A multi-objective fuzzy mathematical model and machine learning algorithms for a blood supply chain considering disruptions

A risk-averse two-stage stochastic programming model for the biomass supply chain planning problem

Under the dual carbon target regulation, this article constructs a collaborative research and development carbon reduction model for the photovoltaic industry supply chain from the perspective of carbon tax and consumer green preferences, using differential game theory. Considering three different scenarios of no research and development carbon reduction, independent research and development carbon reduction, and collaborative research and development carbon reduction, the optimal factors and profit values are obtained, and case analysis and sensitivity analysis are conducted. Research has found that: 1) The optimal carbon reduction achieved by photovoltaic industry supply chain entities through cooperative research and development is higher than that achieved through independent research and development. 2) The increase in carbon tax rates has to some extent increased carbon emissions, but at the same time reduced the overall profit of the photovoltaic industry chain. 3) The higher the proportion of research and development costs borne by photovoltaic system manufacturers, the higher the carbon emission reduction of photovoltaic silicon wafer suppliers' research and development. 4) Consumer green preferences are beneficial for increasing carbon emissions reduction in the photovoltaic industry chain.

The efficient management of reverse supply chains, particularly the collection and remanufacturing of defective products, plays a critical role in reducing production costs and determining the final pricing of remanufactured products. While existing research extensively explores warranty policies and maintenance services to enhance customer satisfaction and profitability, the integration of vehicle routing for product collection and sustainability advertising strategies remains underexplored. Addressing this gap, this study introduces a comprehensive two-stage reverse supply chain model that captures the interactions between manufacturers (MFRs) and remanufacturers (RMFRs) through a Stackelberg game framework. Methods: The proposed model incorporates interactive production constraints, vehicle routing problem (VRP) for optimizing collection logistics, and sustainability advertising to influence consumer behavior towards remanufactured products. Utilizing mixed nonlinear programming (MINLP) and nonlinear programming (NLP) techniques, the model simultaneously optimizes pricing strategies, collection efforts, and advertising investments for both MFRs and RMFRs. Numerical analyses are conducted to solve the optimization problems, accompanied by sensitivity analyses to evaluate the impact of key parameters such as production costs, defect rates, and routing constraints. The numerical results demonstrate that increases in production costs for MFRs lead to higher selling prices, thereby reducing their profit margins and negatively impacting RMFR profitability due to decreased demand for remanufactured products. Sensitivity analysis reveals that higher defect rates (α ≥ 0.8) significantly diminish overall supply chain profitability by lowering customer acceptance of RMPs. Additionally, expanding the allowable vehicle routing distance L effectively reduces collection costs, enhancing RMFR profits and enabling greater investment in sustainability advertising. The study shows that the integration of VRP and advertising strategies proves crucial in balancing cost efficiencies and market competitiveness, ultimately fostering a more sustainable and profitable reverse supply chain.

A simulation-based approach for reconstructing a diverse set of supply chain models with sparse data using a quality diversity algorithm

This paper explores sales mode choice under asymmetric information and varying logistics service quality. A dual-channel supply chain model is examined, comprising a manufacturer, a retail platform, and two heterogeneous logistics service providers, where the retail platform possesses private information regarding channel competition through a game-theoretical analysis. It is shown that under conditions of information symmetry, when the market size is small, the manufacturer can maximize profits under the FA scenario (Agency selling under complete information); otherwise, the retail platform tends to prefer the opposite strategy. When the market size is moderate, the reselling dual-channel strategy emerges as the optimal choice for maximizing the overall profitability of the supply chain. In such cases, all supply chain participants achieve a win-win outcome under the same strategy. Under asymmetric information, the manufacturer predominantly opts for scenario AR (Reselling under asymmetric information), while the manufacturer may achieve profitability under scenario AA (Agency selling under asymmetric information).

