Closed-loop Supply Chain Network Research Articles

Optimization modelling for a sustainable closed-loop supply chain network using IoT: multiobjective metaheuristic algorithms

Optimization modelling for a sustainable closed-loop supply chain network using IoT: multiobjective metaheuristic algorithms

Closed-loop supply chain network of end-of-life wind turbines mathematical model proposal considering environmental, employment, and cost reduction aspects

The worldwide climate issue makes wind energy an attractive renewable energy source. Wind energy reduces carbon emissions, energy prices, creates jobs, improves power access, and ensures energy security. The mass of employed materials in wind turbines indicates a lot of potential raw materials when the whole mass is evaluated. Additionally, a new wind turbine costs $2–$4 million. Remanufacturing or using recycled raw materials from end-of-life wind turbines is practical due to the decreased cost of new wind turbines. To ensure sustainability, CO2 emissions and social advantages like employment must be considered. Given the typical life expectancy of wind turbines at 20–25 years, this issue must be addressed globally in the mid-term. The objective of the conducted study is to provide a multi-objective mathematical framework that encompasses the environmental, social, and economic aspects of wind turbines at the end of their operational lifespan. Hence, this study presents a methodical framework for achieving sustainability in the wind energy sector through the implementation of a closed-loop supply chain. Furthermore, a lexicographic solution approach is offered for each objective of the mathematical model, with the aim of providing decision makers with a comprehensive overview of solutions. The methodology employed in this work involves the utilization of a mixed-integer linear programming (MILP) approach to address the closed-loop supply chain for end-of-life wind turbines. Additionally, a lexicographic solution approach is provided to effectively solve the problem at hand. The novelty of this article lies in its examination of the closed loop supply chain network for end-of-life wind turbines, encompassing the social, environmental, and economic dimensions. This study represents the first of its kind to explore these aspects comprehensively. The proposed mathematical model has three objectives which cover the remanufacturing and recycling and aims to minimize CO2 emissions, unemployment, and transportation cost. In order to evaluate the proposed model, Türkiye is utilized as case with real-life data and Gurobi 9.1.2 is utilized. The locations of wind turbines could not be collected individually, and Euclidian distance was used instead of road distance due to a lack of data. These are the main limitations of the proposed study. The study revealed that the TEC and TCE lexicographic approach has the best recycling and remanufacturing cost-to-manufacturing cost ratio, 52.54 %. The least desired result is 74.50 % from CTE.

Robust optimization in an agricultural closed-loop supply chain network design with a price and freshness-dependent demand: hybrid rat with particle swarm optimization algorithm

Robust optimization in an agricultural closed-loop supply chain network design with a price and freshness-dependent demand: hybrid rat with particle swarm optimization algorithm

Closed-loop supply chain network design with price-greenness-sensitive demand: A distributionally robust chance-constrained optimization approach

In response to the government’s heightened focus on recycling and remanufacturing, as well as the growing awareness among consumers about environmental security, manufacturing companies are currently required to establish efficient closed-loop supply chain networks in order to improve their socialreputation and competitive advantage. This study investigates the optimization of a Closed-Loop Supply Chain (CLSC) network that involves multiple products, multiple periods, and uncertain returns, which also considers the influence of many factors, such as carbon cap-and-trade policy, raw part procurement discounts, and facility capacity constraints, on the supply chain. Simultaneously, customer demand is sensitive to both product pricing and product greenness, and product greenness can be improved by investing in emission reduction technologies. To address the uncertainty in the returns, we propose a two-stage distributionally robust chance-constrained optimization model, which is transformed into a mixed integer linear programming model. To efficiently address the complex problem, we designan improved Benders decomposition (IBD) algorithm. The experimental results confirm that the IBD algorithm has significant advantages when compared to the Benders decomposition algorithm. Additionally, this study conducted a sensitivity analysis on key parameters and proposed operation suggestions of practical importance.

