Search Results (2,303)

Cold supply chains require coordinated inventory and storage decisions to preserve product quality while managing high energy consumption. This paper develops a joint economic lot-sizing model for a two-echelon cold supply chain that explicitly integrates time–temperature-dependent quality degradation with energy consumption in refrigerated warehouses. Unlike traditional approaches, energy is modeled as an endogenous function of warehouse filling level and warehouse temperature, allowing the interaction between inventory volume, energy efficiency, and quality preservation to be captured. The model is formulated under three coordination policies—Lot-for-Lot, traditional agreement, and consignment stock—and solved under joint decision making. Numerical results for chilled and frozen products show that neglecting energy and quality costs can lead to sub-optimal policies with total cost penalties exceeding 300% compared to the proposed integrated optimization. Results further indicate that a consignment stock agreement can reduce total system costs by up to 9% relative to traditional policies, while the optimal lot size is highly sensitive to energy prices, product value, and warehouse temperature. These findings highlight the critical role of jointly optimizing inventory, energy, and quality decisions in cold supply chains and provide actionable insights for designing more sustainable and energy-efficient production inventory systems. Full article

To achieve the goal of the 2015 Paris Agreement to limit global warming to 2 °C compared to pre-industrial levels, South Korea is implementing a policy to use bioethanol as a transportation fuel based on the Renewable Fuel Standard (RFS). This study proposes a mixed-integer linear programming (MILP) model to design an optimal bioethanol supply chain utilizing rice straw, a readily available resource in South Korea. To minimize the total cost of bioethanol production, the proposed model considers optimal facility locations, i.e., those of feedstock collection (farm), refining (refinery), and consumption (market), and transportation volumes. This experiment is conducted to evaluate the blending ratios of bioethanol in gasoline (3%, 6%, and 9%) specified by the Renewable Fuel Standard (RFS) policy, based on actual gasoline consumption data in South Korea. In the RFS 3% scenario, operating a single large-scale refinery was the most economical option, but in the RFS 6% and RFS 9% scenarios, multiple refineries must be utilized to ensure supply chain economics. In conclusion, the proposed MILP model shows the practicality of gradually increasing the number of refineries and selecting the optimal location for each region as future bioethanol demand increases. Full article

The rapid expansion of telecommunication infrastructure in developing countries has increased the demand for sustainable strategies to deploy field engineers in tower maintenance operations. Traditional approaches often neglect spatial factors, resulting in inefficient workforce allocation, excessive travel, and higher carbon emissions. This study develops an applied geospatial deployment framework that integrates spatial analysis with sustainable supply chain management (SSCM) principles to support operational decision-making in resource-constrained telecommunication maintenance environments. Using publicly available tools, tower and homebase coordinates were mapped and analyzed through Haversine-based geodesic distance calculations, with a comparative assessment against Euclidean approximation, while incorporating operational constraints such as service time per tower, available personnel, and work-hour limitations. The results indicate that the existing two-homebase deployment strategy leads to unbalanced workloads and unnecessary travel distances. By introducing a cluster-based restructuring using k-means to identify four sub-homebases, the proposed approach reduces total round-trip travel distance from 9120 km to 5913 km per maintenance cycle, representing a 35.2% reduction. This distance reduction corresponds to an estimated saving of approximately 593 kg of CO₂ emissions per maintenance cycle, representing an operational-scale reduction in travel-related emissions based on distance-derived fuel consumption modeling and assuming typical fuel efficiency for service vehicles. In addition, the optimized spatial configuration enables a more equitable distribution of engineers and reduces travel-related fatigue. These findings demonstrate the value of integrating geospatial optimization with sustainable supply chain management by aligning operational efficiency with quantifiable environmental and social sustainability outcomes. The proposed framework offers a replicable, low-cost, and data-driven solution for telecommunication infrastructure providers seeking to enhance the sustainability of field service operations in resource-constrained environments. Full article

Regional production-consumption imbalances and deficient multimodal connectivity in grain circulation systems have rendered traditional segmented transport inefficient, loss-intensive, and costly, constraining overall supply chain performance. In China, the persistent north-to-south and west-to-east grain transfer patterns, driven by regional production–consumption imbalances, have imposed significant challenges on the grain circulation system, making multimodal transport optimization a critical priority for national food security. Multimodal transport, a critical logistics optimization strategy, integrates diverse transport modes and hub nodes to enable end-to-end coordination, thereby enhancing circulation efficiency and food security. This study systematically reviews the transport configurations and modal characteristics of grain multimodal transport, and employs bibliometric analysis with the VOSviewer tool to map publication trends and keyword co-occurrence networks. Subsequently, recent advances in transshipment hub location selection and route optimization in multimodal transport systems are examined. Finally, existing technical bottlenecks are summarized, and future research directions are outlined from the perspectives of intelligent logistics, green and low-carbon development, coordinated operations, and supply chain resilience. Full article

