Sustainable Supply Chain Network Design Research Articles

Data-driven robust optimization for a sustainable steel supply chain network design: Toward the circular economy

Steel is the indispensable cornerstone of modern life and its remarkable strength and adaptability render it a fundamental pillar of worldwide advancement and economic growth. However, the steel sector faces significant challenges, including the need to incorporate sustainable practices and circular economy principles, especially enhancing occupational safety, optimizing water and energy resource utilization, and effectively utilizing by-products within the steel supply chains. This paper addresses these gaps by presenting a bi-objective data-driven optimization model formulated to design a sustainable forward-reverse steel supply chain network. Sustainable development is studied by examining total cost, occupational safety, and environmental impacts of water and energy consumption and transportation system, concurrently. Incorporating circular economy principles, the collection of steel scrap under take-back policies and utilizing steel slag, a by-product of steel production, is considered. To address uncertainties, a data-driven robust optimization method is employed which harnesses historical data to construct a dynamic uncertainty set using support vector clustering. The model is applied to a real case study in the Iranian steel sector. Results demonstrate the effectiveness and robustness of the proposed method, showing that, on average, a 5 % increase in costs correlates with a 7 % improvement in environmental impacts.

Resilient and sustainable semiconductor supply chain network design under trade credit and uncertainty of supply and demand

Supply Chain (SC) performance appraisal extends beyond economic evaluation to encompass environmental and social impacts, as well as resilience to supply and demand disruptions. Thus, SC network design decisions should simultaneously optimize all these performance metrics as a primary objective. However, existing studies optimized these objectives independently or in a limited scope, failing to comprehensively address the interconnected nature of these performance metrics. Therefore, this study proposes a mixed integer linear programming (MILP) model with four objectives to address a multi-tier resilient and sustainable semiconductor SC network design problem under trade credit, supply, and demand uncertainties. The model aims to determine the optimal number and location of various facilities, the amount to order from suppliers, economic production quantities, quantity allocation between SC entities, and ideal supplier selections. The proposed supply chain network contributes to the global effort to reduce carbon emissions and enhance the circular economy by considering carbon trading, recycling, and the proper disposal of end-of life products. Uncertainties in input parameters are addressed using a fuzzy programming approach, while lexicographic optimization and the ɛ-constraint method are used to solve the proposed model. This paper presents a numerical example to illustrate the applicability of the proposed model and provide managerial insights. The numerical analysis results show that provided an appropriate confidence level for the uncertain parameters, the sets of Pareto optimal solutions can be generated. And it also shows that the total SC cost increases with decreasing carbon emissions level and increasing job opportunities created. This research advances sustainable and resilient supply chain management practices while offering practical guidance for decision-makers in real-world contexts.

A Systematic Review of Sustainable Supply Chain Network Design: Optimization Approaches and Research Trends

In response to the ever-increasing pursuit of competitiveness among organizations in today’s global business landscape, the subject of supply chain management has become a vital domain encompassing a wide range of sectors and industries across the economy. The growing concern about sustainable development has prompted public and private supply chain players to incorporate the three pillars of sustainability, namely, economic, environmental, and social, into the design of their supply chain networks. This study reviews and examines the content of 102 relevant papers to discuss the mathematical models, modeling approaches, and solutions that have been explored in the existing literature on forward sustainable supply chain network design. This paper also investigates the sustainability elements and supply chain network peculiarities including design factors and decision levels. In this review, several limitations in the current literature on sustainable supply chain network design optimization models are highlighted. According to the analysis, it was found that a better understanding of the industry and its sustainability requirements and priorities is essential for designing sustainable supply chain networks that are tailored to the needs of a specific industry rather than achieving general sustainability objectives. In addition, integrating strategic, tactical, and operational decision levels in the design of supply chain networks is critical for evaluating their impact on each other in terms of sustainability. More sophisticated mathematical solution methods for dealing with real-life scenarios including nonlinearity and uncertainty sources are required. The paper concludes with new prospects of research to promote a better integration of sustainability into supply chain networks.