Sustainable performance in SMEs using big data analytics for closed-loop supply chains and reverse omnichannel

The study aims to examine the advantages of utilizing big data analytics (BDA) on circular economy (CE) dual systems, which are closed-loop supply chain (CLSC) and the reverse omnichannel (ROC), aiming to enhance sustainable firm performance (SFP) of the small and medium based enterprises (SMEs). The product return knowledge (PRK) can reinforce the proposed network of relationships and facilitate the approach of active returns in CE. Using the structural equation model (SEM) in AMOS v24, the researchers examined the hypotheses using data from a simple random sample of 232 SMEs in Pakistan. Though ROC solutions provide rare performance-related benefits, however, BDA makes the CE system more efficient. CLSC gets strengthened when the PRK is developed. The improved PRK facilitates the CLSC network and enhances its performance. This study adopts a novel approach to studying CE by considering the dual system of CE in the forms of CLSC and ROC. The research investigates whether the BDA facilitates improved product return processes by improving CLSC operations and achieving service capabilities using ROC. Finally, the proposed framework is the first to investigate the benefits evolving from the PRK, which enhance the capability to sense, seize, and reconfigure the process and facilitate the approach of active return in CE. The findings suggest that firms must decide carefully between CLSC and ROC. Choosing CLSC requires higher operational capabilities, whereas deciding on ROC requires higher service capabilities. Lastly, PRK is necessary for achieving the firm's performance objectives.

The Optimal Logistics Distribution Service Strategy of the E-commerce Closed-Loop Supply Chain Network under Blockchain Technology and the Government Blockchain Subsidy

The booming development of e-commerce has promoted the diversified development of logistics distribution services (LDS). For LDS, e-commerce retailers (e-retailers) often choose either the outsourced logistics distribution services strategy (OLDSS) or the self-built logistics distribution services strategy (SBLDSS). Although there are problems such as products getting lost and damaged during the logistics distribution process, the high transparency and traceability characteristics of blockchain technology (BT) can help solve the problem of products being lost and damaged in the logistics distribution process. However, due to the high cost of BT, e-retailers may encounter reduced sales, which causes the supply chain corporate profits to decrease. To encourage the BT investment enthusiasm of the e-retailers and regulate corporate profits, the government implements subsidies for e-retailers’ BT, namely, the government blockchain subsidy (GBS). In addition, in recent years, environmental degradation has become increasingly severe, causing negative impacts on people’s lives. To promote sustainable development, we use variational inequality to establish an e-commerce closed-loop supply chain (E-CLSC) network equilibrium model in which the network equilibrium decisions of e-retailers choosing the OLDSS and those choosing the SBLDSS are obtained. Then, we analyze the impact of the BT input cost and the GBS quota on equilibrium decisions by studying their properties and verifying the theoretical results by performing numerical examples. Finally, we analyze the profits of the e-retailers to obtain the impact of the BT input cost and the GBS quota on e-retailers’ choice of the optimal LDS strategy; in this way, we provide a scientific basis for e-retailers to choose the optimal LDS strategy. The results show that increasing the BT input costs reduces e-retailers’ product sales under the two LDS strategies, which decreases the production rate and the recovery rate of the products. When the BT input cost is low, SBLDSS is the best choice for e-retailers. When the BT input cost is high, OLDSS is the best choice for e-retailers. Moreover, there is a positive correlation between GBS and e-retailers’ product sales; thus, GBS is conducive to expanding market demand, regulating the profits of manufacturers, increasing the e-retailers’ profits, improving the enthusiasm of the e-retailers for BT investment, and promoting the overall development of supply chain enterprises. For e-retailers, choosing the OLDSS can lead to a better development of the E-CLSC.

