Evaluation and Design of Supply Chains for Bioenergy Production

Abstract

Future energy security and consumption trends for energy products have stimulated the consumption of products such as bioethanol, biodiesel, or biogas, generated from non-petroleum sources. Therefore, the production of these products aims to increase its viability progressively. The supply chain (SC) approach enables the evaluation of the structures used to produce these types of bioenergy. Consequently, the identification of tools to represent the production stages of the SC and their articulation with the objective functions, as well as the strategies and solution software implemented in the design of SC for bioenergy products are presented throughout this bibliographic analysis. Based on systematic and narrative literature analysis, current trends and future research issues are performed. The bibliographic analysis has evidenced that the production of bioenergy is a research topic that has evolved in the last decades. Strategic decisions such as factory capacity and the location of production facilities are the most frequently used decision variables in the design of bioenergy SC. Similarly, it was found that the bioenergy SC designs have focused on the implementation of several feedstocks simultaneously. Finally, due to these evaluation and design trends, the bioenergy SC designs that include environmental and social objectives aimed at sustainability are a future relevant research issue.

1. Introduction

Energy is an essential resource that supports the economic development of humanity. The use of this resource allows the execution of small, medium, and large-scale activities, for instance, heating, cooking, and household appliances operation, aimed at satisfying basic household needs, as well as to execution of industrial-scale activities such as production, transformation, conservation, storage, and transportation processes. However, these activities are accomplished mainly through energy from fossil fuels, which for more than 50 years have shown an increasing trend in their exploitation and consumption [1]. Relying on this trend, although efforts on a global scale to promote the use of other energy sources and reduce oil consumption, 2.94 million barrels per day of oil (MMbbl) will be consumed by the end of 2022 [2], showing an average increase of 2% over the last ten years and 5.7% since 2020.

In addition, a series of negative aspects have been associated with the consumption of this type of fossil energy source [3], including the world-proven crude oil reserves stagnation since 2019 (growth less than 0.3%) and climate change contribution through greenhouse gas (GHG) generation [4]. The latter had increased CO₂ emissions associated with the consumption of oil and derivatives, from 32.2 million tons in 2012 to 34.3 million tons in 2022 [2]. However, despite this background, notwithstanding a slight slowdown in its consumption, oil and its derivatives still supply the world’s energy demand [5].

As a consequence of the aforementioned scenario, since the end of the last century, bioenergy has been implemented as a sustainable development alternative, seeking to diversify the energy supply through the use of organic raw materials from biological sources. These organic raw materials correspond especially to biomass, vegetal material, energy crops, as well as industrial, agricultural, agro-industrial, and forestry residues. Through these raw materials, several types of bioenergy are produced, which are broadly divided into three main categories: (1) liquid biofuels (bioethanol or biodiesel), (2) solid biofuels (pellets, firewood, or biomass), and (3) gaseous biofuels (biogas or biohydrogen). Due to the variety of scenarios in which these bioenergy products can be applied, their consumption has been growing at an average rate of over 12.5% per year since 2012, achieving around 7% of the global energy demand by the beginning of the year 2023 [6]. Therefore, bioenergy leads to a reduction in the dependence on petroleum-based products and, at the same time, generates mechanisms that seek to guarantee energy security in the future.

However, negative aspects have also been associated with the use of bioenergy products. Qin et al. [7] express that issues such as food security still generate conflict for bioenergy production because diverting agricultural products to be utilized in bioethanol, biogas, or biodiesel production, which can be employed to feed the population, can influence the prices of the agricultural raw materials utilized and the land use. Likewise, Slade et al. [8] indicate that the increase in the use of bioenergy leads to environmental and social risks, generating consequences on agriculture, forestry, and land use. Considering that the production and consumption of bioenergy pursue a socially and environmentally friendly context, the aforementioned negative aspects require a rigorous analysis to establish bioenergy as an energy diversification instrument.

Regarding the diversity of aspects that influence bioenergy production, different approaches allow us to analyze its production. Process simulation and supply chain management (SCM) are two approaches that allow us to delve into the various issues associated with the production of bioenergy; through these approaches, aspects associated with the designs of the production process and the designs of the SCM can be addressed. The process simulation and SCM approaches implemented in assessments and research of bioenergy production are shown in

Table 1. Study approaches, objectives, and analytical tools to assess bioenergy production.

Approach	Objectives	Assessment Tools	Reference
Process simulation	Economic assessment Production technologies identification, comparison, and selection Raw materials treatments Yields increase Co-products generation Production cost reduction Optimal operation conditions identification Comparison between Pretreatments Alternatives of unit operation and production process Technology setups Pathways conversion and pinch analysis.	Comparison of costs Energetic analysis Yields comparison Total cost analysis Environmental analysis (LCA, WAR algorithm, emissions) Pareto curves Energetic yields comparison.	[9,10,11,12,13,14,15]
Supply chain management (SCM)	Strategic decision making Reduction in cost production Yields improvement Selection of raw materials Location of: ○ Raw materials ○ Pretreatment facilities ○ Process facilities ○ Storage and distribution points Environmental impact reduction Identification and definition of indicators	Total cost comparison Sensitivity analysis Pareto curves Analysis of several conditions and multiple scenarios approach Comparisons among: ○ Mathematical programming approaches ○ Bioenergy type production ○ Country, regional, and local scale. Cost-benefit assessment, etc.	[16,17,18,19,20]

LCA: life cycle analysis, WAR: waste reduction algorithm.

. Each approach involves specific tools that promote improvement at specific production stages. In this regard, process simulation facilitates the analysis of unit operations and processes required for bioenergy production, allowing the interaction between diverse information sources, i.e., laboratory, pilot, or industrial scale analysis. In general, the assessments under this approach address objectives such as comparison, selection, and adaptation of raw materials, as well as the selection of production technologies or co-product generation. Thus, through process simulation, it is possible to obtain performance improvements in the bioenergy production process supported by the design and assessment processes.

On the other hand, the supply chain (SC) approach allows for a global system analysis. This approach, apart from including information obtained through process simulation, enables a general analysis and design, considering the three SC segments: supply, production, and distribution (or upstream, midstream, and downstream). Therefore, SC analysis involves a large number of elements correlated with bioenergy production, from supply logistics of raw materials and consumables to the entire production and distribution structures, including transportation systems, logistics of distribution, and social or environmental criteria. It should be noted that the inclusion of environmental criteria in the SC analysis is a mechanism used to determine the environmental impacts generated in a production process, hence it is a valuable approach to evaluate the SC of bioenergy production that contributes to the reduction in climate change.

Based on the SCM approach, Rodrigues et al. [21] used the biorefinery concept employing lignocellulosic materials to evaluate a syngas SC. Through economic criteria (total cost) and environmental criteria (impacts generated), these authors analyzed several ways to improve the energy efficiency of the system and the economic benefit of the process. Knoope et al. [20], in turn, used the SC approach to evaluate the environmental impact reduction from a biodiesel SC from soy. This study compared several cases at the national level to identify the environmental impacts (emissions) generated under different scenarios. In this way, it is possible to visualize how both studies apply environmental criteria as an objective to identify an SC design for different bioenergy types, aiming to produce the least amount of environmental impact while reducing total costs. In conclusion, the importance of research approaches, such as process simulation and SCM, associated with bioenergy production, is a consequence of the requirements for the design, implementation, operation, and control of production processes, in order to diversify the global energetic demand through the reduction in bioenergy production costs and the improvement of its sustainability indicators. Consequently, bioenergy products could be offered as sustainable goods with a high degree of environmental and social responsibility.

The SCM applied to bioenergy production, having a global vision of the system, considers simultaneously a large number of variables. These variables account for strategic, tactical, and operational decision-making, such as feedstock selections, bioenergy products to produce, capacity and number of facilities, time horizons, regional, national, or international scale, as well as SC linkages from supply to distribution, and criteria related to economic, environmental and social objectives. Given the interaction between the high volume of information and the criteria required for the bioenergy SC design, it is of paramount importance to analyze the state-of-the-art investigations carried out under this approach, to identify the mechanisms implemented to address SC of diverse bioenergy products, and to determine the strategies and optimization criteria implemented.

Some interesting reviews have been published, which explore the development of SC for certain bioenergy products. These publications are mainly focused on the analysis of mathematical programming models developed to assess the availability and feasibility of feedstocks, especially from biomass and forest residues [22,23,24,25,26]. However, the identification of objective functions and tools used for the evaluation of economic, environmental, and social criteria, linked to the bioenergy SC design scale, has not been the main focus of these reviews. Additionally, the identification of tools to represent the production stage of the SC and their articulation with the objective functions, as well as the strategies and solution software implemented in the design of SC for bioenergy products, have not been sufficiently highlighted. The present study attempts to achieve this aim. Therefore, this review was addressed to identify the goals proposed in the objective functions of the bioenergy SC for different products, to analyze the features of solutions to optimization problems posed, and to determine the strategies to include environmental and social criteria in the design of SC of bioenergy production. This study examines the mechanisms used in different works published in the last two decades to develop optimization strategies applied to regional, national, and international scales in the design of SC from a sustainability perspective.

The structure of this paper is organized into seven major sections. Section 1 introduces a general context of SC and process simulation approaches focused on bioenergy production. Section 2 defines the bioenergy concept and describes the main features of bioenergy products such as biodiesel, bioethanol, drop-in biofuels, solid biofuels, and gaseous biofuels. Section 3 presents the generalities of the supply chain (SC) concept and its particularities for bioenergy production. Section 4 describes the systematic search process related to bioenergy SC, followed by the identification of bioenergy supply chain designs in Section 5. Section 6 provides the research and development trends in bioenergy SC design. Finally, the conclusions are presented in Section 7.

2. Bioenergy

2.1. Generalities

Bioenergy, together with clean energies, constitutes the so-called renewable energies. These types of energies, in principle, come from clean sources, do not deplete, are biodegradable, and are considered environmentally friendly on account of the greenhouse gas emissions (GHGs) can be captured in a shorter cycle [27,28]. Bioenergy also has the advantage of being produced from diverse sources of biological materials, including feedstocks that do not conflict with food security, for instance, agricultural residues, agroindustrial wastes, and some non-food crops [29]. These raw materials, so-called biomass, have been implemented as fuels for centuries. In fact, biomass from wood energy crops, woodlands, rural areas, and forestry residues are still essential sources of thermal energy generation at present, as well as other bioenergy products such as biofuels and electric energy.

Figure 1 shows bioenergy products classified as first, second, or third-generation products (1G, 2G, and 3G) based on the source of the feedstock used to produce them. Regarding this classification, 1G bioenergy products consist of those produced from feedstocks that generate direct competition for food security [30], e.g., products like vegetable oils, wheat, corn, sugar, and rice, which are initially intended for human consumption as food. Conversely, 2G bioenergy products consist of those in which non-food sources are employed for their production; in other words, raw materials that do not directly impact food security or land use [31]. Furthermore, 2G bioenergy production utilizes urban, agricultural, agro-industrial, and forestry waste as raw materials, among which are mainly sugarcane bagasse, wheat or corn straw, residual oils, or animal fats. Similarly, the 3G bioenergy products use another kind of feedstock (regarding 1G), such as microalgae, to produce biodiesel. Instead, based on a more recent concept, fourth-generation products can be found. This concept, which also involves the utilization of microalgae as feedstock, employs reengineering and biological analysis for the exploitation of synthetic microorganisms with specific properties to manufacture some bioenergy products from efficient CO₂ fixation [32].

Bioenergy production requires a specific process regarding feedstock. The transformation of feedstock as by-products and wastes into 2G and 3G renewable energies is based on processes classified in two conversion pathways (see Figure 2). The thermochemical pathway consists of combustion, gasification, pyrolysis, and liquefaction processes. These processes are implemented to generate bioenergy products such as heat, electricity, hydrogen, or biofuels such as diesel and biogas (see Figure 2). On the other hand, the biochemical pathway consists of transesterification and fermentation processes, which are used to generate bioenergy products, for instance, biodiesel, bioethanol, biobutanol, and biogas. Given the properties and structures of raw materials used in the production of 2G renewable energies, the thermochemical and biochemical conversion pathways have lower yields and higher production costs in comparison with those produced from 1G feedstocks. Therefore, the processes to obtain 2G bioenergy products have a lower economic viability [33].

Based on the features of the aforementioned conversion pathways, the main challenges of the thermochemical pathway, from the 2G bioenergy context, are related to improving energy efficiency [34,35]. Conversely, the biochemical pathways seek to increase the conversion yields [36,37], since biological transformations are a critical process, where lower yields and diluted products are usually obtained. Thus, additional requirements for resources, operations, processes, inputs, and energy costs are attributed to this pathway [38,39]. These additional requirements result in high production costs, hence the biochemical pathway is sought to increase its energy efficiency and transformation yields [40]. An overview of the production processes of liquid biofuels and some comparisons in the evaluations carried out in their production processes are presented below.

2.2. Liquid Biofuels

The transportation sector is one of the main energy demanders; the demand of this sector is generally supplied for products such as gasoline and diesel. However, liquid biofuels like bioethanol and biodiesel have increased their participation to supply this demand. Biodiesel and bioethanol began to gain importance as a consequence of the oil crisis occurring from 1973 to 1976, a period in which there was a severe oil shortage. This shortage was one of the drivers for countries such as the USA and Brazil to initiate programs to produce liquid biofuels using alternative sources to meet their consumption requirements. The consumption of bioethanol and biodiesel has been growing as a result of similar events such as the crisis of the 1990s, which resulted in a global recession that drastically reduced oil consumption and increased oil prices [41]. Likewise, the 2003 crisis caused the most significant increase in oil prices as a consequence of the war in Iraq and the Venezuelan recession; this context, which occurred between 2003 and mid-2008, increased the price per barrel from USD 28 to USD 134. Conversely, the opposite behavior occurred at the beginning of 2015, when the price per barrel of oil dropped from USD 112 to USD 47 due to global oversupply and overexploitation through technologies such as fracking [42].