This paper focuses on alternatives to the CAFC-NEV credits policy in the automotive industry of China. It considers a dual-channel supply chain consisting of a manufacturer and a retailer that can simultaneously produce and sell new energy vehicles (NEVs) and internal combustion engine vehicles (ICEVs). Differential game theory is employed to explore dynamic optimal decisions under CAFC-NEV credits and carbon credit policies. The results suggest that the strategies combining CAFC-NEV credits and carbon credit policies are equivalent to a single CAFC-NEV credits policy. Therefore, implementing the carbon credit policy on the basis of the CAFC-NEV credits policy does not affect the increase in NEV range. If the NEV credit score is below a certain threshold, the carbon credit policy will result in a higher range increase and brand goodwill of NEV. In the transition process of implementing the carbon credit policy based on CAFC-NEV credits and subsequently canceling the CAFC-NEV credit policy, the profits of supply chain members change slightly. The findings provide a theoretical basis for the timely exit of the CAFC-NEV credits policy. Full article

The government’s scientific disclosure of food safety inspection information can guide consumers toward rational substitution choices, thereby improving food safety while transforming individual decision-making into collective action, thereby achieving social co-governance. This process activates the “voting with their feet” market mechanism, which exerts pressure on supply chain enterprises to improve quality control. However, the current mismatch between disclosed information and consumer demand significantly weakens this effect. Drawing on evolutionary game theory, this study constructs an evolutionary game model involving producers, sellers, and consumers to explore how information alignment shapes stakeholder behavior. The findings indicate that improving information alignment effectively nudges consumers toward informed substitution choices, reinforcing the market-driven pressure on supply chain enterprises to strengthen quality control; reducing quality control costs is a more effective short-term incentive for sellers than increasing market returns; and when information alignment is low, prioritizing inspections of sellers more efficiently enhances co-governance performance, whereas under high alignment, stronger regulation of producers becomes more effective. Aligning the content, channels, and presentation of government-disclosed inspection information with consumer needs is critical to empowering effective social co-governance. These findings provide theoretical foundations and policy insights to optimize information disclosure strategies and regulatory resource allocation. Full article

With the deepening of international trade and the increasing shortage of land resources, the importance of virtual soil trade in grain has become increasingly prominent. Based on FAO data, this study constructs the virtual soil trade network of wheat, rice, corn and soybean in the major G20 grain trading countries in 2013 and 2023, measures its network characteristics, and uses the exponential random graph model to explore its influencing factors from three dimensions of economic scale, geographical characteristics and resource endowment. The results show that: (1) virtual land trade is essentially a redistribution mechanism of land use pressure, rather than a simple grain flow; (2) the formation of network is driven by exogenous economic factors and endogenous relations; and (3) the role of each country in the network varies with the grain and food category and the development stage, showing a systematic differentiation. It is suggested that the allocation of land resources should be optimized according to the differences in virtual land flows in different countries and food categories. Since the export of virtual land is accompanied by ecological costs (such as deforestation, soil degradation, and water consumption), sustainability must be integrated into trade policies. Rations involve national security strategy, and it is necessary to strengthen domestic productivity and strategic reserves. Feed grain can use the market mechanism to promote trade liberalization and diversification, and reduce the risk of supply chain concentration while giving full play to the global comparative advantage. Full article

Globalization has reorganized industrial spatial patterns, embedding regional economies into complex global production systems. However, the existing literature primarily focuses on the national level, leaving the “global-national-local” multi-scale linkages of sub-national regions underexplored. Focusing on Guangdong, which is China’s most open economic gateway, this study constructs a nested Multi-Regional input–output (MRIO) model to systematically reveal its industrial linkage paths across multiple scales. The results demonstrate that Guangdong features “strong local services and extensive global connections.” Specifically, the network is led by the high-R&D-intensity category and supported by energy and low-R&D categories, highlighted by two core supply paths, which are non-metallic mineral supply for construction and metal product support for optical–electrical manufacturing. Four heterogeneous modes are identified: resource security, innovation-driven dual circulation, cost-competitive regional division, and export-oriented service support. Crucially, the provincial “domestic intermediate chains plus international core chains” logic underscores Guangdong’s role as a bridge connecting Global and Domestic Value Chains. Theoretically, this work enriches the local dimension of Global Production Network theory. Methodologically, it provides an operational tool for nested analysis. Practically, it offers policy evidence for open economies to optimize industrial layouts and enhance supply chain resilience. Full article