A novel multi-objective robust possibilistic flexible programming to design a sustainable apparel closed-loop supply chain network

Designing a sustainable Closed-Loop Supply Chain (CLSC) network is imperative for the apparel industry, given its escalating adverse effects on economic, environmental, and social dimensions. In this study, a novel tri-objective location-allocation optimization model is specifically developed for designing a sustainable apparel CLSC, incorporating the industry's unique facilities. The aim of the model is to simultaneously minimize the costs and negative environmental impacts while maximizing social benefits under demands and returns uncertainty. A notable research contribution lies in addressing the unique challenges of treating different types of returns, including commercial, End Of Use (EOU) and End Of Life (EOL) returns due to their uncertain quality and quantity. Additionally, the model optimizes the environmental performance levels of production facilities, a novel aspect in the apparel CLSC research. Moreover, the flexibility of constraints related to the demand fulfilment is considered. To cope with such flexibility and uncertainties, a new hybrid robust possibilistic flexible programming model is developed, by extending the previous methodologies. A core innovation of this solution approach lies in the pioneering utilization of hexagonal fuzzy numbers for uncertain epistemic parameters, making a significant advancement in the field of CLSC. Comparative analysis with the similar studies demonstrates the superiority of the proposed model, incorporating hexagonal fuzzy numbers over the method using triangular fuzzy numbers. Furthermore, AUGMECON method using lexicographic optimization is applied to handle the multi-objective model. The application of the proposed model is shown focusing on Southwestern Ontario in Canada. The results reveal that commercial and EOU returns have a more detrimental impact on economic, environmental, and social sustainability aspects compared to EOL returns.

A responsive closed-loop supply chain network design under demand uncertainty

A closed-loop supply chain (CLSC) is important in developing a green industrial supply chain and a sustainable economy. This study addresses the challenge of designing a closed-loop supply chain network (CLSCN) under demand uncertainty by integrating forward and reverse logistics. A continuous approximation (CA) approach is proposed to solve a responsive CLSCN model. An inventory-sharing strategy is applied in the continuous approximation (CA) approach to further solve uncertain demand in a CLSCN. We conduct sensitivity analysis to demonstrate how the optimal design characteristics and the responsive supply chain network (SCN) design depend on various scenarios. A responsive CLSCN's network density is higher than a responsive SCN's. The changes in network density of the distribution center (DC) are not significantly affected by increased risks of uncertainties. The proposed model has demonstrated substantial cost savings compared to the traditional responsive CLSCN model.

Closing the Gap: A Comprehensive Review of the Literature on Closed-Loop Supply Chains

Closing the Gap: A Comprehensive Review of the Literature on Closed-Loop Supply Chains

Addressing uncertainty in closed-loop supply chain networks: a multi-objective approach to integrated production and transportation problems

Purpose This study aims to address the inherent uncertainties within closed-loop supply chain (CLSC) networks through the application of a multi-objective approach, specifically focusing on the optimization of integrated production and transportation processes. The primary purpose is to enhance decision-making in supply chain management by formulating a robust multi-objective model. Design/methodology/approach In dealing with uncertainty, this study uses Pythagorean fuzzy numbers (PFNs) to effectively represent and quantify uncertainties associated with various parameters within the CLSC network. The proposed model is solved using Pythagorean hesitant fuzzy programming, presenting a comprehensive and innovative methodology designed explicitly for handling uncertainties inherent in CLSC contexts. Findings The research findings highlight the effectiveness and reliability of the proposed framework for addressing uncertainties within CLSC networks. Through a comparative analysis with other established approaches, the model demonstrates its robustness, showcasing its potential to make informed and resilient decisions in supply chain management. Research limitations/implications This study successfully addressed uncertainty in CLSC networks, providing logistics managers with a robust decision-making framework. Emphasizing the importance of PFNs and Pythagorean hesitant fuzzy programming, the research offered practical insights for optimizing transportation routes and resource allocation. Future research could explore dynamic factors in CLSCs, integrate real-time data and leverage emerging technologies for more agile and sustainable supply chain management. Originality/value This research contributes significantly to the field by introducing a novel and comprehensive methodology for managing uncertainty in CLSC networks. The adoption of PFNs and Pythagorean hesitant fuzzy programming offers an original and valuable approach to addressing uncertainties, providing practitioners and decision-makers with insights to make informed and resilient decisions in supply chain management.