Additive manufacturing (AM), otherwise referred to as 3D printing, has rapidly grown and is considered a core element in the digitalization of supply chain management. It allows for localized and on-demand production, reducing dependence on central storages and mass manufacturing. AM is transforming how modern supply chains operate by enabling more flexible, localized, and sustainable production. The rapid evolution of AM has begun to reshape traditional supply chain models across multiple industries. By enabling localized, on-demand, and customized production, 3D printing challenges conventional approaches to sourcing, inventory management, and logistics. This review analysis current research and industrial applications to examine the transformative impact of 3D printing on supply chain design, efficiency, and sustainability. Key findings highlight that additive manufacturing can significantly reduce lead times, lower transportation and inventory costs, and enhance supply chain agility. Moreover, it supports sustainability goals by minimizing waste and carbon emissions through shorter, localized supply networks. It also discusses how AM helps organizations respond to disruptions while highlighting new risks, such as cybersecurity threats and quality control issues. By analyzing findings from academic sources, this review identifies both the opportunities and limitations of AM adoption. Overall, the literature shows that AM can make supply chains more agile, environmentally friendly, and resilient when combined with strong digital infrastructure and proper management practices. However, challenges remain in standardization, cost, and integration across industries.

Supply chain management is poised for a transformative shift through the adoption of 3D and 4D printing technologies, which enable localized manufacturing, lead time reductions of up to 65%, and inventory cost savings of approximately 67%. These technologies support the growing demand for customized products by fostering a supply chain that is more responsive, adaptive, flexible, efficient, and cost-effective. 4D printing further enhances product lifecycle management by allowing objects to change shape or function over time, improving overall performance and longevity. The proposed impact analysis demonstrates streamlined adaptation of manufacturing processes, contributing to environmental sustainability through significant waste reduction. Empirical results from the case study of Company X reveal transformative outcomes in performance metrics, with supply chain efficiency increasing from 45 to nearly 100 units, and 3D/4D printing adoption surging from 2% to 98% between 2016 and 2024. Integration of high-value design principles and additive manufacturing improved key dimensions like agility, visibility, collaboration, and flexibility by 15-20 points each, while stakeholder support remained strong, with 62.5% endorsing 3D/4D adoption, 67.8% favoring faster delivery, and over 57% supporting digital integration strategies. This research offers a validated framework for industries aiming to implement sustainable, digitally-driven supply chain models across sectors like automotive, aerospace, and healthcare.

By constructing a knowledge supply chain model with both theoretical and practical value, this study proposes a novel approach to integrating multimodal data—such as text, financial reports, video cases, and business models—to generate teaching cases. The experiment employs a privatized Deepseek32b system, utilizing multimodal knowledge embedding technology, cognitive logic injection mechanisms, and systematic design of a teaching logic enhancer to significantly improve interdisciplinary knowledge integration and extraction efficiency. The experimental results show that generative artificial intelligence consistently produces an excess of teaching cases, with a significantly higher coverage of knowledge points compared to traditional NLP and manual methods. While generative AI exhibits stable logical coherence, its content logic is slightly inferior to that of high-quality human-generated works. This study verifies the effectiveness of the cross-modal knowledge extraction training method and provides valuable reference insights.

The increasing popularity of electric vehicles (EVs) as green alternatives to traditional internal combustion engine cars has highlighted the need for sustainable and environmentally friendly supply chain models. In particular, the handling of EV batteries, which are environmentally unfriendly and logistically critical due to their hazardous nature and short life cycle, requires a well-designed closed-loop supply chain (CLSC). This study proposes a new multi-objective optimization model of the CLSC, explicitly tailored to EV batteries under demand and return rate uncertainty. The proposed model incorporates three primary objectives that are typically in conflict with one another: minimizing the total cost, reducing carbon emissions throughout the entire supply chain network, and maximizing the recycling and reuse of batteries. The model employs a neutrosophic goal programming (NGP) methodology to address the uncertainties associated with demand and battery return quantities. The NGP model translates multiple objectives into non-monolithic goals with crisp aspiration levels (i.e., prescribed ideal levels for achieving the best of each goal) and thresholds that capture tolerances, thereby accounting for uncertainty. The efficiency of the proposed method is illustrated by a numerical example, solved using a IBM ILOG CPLEX Optimization Studio 22.1.2 solver. The findings demonstrate that the NGP can offer cost-effective, low-impact, and environmentally friendly solutions, thereby enhancing system robustness and flexibility to adapt to uncertainties. This study contributes to the emerging literature on sustainable operations research by developing a decision-making tool for EV-HV battery supply chain management. It also offers relevant suggestions for policymakers and industrialists who seek to co-optimize economic benefits, ecological sustainability, and logical feasibility in the emerging green society.