Considering the strong influence of petroleum products on account of the previously described phenomena, there is an evident necessity to establish energy alternatives that promote energy security in the future. In this context, bioenergy products such as bioethanol and biodiesel have suitable qualities to be included as alternative energy sources. Furthermore, these liquid biofuels become even more significant since they may contribute to meeting the large consumption volumes of a sector like transportation, which is recognized as one of the sectors with the highest energy demand on a global scale. Consequently, bioethanol and biodiesel are the bioenergy products with the highest production and consumption volumes [2]. This consumption trend of liquid biofuels is due to their capacity to maintain similar behaviors to petroleum products in internal combustion engines, and their feasibility of substituting or being implemented in percentage blends with gasoline and diesel [43]. Accordingly, bioethanol and biodiesel have shown an increasing trend in demand since 2006 [44].

2.2.1. Biodiesel

Oils and fats of vegetable and animal origin are the primary raw materials employed in biodiesel production. These feedstocks are mainly constituted by triglycerides (fatty acid esters with glycerol), which react with light alcohols in the presence of a catalyst, favoring the transesterification reaction; this is the main process for biodiesel production [45]. Comparing the yields obtained by 1G and 2G feedstocks, the 2G feedstocks usually show lower yields in the biodiesel production process [46]. However, the production of biodiesel, especially 2G biodiesel, has several challenges to achieving its economic viability. Therefore, economic evaluations are crucial to analyzing different scenarios for biodiesel production. In this sense, Sun et al. [47] and Sakdasri et al. [48] developed an economic evaluation, supported by simulation, to produce biodiesel from biomass microalgae and oil palm, respectively. Similarly, Aboelazayem et al. [49] and Glisic et al. [50] utilized waste cooking oil and waste vegetable oil, applying economic evaluations for 2G biodiesel production. In the aforementioned studies, the techno-economic analysis leads to finding feasible operating conditions. Through sensitivity analysis, different parameters were varied to identify suitable conditions for feedstocks with specific properties to improve the feasibility of 1G and 2G biodiesel production.

2.2.2. Bioethanol

Bioethanol production is characterized by the fermentation of different sugars, which are transformed into alcohol. Corn and sugar cane are the main 1G products used for their large-scale production, these products are the primary raw materials employed by the USA and Brazil, respectively. These two countries are the most significant worldwide bioethanol producers [51]. In contrast, 2G bioethanol production is recurrently produced from lignocellulosic biomass. These feedstocks are mainly composed of three components (cellulose, hemicellulose, and lignin), constituting around 90% of the dry weight of lignocellulosic biomass [52]. The feedstocks utilized for 2G bioethanol production may be classified into the following groups: agricultural and agro-industrial wastes, for instance, sugarcane bagasse, wheat straw, rice husks, and sawdust; forestry products (e.g., hardwoods, softwoods, and residues); herbaceous biomass; cellulosic wastes and urban wastes (see Figure 2).

Ethanol production from lignocellulosic materials is based on the biochemical conversion of these materials by fragmenting their main polysaccharides (cellulose and hemicellulose) into sugars that can be fermented (saccharification or enzymatic hydrolysis), followed by a fermentation process. Saccharification is usually catalyzed by enzymes such as cellulases, while fermentation is carried out by yeast or bacteria. However, due to the characteristics of this type of raw material, the saccharification and fermentation processes have restrictions caused by the complex accessibility to the surface area of the structure generated for the hemicellulose–lignin matrix, which acts as a barrier to accessing the cellulose [53]. Considering the aforementioned structural properties, the conversion process of lignocellulosic biomass into ethanol involves a production process with the following stages: pretreatment, detoxification, hydrolysis, fermentation, and distillation [54].

The techno-economic evaluation of the 1G and 2G bioethanol production process is widely applied to assess the properties of different raw materials. Among the preliminary stages of the process, especially those feedstock adjustments, the pretreatment and saccharification (enzymatic hydrolysis) stages have particular importance due to the influence on the amount of sugars available for fermentation. In that sense, Mendes et al. [55] and Hasanly et al. [56] developed techno-economic evaluations proposing different scenarios in the 1G and 2G bioethanol production process from sugarcane and wheat straw, respectively. On the one hand, Mendes et al. [55] sought to evaluate different operating conditions in the initial stages of the process (pretreatment); on the other hand, Hasanly et al. [56] identified the operating capacity and costs associated with the production stage. Although each study analyzed different feedstocks from different perspectives, the techno-economic evaluation was used to increase the feasibility of the respective production stages. However, even though all the studies mentioned above (linked to biodiesel and bioethanol production) improve the economic viability of different bioenergy products, it should be noted that all improvements obtained are related to the production process (via simulation). Notwithstanding, the feasibility improvements obtained in the previously mentioned studies can be implemented in SC approaches to include in their analysis of raw materials supply, logistics, and distribution issues for each bioenergy product.

2.2.3. Drop-In Biofuels

Drop-in biofuels are composed of a mixture of different types of hydrocarbons that should be functionally equivalent to petroleum products without being chemically identical [57,58]. The leading quality of drop-in fuels is that they can use the same infrastructure developed for petroleum products, for instance, gasoline, diesel, kerosene, or jet fuel [59]. In this sense, a drop-in biofuel must have the properties to be easily introduced into the existing petroleum infrastructure and be handled similarly to petroleum products without having to make significant modifications to the current infrastructure [60]. Therefore, drop-in biofuels must have specific properties of viscosity, distillation profiles, and acidity values to be easily included in the petroleum infrastructure.

The drop-in biofuels are relatively new in the energy market, where four main production routes can be identified. The oleo-chemical pathway for fats and oils, the thermochemical pathway for lignocellulosic material, the biochemical pathway for sugars and starches, and finally, the hybrid pathway for carbon monoxide and carbon dioxide [61]. Nevertheless, the oleo-chemical conversion applied to feedstocks such as oils and fats is one of the technological routes that provides better operating conditions for its production [62]. It should be noted that the main challenges in the production of drop-in biofuels are associated with the high amounts of oxygen contained in feedstocks such as sugars, lipids, and biomass, which are usually found in percentages greater than 40%. Therefore, the hydrodeoxygenation and decarboxylation processes must be applied to reduce the oxygen quantities in these feedstocks.

Although drop-in biofuel production projects have not been published recently, Okeke and Mani [63] and Thilakaratne et al. [64] have developed investigations on economic evaluations for drop-in biodiesel and drop-in gasoline production. Conversely, Jong et al. [65], Azadi et al. [66], and Sebastião et al. [67] developed environmental assessments for jet fuel, syngas, and drop-in ethanol, respectively. The main focus of these economic and environmental assessments is the production pathways used. The production costs and environmental impacts (life cycle analysis) were determined for the production pathway employed for each type of drop-in biofuel. Even though it is difficult to make a meaningful comparison between the results of the techno-economic and environmental evaluations, due to the methodology utilized by each author, gasification is considered the most promising process for the production of drop-in fuel. The main reason is its flexibility to operate with different feedstocks and its ability to produce liquid fuels with higher yields. Conversely, the other conversion pathways are still in the early development stages. However, drop-in biofuels have been used as the principal subject in new research, analyzing their production pathways to explore their potential as energy products [68].

2.3. Solids Biofuels

Combustion is the most common method of obtaining electrical energy from biomass. The primary biomass employed for this type of bioenergy is pellets, briquettes, wood chips, and several kinds of bales. This method is the most direct process to convert biomass into usable energy, obtaining mainly heat and steam. Despite its apparent simplicity, direct combustion is a complex process from a technological point of view because of high reaction rates, heat release, and the variety of reactants and reaction patterns.

An adequately designed biomass combustion facility (fixed or fluidized bed systems) can burn different biomass types. In this combustion process, volatile hydrocarbons (CxHy) are formed and burned in a combustion zone [69]. It should be noted that the physicochemical properties of the biomass such as nitrogen, sulfur, and ash content, or oxygen levels, have a strong influence on the temperature generated in the combustion chamber, as well as gas emissions (CO₂ y NO_x), and conversion efficiencies [70]. Hence, temperatures over 500 °C and thermal conversion efficiencies ranging from 67% to 90% can be achieved [71,72]. This method is based on producing combustible gases from materials containing carbon, heating feedstock in the absence of O₂, through dry distillation or pyrolysis Bridgwater [73]. Thus, volatile gases and solid carbon are obtained from the thermal decomposition of biomass. The process occurs at 850 °C and at high or atmospheric pressure. As a result, this process obtains synthesis gas as a product mainly composed of combustible gases such as hydrogen and carbon monoxide. At the same time, some remnants such as liquid by-products and mineral material are obtained in the process.

2.4. Gaseousus Biofuels

Gaseous biofuels are another set of bioenergies produced from biomass, similar to liquid biofuels. The following section presents general information on its production and techno-economic evaluations carried out to improve its viability.

2.4.1. Biogas

Biogas production is accomplished through a highly complex biochemical process called anaerobic digestion. This process is generally divided into four sequential stages: hydrolysis, acidogenesis, acetogenesis, and methanogenesis [74]. In the hydrolysis stage, the complex organic substrates are degraded into simple organic compounds by extracellular enzymes produced for hydrolytic microorganisms, resulting in simple soluble monomers. In this stage, complex organic substrates are degraded into simple organic compounds by extracellular enzymes, which are produced by hydrolytic microorganisms, generating simple soluble monomers. In this stage, proteins, lipids, and carbohydrates are hydrolyzed to amino acids, long-chain fatty acids, and sugars, respectively. Through acidogenesis, these monomers are converted into a series of volatile fatty acids and other small molecular compounds by fermentative bacteria. Furthermore, in the acetogenesis stage, acetogenic bacteria convert the free fatty acids into acetate, carbon dioxide, and hydrogen, which provide the direct substrate for conversion to methane in the methanogenesis stage [75].

The techno-economic analysis applied to biogas production presents a different approach compared to bioethanol and biodiesel production. Biogas production is often accomplished as a co-product of another production process; for instance, it is a co-product to improve yields in the production of bioethanol [76], biodiesel [63] or electric energy from biomass [77]. Biogas is usually used as a source of heat or electric energy generation to reduce the energy cost of the production processes for other bioenergy products. Nevertheless, due to the diverse feedstocks that can be transformed into biogas, Wu et al. [78], Al-Wahaibi et al. [79], and Aui et al. [80] carried out techno-economic evaluations, analyzing the suitability processes of feedstocks such as microalgae, food waste, agricultural waste, and agro-industrial waste, respectively. Based on that information, these authors determined the costs associated with the capacity and operation of production plants, while analyzing the yields achieved after applying anaerobic digestion. Although the composition of the raw material plays a vital role in biogas production yields, the works previously mentioned showed the importance of improving energy efficiency and operating costs. This approach is used because biogas is usually transformed into other types of energy such as electricity or steam.

2.4.2. Biohydrogen

Biohydrogen is a bioenergy product with a high capacity of use due to its high energy potential, minimal environmental impacts given its null CO₂ emissions, and the possibility of being produced from multiple raw materials [81,82]. In that sense, lignocellulosic biomass, industrial waste, or microalgae are included in the biological materials used for its production [83]. Nevertheless, similar to biofuels such as ethanol, biohydrogen production requires a feedstock adjustment process (pretreatment), in which feedstock is processed to access the metabolites of interest [84]. The application of this adjustment process encourages, in addition to the exposure of these metabolites, the generation of inhibitory compounds such as phenols and other aromatic compounds, as well as furfural, hydroxymethylfurfural, and furan by-products [85,86]. As a result of these particularities, the inclusion of an additional detoxification process becomes necessary to obtain superior yields in a subsequent fermentation process [84].

Although fermentation is one of the biological methods for biohydrogen production, other methods such as direct and indirect biophotolysis [87], photo-fermentation, gas exchange reaction [88], and dark fermentation [88] are also employed for its production. For this purpose, microorganisms of the genus Citrobacter, Clostridium, Enterobacter, Klebsiella, and Thermotoga are usually utilized in anaerobic processes depending on the features of the biological material used as raw material and the process implemented [89,90]. Hydrogen production is focused on the dissociation of a mixture of gases (biogas) produced by a microorganism from biological material used as substrate. Hence, removing gases such as CO₂ from the mixture is necessary. This continuous removal is necessary to avoid the accumulation of other gases, reducing inhibitions in the process and simultaneously increasing the concentration of biohydrogen, which is necessary to be used as a source of bioenergy [91].

2.4.3. Syngas

Synthesis gas or syngas is a product obtained through a thermochemical process at high temperatures (between 500 °C and 1200 °C) applied to raw materials such as lignocellulosic materials, urban waste, or by-products generated by wastewater treatment. Through gasification, the raw materials, together with a gasifying agent (generally air or oxygen), lead to combustion reactions, Boudourad reaction, methanation, or oxidation [92]. In these reactions, feedstocks are decomposed, generating a mixture of gases, mainly carbon monoxide and hydrogen, with methane, carbon dioxide, heavy hydrocarbons, water, and ashes in smaller proportions [93]. Carbon monoxide and hydrogen, the main components of syngas, show the highest participation ranges. Their ranges oscillate between 30% and 45% for carbon monoxide and 25% to 30% for hydrogen, according to the feedstock type employed for its production [94]. The other components of the mixture of gases, such as methane, carbon dioxide, and others, oscillate from 5% to 15%.