Metaheuristic-driven extended exergy accounting for sustainable closed-loop food supply chain management

In this research, an exergetic mathematical model is proposed for closed-loop food supply chain network design considering economic, environmental, and social aspects. The studded closed-loop food supply chain consists of six echelons, four echelons for forwarding the products (suppliers, factory, distribution centers, and customers), and two echelons for returning the products (collecting centers and disposal centers). The exergetic model is based on the extended exergy accounting method, which calculates the consumed exergy in all six echelons by considering the equivalents of labor and capital exergy. This model aims to minimize both the economic and exergetic costs associated with the food supply chain. To solve the proposed mathematical model, an ensemble global–local search metaheuristic algorithm utilizing a combined whale optimization algorithm (WOA) and simulated annealing (SA), called CWOASA, is proposed. In the CWOASA algorithm, first, WOA is used to search the entire search space. Next, SA is applied to further improve the best solution obtained by WOA. A real dataset of a dairy supply chain is used to evaluate the efficiency of the proposed metaheuristic-driven exergy analysis model and claim its benefits over the existing methods. Simulation results revealed that the proposed method leads to a 6.74% reduction in the total exergy consumption of the supply chain. The proposed methodology offers valuable insights, shedding light on the prospective gains in terms of mitigated environmental impact for each incremental economic expenditure, thus laying the foundation for promising avenues of future research.

Circular closed-loop supply chain network design considering 3D printing and PET bottle waste

AbstractOne of the most critical pillars of Industry 4.0 (I4.0) is Additive Manufacturing (AM) or 3D Printing technology. This transformative technology has garnered substantial attention due to its capacity to streamline processes, save time, and enhance product quality. Simultaneously, environmental concerns are mounting, with the growing accumulation of plastic bottle waste, offering a potential source of recycled material for 3D printing. To thoroughly harness the potential of AM and address the challenge of plastic bottle waste, a robust supply chain network is essential. Such a network not only facilitates the reintegration of plastic bottle waste and 3D printing byproducts into the value chain but also delivers significant environmental, social, and economic benefits, aligning with the tenets of sustainable development and circular economy. To tackle this complex challenge, a Mixed-Integer Linear Programming (MILP) mathematical model is offered to configure a Closed-Loop Supply Chain (CLSC) network with a strong emphasis on circularity. Environmental considerations are integral, and the primary objective is to minimize the overall cost of the network. Three well-known metaheuristics of Simulated Annealing (SA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) are employed to treat the problem which are also efficiently adjusted by the Taguchi design technique. The efficacy of our solution methods is appraised across various problem instances. The findings reveal that the developed model, in conjunction with the fine-tuned metaheuristics, successfully optimizes the configuration of the desired circular CLSC network. In conclusion, this research represents a significant step toward the establishment of a circular supply chain that combines the strengths of 3D printing technology and the repurposing of plastic bottle waste. This innovative approach holds promise for not only reducing waste and enhancing sustainability but also fostering economic and social well-being.

Factor analysis of environmental effects in circular closed‐loop supply chain network design and modelling under uncertainty in the manufacturing industry

AbstractSustainable development and competitive advantage are impacted by strategic choices that maximize resource value and reduce waste. Numerous instances of thriving OEM remanufacturing can be observed, predominantly in the business‐to‐business domain. The significance of Closed‐Loop Supply Chain (CLSC) in generating value and managing the recovery process is widely acknowledged within the supply chain industry. Manufacturing companies now have to come up with green supply chain and process design strategies in response to recent changes in environmental regulations. This study designates the specific features of circular closed‐loop supply‐chain design considering end‐of‐life products. Uncertainty in various aspects, such as acquisition, processing, and market stages, is impeding the progress of circular economies and also sustainable development in closed‐loop supply chains (CLSCs). This has led to increased complexity in remanufacturing processes and decreased efficiency. To address this issue, the study proposes a comprehensive, integrative approach for establishing a sustainable CLSC network that adapts to fluctuating demand through a questionnaire analysis. Moreover, the study introduces a multi‐objective optimization model for a dual‐channel supply chain network, aiming to enhance the flow. This model considers both economic and environmental objectives to achieve a sustainable and efficient supply chain system. To determine the ideal circular closed‐loop supply chain (CLSC) network design, the research proposes linear programming model with mixed integer, where recovery can take place in one of three ways: through material recovery, component recovery, or product recovery. Numerous findings from a thorough analysis by data backup that inform CLSC managers of ways to improve product returns in terms of quantity and quality. To address the uncertainty problem, the research also developed the fuzzy credibility constraint technique with a Simulated Annealing algorithm. Using cutting‐edge methods, it is explored and compared how sensitivity analysis results, the impact of altering the problem parameters, and the performance of the suggested model respectively.

