Search Results (341)

Achieving the construction sector’s dual carbon objectives necessitates scaling green energy adoption in new residential buildings. The current literature critically overlooks four unresolved problems: oversimplified penalty mechanisms, ignoring escalating regulatory costs; static subsidies misaligned with market maturity evolution; systematic exclusion of innovation feedback from energy suppliers; and underexplored behavioral evolution of building owners. This study establishes a government–suppliers–owners evolutionary game framework with dynamically calibrated policies, simulated using MATLAB multi-scenario analysis. Novel findings demonstrate: (1) A dual-threshold penalty effect where excessive fines diminish policy returns due to regulatory costs, requiring dynamic calibration distinct from fixed-penalty approaches; (2) Market-maturity-phased subsidies increasing owner adoption probability by 30% through staged progression; (3) Energy suppliers’ cost-reducing innovations as pivotal feedback drivers resolving coordination failures, overlooked in prior tripartite models; (4) Owners’ adoption motivation shifts from short-term economic incentives to environmentally driven decisions under policy guidance. The framework resolves these gaps through integrated dynamic mechanisms, providing policymakers with evidence-based regulatory thresholds, energy suppliers with cost-reduction targets, and academia with replicable modeling tools. Full article

Supplier selection is a crucial factor for ensuring compliance with the circular economy’s principles. Existing approaches often overlook the integration of circularity and risk assessment in supplier evaluation, limiting their effectiveness in achieving sustainability goals. This paper addresses this gap by applying suitable criteria and proposing a structured decision-making model for circular supplier selection. The model innovatively integrates Multi-Criteria Decision Analysis (MCDA) techniques with risk evaluation, providing a comprehensive framework for assessing suppliers in circular supply chains. By advancing the theoretical understanding of circular supplier selection, this research contributes to both academia and practice, reinforcing the alignment between supply chain decision-making and the Sustainable Development Goal (SDG), particularly Target 12.5. Full article

Manufacturing serves as a vital indicator of a nation’s economic strength, technological advancement, and comprehensive competitiveness. In the context of the VUCA (Volatility, Uncertainty, Complexity, Ambiguity) business environment and globalization, uncertain market demand has intensified supply chain disruption risks, necessitating resilience strategies to enhance supply chain stability. This study proposes five resilience strategies—establishing an information sharing system, multi-sourcing, alternative suppliers, safety stock, and alternative transportation plans—while integrating sustainability requirements. A multi-objective mixed-integer optimization model was developed to balance cost efficiency, resilience, and environmental sustainability. Comparative analysis reveals that the resilience-embedded model outperforms traditional approaches in both cost control and risk mitigation capabilities. The impact of parameter variations on the model results was examined through sensitivity analysis. The findings demonstrate that the proposed optimization model effectively enhances supply chain resilience—mitigating cost fluctuations while maintaining robust demand fulfillment under uncertainties. Full article

Effective management of the hydrogen supply chain (HSC), starting with supplier selection, is crucial for advancing the hydrogen industry and economy. Supplier selection, a complex Multi-Criteria Decision Analysis (MCDA) problem in an inherently uncertain environment, requires careful consideration. This study proposes a novel hybrid MCDA framework that integrates the Bayesian Best–Worst Method (BWM), Fuzzy Analytic Hierarchy Process (AHP), and Entropy Weight Method (EWM) for robust criteria weighting, which is aggregated using a game theory-based model to resolve inconsistencies and capture the complementary strengths of each technique. Next, the globally weighted criteria, emphasizing sustainability performance and techno-risk considerations, are used in the TODIM method grounded in prospect theory to rank hydrogen suppliers. Numerical experiments demonstrate the approach’s ability to enhance decision robustness compared to standalone MCDA methods. The comparative evaluation and sensitivity analysis confirm the stability and reliability of the proposed method, offering valuable insights for strategic supplier selection in the evolving hydrogen landscape in the HSC. Full article