Held [95] and Alamia et al. [96] economically assessed the production of syngas under different plant capacities, supported by the mass and energy balances generated in the wastewater methanization process, thus determining the average that could be achieved. Based on this information, these authors analyzed different plant capacities (through process simulation) to identify the operating conditions that allow an improvement in the process efficiency, making comparisons between the total cost of production. Similar evaluation approaches were implemented by Thunman et al. [97] and Menin et al. [98] to produce syngas from forest biomass, in which a techno-economic analysis allowed them to compare the minimum selling prices obtained by the variation in production capacities. In addition to the aforementioned approaches, syngas is also used as a basis for the production of other bioenergy products, such as bioethanol and biodiesel, via syngas fermentation [99]. Nevertheless, this production pathway has many limitations linked to gas–liquid mass transfer, low productivity levels, and high production costs. In that sense, works searching for innovation in these production processes have been developed, focusing on the genetic development of modified microorganisms [100], as well as the improvement of bioreactors to have better control of the gasification reactions [101]. In that sense, it is possible to observe that the aforementioned works focus on the analysis to increase the conversion yields based on a better understanding of the process, such as operational parameters, instead of economic evaluations.

2.5. Regulations Associated with the Use of Bioenergy Products

The diversity of bioenergy products that can be produced to satisfy different energy requirements is one of the reasons that bioenergy is becoming an increasingly important energy source. In that sense, many countries have applied specific regulations to encourage bioenergy consumption. In this section, the blending percentages with other fuels that have been implemented for some bioenergy products are shown.

In order to mitigate the climate change caused by greenhouse gases on the planet, member countries of the European Union (EU), as well as several countries in Africa, Asia, and America, have included government policies that encourage the consumption of biofuels such as bioethanol and biodiesel. The objectives of this strategy implemented by the EU are, firstly, to reduce greenhouse gas production by 55%; secondly, the goal is that around 30% of energy consumption in the transportation sector should be supported by renewable energies [102]. For instance, a pioneer in biofuel production policies such as the USA, through the Energy Independence and Security Policy established in 2007, ordered a progressive increase in the proportion of biofuel to be added to gasoline between 2008 and 2023, from 34 to 136 billion liters, respectively. Based on those blending proportions, 60 billion liters must be produced from lignocellulosic materials. Similarly, Brazil, which shares the top positions on a global scale with the USA in bioethanol production, is also characterized by a significant trajectory in applying biofuel production and consumption policies (see

Table 2. Biofuel blends government regulations implemented by different countries.

Country	Blending Proportion Implemented by Policy	Country	Blending Proportion Implemented by Policy
Angola	E10	Malaysia	B10
Germany	E5 and B5	Mexico	E7
Argentina	E25 and B10	Nigeria	E10
Australia	E7 and B4	Norway	B10
Austria	B10	New Zealand	B5
Belgium	E10 and B7	Zimbabwe	E15
Brazil	De E18 a E25 and B10	Mozambique	E15
Bolivia	E12 and B3	Panama	E12
Bulgaria	E12	Paraguay	E24 and B1
Canada	E7 and B2	Peru	B10 and E7.8
Chile	B5	Poland	B12
China	E12 and B10	Portugal	E10 and B12
Colombia	E10 and B10	Netherlands	E10 and B7
South Korea	E2	Philippines	E12 and B2
Costa Rica	E7 and E20	Romania	E5 and B7
Croatia	E10 and B10	United Kingdom	E10 and B10
Denmark	E7	Czech Republic	B7
Ecuador	B5	Dominican Republic	E15
Slovenia	B5	South Africa	E10 and B5
Spain	E5 and B5	South Korea	B2.5
Ethiopia	E10	Sudan	E7
France	E10 and B10	Sweden	E10 and B10
Finland	E18	Thailand	E5 and B10
Greece	E10	Turkey	E7
Guatemala	E5	Hungary	E7 B10
India	E5	Ukraine	E7
Indonesia	E3 and B20	Uruguay	B5 and E7 para 2015
Ireland	E12 and B10	Vietnam	E5
Italy	E10 and B7	Zambia	E10 and B5
Jamaica	E10	United States	Producing 136 billion liters of renewable fuels by 2023
Kenya	E10
Malawi	E10

E: blending proportion gasoline-ethanol (e.g., E5 = 5% ethanol); B: blending proportion of diesel-biodiesel (e.g., B5 = 5% biodiesel).

).

Table 2 shows a group of 63 countries with their respective governmental policies linked to biofuel consumption. The USA and Brazil have the highest biofuel blends, with implementation percentages above 20%. The blends primarily implemented are between 10% and 15%, being implemented by countries such as Turkey, Thailand, Vietnam, and Australia [103], as well as South American countries such as Paraguay, Costa Rica, Argentina and Colombia [44]. In the case of Colombia, the Ministry of Mines and Energy, through Law 693 of 2001, established that since the second semester of 2006, supply stations in all cities with more than 500,000 citizens must distribute a gasoline blend with 10% fuel alcohol (bioethanol) content. Furthermore, through Decree 2629 of 2007 [104], all new cars arriving in the country must have the Flex-fuel qualification, which can operate with blends from 20% to 80% ethanol. On the other hand, countries that have a shorter trajectory using biofuels have regulations with lower blending proportions. This group includes countries such as Australia, Chile, Ecuador, Slovenia, Guatemala, and New Zealand (see Table 2), implementing blends between 5% and 7% [105].

3. Supply Chain (SC)

3.1. Overview of SC

Since the 1980s, terms such as supply networks, distribution channels, and value chain began to be important in the management approach. These concepts were applied to increase customer satisfaction while improving aspects linked to quality, logistical parameters, and economic benefits [106,107]. Thus, production systems began to be considered as a network, which, firstly, focuses on the production process, and secondly, began to include the supply and distribution linkages. Figure 3 shows a general SC structure, which contains relevant elements such as the supply of raw materials, inventories, and linkages between their sub-elements. The production process analysis based on the SC approach is characterized by the flow of materials and information between upstream, midstream, and downstream. According to the Council of Logistics Management, supply chain management (SCM) is the process of efficiently planning, implementing, and controlling the flow of material and information from suppliers of raw materials and inputs, through storage, processing, and inventories to the distribution of the finished product to the final consumer [108].

The SCM concept has been complemented by authors such as Stock and Boyer [109], who define it as the management of a supply network with internal and external relationships between interdependent companies and business units that support the two-way flow (forward and reverse) of materials, information, services, and finances. It should be noted that these internal and external relationships cover from the original supplier to the final consumer, having as its objective to achieve customer satisfaction. In the same sense, Cerchione and Esposito [110] complement the SC definition, indicating that the supply network includes the vertical connection between companies with one or more customers and suppliers, as well as the horizontal relationship between suppliers. In summary, the general idea of the SCM is to carry out a global analysis of a production system, coordinating the material balances and information flows established by connecting the elements that constitute the supply, production, and distribution stages (see Figure 3).

Subsequently, market behavior and consumer demands have undergone some modifications. Given the development of the Sustainable Development concept, fostered by the Kyoto Protocol of Copenhagen (2006–2009) and implemented by the World Commission on Environment and Development (WCED); it is established that the demand for current requirements must be covered without compromising the capability of future generations to satisfy their requirements [111]. As a result, in the last decades, the importance of environmental and social criteria has increased, creating a more significant environmental and social responsibility when an economic activity is carried out. Hence, consumption behavior has diversified its focus, from being governed by a purely economic point of view to being governed by a consumer who is more aware of the impacts generated in his environment due to the satisfaction of his necessities—in essence, a consumption aimed at a sustainable approach.

As a consequence of implementing the Sustainable Development concept, the SCM evolved from being a tool primarily governed by aspects linked to economic analysis and quality to a tool that included variants with analysis approaches that can include economic, environmental, and social criteria. Considering the relevance acquired by environmental and social criteria, SCM includes these criteria through green supply chains (GSCMs) [112] and sustainable supply chains (SSCMs) [113], respectively. The GSCM is characterized by prioritizing environmental criteria, which can be implemented in combination with economic approaches or focused solely on measuring environmental impacts. The GSCM aims to reduce the environmental effects caused by a production system. Consequently, GSCM has the minimization of aspects such as pollution, water, and air contamination as an objective, as well as the reduction in waste materials, energy, or products generated by activities carried out inside the three SC stages [114].

SSCM, in turn, is characterized by the simultaneous inclusion of economic, environmental, and social criteria. The inclusion of social criteria into the SCM, contrary to economic and environmental criteria, is a relatively new approach. SSCM is usually utilized to increase economic performance while ensuring environmental and social goals [115]. The aforementioned environmental objectives are linked to reducing the impacts generated by a production system, while the social objectives are related to the impact generated in the community. Among several social objectives considered in the SSCM are: the health effects on people generated by the execution of a productive project, the impact on its life quality, equitable opportunities generation, appropriate waste management, as well as the number of jobs generated [116]. Based on these guidelines, SCM, GSCM, and SSCM, supported by simulation, mathematical modeling, reverse logistics, data management, and computational tools, have contributed to the solution of optimization problems applied to reduce the costs and risks of the chain. In this sense, efficiency and customer satisfaction are increased through design, optimization, and coordination provided by GSCM and SSCM approaches [117].

Considering the market behavior as well as environmental and social responsibilities previously mentioned, SCM, GSCM, and SSCM have been used for the design and coordination of production systems in several sectors. For instance, manufacturing [118], food industries [119], and hospitals [120], are some of the sectors where the SCM concept has been applied. Bioenergy production is one of the sectors where SCM is used mainly for decision-making. The SCM, GSCM, and SSCM, based on tools applied by them, are widely employed to address the high complexity of utilizing alternative feedstocks in the production of 1G, 2G, and 3G bioenergy products, for instance, bioethanol [121], biodiesel [122], electric energy from biomass and gaseous biofuel [123]. The following section describes the main aspects of the SC approach aimed at the bioenergy sector.

3.2. Bioenergy SC

The book Climate Impacts on Energy Systems, written by the World Bank in 2011 [124], extensively analyzes the strong relationship between climate change and energy use. Similarly, Yalew et al. [125] correlated aspects such as energy generation sources, supply, demand, transportation, production, infrastructure, and energy efficiency, analyzing the direct effects on other economic sectors such as transportation. In that sense, the implementation of the SCM for bioenergy production aims to assess the production cost, focusing on the evaluation of the cost-benefit obtained through efficiency improvements and profitability of a finished bioenergy product. Therefore, considering that costs associated with energy consumption can represent about 30% of total production costs [126], the implementation of SCM, GSCM, and SSCM in the bioenergy sector is used to support decision-making at strategic, tactical, and operational levels. For instance, these decision-making levels include strategic decisions, such as the location of production facilities and defining their respective distribution and supply networks, to make specific decisions, such as inventory management, the definition of purchasing systems, and transportation schedules.

The implementation of SCM, GSCM, or SSCM for producing bioenergy products (bioethanol, biodiesel, biogas) is a problem with a higher degree of complexity. The bioenergy SC differs from the traditional SC due to the uncertainty of variables such as availability and supply of raw materials, physical–chemical properties of feedstocks, geographical distribution of supply sources, facilities of processing and distribution sites, as well as fluctuations in the demand of bioenergy products. As a consequence of the aforementioned variabilities, the complexity of bioenergy SC is increased by the large number of linkages that constitute each stage and the logistical challenge of keeping a constant supply of the type of raw materials utilized for their production [127].

Given the specific area of the bioenergy green supply chain (GSC), the works of Ascenso et al. [128] and Garofalo et al. [123] carried out case studies of electric energy and ethanol. Both studies consider environmental goals; however, the study by Ascenso et al. [128] also includes an evaluation of economic performance in addition to its environmental goal. Similarly, He-Lambert et al. [129] and Ahmed and Sarkar [122] assessed economic criteria in biofuel GSC while simultaneously including environmental measurements. In the aforementioned studies, the implementation of SCM (including GSC and SSC) was used to obtain cost-effectiveness improvements in production processes, enhancing the viability of different 1G, 2G, and 3G bioenergy products. Based on the generalities of SCM and its implementation in the bioenergy sector, a systematic search is presented in Section 4 and Section 5 to deepen the evaluation methods and trends employed for the design of SC, GSC, and SSC in the bioenergy sector.

4. Systematic Search Methodology

The literature search aimed at identifying SCM, GSCM, and SSCM designs of bioenergy products was conducted mainly in databases such as Scopus, Web of Science, and Science Direct. The words “supply chain” were used in combination with the words “bioethanol”, “ethanol”, “biodiesel”, “biogas”, or “bioenergy”; those words were included in the search filters to ensure their presence in paper sections such as the title, abstract, and keywords. Subsequently, search parameters were defined, ensuring that the results corresponded only to bibliographic reviews and scientific articles, excluding reports of academic events (conferences and congresses) and publications before 2010.

After applying the search methodology, 990 articles were obtained, analyzed, classified, and subjected to a selection process. The main characteristics to determine the relevance of the papers to be included in this review were the following: the published papers must be related to SCM, GSC, or SSC of bioenergy products, have the formulation of mathematical models (focused on optimization) for strategic, tactical or operational decision-making, and apply these optimization problems in case studies to obtain SCM, GSCM and SSCM designs for bioenergy products. A total of 187 articles were selected and subsequently classified (see

Table 3. Publications per scientific journal related to bioenergy SC between 2009 and 2023.