Green-resilient model for smartphone closed-loop supply chain network design: A novel four-valued refined neutrosophic optimization

This study endeavors to integrate green supply chain and resilience proactive strategies within a closed-loop supply chain network. The research employs a three-step methodology. In the first phase, a multi-objective, multi-period, multi-node, mixed integer linear programming model (MILP) is developed. The objective of this study is to maximize resilience while minimizing both greenhouse gas (GHG) emissions and costs. The environmental footprint is assessed using the life cycle assessment (LCA) tool as part of the green supply chain strategy. To optimize resilience strategies, proximate nodes in the closed-loop supply chain network implement measures involving node criticality and inventory availability. In the second step, a novel four-valued refined multi-objective neutrosophic optimization algorithm is introduced to resolve the model. In contrast to traditional optimization approaches, neutrosophic optimization integrates elements of truth, falsity, contradictions, and uncertainty into the decision-making process. In the third phase, a case study of the smartphone supply chain is presented, advocating for the adoption of circular economy practices to prevent future waste. To assess the resilience of the model, a sensitivity analysis has been conducted, examining various key parameters associated with the market, and studying their impact on the objective function. The model's performance is validated by comparing the solution methodology with other goal programming and interactive fuzzy multi-objective methods. The results highlight the better performance of four-valued refined neutrosophic optimization, achieving 3.59 and 3.38 times greater cumulative gap reduction than other methods. The optimal solution recommends green strategies for an optimal trade-off among cost, GHG emissions, and resilience.

Multi-objective mixed integer programming modelling for closed-loop supply chain network design: an enhanced Benders decomposition algorithm

Considering a closed-loop supply chain (CLSC) network with uncertain demand and recycling rates, this article innovatively designs a multi-objective mixed-integer programming model that incorporates corporate social responsibility (CSR), a facility retrofit strategy (FRS), a flexible supply strategy (FSS) and a vehicle selection strategy (VSS). Then, robust optimization methods are applied to construct robust models under three situations of uncertainty. For the computational complexity of large-scale problems, an enhanced Benders decomposition algorithm (EBDA) is designed. A numerical case analysis is conducted using five different scale instances. First, compared to other algorithms, EBDA accelerates the solution efficiency while ensuring convergence. Secondly, the sensitivity of the objective weights and the trade-offs of multiple objectives are analysed. Finally, the impact is analysed of the uncertain environment, CSR, an FRS, an FSS and a VSS on the CLSC network. Decision makers need to balance three objectives to manage CLSC and use these strategies appropriately to address the negative impact of an uncertain environment.

Net-zero, resilience, and agile closed-loop supply chain network design considering robustness and renewable energy.