Prevention of post-pandemic crises: A green sustainable and reliable healthcare supply chain network design for emergency medical products

The COVID-19 pandemic happened exactly when no one expected it and many people died due to the lack of medical equipment. Although pulse oximeters and thermometers were only one of the virus detection equipment, the lack of that equipment could lead to people not knowing about the disease and then the death of those people. Given that these medical devices are very vital in the era of a pandemic, and much less in the later, it is required to provide the best conditions for their use in a closed-loop healthcare supply chain network in post-pandemic. In this paper, a scenario-based two-stage stochastic programming three-objective model for designing a green closed-loop supply chain network is presented. Cost minimization, reliability maximization, and critical response maximization are the objectives of the proposed model. The third objective function is considered for the critical response based on the choice of transportation method. The greenhouse gas emissions for all supply chain elements are considered uncertain and controlled in several possible scenarios. By using an accelerated Benders decomposition algorithm, the mathematical model was solved in different dimensions and the result was analyzed and evaluated in different scenarios. The results demonstrate that the acceleration of Benders bonds reduces the convergence speed of the algorithm by 41%. Our study provides managerial insights into sustainability and reliability, enhancing healthcare supply chain resilience during and after pandemics.

A fuzzy multi-objective optimization model for sustainable healthcare supply chain network design

Healthcare supply chains play a crucial role, which enables the implementation of optimization strategies that have rapidly emerged as highly effective means for improving the overall structure of pharmaceutical and healthcare supply chains.. In the healthcare industry, parameters such as increasing the quality of service, as well as optimizing costs, environmental, and social factors play a unique role in supply chain management. To improve the healthcare supply chain network, this study proposed a novel optimization model to optimize multiple objectives, including minimizing the total costs and environmental impacts, while maximizing the social factors by creating jobs simultaneously. To address the effects of uncertain parameters, a fuzzy optimization method alongside the multi-objective gray wolf optimizer (MOGWO), non-dominated sorting genetic algorithm II (NSGA-II), multi-objective differential evolution algorithm (MODEA), and ε-constraint are applied to optimize the model. Also, a case study of the pharmaceutical industry demonstrates the model's efficacy in a real-life context. The numerical results show the MOGWO manages to create high-quality Pareto solutions with a good spread at the Pareto boundary within a short time compared to the ε-constraint approach. Further, it has shown a more robust performance compared to MODEA and NSGA-II, indicating the efficiency of MOGWO, among other solution methods and other objective indicators.

Antifragile, sustainable, and agile supply chain network design by considering resiliency, robustness, risk, and environmental requirements.

This research suggests an Antifragile, Sustainable and Agile Supply Chain Network Design (ASASCND) as a new network design that integrates these concepts considering resiliency, robustness, risk, and environmental requirements. The cost function combines a novel method with robust stochastic optimization and Entropic Value atRisk (EVaR). This model combines expected value, maximum and EVaR of cost as an objective function. This research adds antifragility by the effect of learning on variable parameters, sustainability by considering the environmental and social issues, resiliency and agility by flexible capacity, and multi-resource and demand satisfaction constraints to the model. The case study is in the automotive industry. This model compares the main problem by considering antifragility without thinking about antifragility. The ASASCND cost is - 0.3% less than without considering antifragility. In addition, when the conservatism coefficient grows, the cost function increase. In addition, the antifragility coefficient and the confidence level affect positively, and the agility coefficient negatively affects the cost function. Expanding the model scale changes the cost function and time computation because the antifragility coefficient changes variable cost. Finally, managerial insights and practical implications are explained.

Sustainable pomegranate supply chain network design considering cultivation process and water consumption

Sustainable pomegranate supply chain network design considering cultivation process and water consumption

A sustainable circular supply chain network design model for electric vehicle battery production using internet of things and big data

AbstractDesigning and developing sustainable circular supply chain networks for electric vehicle (EV) lithium‐ion battery recycling and production requires complex environmental sustainability and economic viability assessment. EVs use a lot of data for battery management and delivering optimum performance, and the Internet of Things (IoT) plays a major role in managing this data. This study develops a bi‐objective mixed‐integer linear programming model for designing a sustainable circular supply chain to manage the manufacturing, remanufacturing, and distribution of EV lithium‐ion batteries under uncertainty using the IoT and big data. The proposed model simultaneously minimizes total costs and CO2 emissions and uses IoT to improve network performance and create a traceable and secure environment. A fuzzy multi‐objective method solves the bi‐objective optimization model under uncertainty, and a simulation algorithm examines the effectiveness of the proposed model through simulated problems.

Sustainable edible vegetable oils supply chain network design considering big data: a fuzzy stochastic approach

Sustainable edible vegetable oils supply chain network design considering big data: a fuzzy stochastic approach

Robust sustainable canola oil-based biodiesel supply chain network design under supply and demand uncertainty.