A considerable proportion of perishable goods, including fruits and vegetables, deteriorate prior to reaching customers. Inadequate refrigeration infrastructure, particularly in developing nations with arid climates and markets distant from agricultural sources, accounts for most of these losses. A food cold chain has three primary phases: pre-cooling, cold storage, and refrigerated transportation. All phases of the cold chain rely fundamentally on refrigeration to preserve perishable products at designated temperatures, relative humidity, and CO₂ concentrations, thus prolonging their shelf life. Solar-driven or aided refrigeration systems use solar energy to power cooling systems and preserve the food in the cold chain. These systems are especially beneficial in off-grid or developing areas for preserving perishable goods such as fruits, vegetables, and other food items, mitigating postharvest losses that can exceed 30–50% in areas with inconsistent energy supplies. Despite progress in efficiency and scalability, numerous research gaps remain across technological, economic, social, policy, and regional dimensions, including technical aspects, optimization, and integration. There is a need to enhance energy-efficient designs, particularly by managing solar intermittency to address non-uniform cooling, which leads to inconsistent ripening and spoilage, and by integrating sustainable refrigerants to mitigate environmental impact. Further development is necessary for micro-scale, transportable, or decentralized systems designed for small farms, while economic and financing obstacles include high upfront costs and limited financial accessibility. Substantial deficiencies exist in creating affordable models and funding channels for small-scale agriculturalists. Addressing these deficiencies could expedite adoption, thereby reducing global food loss and waste (accounting for 8–10% of GHG emissions) while improving food security. Future research must emphasize multidisciplinary methodologies that amalgamate engineering, economics, and social sciences to provide comprehensive solutions. Full article

Global value chains (GVCs) have evolved into highly interconnected and geographically fragmented production networks, increasing exposure to systemic disruptions and revealing the limitations of static input–output and conventional network approaches. This study develops a unified analytical framework for modeling the structure, dynamics, and resilience of GVCs by integrating input–output economics with network theory, control theory, optimal transport, information theory, and cooperative game theory. The framework represents GVCs as time-varying, multi-level networks and formalizes shock propagation through stochastic normalization and state-space dynamics. Entropy-regularized optimal transport is employed to model friction-dependent substitution and supply chain reconfiguration, while Koopman operator methods approximate nonlinear adjustment dynamics. Cooperative flow-based indices are introduced to assess systemic importance and bargaining power. The analysis produces a coherent set of structural and dynamic indicators capturing vulnerability, adaptability, and controllability across country–sector nodes. Overall, the framework provides an empirically applicable toolkit for diagnosing structural fragilities, comparing resilience across economies, and supporting scenario-based evaluation of industrial and trade policies in complex global production networks. Full article

Background: The constant growth in demand for sustainable energy products and the development of the circular economy have created a critical need for an efficient supply chain for biomass. However, the inherent challenges of biomass make its harvesting, collection, storage, and transport difficult, impacting logistical efficiency and the viability of bioenergy and bioproduct production. This study analyzes how combining artificial intelligence (AI) with multimodal transport can optimize and improve efficiency, as well as reduce costs, in biomass logistics. Methods: The study uses a tiered research framework that encompasses the physical domain (biomass limitations), the structural domain (mathematical modeling for multimodal transport), the intelligence domain (AI-based decision making), and the strategic approach. Results: The outcomes indicate that while truck transport is ideal for short distances, integrating rail and water transport through AI-driven optimization reduces costs and greenhouse gas emissions for long-distance travel. AI technologies, such as digital twins and machine learning, improve demand forecasting, real-time routing, and cargo consolidation, leading to enhanced prediction accuracy for transport costs. Conclusions: The integration of AI and multimodal networks builds resilient and sustainable biomass supply chains. However, full implementation requires addressing data fragmentation and investing in digital infrastructure to enable seamless coordination between supply chain stakeholders. Full article