Journal	Art.	Journal	Art.
Computers and Chemical Engineering	17	Energy Policy	4
Energy	15	Bulgarian Chemical Communications	2
Applied Energy	14	Transportation Science	2
Industrial and Engineering Chemistry Research	10	Computers and Industrial Engineering	2
Journal of Cleaner Production	10	Agricultural Research	2
Renewable Energy	9	Agricultural Systems	2
Biomass and Bioenergy	7	AIChE Journal	2
Bioresource Technology	6	Bioenergy Research	2
Chemical Engineering Research and Design	6	Biofuels Bioproducts and Biorefining	2
Chemical Engineering Transactions	6	Biomass Conversion and Biorefinery	2
Transportation Research	5	Clean Technologies and Environmental Policy	2
Energies	5	Industrial Crops	2
Sustainability	5	Computers and Operations Research	2
Renewable and Sustainable Energy Reviews	4	ACS Sustainable Chemistry and Engineering	2
Energy Conversion and Management	4	Other journals with one publication	34

Art.: number of scientific articles published.

). The aspects considered were the following: bioenergy product produced, raw materials implemented, chain stages included in the models proposed, solution methods, and economic, environmental, or social design criteria, as well as the strategies utilized to include uncertainty.

The articles selected, as a result of the search methodology, were classified according to the topics considered. Table 3 shows the total number of papers analyzed, which were grouped according to the journals used for their publication. In this sense, the journals with the highest number of papers are Computers and Chemical Engineering and Energy, with 17 and 15 articles, respectively. These two journals account for 17% of the publications analyzed, followed by journals such as Applied Energy (14), Industrial and Engineering Chemistry Research (10), and Journal of Cleaner Production (10), and Journal of Cleaner Production (10).

Once the systematic search methodology previously described was applied, the resulting articles were classified for their respective analysis. Section 5 shows this analysis, which is supported by the studies selected according to the bioenergy products, feedstocks, criteria, scale, context, and solution strategies applied in the SC bioenergy design.

5. SC Designs of Bioenergy Production

5.1. Products Used in the Design of Bioenergy SC

According to the search and selection parameters of the research papers analyzed, Figure 4 shows the number of papers associated with the main bioenergy products through SC design of bioethanol, biodiesel, electricity, and biogas. Considering the period between 2009 and 2021 as a reference, it is possible to identify, although there were fluctuations in the number of articles in 2015 and 2023, an incremental trend in the number of papers addressing SC design for bioenergy. While there was a considerable increase between 2015 and 2016, from 5 to 15 papers, the most abrupt increment occurred between 2019 and 2020; this last year, 36 papers were reached. In this vein, it is possible to identify bioethanol production as the bioenergy product with the highest number of papers per year between 2010 and 2023 (except in 2019), followed by biodiesel (see Figure 4).

Moreover, the publications associated with the design of biogas and biomass-based electric energy SC in the same period (2010–2023) show a lower participation regarding the design of bioethanol and biodiesel SC. This trend could be associated mainly with two factors. The first factor is related to the consumption trends of the areas where the assessment is performed, for instance, countries that produce and use biogas as one of their main energy sources, such as Germany, the United Kingdom, and Italy [130]. However, the number of countries that consume this type of bioenergy as a primary source is considerably lower compared to the countries that consume products such as bioethanol and biodiesel. The second factor is related to the final use of bioenergy products; for instance, electric energy and biogas have been aimed to supply requirements with a lesser volume of consumption in comparison to bioethanol and biodiesel. Hence, the SC designs for electric energy and biogas show fewer studies published (see Figure 4). In contrast, bioethanol and biodiesel promote the assessment of these SC designs to improve their production and profitability due to their high volume of demand since they have the features to be implemented in the transportation sector.

As a result of the production and profitability improvement requirements for products such as bioethanol and biodiesel, the generation of biogas and electric energy from biomass is sometimes not considered the main product for SC design; instead, these latter products are produced as secondary products (co-product) in which the main product of the SC is bioethanol or biodiesel. In this sense, biogas and electric energy are used to supply the energy demand required to improve the profitability of the bioethanol and biodiesel production processes. The application of these strategies can be evidenced in the works of d’Amore and Bezzo [131], Gan and Smith [132], and Ortiz-Gutiérrez et al. [133], in which biogas and electrical energy are used to provide part of the energy required in the process of obtaining another bioenergy product such as bioethanol. In conclusion, based on the trend of the studies carried out in the analyzed time range, it is possible to prove that the subject associated with the design of bioenergy SC is a dynamic subject that has aroused interest in the last few years. This interest is primarily associated with the search for future energy security and the advantages that a global vision of bioenergy production systems offer to improve the viability of these alternative energies.

5.2. Decision-Making Addressed in the Bioenergy SC Design

The design of a bioenergy SC should consider different levels of decision-making supported by aspects such as the type of bioenergy product to be generated and the possible raw materials to be used. This section identifies the decisions utilized in the different published works and their impact on the structure and design of bioenergy SC.

Bioenergy SC presents a higher degree of complexity than SC of other economic activities because of the raw materials used for their production. These raw materials, being alternative sources, present a logistical challenge to achieve a constant supply and sufficient volumes for bioenergy production. The bioenergy SCM contributes to the design of these types of production systems since it is used for decision-making, especially at strategic and tactical levels. The strategic decisions require a higher investment and are applied for longer time horizons, between 6 and 8 years [134]. In the bioenergy SC context, these strategic decisions are implemented to determine aspects such as the location and capacity of production plants and storage sites, as well as the location of supply, collection, and pretreatment places [135].

On the other hand, tactical decisions have a shorter time horizon compared to strategic decisions. Tactical decisions are generally medium-term; namely, they are decisions with a time horizon of a few months, such as the management of inventory levels [136] or the logistics for bioenergy plant operation [137]. In that sense, the strategic and tactical decisions, included in the bioenergy SC design, have a strong influence on its final structure (see

Table 4. Strategic and tactic decisions according to structures in the bioenergy SC designs.

Structure SC	Strategic Decision	Tactical Decision	Ref.
Combined (Nw)	Location and capacity plant, transport, Location (feedstock)	Conversion technology selection	[141]
Divergent	Capacity plant, transport, Location storage (feedstock)	Quantity of product and storage, flow material transported between production facility, storage, and sale points	[142]
Convergent	Location facility, selection transport mode	Quantity of feedstock to supply and storage	[143]
Combined (Nw)	Location de facility, capacity plant, selection transport mode	Conversion technology selection	[144]
Combined (Nw)	Location facility, capacity plant, selection transport mode	Conversion technology selection, production scheduling, storage control	[145]
Combined (Nw)	Location facility, Capacity facility, selection transport mode	Conversion technology selection	[146]
Combined (Nw)	Location facility, Instalación SA (feedstock), selection transport mode	Quantity of biomass harvesting, quantity of biomass shipped between storage and facility	[147]
Divergent	Location facility, selection transport mode	Selection of temporal demand zones	[139]
Combined (Nw)	Location facility, Capacity plant, selection transport mode, locations storage (feedstock and final product)	Range of proportion blending (bioethanol-gasoline), gasoline imports	[148]
Combined (Nw)	Location facility, Capacity plant, selection transport mode, location storage (feedstock)	Conversion technology selection, Shipments Stock levels, quantities of ethanol to import	[136]
Convergent	Location facility, Capacity plant, location storage (feedstock)	Storage levels of feedstocks by year, flows between storage and process facility	[138]
Combined (Nw)	Location facility, Capacity plant, selection transport mode, location storage (final product)	Conversion technology selection	[149]
Convergent	Location facility, Capacity plant	Increment capacity plant, feedstock storage	[150]
Convergent	Location facility, Capacity plant, location storage (feedstock)	Quantity of 1G feedstocks to transport	[151]
Combined (Nw)	Location facility, Capacity plant, location storage (feedstock)	Stockc level feedstock	[152]

SC: supply chain, Nw: network, Ref.: reference.

). As a result, the structure of the bioenergy SC is influenced based on the SC stage that these decisions focus on (upstream, midstream, or downstream). Table 4 shows a set of strategic and tactical decisions in different studies correlating with the final structures obtained from the bioenergy SC design. In that sense, if the strategic or tactical decisions are mainly addressed to the upstream logistics, such as obtaining raw material, defining the supply, or processing points, the bioenergy SC will be a convergent structure [138]. In contrast, if the SC design is focused on the process and distribution stages, the bioenergy SC will be a divergent structure [139]. Finally, if the SC design considers aspects of the supply, production, and distribution stages equally, this SC will be a combined or network structure [140].

Through the analysis of the selected papers, the strategic decisions most frequently considered in defining the design of bioenergy SC were identified. Figure 5 shows the most relevant strategic decisions utilized in the last 13 years. The facility location and the capacity of the production plant are the decision variables most frequently employed in the analyzed papers for the time horizon evaluated. These decisions reached the highest values in 2020, considering them in 29 and 23 studies, respectively. On the other hand, decisions related to the storage location and the selection of transportation modes were considered in a smaller proportion than facility location and capacity production plant. Finally, the decision variable least utilized is the strategic decision associated with the conversion selection technology for the production of bioenergy products. It is possible to identify that this decision variable, except for 2015 and 2019, is considered in an average of five studies per year.

Based on the information shown in Table 4 and Figure 5, it was noted that the location and capacity of the production facilities are the most critical decision variables in the design of SC for bioenergy products. This trend is due to these decisions having the most extended duration since they will remain throughout the entire time horizon considered for the SC design. On the other hand, regarding tactical decisions, some examples are presented by authors such as Liu et al. [143], who contemplated in their SC optimization models the following tactical decisions regarding supply and production stages: quantity of feedstock purchased from a specific supplier, quantity of feedstock to be stored, quantity of feedstock consumed, and quantity of energy required in production (all of them for a time horizon less than one year). In that work, these tactical decisions were considered to evaluate their influence on the sale prices of 2G bioethanol produced from forest residues. In conclusion, they found that through the reduction in the amount of energy required in the process and slightly improved coordination of storage times, a reduction in the costs associated with production and emissions was achieved, thus improving the profitability of the SC and reducing the costs associated with emissions.

Similarly, Park et al. [147] designed an SC of bioethanol produced from switchgrass, including in their optimization model tactical decisions such as the quantities of biomass harvested and shipped, considering a multimodal transport, as well as the management of biomass inventories and ethanol produced. The results obtained in this study indicate that implementing a multimodal system would be more cost-effective than a single-modal system in terms of the total cost of bioethanol SC by reducing shipping costs. In this sense, it could be inferred that tactical decisions, conjointly with strategic decisions such as the location and capacity of production plants, are used in the formulation of optimization models to establish robust bioenergy SC designs, which allow more efficient use of the conditions presented in a specific geographical zone.

5.3. Raw Materials Considered in Bioenergy SC

A critical factor in a successful Bioenergy SC design is directly related to the feedstock to be used since relevant aspects, such as the bioenergy product to be produced and the logistics requirements for the supply, are determined based on the availability and characteristics of these feedstocks. Therefore, this section presents the main feedstocks utilized in the design of bioenergy SC.

The raw materials (RMs) used for bioenergy production have been a constant subject of controversy. The diversion of food products such as corn, wheat, or sugarcane for bioenergy production generates a social conflict due to the competition for these products and their direct impact on food security [153]. Figure 6 shows the works that employed 1G and 2G RM in bioenergy SC designs. As shown in the time series between 2010 and 2023, until 2013, the number of studies using 1G and 2G PM was similar. The year 2014 presents the inflection point where the number of studies focused on 2G bioenergy SC design considerably exceeds the number of studies that used 1G. After 2014, the designs that employ 2G RM remain higher in volume until 2023, where the year 2022 presents the most significant difference in the number of studies, with a total of 19 studies associated with 2G RM in comparison with a total of 3 studies that focused on 1G RM.

Examples of bioenergy SC designs using 1G RM were developed in studies such as [154] and [155], which utilized sugarcane for ethanol production. On the other hand, Kheybari et al. [156] and Esmaeili et al. [139] designed SC for the same bioenergetic product but utilized corn as RM. The aforementioned studies consider ethanol the main product in their SC designs, producing it from 1G RM, such as sugar cane and corn, which provide sugars and starch, respectively, for ethanol production. Conversely, 2G sources such as agricultural [136], agro-industrial [157] and forestry residues [158], as well as non-food feedstocks such as algae [159] and switchgrass [147], are examples of RM used in 2G bioethanol SC designs. In that sense, the diversity of RM that can be used for bioethanol production is one factor that influences the high number of studies focused on producing this bioenergy product. Consequently, the bioenergy SC design could be established as a tool that enables the production of bioenergy products in countries with diverse agroecological conditions and agricultural vocations, improving the SC adaptability and exploiting a wide variety of alternative feedstocks that will not compete for food security.

Given the diversity of 1G and 2G RM that can be used for the production of bioethanol, biodiesel, biogas, and electric energy, the bioenergy SC designs are also considered as a decision to utilize one or multiple RM. Figure 7 shows the number of studies that implemented SC designs for bioethanol, biodiesel, biogas, and electric energy based on the quantity of PM utilized as supply. The studies that considered one RM for bioenergy production are shown in Figure 7A, while the studies that considered multiple RM are shown in Figure 7B. From 2010 to 2013, two trends can be particularly identified; the first trend identifies bioethanol, produced with one or multiple RM, as the main bioenergy product analyzed in this time interval. The second trend indicates that, until 2014, bioenergy SC designs employing one RM were considered in more studies.