The excessive consumption of fossil fuels has sparked debates and caused environmental damage, leading the global community to search for a suitable alternative. To achieve sustainable development goals and prevent harmful climate scenarios, the world needs to increase its use of renewable energy. Biodiesel, a clean and eco-friendly fuel with a high flash point and more lubrication than petroleum-based fuels, and without the emission of harmful environmental gases, has emerged as one of the fossil fuel alternatives. To promote the mass-level production of biodiesel, a sustainable supply chain (SC) that does not depend on laboratory production is necessary. For this purpose, this research proposes a multi-objective mixed-integer non-linear mathematical programming (MINLP) model to design a sustainable canola oil-based biodiesel supply chain network (CO-BSCND) under supply and demand uncertainty. This mathematical model aims to minimize the total cost (TC) and total carbon emission while maximizing the total number of job opportunities simultaneously. A scenario-based robust optimization (SBRO) approach is applied to deal with uncertainty. The proposed model is implemented in a real case study in Iran, and numerical experiments and sensitivity analysis are conducted to demonstrate its applicability. The results of this research demonstrate that designing a sustainable supply chain network for the production and distribution of biodiesel fuel is achievable. Moreover, this mathematical modeling makes mass-scale production of biodiesel fuel a possibility. In addition, the SBRO method adopted in this research enables managers and researchers to explore the design conditions of the supply chain network by controlling the uncertainties that affect it. This approach allows the chain's performance to be as close as possible to the actual conditions. As a result, the SBRO method enhances the efficiency of the supply chain network and boosts productivity toward achieving desired goals.

Multi-tier supply chain network design: A key towards sustainability and resilience

The inescapable need for sustainable supply chains has fascinated controversial attention, in the last two decades, due to increasing governmental regulations and societal awareness. But, facilities and their linkages, within the supply chain network, are subject to perturbations due to natural or human-made events (e.g., strikes and floods). Therefore, managers are concerned about re-engineering a supply chain network that is simultaneously sustainable and resilient. Hence, there is an inevitable need to embrace sustainability and resilience at a strategic level for the supply chain management. Motivated by this necessity, this paper presents a new panacea methodology towards a sustainable and resilient (susilient, henceforth) two-tier supply chain network design (S-2TSCND). To this end, an extended exploration regarding the relationship and completeness between sustainability and resilience, in the supply chain context, was explored and discussed. It presents a new term for “susilient” supply chain due to the strong correlation found between these two paradigms. Then, a holistic framework was developed to identify dimensions, enablers, and criteria towards susilient development. Next, a fuzzy four-objective optimization model (FFOOM) was developed to (1) solve the susilient facility location problem; (2) assign the optimal order quantities among the selected facilities considering the susilience aspect; and (3) derive a trade-off between sustainability and resilience. The susilience values of nominated facilities were quantified via a hybrid AHP-OCRA method. These values were then merged into the FFOOM to consolidate susilience performance into the strategic and tactical decisions. A set of trade-offs was then derived via the ε-constraints method. Finally, each trade-off was examined via the global criterion approach to measure its distance from the ideal solution aiming to select the final supply chain network design. The validity of the developed methodology was proved on a re-configuration of an existing real 2TSCND.

A multi-objective simulation-based decision support tool for wine supply chain design and risk management under sustainability goals

Sustainable Supply Chain Network Design (SCND) poses a complex challenge, as it often requires trade-offs in decision-making. The problem becomes even more challenging when there is a need to integrate risk management into the design process. It is steered by this challenge that the purpose of the paper is twofold. First, it proposes a Simulation-Based Decision Support System (SBDSS) for a real case study of a Portuguese wine SCND problem. Second, it analyzes different wine Supply Chain (SC) configurations under risk situations and sustainability objectives. The main contributions of this research are as follows. First, it concerns the method that was employed, which, contrarily to traditional approaches in literature (e.g., stochastic or genetic programming), consists of a simulation approach, in which agents and their interactions in an environment with uncertainty are modeled. Second, for the generation of non-dominated solutions, a simheuristic was employed, which combines multi-objective optimization with simulation. Finally, the type of industry that was considered, due to the lack of cases focusing on it in literature. The results suggest that the proposed approach determines a set of Pareto-front solutions that consider the sustainability dimensions of SCND, as well as risk situations. Furthermore, the implications that several parameters have on the sustainability of the designed SCs were assessed, as well as the impacts that the considered risks have on the performance of SCs. In particular, it was found that transportation risks impacted considerably more the performance of the SC than the risks on the prices of energy and raw materials.