Objectives: This study systematically reviews empirical literature from the past five years to explore how improvements in agricultural supply chain efficiency contribute to specifically identifying the pathways that align with the Sustainable Development Goals (SDGs) across economic, environmental, and social dimensions. Theoretical Framework: Drawing on supply chain management theory, transaction cost theory, and the concept of sustainable development, the research constructs an “efficiency to SDGs” framework that explains the multifaceted benefits of efficiency optimization. Method: Using qualitative document analysis, this study synthesizes recent empirical studies and high-quality case evidence from Web of Science and Scopus to identify mechanisms through which efficiency improvements translate into sustainability outcomes. Results and Discussion: The findings highlight that lowering transaction costs, advancing supply chain finance, and integrating digital innovations stimulate rural economic vitality, while green technologies and smart logistics reduce carbon emissions and optimize resource use. Also, inclusive supply chain models—such as cooperatives, digital platforms, and community-supported agriculture—enhance the welfare of smallholders, women, and youth, fostering social inclusion and strengthening trust in both rural and urban areas. The study further emphasizes the critical role of institutional design and policy support in scaling these impacts globally. Research Implications: The review provides insights into how efficiency-oriented strategies can be transformed into sustainable development pathways, offering valuable guidance for policymakers, practitioners, and researchers in designing low-carbon, resilient, and inclusive agricultural systems. Originality/Value: By integrating theoretical and empirical evidence, this study constructs a novel “efficiency–sustainability” logical framework that bridges the gap between efficiency improvement and sustainability outcomes, laying a foundational basis for future research and policy innovation in agricultural supply chain governance.

In the era of global connectivity, supply chain management has emerged as a significant challenge. AI holds the promise to revolutionize the approach and handling of supply chain management. Market transformations underscore the need for agility. Incorporating AI into supply chain management rapidly caters to optimization and adaptability. This research zeroes in on the positive facets of AI within supply chain management. The outcomes bolster AI implementation, propel higher-level administration, and foster the development of advanced supply chain models.

Construction logistics is central to optimising site operations and delivery processes, yet the need to meet dynamic site requirements while minimising transport movements presents a persistent challenge. Transport efficiency can be improved through both strategic and operational interventions at the business-unit level. This study examines transport-related distribution practices within the plasterboard supply chain in Auckland, New Zealand, and evaluates opportunities to enhance efficiency using established performance metrics. By integrating supply chain management and circular economy principles through spatial analysis and supply chain modelling, the research demonstrates the potential to achieve up to a three-fold improvement in vehicle capacity utilisation. The operational analysis—focused on general-purpose (non-specialist) transport—is grounded in real-world transport data that extends beyond conventional trip-centricity to capture a broader supply chain perspective. This approach addresses a key methodological gap by empirically validating analytical models in a specific operational context. In addition to quantifying efficiency gains, the study identifies context-specific inefficiencies that constrain construction transport performance and proposes sustainable solutions that extend beyond technological fixes. These include strategic organisational measures for improving fleet management, transport contracting and pricing, collaborative planning across supply chain actors, waste management practices, and collaborative logistics through integrated warehousing. By linking technical analysis with business-oriented insights, the research provides proof-of-concept for practical, scalable strategies for improved construction logistics and wider freight transport efficiency grounded in empirical evidence.

The aim of this paper is to explore how sustainable supply chains contribute to enhancing the competitiveness of companies in international markets, particularly through the integration of sustainability principles into marketing strategies and global business practices. Building upon contemporary theoretical approaches to international marketing, circular economy, and corporate sustainability, the study applies a comparative and synthetic analysis of secondary sources, complemented by case studies of global companies that have implemented sustainable supply chain models. The methodological framework includes qualitative analysis of strategic documents, corporate social responsibility reports, and market performance indicators, with the objective of identifying the relationship between sustainability, reputation, and market position. The research results indicate that companies actively developing sustainable supply chains achieve long-term competitive advantage through increased consumer trust, reduced resource costs, and stronger relationships with partners across the value chain. Sustainable practices in procurement, logistics, and distribution have become a core element of marketing strategy, as they contribute to building brands perceived as responsible, innovative, and reliable. The conclusions confirm that sustainability is no longer an additional value but a strategic imperative for competitiveness in international marketing. The paper recommends integrating green logistics, digital tools for supply chain monitoring, and standardized sustainability criteria into all stages of business operations. The additional contribution of this research lies in the development of a conceptual model linking sustainable practices with the elements of the international marketing mix, providing both a theoretical framework and practical guidelines for companies striving for global competitiveness based on sustainable development.