Moreover, throughout the period from 2014 to 2022, a pluralization of bioenergy products included in the SC designs was identified; for instance, products such as biodiesel, biogas, and electric power were contemplated in a higher number of studies after 2014 (see Figure 7A,B). Among the bioenergetic products identified, biogas is the product with the lowest number of bioenergy SC designs, regardless of whether one or multiple RM were utilized. Furthermore, the SC for biodiesel and electric energy were the bioenergy products with the highest increase, after ethanol, especially since 2015.

The inclusion of multiple RM simultaneously in a bioenergy production process presents a high level of complexity. This complexity is caused by the considerations that must be included in the optimization models employed to obtain the bioenergy SC designs, including the incorporation of binary variables in these models (see Section 5.4). Additionally, it is necessary to include specific constraints related to RM volumes, RM procurement locations, and transformation technologies. The implementation of these considerations in optimization models is helpful to obtain robust bioenergy SC designs since they enable the achievement of the following benefits: increase profitability with the transformation of greater RM volumes, ensure the availability of feedstocks, produce diverse bioenergy products, and ensure a sufficient supply for the SC while simultaneously allowing for a more detailed analysis in the economic evaluations and thus generating positive environmental and social impacts. Regarding the assessment of multiple RM, Memişoğlu and Üster [160] carried out an SC design to produce ethanol, which aimed to minimize the strategic costs related to the supply network. In this study, 2G feedstocks such as switchgrass, forest, and threshing residues were considered in the design. Furthermore, these authors included surrogate constraints associated with inventory volumes and biomass deterioration rates. Yue et al. [161], in turn, analyzed wheat, wheat straw, and miscanthus for ethanol production, while the analysis of environmental impacts is the aim of the bioenergy SC assessment.

5.4. Model Types and Assessment Criteria Applied to Bioenergy SC

Each economic, environmental, or social criterion that can be included in the design of bioenergy SC is utilized as an objective in optimization models to obtain SCM, GSCM, or SSCM designs. However, to implement these optimization models, multi-objective approaches must be applied. This section identifies the types of models used in bioenergy SC designs (SCM, GSCM, or SSCM) and the criteria implemented for this process.

The optimization problems are applied to obtain SC designs for bioenergy products through the formulation of objective functions, variables, and constraints. Figure 8 shows the main integer programming models used by researchers to design and evaluate bioenergy SC. It should be noted that each optimization problem presents particular programming features, which are adjusted to the response requirements that the researcher aims to obtain for the design of the bioenergy SC. Mathematical programming is employed to determine an optimal value (maximum or minimum) for the objective function and to establish the value of the decision variables while satisfying all the constraints [162].

Linear programming (LP) models are characterized by linear objective functions and linear constraints. This type of mathematical programming was applied by authors such as Munir et al. [163] and Aboytes-Ojeda et al. [164], which employed LP models to minimize total SC costs when determining the best location of the production facilities. Munir et al. [163] analyzed coconut oil to produce biodiesel; Aboytes-Ojeda et al. [164], in turn, studied herbaceous biomass (switchgrass) for ethanol production. Although these authors handle different bioenergy feedstocks and products, the objective function approach in both studies is based on economic criteria. The objective functions focused on economic criteria are the main objective functions utilized in investigations related to bioenergy SC. Accordingly,

Table 5. Optimization model types, evaluation criteria, and application context used for bioethanol SC design.

Raw Material (MP)	MP Source		Criteria			Model Type	Scope	Country	Ref.
	1G	2G	Ec.	Env.	Soc.
Rice (Straw)		X	X	X	X	MINLP	Country	Indonesia	[19]
Corn, sorghum, Wheat and its waste	X	X	X	X		MILP	Region	Iran (Fars)	[138]
Corn	X	X	X	X		LP	Region	USA (North Dakota)	[139]
Switchgrass		X	X			MILP	Region	USA (North Dakota)	[147]
Wastewater		X	X	X	X	MILP	Country	Iran	[148]
Sugar cane	X		X			MILP	Country	Brazil	[149]
Corn (straw)		X	X	X		MILP	Region	USA (North Dakota)	[150]
Corn and straw	X	X	X	X			Region	USA (Missouri)	[151]
Sugarcane	X		X	X		MILP	Country	Argentina	[154]
Sugarcane	X		X	X	X	MILP	Country	South Africa	[155]
Crop residue, and woody biomass		X	X	X		MILP	Region	USA	[157]
Forestry and wastes		X	X	X		MILP	Region	USA (Michigan Northern)	[158]
Agricultural wastes		X	X			MILP	Region	USA (Texas)	[160]
Wheat, Wheat (Straw), miscanthus	X	X	X	X		MILP	Country	UK	[161]
Agricultural wastes	X		X			MILP	Country	South Africa	[165]
Sugar cane	X		X			MILP		Not specified	[166]
Corn grain and stover	X	X	X			Other	Region	USA (Illinois)	[167]
Corn (straw)		X	X			MINLP	Region	USA (Illinois)	[168]
Cassava (mandioca)	X		X	X		Other	Region	China	[169]
Biomass		X	X			MILP	Region	Italy (north)	[170]
Switchgrass		X	X			MILP	Region	US (North Dakota)	[171]
Agricultural wastes		X	X			MILP	Region	USA (California)	[172]
Wheat and Corn (straws)		X	X	X	X	MILP	Country	Iran	[173]
Corn	X		X			MILP	Region	Italy (north)	[174]
Agave (Sugar cane bagasse)		X	X			MILP	Country	Mexico	[175]
Wheat, Corn, Cassava (mandioca)	X			X		MILP	Region	China	[176]
Agricultural wastes		X	X			MILP	Region	USA (North Dakota)	[177]
Corn and straw	X	X	X	X		MILP	Country	Italia (northern)	[178]
Sugar cane	X		X			MILP	Country	Argentina	[179]
Agricultural wastes		X	X			MILP	Country	Iran	[180]
Corn (straw) and Forestry and wastes		X	X			MILP	Region	USA (California)	[181]
Corn	X	X	X	X	X	MILP	Region	France (southwest)	[182]
Switchgrass	X	X	X	X	X	MILP	Region	USA	[183]
Corn and straw		X	X	X		MILP	Region	Italy (north)	[184]
Sugar cane	X		X	X		MILP	Country	Argentina	[185]
Corn Silage and straw	X	X	X	X		MILP	Region	Italy (north)	[186]
Straw		X	X			MILP	Region	Canada	[187]
Wheat	X		X	X		MILP	Region	Italy (north)	[188]
Corn	X		X			MILP	Country	Italy (north)	[189]
Sugar cane	X		X			MILP	Country	Argentina	[190]
Sugar cane bagasse		X	X	X		MILP	Country	Iran	[191]
Wheat (Straw) and Corn waste	X	X	X	X	X	MILP	Country	Bulgaria	[192]
Not specified	X	X	X		X	MILP	Region	China	[193]
Agricultural wastes	X	X	X	X	X	MILP	Country	Iran	[194]
Switchgrass		X	X	X	X	MILP	Country	Iran	[195]
Biomass (Wheat, Corn, etc.)	X	X	X	X	X	MILP	Country	Bulgaria	[196]
Sugar cane		X	X	X	X	MILP	Country.	Iran	[197]
Switchgrass		X	X	X		MILP	Region	USA (Tennessee)	[198]
Corn and Barley		X	X			MILP	Region	Mexico	[199]
Wheat (Straw), miscanthus	X	X	X	X		MILP	Country	UK	[200]
Switchgrass		X	X	X		MINLP	Country	Iran	[201]
Corn and Straw	X	X	X	X		MILP	Region	Italy (north)	[202]
Wheat (straw)			X	X		MILP	Country	Germany	[203]
Rice, Barely, wheat		X	X	X	X	MILP	Country	Iran	[204]
Corn and Barley		X	X			MILP	Region	Mexico	[199]
Sugar cane	X	X	X	X	X	MILP	Country	Iran	[205]
Corn, Corn Stover and Switchgrass	X	X	X	X		MILP	Region	USA	[206]
Bean, rice, and Barley	X		X			MILP	Local	Korea	[207]
Organic waste		X	X			MILP	Country	South Korea	[208]
Agricultural residues	X	X	X			MILP	Country	Mauritius	[209]
Sugarcane	X		X	X		MILP	Country	Brazil	[210]
Corn, sorghum, Wheat and, Barley	X		X			MILP	Country	Mexico	[211]
Pine and Eucalyptus	X	X	X			MILP	Country	Colombia	[212]
Corn stover		X	X			MILP	Region	USA	[213]

Note: Ec.: economic criteria, Env.: environmental criteria, LP: linear programming MILP: mixed integer linear programming, MINLP: mixed integer nonlinear programming, Soc.: social criteria, 1G: first generation, 2G: second generation, Ref.: references.

and Table 6 show a classification of the investigations published in SC for bioethanol and biodiesel, respectively; where the type of optimization model type, RM, as well as the objective function (assessment criteria) and application context (city, region, or country scale) were discriminated. Additionally, the design and evaluation of biogas, electric energy SC, and SC that include two or more bioenergy products simultaneously are listed in Table 7 and Table 8, respectively. The references in the Table 5, Table 6, Table 7 and Table 8 cites a few references from [19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280].

As shown in Figure 8, the LP models present the lowest levels of implementation in comparison with the MINLP and MILP optimization models. Studies that involve LP models have been developed by Ren et al. [214] and Esmaeili et al. [139] for bioethanol SC designs. However, the low implementation of LP models could be attributed to their limitations since these models are only applicable to static situations; for instance, this type of model is incapable of solving stochastic problems on its own. Moreover, LP models can especially handle single-objective problems. Nevertheless, it can solve multi-objective problems with the condition that an LP model must be created for each objective [215]. Similar to LP models, mixed integer linear programming (MILP) models have objective functions and linear constraints. However, MILP models are characterized by the integer variables included in their constraints; these constraints could be related to quantities of raw materials or the number of production and storage facilities to be installed, which are considered due to the presence of discrete events. Additionally, the MILP models include binary variables, which enable the model to select between different alternatives with specific options. This feature is carried out by mutually exclusive variables comprising only two values (0 or 1).

Based on the implementation of MILP models, Mutenure et al. [165] formulated an optimization model to maximize the profitability of 1G and 2G ethanol SC involving several feedstocks such as sugarcane with its respective residues, as well as sorghum, wheat, and corn (see Table 5). To solve this optimization problem, a MILP model was used to make decisions between the previously mentioned feedstocks and their different transformation technologies, including whether the installation of storage and pretreatment locations was relevant. Similarly, Cambero et al. [216] evaluated the design of a 2G bioenergy production SC employing forest residues from a wood production area in Canada (see Table 6); it stands for a MILP model formulated with a multi-objective function since these authors include economic, environmental, and social aims to evaluate the production of heat, pellets, and electric energy as final products.

Table 6. Types of optimization models, evaluation criteria, and application context used for biodiesel SC design.

Raw Material	MP Source		Criteria			Model Type	Scope	Country	Ref.
	1G	2G	Ec.	Env.	Soc.
Algae			X			MILP	Country	Korea	[217]
Jatropha curcas		X	X	X		MILP	Country	Iran	[218]
Wastewater sludge		X	X	X		MILP	Region	USA (Mississippi)	[137]
Waste cooking oil		X	X	X	X	MILP	City	Suzhou (China)	[219]
Soybean	X		X			MILP	Country	Iran	[220]
Microalgae		X	X			MILP	Country	Iran	[159]
Forestry wastes		X	X	X		MILP	Region	Canada	[221]
Wastewater sludge		X	X	X		MILP	Region	USA (Mississippi)	[145]
Vegetable oil	X		X			MILP	Country	Brazil	[222]
Oil palm	X		X	X		MILP	Country	Colombia	[223]
Waste cooking oil		X	X	X	X	MILP	Region	China (Jiangsu)	[219]
Oil palm	X		X	X	X	MILP	Country	Colombia	[224]
Sunflower and rapeseed	X		X	X		MILP	Country	Bulgaria	[225]
Waste cooking oil and Jatropha		X	X			MILP	Country	Iran	[226]
Jatropha y Waste Cooking oil		X	X	X		MPRO	Country	Iran	[226]
Jatropha	X		X	X		MILP	Country	Iran	[227]
Jatropha seeds and Waste cooking oil	X		X			MILP	Country	Iran	[228]
Wastewater sludge		X	X			MILP	Country	Iran	[229]
Camellia pleifera		X	X			MILP	Region	USA (Montana)	[230]
Jatropha and Waste Cooking oil		X	X	X		MILP	Region	Iran	[231]
Not specified			X	X	X	LNRM	Country	Iran	[232]
Kitchen waste		X	X			MILP	Region	China	[233]
Waste cooking oil		X	X	X		Other	Region	China	[234]
Chicken fat, mutton fat, and beef fat		X	X	X		MILP	Country	Pakistan	[235]
Chicken fat, beef and Mutton tallow		X	X	X	X	MILP	Country	Pakistan	[236]
Algae		X	X			MINLP	Country	USA	[237]
Fat residue	X	X	X	X	X	MILP	Country	Bulgaria	[238]
Jatropha		X	X	X		MILP	Inter.	Iran	[239]
Moringa	X		X	X		MILP	Country	Iran	[240]
Forest and residues	X	X	X	X		MILP	Region	Iran	[241]
Jatropha		X	X	X	X	MILP	Country	Iran	[242]
Microalgae		X	X			MILP	Country	Korea	[243]
Castor bean seeds		X	X			MILP	Region	Brazil	[244]
Soybean, rapeseed, and sunflower	X		X	X		MINLP		Not specified	[245]

Note: Ec.: economic criteria, Env.: environmental criteria, LNRM: linear non-radial model, MILP: mixed integer linear programming, MINLP: mixed integer nonlinear programming, MPRO: mathematical programming robust model, Soc.: social criteria, 1G: first generation, 2G: second generation, Ref.: references.