A Bi-objective two-stage stochastic optimization model for sustainable reverse supply chain network design under carbon tax policy and government subsidy considering product quality

ABSTRACT There is a growing concern over environmental pollution resulting from the production. Moreover, the expansion of various industries has led to an increased demand for raw materials. To address these challenges, this paper aims to investigate the bi-objective optimization of a sustainable reverse supply chain network while considering two key sources of uncertainty in the returned product quality and the remanufacturing capacity. Additionally, the study considers the effects of carbon tax policies and government subsidies on remanufactured products, while also focusing on three important sustainability aspects - economic, social, and environmental. The study uses two quality thresholds at inspection centers to sort products, and the epsilon constraint and NSGA-II are applied to solve the model. Through numerical analysis, the research demonstrates that objective functions are sensitive to uncertain parameters and minimum acceptable quality levels. Furthermore, the study reveals that government subsidies can offset the negative effects of carbon tax policies.

RETRACTED ARTICLE: Sustainable and optimal design of Chinese herbal medicine supply chain network based on risk dynamic regulation mechanism

We propose a robust fuzzy design model for a sustainable closed-loop supply chain network. The model is based on a risk dynamic regulation mechanism. In this way, we can solve the problem of sudden disruptions and uncertain demand in the supply chain of Chinese herbal medicines. We also develop a hybrid algorithm solution to solve the model and design a resilient supply chain network. The specific steps are as follows: (1) The risk dynamic regulation mechanism is created with strong risk resistance by considering the information sharing platform, facility defense, drying station scheduling, safety stock, and shared inventory. (2) Based on the dynamic risk regulation mechanism, we establish a sustainable Chinese herbal medicine supply chain network design model. Then, we use the robust fuzzy method and the epsilon constraint to deal with the uncertainty and integrate the model. (3) We introduce opposition-based learning, cosine convergence factor, and levy flight to the original Whale and Grey wolf algorithms to obtain the Hybrid algorithm, which is used to solve the processed model. The results show the model and algorithm proposed in this paper have strong applicability and advantages in designing closed-loop supply chain networks for Chinese herbal medicine and provide references for relevant decision-makers.

Leagile sustainable supply chain network design considering disruption risk by C-Var approach: a case study

Leagile sustainable supply chain network design considering disruption risk by C-Var approach: a case study

A multi-objective optimization for a closed-loop sustainable pharmaceutical supply chain network design: a case study

PurposeThis paper deals with the combined management and design of a sustainable pharmaceutical supply chain network with considering recycling.Design/methodology/approachThis paper first utilizes the analytical hierarchy process to select and rank green manufacturers. Second, the authors proposed a multi-objective nonlinear mathematical model to design a sustainable pharmaceutical supply chain network. The proposed model has been linearized and solved using the LP-metric method using GAMS software.FindingsA real case study has been conducted in Iran. The results show that environmental and social issues can be improved while minimizing total costs.Originality/valueGiven the criticality and importance of drugs in human health and the importance of recycling in today's world, proper management and design of a sustainable drug supply chain are necessary. This study pays special attention to environmental issues by utilizing multi-criteria decision approaches and customer satisfaction.

Metaheuristic optimizers to solve multi-echelon sustainable fresh seafood supply chain network design problem: A case of shrimp products

Seafood products are sought-after among communities all over the globe and are the main sources of essential nutrition for humans. Recently, the seafood supply chain networks have encountered obstacles that new sustainability regulations and the pandemic have brought forward. In this study, a novel supply chain network is developed for fresh seafood, considering sustainability aspects, to ideally balance the financial aspect of the network while enhancing the recycling of waste products. Moreover, four metaheuristics are employed to conquer the computational complexity of exact solution methods. To evaluate the performance of the algorithms in addressing the complexity of the proposed seafood supply chain model, some numerical examples in three different scales are designed. The obtained results from metaheuristic optimizers are assessed based on five effective measures. To facilitate the statistical analysis process, each measure is normalized using the relative deviation index indicator. According to the results obtained from the implementation of metaheuristic algorithms, it can be concluded that the multi-objective grey wolf and multi-objective golden eagle optimizers outperform the other two solution methods in terms of quality of solutions. Therefore, they can be applied efficiently in solving real-world seafood supply chain network problems.