Retraction: Water Supply (2022) 22 (12): 8540–8556: Construction of pollution risk early warning model for urban drinking water supply chain, Yongxiao Cao, Xianglong Zhang, Zihan Chen, Zhixiao Zhang, Huaibin Wei, https://dx.doi.org/10.2166/ws.2023.255

Purpose In today’s intensely fiercely contested market, supply chain participants emphasize enhancing channel quality to attract and retain customers. As channel quality becomes a strategic differentiator, understanding its role in supply chain dynamics is increasingly vital. Design/methodology/approach This study develops a competitive supply chain model involving two manufacturers, two retailers and one online platform, explicitly incorporating channel quality as a strategic decision variable. The manufacturers select one of three contract types to sell their products: exclusive partnerships with individual retailers, fully nonexclusive partnerships with both retailers and hybrid non-exclusive partnerships involving individual retailers and a common online platform. Customer demand in each channel is modeled as a function of selling price and channel quality. A Nash game framework determines the optimal strategic decisions of all supply chain participants under each contractual scenario. Findings The results indicate that when supply chain members compete to sell the same product, they are compelled to offer higher levels of channel quality to remain competitive. Furthermore, participants engaged in nonexclusive contractual arrangements consistently outperform those in exclusive agreements in terms of profitability. Manufacturers can enhance their profits by adopting a hybrid nonexclusive strategy combining retail and online platform channels, rather than relying solely on traditional retail distribution. Originality/value The study is novel in its integration of channel quality as a central element in supply chain competition and in expanding nonexclusive contracts to include both online and offline sales channels. This dual-platform approach provides deeper insights into modern supply chain strategies in a multichannel retail environment.

Problem definition: We examine how market and economic factors influence the occurrence of forced labor in supply chains and how buying companies can develop optimal contracts to prevent forced labor in the presence of information asymmetry between the buyer and the agent. Methodology/results: We develop a game-theoretic model of a labor supply chain comprising a socially aware buyer and a profit-maximizing labor agent. Our equilibrium analysis shows that the audit cost affects the extent to which the buyer can extract surplus from the agent. In the asymmetric information case, we design a menu of contracts and show that the difference in the agent’s earnings dictates how an unconstrained optimal contract can be adjusted to be incentive compatible. Our result suggests that it is optimal for the buyer to leave a surplus to agents with high recruitment capability (measured in terms of the labor pool size) regardless of the audit cost. As we extend our analysis to the multiagent case, we develop a “sequential” menu of contracts that ensures no coercion and maximum buyer profit. We apply our model to a data set of labor agents for recruiting foreign agricultural workers in the United States. Managerial implications: To implement incentive-compatible contracts that deter coercive labor outcomes, the buyer may need to allocate informational rents to the agent by foregoing a portion of its surplus. We find that information asymmetry regarding the agent’s true recruitment capability necessitates that the buyer offers a “menu of contracts” to prevent the risk of forced labor. This menu benefits exactly one type of agent, depending on the earnings differential between the available contracts, but always leaves a financial surplus for agents with strong recruitment capabilities. When multiple potential agents are available, the buyer can employ a reverse auction mechanism to select one agent. Though the core insights from contracting with a single agent continue to apply, their impact diminishes as the number of potential agents grows. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0438 .