In light of the current challenges posed by complex multi-agent interactions and competing interests in integrated energy systems, an economic optimization operation model is proposed. This model is based on a two-layer game comprising a one-master–many-slave structure consisting of an energy retailer, energy suppliers, and a user aggregator. Additionally, it considers energy suppliers to be engaged in a non-cooperative game. The model also incorporates a carbon trading mechanism between the energy retailer and energy suppliers, considers integrated demand response at the user level, and categorizes users in the community according to their energy use characteristics. Finally, the improved differential evolutionary algorithm combined with the CPLEX solver (v12.6) is used to solve the proposed model. The effectiveness of the proposed model in enhancing the benefits of each agent as well as reducing carbon emissions is verified through example analyses. The results demonstrate that the implementation of non-cooperative game strategies among ESs can enhance the profitability of ES1 and ES2 by 27.83% and 18.67%, respectively. Furthermore, the implementation of user classification can enhance user-level benefits by up to 39.51%. Full article

Suppliers nowadays can be directly approached in business-to-business (B2B) E-commerce websites. This makes the product and service information of certain suppliers accessible in online reviews. Therefore, online reviews have become important for B2B supplier evaluation and selection. Recently, the sustainability of food packaging has attracted increasing attention from companies and consumers. This study developed a novel multi-criteria decision making (MCDM) method called Percentage Assessment with Synergistic Comparisons And Aggregated Ranks (PASCAAR) to support the selection of sustainable food package suppliers based on online review information in B2B E-commerce websites. Such a method used three different percentage comparisons between alternatives and the minimal options, and then aggregates the comparisons with their ranks. This study confirmed the effectiveness of PASCAAR by applying it to a case study to select the supplier of sustainable food packages (i.e., biodegradable food containers) from six candidates in the B2B E-commerce website by considering multi-dimensional online review information and their own product properties. Using PASCAAR, this study obtained the outcome that the third candidate is the most suitable one, as quantitative results indicate this supplier has the highest PASCAAR score. Based on the results, this study further conducted thorough sensitivity tests to validate the results. It can be found that, compared with the classical MCDM methods in measuring the performance of alternatives and aggregating evaluation scores, the PASCAAR method can have more robust and informative results. This study also developed a PASCAAR Solver to enable easy implementation of this method. This study contributes to the existing literature by providing new ranking and aggregation ideas in MCDM and can offer practitioners a more informative and highly actionable method for supplier selection and decision support system development by utilizing online review information. Full article

Overmilking occurs when the teat cups remain attached to the udder during milking, even though there is little or no milk flow. This puts pressure on the teat tissue and reduces milk production due to longer milking times, meaning fewer cows are milked per hour. Therefore, the correct removal of the teat cup at the end of mechanical milking is crucial for the milking process. The aim of this study was to describe the factors influencing automatic teat cup removal (ATCR) at the end of mechanical milking and to demonstrate its importance for udder health, milk production and milk quality. There are considerable differences between milking system suppliers and countries regarding the minimum removal of the teat cup at the end of the milking process. However, to ensure good milk quality, prevent teat damage and reduce the risk of mastitis, it is important to shorten the working time of the milking machine on the udder in both automatic and conventional milking systems. For this reason, several studies have shown that increasing the milk flow switch point effectively reduces milking time, especially in automatic milking systems where dairy cows are milked more than twice a day. However, when the ATCR setting was increased above 0.5 kg·min⁻¹, milk production decreased, and the number of somatic cells in the milk produced increased. Therefore, the use of ATCR at a milk flow rate of 0.2 kg·min⁻¹ significantly increased milk production, improved milk quality and maintained udder health when a low vacuum level (34–36 kPa) was used in milking machines such as MultiLactor and StimuLactor (Siliconform, Germany). In conclusion, ATCR at a milk flow of 0.2–0.3 kg·min⁻¹ is a useful level to achieve various goals on dairy farms when a low vacuum of 34–36 is used in the milking machine. If the milking machine uses a higher vacuum, it is possible to program a higher ATCR at a milk flow of up to 0.5 kg·min⁻¹. Full article