The Net-zero, Resilience, and Agile Closed-Loop Supply Chain Network (NZRACLSCND) concept integrates net-zero, resiliency, and agility in a circular economy. Regarding net-zero, this research embeds renewable energy like solar energy and hybrid trucks to supply energy for facilities and transportation of goods and products between components. Applying redundancy, multi-source, and flexible capacity as resiliency strategies is suggested to cope with the demand disruption. Satisfaction demand level is utilized for the agile approach. This research proposes Robust Stochastic Optimization (RSO), including the weighted expected value and maximum CO2 for NZRACLSCND. This study locates and determines the flow of CLSC in the home appliance industry by considering NZRA, robustness, and risk against demand disruption. CO2 emission using the NZRA concept is 233.33% less than without considering NZRA concepts. In addition, the conservative coefficient, agile coefficient, decreased CO2 coefficient, and the model scale are analyzed. The results show that when the conservative coefficient increases, the risks of CO2 emission increase. In addition, when the agile coefficient increases, as a result, CO2 emission increases. Finally, when the decreased CO2 coefficient and the model scale increase, we can see that CO2 emission and cost are increased.

Robust optimization of a closed-loop supply chain network based on an improved genetic algorithm in an uncertain environment

Aiming at the network structure design problems of facility location, path planning, and flow distribution in an e-commerce closed-loop supply chain network under an uncertain environment, the uncertainties of both the forward and reverse logistics demands and the impact of facility disruptions are analyzed. A robust optimization model is established using the robust peer-to-peer optimization method. An algorithm is designed that combines the minimum spanning tree algorithm with a two-layer adaptive genetic algorithm (Prim-DMGA) to solve the model. The results show that the Prim-DMGA not only requires less computation time, but also generates solutions of higher quality. In addition, the application of the robust optimization model reduces the adverse effects of uncertain factors on the e-commerce closed-loop supply chain network, thereby improving its robustness.

Optimizing closed-loop supply chain design with multi-objective demand fulfillment in a fuzzy environment using novel metaheuristic algorithm

Establishing a closed-loop system that could facilitate the reusing, renovation, and recycling of the various garbage products generated by this business could prove significant value to the particular business chains involved. A system of shipping that is mindful of the surroundings and takes accountability regarding all the relevant money, sustainable, and societal concerns. The sustainability Closed-Loop Supply-Chain Networks (CLSCN) architecture and the marketplace are brought together in the present article, which serves as the study’s primary part in the body of knowledge. As a result, an optimization with multiple objectives paradigm has been offered to arrive at their choices regarding position, allocations, and stock in relation to the challenge under consideration. The goals of the optimized model, derived from the triple bottom line strategy, are aimed at lowering overall expenditure and emissions of CO2 as much as possible while increasing the number of employment possibilities. In this study, we have proposed Hybrid electromagnetism with a genetic algorithm (HEGA) and compare our proposal with the existing methods. The obtained results show that the proposed model integrated with HEGA gives significant improvements with significant outcomes in terms of sensitivity (97%), specificity (95%), transportation cost (30%), and computational time (5.3s). This knowledge serves as a driving force behind the development of CLSN in the sector to establish a viable and affordable approach.

Design of distributionally robust closed-loop supply chain network based on data-driven under disruption risks

In globalised and highly uncertain business environment, it is necessary to design a supply chain that is not only efficient but also resilient, with continuity to operate and meet demand in the face of disruption. Aiming at this problem, a two-stage distributionally robust optimisation model based on data-driven is established to design a closed-loop supply chain network that can be flexibly executed in case of disruption. To analyse the resilience of the supply chain, the model considers the possible random disruptions of two types of facilities, and aims to deal with them through active and passive strategies such as supplier fortifying, recovery, signing with backup suppliers, and lateral transshipment. In addition, in view of the uncertainty of disruption scenarios and the limited disruption historical data available, distributionally robust optimisation method with the Wasserstein ambiguity set is used. In solving, the established robust model is transformed into a tractable model form using duality and linearisation technology, and solved by Gurobi solver. The numerical results show: Considering resilient measures effectively mitigate disruption hazards; Adding the recycling strategy can significantly reduce the production costs; Comparing with stochastic and classical robust optimisation models, the performance of the model established in this paper is highlighted.

Multi-objective programming for multi-period multi-product closed-loop supply chain network design: a fuzzy robust optimization approach

Multi-objective programming for multi-period multi-product closed-loop supply chain network design: a fuzzy robust optimization approach