The intensification of pig production and the restructuring of pork supply-demand patterns have profoundly reshaped greenhouse gas (GHG) emissions across the pork supply chain. Understanding the environmental consequences of these food system transitions is essential for developing effective mitigation strategies. Focusing on technological and spatial transformations between 2002 and 2022, this study employed linear programming and life cycle assessment (LCA) to systematically quantify GHG emissions from China’s pork supply system, applied the Logarithmic Mean Divisia Index to decompose the key drivers of emission changes, and conducted scenario analysis to assess mitigation potential by 2030. The results show that geographic shifts in pork production and consumption increased interprovincial food miles and associated transport emissions. With the intensification of pig production, carbon dioxide (CO₂) emissions surpassed methane (CH₄) to become the second-largest GHG source, driven primarily by greater reliance on commercial feed, synthetic fertilizers, and fossil energy inputs. Although the transition from smallholder to intensive production systems exerted a mitigation effect, this was outweighed by a substantially larger increase in emission intensities across all production systems. Between 2002 and 2022, total emissions rose by 110.1%, reaching 164.05 Mt CO₂eq. A full-chain optimization strategy integrating low-opportunity-cost feed substitution, enhanced manure recycling, biogas production, and green transportation could reduce emissions by 49.1% by 2030 while enabling an 8.2% increase in pork output. This work not only reveals the evolving emission structure of China’s pork supply system but also identifies critical pathways for the low-carbon transformation of livestock systems globally. Full article

Supply Chain Management (SCM) has received considerable attention from the industrial community in recent decades. SCM continues to be an interesting and relevant research topic in many business areas such as revealing supply chain integration benefits, uncertainty and risk mitigation methods, decision-making and optimization methodologies, etc. In current supply chain management, huge volumes of data are being developed each second, and emerging technologies such as Radio Frequency Identification (RFID) have amplified the availability of online data. Using Artificial Intelligence (AI) methods that go beyond simply using the huge volume of online data enables Supply Chain (SC) managers to monitor everything in a timely fashion. There are several aspects of an SC that AI—and specifically Artificial Neural Networks (ANNs)—can be applied to better help them manage and optimize. This study aims to review state-of-the-art ANNs and Deep Neural Networks (DNNs) in the field of supply chain management. One hundred high-quality research studies that applied ANNs in supply chain management are reviewed and categorized into four classes: performance optimization, supplier selection, forecasting, and inventory management studies. Our study shows that there is a significant possibility that we could use ANNs and DNNs to better manage supply chains. Across the reviewed studies, neural networks are frequently reported to improve predictive performance and support monitoring/control in complex, nonlinear supply chain settings, often complementing traditional operations research approaches. Finally, the limitations of ANN models and the possibilities for future studies are presented at the end of this study. Full article

Biological wastewater treatment relies primarily on activated sludge and anaerobic digestion for the removal of organic matter. In urban wastewater treatment plants discharging into eutrophication-sensitive environments, the simultaneous removal of carbon, nitrogen, and phosphorus is required to meet increasingly stringent discharge limits. Under these conditions, the transformation of complex organic matter into volatile fatty acids (VFAs) represents a more efficient strategy than complete mineralization, as biodegradable carbon is essential to sustain biological nitrogen and phosphorus removal processes. In this study, an anaerobic sequencing batch reactor was operated under acidogenic conditions to promote the conversion of organic matter into VFAs. For the first time, this study demonstrates how temperature-controlled acidogenic pretreatment can reliably supply biodegradable carbon to support efficient downstream nitrogen and phosphorus removal in municipal wastewater treatment. A kinetic model was developed to describe the temporal evolution of the different carbon fractions involved in anaerobic digestion, including biodegradable and non-biodegradable organic matter, intermediate compounds, short-chain volatile fatty acids, and biogas. The model assumes first-order kinetics and constant biomass concentration and was successfully validated against experimental data, with deviations below 10%. Estimated kinetic constants exhibited a strong temperature dependence, particularly for hydrolysis and acidogenic pathways, whereas methanogenic steps showed lower sensitivity. Overall, the results demonstrate that temperature is a key operational parameter governing acidogenic performance and carbon transformation pathway. The simple and novel proposed kinetic model provides a useful tool for predicting VFA production and optimizing anaerobic pretreatment strategies aimed at enhancing downstream nutrient removal processes. Optimizing SBR operation for nutrient removal also offers sustainability benefits by improving resource efficiency and reducing energy and chemical inputs. Full article