Table 6. Types of optimization models, evaluation criteria, and application context used for biodiesel SC design.

Raw Material	MP Source		Criteria			Model Type	Scope	Country	Ref.
	1G	2G	Ec.	Env.	Soc.
Algae			X			MILP	Country	Korea	[217]
Jatropha curcas		X	X	X		MILP	Country	Iran	[218]
Wastewater sludge		X	X	X		MILP	Region	USA (Mississippi)	[137]
Waste cooking oil		X	X	X	X	MILP	City	Suzhou (China)	[219]
Soybean	X		X			MILP	Country	Iran	[220]
Microalgae		X	X			MILP	Country	Iran	[159]
Forestry wastes		X	X	X		MILP	Region	Canada	[221]
Wastewater sludge		X	X	X		MILP	Region	USA (Mississippi)	[145]
Vegetable oil	X		X			MILP	Country	Brazil	[222]
Oil palm	X		X	X		MILP	Country	Colombia	[223]
Waste cooking oil		X	X	X	X	MILP	Region	China (Jiangsu)	[219]
Oil palm	X		X	X	X	MILP	Country	Colombia	[224]
Sunflower and rapeseed	X		X	X		MILP	Country	Bulgaria	[225]
Waste cooking oil and Jatropha		X	X			MILP	Country	Iran	[226]
Jatropha y Waste Cooking oil		X	X	X		MPRO	Country	Iran	[226]
Jatropha	X		X	X		MILP	Country	Iran	[227]
Jatropha seeds and Waste cooking oil	X		X			MILP	Country	Iran	[228]
Wastewater sludge		X	X			MILP	Country	Iran	[229]
Camellia pleifera		X	X			MILP	Region	USA (Montana)	[230]
Jatropha and Waste Cooking oil		X	X	X		MILP	Region	Iran	[231]
Not specified			X	X	X	LNRM	Country	Iran	[232]
Kitchen waste		X	X			MILP	Region	China	[233]
Waste cooking oil		X	X	X		Other	Region	China	[234]
Chicken fat, mutton fat, and beef fat		X	X	X		MILP	Country	Pakistan	[235]
Chicken fat, beef and Mutton tallow		X	X	X	X	MILP	Country	Pakistan	[236]
Algae		X	X			MINLP	Country	USA	[237]
Fat residue	X	X	X	X	X	MILP	Country	Bulgaria	[238]
Jatropha		X	X	X		MILP	Inter.	Iran	[239]
Moringa	X		X	X		MILP	Country	Iran	[240]
Forest and residues	X	X	X	X		MILP	Region	Iran	[241]
Jatropha		X	X	X	X	MILP	Country	Iran	[242]
Microalgae		X	X			MILP	Country	Korea	[243]
Castor bean seeds		X	X			MILP	Region	Brazil	[244]
Soybean, rapeseed, and sunflower	X		X	X		MINLP		Not specified	[245]

Note: Ec.: economic criteria, Env.: environmental criteria, LNRM: linear non-radial model, MILP: mixed integer linear programming, MINLP: mixed integer nonlinear programming, MPRO: mathematical programming robust model, Soc.: social criteria, 1G: first generation, 2G: second generation, Ref.: references.

Table 7. Types of optimization models, evaluation criteria, and application context used for electric energy and biogas SC design.

Product	Raw Material	RM Source		Criteria			Model Type	Scope	Country	Ref.
		1G	2G	Ec.	Env.	Soc.
EE	Woody biomass		X	X			MILP	Region	USA (Tennessee)	[246]
Biogas	Animal manure		X	X			MILP	City	Turkey (Izmir)	[247]
Biogas	Agricultural wastes		X	X	X		MILP	Region	Italy	[248]
EE	Grass straw		X	X	X	X	MILP	Country	Iran	[249]
EE	Forestry wastes		X	X			MILP	Country	Portugal	[146]
EE	Forestry wastes		X	X			MILP	Region	Canada (British Columbia)	[216]
Biogas	Animal manure		X	X	X		MILP	Region	USA (North Dakota)	[250]
Biogas	Artichoke by-products		X	X	X		MGLP	Region	Italy (Sardina)	[251]
Biogas	Livestock manure,		X	X	X		MILP	Region	Mexico	[252]
Biogas	Agricultural wastes and manure		X	X			MINLP	Region	Iran	[253]
Biogas	Crop, pasture, and livestock, and wood residues	X	X	X			MINLP	Region	USA	[152]
Biogas	Manure, Sewage Sludge		X	X	X		MILP	Region	USA (Wisconsin)	[254]
Biogas	Corn silage and livestock manure		X	X			MILP	Region	Italy (North)	[255]
Biogas	Residual crops		X	X	X		NLP			[256]
Biogas	Chicken manure		X	X			MILP	Region	Turkey	[257]
Biogas	Manure, organic waste, and wastewater		X	X	X		MINLP	Region	Mexico	[258]
EE	Crop residue		X				MILP	Country	Pakistan	[259]
EE	Wood pellets	X		X			MINLP	Region	Canada	[260]
EE	Woody biomass		X	X			MILP	Region	USA	[261]
EE	Agricultural wastes		X	X	X		MILP	Inter.	European Union	[262]
EE	Agricultural wastes		X	X			MILP	Country	Egypt	[263]

Note: Ec.: economic criteria, EE: electric energy, Env.: environmental criteria, Inter.: international scope, MGLP: multiple goal linear programming, MILP: mixed integer linear programming, MINLP: mixed integer nonlinear programming, NLP: nonlinear programming, Soc.: social criteria, 1G: first generation, 2G: second generation, Ref.: references.

Table 7. Types of optimization models, evaluation criteria, and application context used for electric energy and biogas SC design.

Product	Raw Material	RM Source		Criteria			Model Type	Scope	Country	Ref.
		1G	2G	Ec.	Env.	Soc.
EE	Woody biomass		X	X			MILP	Region	USA (Tennessee)	[246]
Biogas	Animal manure		X	X			MILP	City	Turkey (Izmir)	[247]
Biogas	Agricultural wastes		X	X	X		MILP	Region	Italy	[248]
EE	Grass straw		X	X	X	X	MILP	Country	Iran	[249]
EE	Forestry wastes		X	X			MILP	Country	Portugal	[146]
EE	Forestry wastes		X	X			MILP	Region	Canada (British Columbia)	[216]
Biogas	Animal manure		X	X	X		MILP	Region	USA (North Dakota)	[250]
Biogas	Artichoke by-products		X	X	X		MGLP	Region	Italy (Sardina)	[251]
Biogas	Livestock manure,		X	X	X		MILP	Region	Mexico	[252]
Biogas	Agricultural wastes and manure		X	X			MINLP	Region	Iran	[253]
Biogas	Crop, pasture, and livestock, and wood residues	X	X	X			MINLP	Region	USA	[152]
Biogas	Manure, Sewage Sludge		X	X	X		MILP	Region	USA (Wisconsin)	[254]
Biogas	Corn silage and livestock manure		X	X			MILP	Region	Italy (North)	[255]
Biogas	Residual crops		X	X	X		NLP			[256]
Biogas	Chicken manure		X	X			MILP	Region	Turkey	[257]
Biogas	Manure, organic waste, and wastewater		X	X	X		MINLP	Region	Mexico	[258]
EE	Crop residue		X				MILP	Country	Pakistan	[259]
EE	Wood pellets	X		X			MINLP	Region	Canada	[260]
EE	Woody biomass		X	X			MILP	Region	USA	[261]
EE	Agricultural wastes		X	X	X		MILP	Inter.	European Union	[262]
EE	Agricultural wastes		X	X			MILP	Country	Egypt	[263]

Note: Ec.: economic criteria, EE: electric energy, Env.: environmental criteria, Inter.: international scope, MGLP: multiple goal linear programming, MILP: mixed integer linear programming, MINLP: mixed integer nonlinear programming, NLP: nonlinear programming, Soc.: social criteria, 1G: first generation, 2G: second generation, Ref.: references.

The application of MILP models is suitable for multi-objective functions; thus, they are commonly used in bioenergy SC optimization problems. The methods to apply multiobjective optimization in SC designs are characterized by including economic, environmental, and social criteria in the mathematical programming problem as part of the objection function or as constraints that establish the boundaries of the values to be achieved (see Table 8). This approach is used to make decisions about the location of facilities (production plants, inventories, or pretreatment centers), conversion technologies, raw materials, demand zones, investment capital, production planning, or inventory management [264]. Consequently, the MILP optimization problems are the most commonly used due to their capability to provide bioenergy SC designs that satisfy the economic, environmental, or social objectives defined by the researcher.

Figure 9 shows the distribution of studies published in the last two decades on bioenergy SC designs according to the criteria included in the objective functions of their respective optimization problems. The economic objective is the criteria most frequently observed in the analyzed works. In this sense, an economic objective was applied by Mahjoub et al. [265], Egieya et al. [266], and León-Olivares et al. [199] for the SC designs of biodiesel, biogas, and bioethanol, respectively. It was noted that all these authors utilized the minimization of the SC total cost as the economic objective in their optimization problems, regardless of the previously mentioned bioenergetic products.

Table 8. Types of optimization models, evaluation criteria, and application context used for SC designs that include two or more bioenergy products simultaneously.

Raw Material	MP Source		Criteria			Model Type	Scope	Country	Ref.
	1G	2G	Ec.	Env.	Soc.
Corn	X		X	X		MILP	Region	Italy (North)	[133]
Corn and straw	X	X	X	X		MILP	Region	Italy (north)	[131]
Coffee waste	X	X	X	X		MILP	Country	Colombia	[140]
Corn, straw	X	X	X	X		MILP	Country	Italy (north)	[144]
Straw, manure, and sugar beet		X	X			MILP	Country	Denmark (northwest)	[267]
Corn manure and silage			X	X		MILP	Region	Turkey (Izmir)	[268]
Agricultural residues	X	X	X			MILP	Region	Slovenia	[269]
Forestry residues		X	X	X		MILP	Country	Canada	[143]
Agricultural, forestry, and energy crop wastes	X	X	X			MILP	Region	Korea (Jeju Island)	[270]
Rice, wheat, barley, and corn straw		X	X	X		MILP	Country	Iran	[136]
Agricultural wastes		X	X			MILP	Country	Iran	[265]
Corn and oil	X		X				Country	USA	[271]
Biomass and manure		X	X			MILP	Country	Slovenia	[266]
Animal and agricultural wastes		X	X		X	MILP	Region	Iran	[272]
Azadirachta indica and Eruca sativa		X	X	X		Other	Country	Iran	[273]
Food waste		X	X			MILP	Local	China	[274]
Agricultural wastes	X	X	X			MILP	Country	Ethiopia	[275]
Sugarcane	X		X	X		MILP	Country	Iraq	[276]
Biomass	X		X			Other			[132]
Forest residues and Agricultural wastes		X	X			MILP	Country	Iran	[277]
Cereal straw		X	X	X		MILP	Country	Germany	[278]
Jatropha		X	X	X		MILP	Country	Iran	[279]
Agricultural wastes		X	X			MILP	Region	California	[181]

Note: Ec.: economic criteria, Env.: environmental criteria, MILP: mixed integer linear programming, Soc.: social criteria, 1G: first generation, 2G: second generation, Ref.: references.

Table 8. Types of optimization models, evaluation criteria, and application context used for SC designs that include two or more bioenergy products simultaneously.

Raw Material	MP Source		Criteria			Model Type	Scope	Country	Ref.
	1G	2G	Ec.	Env.	Soc.
Corn	X		X	X		MILP	Region	Italy (North)	[133]
Corn and straw	X	X	X	X		MILP	Region	Italy (north)	[131]
Coffee waste	X	X	X	X		MILP	Country	Colombia	[140]
Corn, straw	X	X	X	X		MILP	Country	Italy (north)	[144]
Straw, manure, and sugar beet		X	X			MILP	Country	Denmark (northwest)	[267]
Corn manure and silage			X	X		MILP	Region	Turkey (Izmir)	[268]
Agricultural residues	X	X	X			MILP	Region	Slovenia	[269]
Forestry residues		X	X	X		MILP	Country	Canada	[143]
Agricultural, forestry, and energy crop wastes	X	X	X			MILP	Region	Korea (Jeju Island)	[270]
Rice, wheat, barley, and corn straw		X	X	X		MILP	Country	Iran	[136]
Agricultural wastes		X	X			MILP	Country	Iran	[265]
Corn and oil	X		X				Country	USA	[271]
Biomass and manure		X	X			MILP	Country	Slovenia	[266]
Animal and agricultural wastes		X	X		X	MILP	Region	Iran	[272]
Azadirachta indica and Eruca sativa		X	X	X		Other	Country	Iran	[273]
Food waste		X	X			MILP	Local	China	[274]
Agricultural wastes	X	X	X			MILP	Country	Ethiopia	[275]
Sugarcane	X		X	X		MILP	Country	Iraq	[276]
Biomass	X		X			Other			[132]
Forest residues and Agricultural wastes		X	X			MILP	Country	Iran	[277]
Cereal straw		X	X	X		MILP	Country	Germany	[278]
Jatropha		X	X	X		MILP	Country	Iran	[279]
Agricultural wastes		X	X			MILP	Region	California	[181]

Note: Ec.: economic criteria, Env.: environmental criteria, MILP: mixed integer linear programming, Soc.: social criteria, 1G: first generation, 2G: second generation, Ref.: references.