Sustainable supply chain network design for municipal solid waste management: A case study

Design and planning of waste transfer stations, treatment facilities, and landfills are essential components of municipal solid waste management. The source separation process reduces waste separation costs and improves the quality of recycled products by preventing the generation of mixed waste and assisting the waste recycling process. The purpose of this paper is to present a multi-period, multi-objective model for designing an integrated and sustainable supply chain network for municipal solid waste management that considers source separation and employs a reward-penalty mechanism. The objective functions include minimizing the system's total cost, greenhouse gas emissions, and adverse environmental impacts on residential areas. The proposed model is illustrated through a case study in Kermanshah, Iran. Three scenarios including the optimal solution of the proposed model considering the landfill location as a decision variable, the optimal solution of the proposed model with the current landfill location fixed, and the existing waste management system are introduced and compared. The results indicate that when the proposed system is used instead of the previous two scenarios, waste management costs are reduced by 12.4% and 66.6%, greenhouse gas emissions are reduced by 0.5%, and increased by 3.3%. Environmental impacts are increased by 16.5% and reduced by 8.9%, respectively. Finally, a sensitivity analysis is conducted to determine the effect of varying the waste impurity percentage on supply chain performance, as source separation is believed to reduce system costs.

Sustainable fertilizer supply chain network design using evolutionary-based resilient robust stochastic programming

Phosphorus is an unsustainable substance that plays an essential role in modern agricultural systems and crop yield. Due to phosphorus growing demand and the importance of sustainable application of this critical resource, there is increasing concern about its supply chain network sustainability and resiliency. In this paper, a multi-objective, multi-product, multi-period mathematical model is developed for the sustainable phosphorus supply chain management in an uncertain environment. The parametric uncertainties such as demand and supply are aggravated by disruptions with devastating effects on strategic, tactical, and operational decisions. Given the potential adverse effects of the phosphorus supply chain on the environment and human beings, a sustainable-resilient supply chain network of the fertilizer industry is designed by considering the related environmental, social, and economic challenges of the phosphorus managing. A reactive strategy is adapted to encounter the disruptions and breakdowns along with the network, while a robust stochastic programming is extended and solved using genetic algorithm to cope with the real-world uncertainties. The proposed model effectively controls the uncertainty and risk-aversion of output decisions and confronts the adverse effects of disruptions. The effectiveness and applicability of the model are validated through a real case study. Besides, the performance and reliability of the model are proved by the realization under new scenarios. The results indicate that the proposed model performs well in capturing real-world uncertainties and promoting the sustainability and resiliency of the network.

A Performance-Oriented Optimization Framework Combining Meta-Heuristics and Entropy-Weighted TOPSIS for Multi-Objective Sustainable Supply Chain Network Design

The decision-making of sustainable supply chain network (SSCN) design is a strategy capacity for configuring network facility and product flow. When optimizing conflicting economic, environmental, and social performance objectives, it is difficult to select the optimal scheme from obtained feasible decision schemes. In this article, according to the triple bottom line of sustainability, a multi-objective sustainable supply chain network optimization model is developed, and a novel performance-oriented optimization framework is proposed. This framework, referred to as performance-oriented optimization framework, integrates multi-objective meta-heuristic algorithms and entropy-weighted technique for order preference by similarity to an ideal solution (EW-TOPSIS). The optimization framework can comprehensively evaluate the performance of overall SSCN by EW-TOPSIS and guide the evolution process of algorithms. In this framework, decision-makers can obtain the feasible schemes calculated by meta-heuristics and determine the optimal one according to the performance value evaluated by EW-TOPSIS. This article combines three performance evaluation strategies with four meta-heuristic algorithms, namely, non-dominated Sorting Genetic Algorithm-II (NSGA-2), multi-objective differential evolutionary (MODE), multi-objective particle swarm optimization (MOPSO), and multi-objective gray wolr optimization (MOGWO), for verifying the effectiveness of the performance-oriented optimization framework. The results validate that the proposed framework has much better sustainability performance than the traditional optimization algorithms and evaluation methods. Furthermore, the proposed performance-oriented optimization framework can provide managers with a special optimal scheme with the best sustainability performance. Finally, some research prospects are presented such as more multi-criteria decision making methods.