This paper presents a novel approach to supplier clustering by utilizing the Arithmetic Optimizer Algorithm (AOA), addressing the complex challenge of supplier segmentation in modern supply chain management. The AOA framework is applied to solve the multi-criteria clustering problem inherent to supplier classification. Using a real-world dataset of 500 suppliers with 12 performance criteria, including cost, quality, delivery reliability, and sustainability metrics, our method demonstrates effective clustering performance compared to conventional techniques. The AOA achieves a silhouette coefficient of 56.5% and a Davies–Bouldin index of 56.6%, outperforming several other state-of-the-art metaheuristic algorithms, including the Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA), and Harris Hawks Optimization (HHO). The algorithm’s robustness is validated through extensive sensitivity analysis and statistical tests. The results indicate that the proposed approach successfully identifies distinct supplier segments with approximately 85% accuracy, enabling more effective supplier relationship management strategies. Full article

Background: The agri-food sector is a major contributor to environmental degradation and emissions, highlighting the need for sustainable practices to mitigate its impact. Within this sector, perishable food crops require targeted efforts to reduce their environmental footprint. Vertical integration is crucial for ensuring alignment between strategic and tactical decision making in supply chain management. This article presents a multi-objective mathematical model that integrates both economic and environmental considerations within the perishable food supply chain, aiming to determine optimal solutions for conflicting objectives. Methods: In this research, we employed combining goal programming with the epsilon constraint approach; this comprehensive methodology reveals optimal solutions by discretizing the values derived from the payoff table. Results: The model is applied to a real case study of the tomato paste supply chain in Ethiopia. To identify Pareto-efficient points, the results are presented in two scenarios: Case I and Case II. Conclusions: The findings emphasize the significant influence of the geographical location of manufacturing centers in supplier selection, which helps optimize the trade-off between environmental impact and total cost. The proposed solution provides decision makers with an effective strategy to optimize both total cost and eco-costs in the design of perishable food supply chain networks. Full article

Modular integrated construction (MiC) is a cutting-edge approach to construction that significantly improves efficiency and reduces project timelines by prefabricating entire building modules off-site. Despite the operational benefits of MiC, the carbon footprint of its extensive supply chain remains understudied. This study develops a hybrid approach that combines multi-agent simulation (MAS) with deep learning to provide scenario-based estimations of CO₂ emissions, costs, and schedule performance for MiC supply chain. First, we build an MAS model of the MiC supply chain in AnyLogic, representing suppliers, the prefabrication plant, road transport fleets, and the destination site as autonomous agents. Each agent incorporates activity data and emission factors specific to the process. This enables us to translate each movement, including prefabricated components of construction deliveries, module transfers, and module assembly, into kilograms of CO₂ equivalent. We generate 23,000 scenarios for vehicle allocations using the multi-agent model and estimate three key performance indicators (KPIs): cumulative carbon footprint, logistics cost, and project completion time. Then, we train artificial neural network and statistical regression machine learning algorithms to captures the non-linear interactions between fleet allocation decisions and project outcomes. Once trained, the models are used to determine optimal fleet allocation strategies that minimize the carbon footprint, the completion time, and the total cost. The approach can be readily adapted to different MiC configurations and can be extended to include supply chain, production, and assembly disruptions. Full article

Digital agriculture has rapidly developed in the last decade in many countries where the share of agricultural production is a significant part of the total volume of gross production. Digital agroecosystems are developed using a variety of IT solutions, software and hardware tools, wired and wireless data transmission technologies, open source code, Open API, etc. A special place in agroecosystems is occupied by electronic payment technologies and blockchain technologies, which allow farmers and other agricultural enterprises to conduct commodity and monetary transactions with suppliers, creditors, and buyers of products. Such ecosystems contribute to the sustainable development of agriculture, agricultural engineering, and management of production and financial operations in the agricultural industry and related industries, as well as in other sectors of the economy of a number of countries. The introduction of crypto solutions in the agricultural sector is designed to create integrated platforms aimed at helping farmers manage supply lines or gain access to financial services. At the same time, there are risks of illegal use of computing power for cryptocurrency mining—cryptojacking. This article offers a thorough risk assessment of cryptojacking attacks on endpoint systems, focusing on identifying critical vulnerabilities within IT infrastructures and outlining practical preventive measures. The analysis examines key attack vectors—including compromised websites, infected applications, and supply chain infiltration—and explores how unauthorized cryptocurrency mining degrades system performance and endangers data security. The research methodology combines an evaluation of current cybersecurity trends, a review of specialized literature, and a controlled experiment simulating cryptojacking attacks. The findings highlight the importance of multi-layered protection mechanisms and ongoing system monitoring to detect malicious activities at an early stage. Full article