Figure 9 shows that the bioenergy SC designs that exclusively use objectives of economic criteria in their optimization problems represent 45% of the total number of works analyzed. Furthermore, implementing multi-objective problems, including economic and environmental objectives (GSCM), is increasingly relevant since they are included in 40% of the published works. Finally, multi-objective problems focused on sustainability, which jointly include economic, environmental, and social objectives, presented the lowest percentage of participation (16%). The work reported by Costa et al. [224], who performed a 1G biodiesel SSCM design considering economic, environmental, and social objectives to determine the most sustainable location for a new production facility for this liquid biofuel, stands out as an example of the formulation of these multi-objective problems. However, it is essential to note that the inclusion of environmental and social criteria in bioenergy SC designs inevitably generates conflicts when they are simultaneously considered with economic criteria.

Economic objectives are generally formulated in the objective functions of optimization problems through indexes to maximize the NPV and profit or minimize the total cost (see Figure 10). The environmental objectives, in turn, are formulated to achieve bioenergy SC designs with a higher degree of environmental responsibility, having as aims the reduction in greenhouse gas (GHG) emissions, reduction in impacts on land use, or the payment of penalties for the generation of pollutants in the system (see Figure 10). Nevertheless, fulfilling these environmental aims is directly related to increased costs in the SC of bioenergy. The same situation arises when social objectives are included, where one of the most commonly used social criteria is employment generation. Consequently, if this criterion was optimized to fulfill a social objective in a bioenergy SC, it would automatically have a negative influence on the economic evaluation by increasing the costs of the bioenergy production SC.

Multi-objective optimization models for bioethanol SC design with their respective evaluation criteria are presented by Ortiz-Gutiérrez et al. [133] and Yue et al. [161], while a biodiesel SC design is presented by García-Cáceres [223]; for the aforementioned works the GSCM were evaluated. These authors simultaneously evaluated economic and environmental criteria through MILP optimization models. Life cycle analysis (LCA) was used to determine the GHG emissions and the direct and indirect impacts generated, which were included in the objective function. This approach to include environmental analysis is widely used in the evaluation of bioenergy SC designs (see Figure 10). The multi-objective functions formulated by García-Cáceres [223] and Yue et al. [161] aimed to minimize the total costs and GHG emissions for a GSCM to produce biodiesel. Similarly, Ortiz-Gutiérrez et al. [133] formulated a multi-objective function to minimize emissions and minimize NPV for a GSCM to produce bioethanol. These works illustrate some interactions between environmental and economic criteria employed by authors to design bioenergy SC.

The interactions between economic, environmental, and social criteria for bioenergy SSCM design are shown in Figure 10. Job creation, responsible land use, the influence on life quality, and population health are considered in social assessments to determine the impacts of bioenergy SSCM design. Rabbani et al. [148], Ghaderi et al. [195], and Gilani y Sahebi [197] designed bioethanol SSCM. Based on this approach, the authors mentioned above formulated the minimization of GHG emissions as an environmental objective, while the economic objectives differed. In that sense, Rabbani et al. [148] and Ghaderi et al. [195] employed the minimization of total cost as the economic objective, while Gilani and Sahebi [197] used the maximization of utility. Finally, the number of jobs generated was used as a social criterion by all authors in their SSCM designs. Although this social criterion is the most commonly used in bioenergy SSCM designs, it should be noted that this interaction between economic, environmental, and social criteria is the least common in bioenergy SSCM design (see Figure 10). Mixed integer non-linear programming models (MINLP) also include functions, constraints, and interactions between integer and binary variables; the MINLP differ regarding LP since the objective functions included in MINLP can implement inverse, quadratic, or exponential functions. Based on the capability of handling objective functions with the aforementioned features, MINLP could generate a representation that better fits real-world phenomena. However, the solution of these models presents a high degree of complexity.

Throughout the use of MINLP models to solve optimization problems, Yue and You [168] evaluated the profitability of a bioethanol SC, while Khishtandar [253] and Sarker et al. [152] evaluated the total cost and computational performance of a biogas SC. The implementation of MINLP in these works was presented given the complexity of the decisions analyzed in their formulations, since in their designs, they aimed to find optimized solutions for production facility locations, as well as the location of storage centers and the quantities of waste to be transported between facilities. Yue and You [168], in turn, included in their SC design making decisions such as RM transfer prices and biofuel sales; these authors also considered revenue distribution parameters between each feedstock supplier and feedstock receiver. Moreover, Sarker et al. [152] included in their optimization model the modification of feedstock quantity caused by seasonal variations. Due to the diversity of decisions that MINLP models enable, these can be widely applied in the bioenergy SC design. The solution method for MINLP models is usually based on the convexity of the nonlinear objective function, which can be classified into two categories: convex and non-convex. In convex MINLP, the Branch and Bound (B&B) method is applied, where each node of the problem is solved by nonlinear programming, thus ensuring a minimum bound for the original MINLP while the existence of a feasible solution is guaranteed. In contrast, the challenge is greater if it is not a convex function since applying a Branch and Bound method for each node does not guarantee a global minimum. Due to this type of inconvenience, several authors have used the linearization of the formulation functions as a solution strategy, thus solving an optimization problem using a MILP model instead of a MINLP model. Consequently, the MILP model is the most relevant approach to solving optimization problems for the bioenergy SC design in the analyzed works.

5.5. Bioenergy SC Application Context Scale

In previous sections, we have analyzed the decisions involved in the optimization problems for bioenergy SC; in that sense, the evaluation criteria and the type of mathematical programming for solving these problems have also been discussed. However, in this section, we analyze the application context of the studies in order to identify the scale of implementation used for the models formulated for bioenergy SC design.

The design of bioenergy SC involves the interaction between the different elements that compose the supply (upstream), transformation (midstream), and distribution (downstream) stages. The identification of the RM that can be used, its availability, quantity, and proximity to processing facilities are some of the requirements that delimit the bioenergy SC design. This identification is carried out in the supply stage; thus, implementing an optimization problem that encompasses as many of these decisions as possible enables an optimal SC design. Consequently, it will be possible to exploit the potential of a specific geographical area to a greater extent. In this sense, these bioenergy SC designs are evaluated through case studies, where the optimization problem defined is applied in a real context to be used for decision-making on the production system designed.

The design of bioenergy SC must incorporate different approaches to take advantage of the infrastructure conditions, geographical conditions, and production systems available in a geographical area to develop production processes that are adjusted to the conditions offered by the environment. Therefore, the bioenergy SC design must contemplate using alternative raw materials with specific logistical difficulties to obtain and transform. These difficulties could be attributed to the dispersion of supply points, process requirements, or the necessity of a particular treatment to exploit unconventional feedstocks. The bioenergy SC approach considers these difficulties in their designs; thus, it could be used to improve the viability of these types of projects in geographic areas with production potential. As a result, it can diversify the economic activities of a region, impacting the quality of life of its population.

Figure 11 shows the number of case studies that evaluate bioenergy SC designs according to country and application context. The application contexts (scale) identified were international, national, regional, and local (or City). The local context is the smallest application scale, as evidenced by the works analyzed. This evaluation context is characterized by the fact that the bioenergy SC scopes are applied to a relatively small geographic area; therefore, the magnitude of the costs and volumes of the bioenergy product to be produced is relatively small in scale (usually city scale). Bioenergy SC designs generally applied in this local context are implemented to solve specific problems in geographic areas such as cities or small provinces [274]. Consequently, bioenergy SC designs applied in local (or city) contexts are usually used for the exploitation of specific types of waste generated by economic activities previously established, which have a clear predominance in these areas [280].

The regional and country evaluation scales were the most frequently utilized application contexts in the case studies analyzed since they were employed in 41.6% and 51.9% of the studies, respectively (see Figure 11). Nevertheless, the regional and country contexts show a significant difference according to the country of application. This behavior is evident in Iran and the USA, with the highest number of works reported from 2010 to 2023. In Iran, only 6 cases were reported at the regional scope and 36 at the country scope, while in the USA, the proportion is the opposite, with 26 cases at the regional scope and only 4 at the national scope. This difference in the context implemented between Iran (36 country cases) and the USA (26 regional cases) can be attributed to the particular conditions of each country. Moreover, the USA, which has been a reference in implementing policies for the use and production of bioenergy, is a country with a large land area and high production and demand for products such as corn, soybeans, wheat, and livestock. Based on these properties, its economy is supported by large-scale private companies distributed in different states of its territory. Consequently, the development of bioenergy SC in the regional context enables the USA to better identify the characteristics of mid-range geographic zones (state or group of states), segmenting its large land area into zones with relatively similar economic activities, thus limiting the supply network to manageable volumes of feedstock.

Iran, in turn, has a considerably smaller geographic area (compared to the USA) with a centralized economy, which stands out as one of the top five countries in the world with the largest oil reserves and the second largest natural gas reserves. Consequently, the economic activity and revenues of its government still depend mainly on oil exports. However, the fact that it is the country with the most bioenergy SC case studies published in recent years (see Figure 11) is evidence of its intention to reduce its economic dependence on oil, reduce emissions, diversify its economy, and enhance the agricultural sector as another of its economic pillars [136]. Given the aforementioned factors, in addition to the plan adopted by its government authorities for economic development based on progress in science and technology, the country context for bioenergy SC is the most appropriate for its environment. Due to its land extensions and production of a limited variety of agricultural products, mainly wheat, barley, and rice [281], Iran requires the projection from the national scale point of view to provide enough raw material, according to the facility sizes analyzed and bioenergy production volumes to achieve better economic viability [138,226].

Based on the context applied in the case studies and the tools used in the works published, it can be concluded that economic criteria are the main evaluation methods to determine the feasibility of bioenergy SC. The total cost of bioenergy SC, as well as NPV, viability, and logistic costs, are the indicators that primarily determine the design of bioenergy SC, followed by environmental criteria such as LCA and GHG emissions, while social criteria such as jobs generated are relegated to the last place. Nevertheless, if a correlation is made between the aforementioned criteria and the application context of the bioenergy SC, it is possible to infer that the regional scope is the most versatile. This versatility is due to the regional context allowing the utilization of different types of RM, which enables better use of the properties in a specific geographic area. In order to define the supply logistics required to estimate, with sufficient detail, the production facility sizes to achieve a better viability.

5.6. Uncertinity in Bioenergy SC Design

The diversity of variables, parameters, and decisions that interact within the optimization models to represent bioenergy SC has been described in previous sections of this document. Nevertheless, parallel to the inclusion of strategic decisions, as well as the inclusion of more specific tactical decisions, the diversity of bioenergy types, and the application of case studies in different world locations, it is necessary to consider the fluctuations in parameters such as the demand for bioenergy products, quantities of available feedstock and feedstock prices. These fluctuations in input parameters are considered through uncertainty. Table 9 shows a set of investigations that included uncertainty in the design of different bioenergy SC, correlating the type of uncertainty applied, the strategy of solution, and the software employed to solve the optimization problem.

Table 9 shows a compilation of works where the parameters usually analyzed under uncertainty are identified. The parameters most commonly studied under uncertainty include fluctuations of feedstock availability, feedstock prices, crop yields, transport capacities, and costs, the performance of transformation technologies [145], and the demands of the different bioenergy products, as well as parameters related to environmental criteria such as environmental impact coefficients and emissions per biomass crop [244]. It should be noted that the bioenergy SC assessment approach defined by the researcher and the parameters that are subject to uncertainty determine the guidelines for the selection of strategies of solution. In that sense, the analyzed works have reported random, epistemic, and deep uncertainty.

Epistemic uncertainty refers to the lack of knowledge or information about the behavior of a system. Random uncertainty, in turn, is associated with inherent variation in an analyzed system or environment and subdivided into homoscedastic and heteroscedastic uncertainty [282]. Furthermore, deep uncertainty is a situation in which analysts of a phenomenon are unaware of the models that relate the key forces shaping a future event (or scenario), the probability distributions of key variables and the parameters in these models are unknown, or the value of alternative outcomes is unknown [283]. Thus, Epistemic and random uncertainties are associated with the quantity of information that can be supplied to the model that represents the SC. This information establishes the proportion between the information needed and the information available to run the assessment. Consequently, uncertainty can be defined as the application of a model for decision-making based on incomplete information. The references in the Table 9 cites a few references from [128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290].

Table 9. Uncertainty and strategy of solution used for bioenergy SC design.