Purpose This paper examines donor interventions aimed at improving the performance of underdeveloped Pathogen Genomic Sequencing (PGS) supply chains in Sub-Saharan Africa. Specifically, we investigate in-kind donations and supply chain management (SCM) capability-building at laboratories performing PGS. In-kind donations have historically been the primary tool used by donor-led initiatives to scale up PGS capacity, while SCM capability-building represents a more recent, complementary strategy. Design/methodology/approach We develop a system dynamics model of the PGS supply chain, grounded in extensive empirical data, to analyze the short- and long-term impacts of each type of intervention. Findings The results reveal a core trade-off: while in-kind donations can mitigate acute shortages, frequent use risks creating dependency and suppressing learning. In contrast, SCM capability-building supports sustainable improvements, particularly when targeted at labs that are unlikely to improve without external support. Research limitations/implications We derive six testable propositions from the analysis and offer a decision framework to support donors in allocating resources more effectively, balancing immediate shortage mitigation with longer-term supply chain improvements. Originality/value By applying a system dynamic modeling approach tailored to the development of PGS supply chains, we capture the nuanced interactions between donor interventions and lab performance, that is: the ability of labs to timely meet disease surveillance needs in their catchment areas. By evaluating both short- and long-term performance impacts of donor interventions, we identify contexts in which each intervention is most effective.

Against the backdrop of the rapid growth in the scale of the prepared food market, safety issues have gradually become prominent. Establishing a traceability system has become crucial to safeguarding consumer rights and promoting the sustainable development of the industry, with traceability information sharing serving as the core link. However, affected by differences in interest demands and information asymmetry between manufacturers and retailers in the prepared food supply chain, there are obstacles to traceability information sharing. To explore the coordination mechanism of traceability information-sharing behavior in the prepared food supply chain under different decision-making models and its impact on profit distribution, this paper constructs a two-level supply chain model including manufacturers and retailers, comprehensively considers the online–offline dual-channel sales model, and distinguishes four scenarios: centralized decision-making, decentralized decision-making, retailer-led cost-sharing contract decision-making, and manufacturer-led cost-sharing contract decision-making. Using a differential game model, the equilibrium results under different decision-making models are discussed. The validity of the model is verified through fitting with empirical analysis and numerical example analysis. The research results show the following: (1) The centralized decision-making model has the best effect on increasing the market share of the prepared food supply chain, and although the cost-sharing contract model can improve it, there is still a gap. (2) The centralized decision-making model is not the one with the maximum profit, and manufacturer-led cost-sharing decision-making basically achieves Pareto optimality. The main reasons are the insufficient incentive mechanism, high coordination costs, and uneven profit distribution in centralized decision-making. (3) The impact of manufacturers’ offline channel traceability information-sharing behavior on profits is more significant than that of online channels. (4) In a market environment with information asymmetry, the impact of goodwill on the profits of prepared foods is more prominent. This research provides a theoretical basis for the management of the prepared food supply chain, helps optimize the traceability information-sharing mechanism and profit distribution plan, and promotes the healthy development of the industry. (5) When the coefficient measuring the intensity of traceability information sharing’s impact on product quality across manufacturers’ online and offline channels increases, only under the retailer-led model does product quality and goodwill exhibit a fluctuating trend of “rising from the bottom to the second place and then falling back to the bottom,” while the profits of all subjects increase simultaneously. (6) As the system attenuation coefficient increases, the evolution of product quality and goodwill under different cooperation models shows significant differences; in terms of profits, the profits of manufacturers’ online channels increase over time, while those of other subjects decrease. (7) When the discount rate rises, the manufacturer-led model presents distinct characteristics: both the ranking and absolute value of product quality decline synchronously, the ranking of goodwill falls, but its absolute value rises against the trend, the evolution of product quality and goodwill shows obvious model heterogeneity, and the profits of all subjects generally decrease.

Supply chain members manage inventory levels through vendor-managed inventory and sourcing strategies to achieve customer satisfaction while reducing costs. The juxtaposition of environmental responsibility and cost-effectiveness presents a unique challenge in supply chain management. This paper addresses the research question: How can a vendor-managed inventory policy improve environmental and cost performance simultaneously when considering supplier selection? Through the theoretical lens of the relational view, we presented a conceptual model to test hypothesized relationships. Moreover, this paper develops a sourcing-based vendor-managed inventory model for a single-product supply chain consisting of a retailer, a manufacturer, and multiple suppliers. This study uses data from a pharmaceutical company as a case study to explore its operations and performance. A machine-learning algorithm, the recurrent neural network, is used to predict demand in the proposed model. The findings suggest that the proposed model can significantly improve environmental and cost performance. The main contribution of this paper is analytical and theoretical support to explain how production flexibility and carbon tax can impact firms’ performance.