The growing frequency and complexity of both natural and man- made crises like pandemics have highlighted the need for robust emergency logistics strategies during the last decade. Procurement is one of the critical areas impacting any emergency supply chain. Dealing with disruptions usually involves multi-sourcing, option contracts, and back-up suppliers as resilient strategies. The proposed review classifies emergency procurement strategies into three capacities following the capacity resilience framework: absorptive, adaptive, and restorative. Also, quantitative resilience metrics, modelling methods, and objectives for practical scenarios are discussed. Conclusions are extracted, and future research agendas outlined. Full article

Background: Growing environmental concerns and the vulnerability of global supply chains to disruptions, such as pandemics, natural disasters, and logistical failures, necessitate the design of sustainable and resilient supply chains. Methods: A novel multi-period mixed-integer linear programming model is developed with the objective of maximizing supply chain profit to design a complete citrus supply chain, which incorporates the production of citrus fruit and juice, and accommodates resilience and sustainability perspectives. Results: A comprehensive citrus supply chain scenario is presented to support the applicability of the proposed model, leveraging real data from citrus supply chain stakeholders in Egypt. Moreover, an actual case study involving a citrus processing company in Egypt is demonstrated. Gurobi software is used to solve the developed model. To build a resilient supply chain which can cope with different disruptions, different scenarios are modeled and strategies for having multiple suppliers, backup capacity, and alternative logistics routes are evaluated. Conclusions: The findings underscore the critical role of resilience in supply chain management, particularly in the agri-food sector. Moreover, the proposed model not only maximizes supply chain profitability but also equips stakeholders with the tools necessary to navigate challenges effectively. Full article

Traditional downstream-initiated accounts receivable financing (TF) is often constrained in assisting immediate upstream suppliers. Conversely, blockchain-enabled accounts receivable financing (BF) offers an efficient solution for deep-tier suppliers to bridge capital gaps and ensure smooth production processes. This study constructs a three-level supply chain model comprising a capital-constrained tier-2 supplier, a similarly constrained tier-1 supplier, and an established retailer. To alleviate financial strain among suppliers, we analyze and compare two financing mechanisms: TF and BF. The investigation explores the effects of blockchain adoption and the time value of capital on a multi-tier supply chain. Our findings indicate that while BF typically features a lower interest rate than TF, it may still perform less favorably under certain conditions. Specifically, when the time value of capital for the tier-1 supplier is sufficiently high, the profitability of all supply chain members under BF falls short of that achieved through TF. In other words, adopting blockchain is not universally the best strategy for multilevel supply chains. Additionally, we delineate the impacts of the accounting period and financing rates on the optimal choice of financing model. These insights provide substantial evidence and managerial guidance regarding the appropriate circumstances for blockchain adoption and its interplay with accounts receivable financing. Full article

With the soaring demand for electric power and the limited spinning reserve in the power system in Taiwan, the comprehensive management of both thermal power generation and load demand turns out to be a key to achieving the robustness and sustainability of the power system. This paper aims to design a demand bidding (DB) mechanism to collaborate between customers and suppliers on demand response (DR) to prevent the risks of energy shortage and realize energy conservation. The concurrent integration of the energy, transmission, and reserve capacity markets necessitates a new formulation for determining schedules and marginal prices, which is expected to enhance economic efficiency and reduce transaction costs. To dispatch energy and reserve markets concurrently, a hybrid approach of combining dynamic queuing dispatch (DQD) with direct search method (DSM) is developed to solve the extended economic dispatch (ED) problem. The effectiveness of the proposed approach is validated through three case studies of varying system scales. The impacts of tie-line congestion and area spinning reserve are fully reflected in the area marginal price, thereby facilitating the determination of optimal load reduction and spinning reserve allocation for demand-side management units. The results demonstrated that the multi-area bidding platform proposed in this paper can be used to address issues of congestion between areas, thus improving the economic efficiency and reliability of the day-ahead market system operation. Consequently, this research can serve as a valuable reference for the design of the demand bidding mechanism. Full article