Uncertainty Type	Parameter with Uncertainty	Strategy of Solution	Solver	Reference
Deterministic	Biomass purchase cost, Transportation cost, Fertilizer sales prices	Sensitivity analysis	LINGO	[284]
Epistemic	Costs and single setup multiple delivery, carbon emissions	Sensitivity analysis and Fuzzy parameters	Methauristic method	[285]
Deterministic	RM supply and Biodiesel demand	Sensitivity analysis and AEC method	Unspecified	[286]
Epistemic	Biodiesel demand, RM availability, biofuel prices	Sensitivity analysis	LINGO 18	[163]
Random	Biodiesel demand, Jatropha yields	Scenarios analysis	GAMS	[227]
Random	RM availability, Biodiesel demand	Chance constrained	Unspecified	[287]
Epistemic	Size of leased land, Target to be achieved	Fuzzy objective and constraints	IBM ILOG (CPLEX)	[247]
Random	Biomass supply, Technology	Two-stage stochastic programming	GAMS (CPLEX)	[145]
Random	Biomass price, Biomass crop emissions	Scenarios analysis	GAMS (CPLEX)	[128]
Deterministic, Random	Technology (yields)	Sensitivity analysis	Gurobi	[254]
Random	Biomass purchase prices, Bioethanol demand, Ethanol purchase prices	Scenarios analysis	GAMS	[157]
Deterministic	Biofuel Demand, RM crop yields, Transportation capacities	Scenarios analysis	LINGO 11.0	[176]
Random	RM supply, Bioethanol Demand	Two-stage stochastic programming	AMLP (CPLEX)	[172]
Deterministic, Random	RM crop yields	Scenarios analysis	Unspecified	[169]
Deterministic, Random	RM crop yields, Purchase prices, Bioethanol demand	Two-stage stochastic programming	GAMS	[177]
Random	Biodiesel demand, RM supply	Scenarios analysis	CPLEX	[219]
Random	RM availability	Two-stage stochastic programming	AMPL-CPLEX	[288]
Random	Biomass availability, transportation costs, Fixed and variable costs	Scenarios analysis	GAMS (CPLEX)	[146]
Epistemic	Bioethanol demand, RM and Bioethanol sales price, Environmental impact factor	Robust possibilistic programming	GAMS (CPLEX)	[173]
Deterministic	Bioethanol demand	Two-stage stochastic programming	GAMS (CPLEX)	[179]
Random	Environmental factor	multistage stochastic	AIMMS (CPLEX)	[244]
Epistemic	Risk coefficient	E-constraint	GAMS (CPLEX)	[218]
Deterministic, Random	Supply sources, Critical technical factors, Biodiesel demand	Two-stage stochastic programming	GAMS (GAMS)	[159]
Random	Biomass demand, Biomass availability, Biomass price	Chance constrained	Hybrid framework of Montecarlo	[253]
Epistemic	RM availability, Bioethanol demand	Robust possibilistic programming	GAMS (CPLEX)	[289]
Epistemic	Bioethanol export prices, Domestic bioethanol demand, External bioethanol demand	Robust possibilistic programming	GAMS (CPLEX)	[197]
Epistemic	Biodiesel demand	Scenarios analysis	Interior-point algoritm—CPLEX	[229]
Random	Biomass supply	Multistage stochastic	AIMMS (CPLEX)	[290]

Note: RM.: raw material.

Table 9. Uncertainty and strategy of solution used for bioenergy SC design.

Uncertainty Type	Parameter with Uncertainty	Strategy of Solution	Solver	Reference
Deterministic	Biomass purchase cost, Transportation cost, Fertilizer sales prices	Sensitivity analysis	LINGO	[284]
Epistemic	Costs and single setup multiple delivery, carbon emissions	Sensitivity analysis and Fuzzy parameters	Methauristic method	[285]
Deterministic	RM supply and Biodiesel demand	Sensitivity analysis and AEC method	Unspecified	[286]
Epistemic	Biodiesel demand, RM availability, biofuel prices	Sensitivity analysis	LINGO 18	[163]
Random	Biodiesel demand, Jatropha yields	Scenarios analysis	GAMS	[227]
Random	RM availability, Biodiesel demand	Chance constrained	Unspecified	[287]
Epistemic	Size of leased land, Target to be achieved	Fuzzy objective and constraints	IBM ILOG (CPLEX)	[247]
Random	Biomass supply, Technology	Two-stage stochastic programming	GAMS (CPLEX)	[145]
Random	Biomass price, Biomass crop emissions	Scenarios analysis	GAMS (CPLEX)	[128]
Deterministic, Random	Technology (yields)	Sensitivity analysis	Gurobi	[254]
Random	Biomass purchase prices, Bioethanol demand, Ethanol purchase prices	Scenarios analysis	GAMS	[157]
Deterministic	Biofuel Demand, RM crop yields, Transportation capacities	Scenarios analysis	LINGO 11.0	[176]
Random	RM supply, Bioethanol Demand	Two-stage stochastic programming	AMLP (CPLEX)	[172]
Deterministic, Random	RM crop yields	Scenarios analysis	Unspecified	[169]
Deterministic, Random	RM crop yields, Purchase prices, Bioethanol demand	Two-stage stochastic programming	GAMS	[177]
Random	Biodiesel demand, RM supply	Scenarios analysis	CPLEX	[219]
Random	RM availability	Two-stage stochastic programming	AMPL-CPLEX	[288]
Random	Biomass availability, transportation costs, Fixed and variable costs	Scenarios analysis	GAMS (CPLEX)	[146]
Epistemic	Bioethanol demand, RM and Bioethanol sales price, Environmental impact factor	Robust possibilistic programming	GAMS (CPLEX)	[173]
Deterministic	Bioethanol demand	Two-stage stochastic programming	GAMS (CPLEX)	[179]
Random	Environmental factor	multistage stochastic	AIMMS (CPLEX)	[244]
Epistemic	Risk coefficient	E-constraint	GAMS (CPLEX)	[218]
Deterministic, Random	Supply sources, Critical technical factors, Biodiesel demand	Two-stage stochastic programming	GAMS (GAMS)	[159]
Random	Biomass demand, Biomass availability, Biomass price	Chance constrained	Hybrid framework of Montecarlo	[253]
Epistemic	RM availability, Bioethanol demand	Robust possibilistic programming	GAMS (CPLEX)	[289]
Epistemic	Bioethanol export prices, Domestic bioethanol demand, External bioethanol demand	Robust possibilistic programming	GAMS (CPLEX)	[197]
Epistemic	Biodiesel demand	Scenarios analysis	Interior-point algoritm—CPLEX	[229]
Random	Biomass supply	Multistage stochastic	AIMMS (CPLEX)	[290]

Note: RM.: raw material.

Random uncertainty can be addressed through the stochastic programming approach; this approach is implemented when enough data are available that are reliable to construct a probability distribution. In this sense, future conditions are assumed based on past trends constructed from statistical data [128,287]. Epistemic uncertainty, in turn, is addressed through the robust solution strategy. In this possibilistic programming strategy, the researcher aims to ensure that the model can cover an extreme fluctuation in specific input parameters, or that the model is capable of predicting the worst-case scenario when a factor of high influence for the SC fluctuates. Kanan et al. [291] implemented this type of uncertainty approach by employing a multi-objective SC and a flexible uncertain constraints strategy, while [180,289] implemented robust possibilistic programming as a strategy of solution based on epistemic uncertainty applied to the chosen input parameters.

Different types of software with specific solvers are used to implement strategies for solving these optimization problems under uncertainty (see Table 9). It should be noted that the selection of the software is conditioned to the preferences of the researchers, according to the programming language and the different tools available to execute the respective analysis. In that sense, the most implemented software to solve the optimization problems formulated in the studies analyzed is GAMS, which has been used in 60% of the bioenergy SC designs, followed by LINGO (4%), Advance Interactive Multidimensional Modeling—AIMMS (4%), Mathematical programming Language—AMPL, and ILOG (4%).

6. Research and Development Trends in Bioenergy SC Design

Research aimed at the design of SC of bioenergy products such as bioethanol, biodiesel, biogas, and electric energy has been implemented in different countries. However, issues related to the approach, information management, and sustainability require a higher level of analysis to improve the viability of these bioenergy products. In that sense, countries have created regulations to stimulate the production and consumption of some of these products. This trend has caused countries with recent interest in bioenergy generation to identify local advantages that allow them to diversify energy generation sources, enabling them to produce bioenergy products that could contribute to satisfying their energetic demand, thus achieving energy security in the future.

Table 10. Research and development trends in the design of bioenergy SC.

Issue	Trend	Strategy
Bioenergy products	Increase in the number of SC designs for electric energy from biomass	To develop SC designs of electric energy or use cogeneration to improve the economic performance of other bioenergy products enhancing their production. To exploit the advantage of its distribution using existing electrical grids to obtain lower distribution costs in comparison with other bioenergy products.
	Increase in the number of biogas SC designs	To implement the production of biogas from the decomposition of organic residues obtained in the production of other bioenergy products; implementation of the biorefinery approach. To generate and use biogas as a source of energy to obtain other bioenergy products.
Design platforms integrating several assessment tools	Implementation of platforms that include different tools (python, AI, specific software, Apps)	To develop technology platforms that integrate tools such as simulation, modeling, environmental analysis, regional characterizations, programming languages, and AI.
Strategic and tactic decision making	Use of multiple RM	To consider varied raw materials, especially residues generated in agricultural and agro-industrial processes. To utilize sub-products generated in other industrial sectors, reducing environmental and social impacts through the design of bioenergy SC, GSC, or SSC.
	Increased implementation of SC with combined (or network) structure	To perform more specific analyses of the production stage and consider the results of these analyses in the SC designs of combined (or network) structures.
Optimization models types and criteria for bioenergy SC design	Consideration of new environmental and social objectives	To implement multi-objective optimization that includes economic, environmental, and social criteria.
	Diversification of social criteria in the bioenergy SC design	To consider other environmental criteria such as influence on quality of life or land use.
Application context	Designs for regional scales	To develop bioenergy SSC at the regional scale involving several types of RM (Including the biorefinery approach).
Bioenergy SC designs	Balance between economic, environmental, and social criteria throughout multi-objective optimization	To apply evaluations that could include sensitivity analysis, to identify environmental and social goals that have the lowest economic impact.
		To produce different types of bioenergy products in the same area for more efficient exploitation of available RM.
Uncertainty	Optimization and SC design under uncertainty	To implement uncertainty that considers more complex phenomena, for instance, pandemics, irregular market trends, and environmental regulations.

SC: supply chain; RM: raw material; GSC: green supply chain; SSC: sustainable supply chain; AI: artificial intelligence.

shows the research and development trends in bioenergy SC design. From the perspective of bioenergy products used in SC designs, it is evident that the increase in their demand, as well as the increase in the number of published studies that address their production, is the result of their potential as possible alternatives to diversify the energetic offer from non-conventional raw materials. Accordingly, using different RM simultaneously could increase the probability of maintaining a sufficient supply while allowing the production of different bioenergy products. Additionally, the design and implementation of this type of bioenergy SC can be applied to comply with the indications established in the Paris Agreement and Climate Change Conference of the Parties—COP26, which stipulate that the global temperature increase must be limited to 1.5 °C, decelerate the consumption of fossil fuels and reduce net CO₂ emissions to zero by 2025 [292].

Bioethanol and biodiesel currently have their places in the fuel market, mainly due to their implementation as energetic alternatives in different countries and as a mechanism to reduce environmental impacts. Nevertheless, biogas and electric energy produced from biomass have the potential to increase their production due to the variety of raw materials and the processes for their production. Additionally, biogas also has the potential to be used as a precursor in the generation of other products such as bioethanol, syngas, or electric energy. Consequently, biogas and electric energy from biomass have the potential to be used in different scenarios. On the one hand, biogas and electric energy can be used to improve the viability of other bioenergy products, such as bioethanol and biodiesel, since biogas or electric energy are frequently generated as co-products or used as a source of energy generation in the production process of a main bioenergetic product. Nevertheless, their distribution advantages can be used, for instance, electricity from biomass, which can be sold economically directly to the electricity distribution network.

Based on the sustainability concept, the consumption trend given by the market in recent years is aimed at a more responsible use of resources, which includes a greater awareness of the impacts generated on the environment and society. The continuous inclusion of environmental and social objectives, together with economic objectives, in the design of bioenergy SC will allow a deeper understanding of the adverse effects (in economic terms) derived from the inclusion of environmental and social objectives in the sustainable design of bioenergy SC (SSCM). Therefore, targets associated with these environmental and social criteria representing lower costs in bioenergy SC will be identified; thus, mechanisms could be evaluated to improve the viability of SSCM of bioenergy. Furthermore, the application context scale considered for the design of the bioenergy SC also influences the sustainability of bioenergy production. According to the studies analyzed, a higher production facility capacity derived from a higher volume of RM reduces the total costs of bioenergy SC. Nevertheless, these facility capacities are dictated by the demand for bioenergy products; therefore, identifying the context of the application to obtain a sufficient level of detail on aspects such as RM availability or finished product requirements for obtaining a viable SC design is crucial. Consequently, the design of a bioenergy SC in a regional context is a mechanism to satisfy these requirements since this context considers the properties of a specific geographical area, with several feedstocks that could provide a continuous and constant supply.

7. Conclusions

The bibliographic analysis has evidenced that bioenergy production is a research topic that has evolved between 2010 and 2023. The increase in the production of bioenergy products such as bioethanol, biodiesel, biogas, and electrical energy from biomass allows us to conclude that there is currently interest in developing production processes with greater viability. Based on the works analyzed, it was identified that strategic decisions such as factory capacity and the location of production facilities are the most frequently used decision variables in the design of bioenergy SC. Similarly, it was found that since 2013, the bioenergy SC designs have focused on considering several feedstocks simultaneously, especially 2G feedstocks. Due to these evaluation and design trends, bioenergy SC, especially in the last decade, often includes environmental and social objectives aimed at sustainability. Consequently, the MILP optimization model statement is the most relevant strategy for including multiple objectives. These approaches are complemented by including uncertainty, especially random and deterministic uncertainty, which are solved by solvers such as GAMS and AIMMS.

The implementation of sustainability in bioenergy SC is a complex objective. Implementing this concept requires the interaction between different disciplines, which focus on relevant aspects such as energy transformation, raw material properties, the evolution of energy transformation pathways, distribution and supply networks, as well as the analysis and design of processes or supply chain management, including environmental, economic, and social analysis. Due to the correlation required between this diversity of disciplines, developing platforms that allow the interaction between different technological tools is a valuable approach to obtaining, processing, and analyzing the many aspects that must be considered in bioenergy production. Consequently, the design and implementation of platforms that enable different technological advances to evaluate bioenergy production is a necessary strategy for improving the viability of bioenergy products obtained from non-conventional raw materials.