Sustainable Closed-loop Supply Chain Research Articles

Integrated multi-manned disassembly line balancing problem with reverse supply chain design strategies by considering lot sizing

ABSTRACT Due to strict governmental regulations, manufacturers have increasingly focused on recovering end-of-life (EoL) products through disassembly lines, aiming to achieve economic benefits while addressing the environmental aspects of supply chain sustainability. Disassembly line balancing (DLB) has become a key challenge for enterprises seeking to perform environmentally conscious manufacturing as part of a closed-loop supply chain policy. Therefore, designing a sustainable closed-loop supply chain requires analyzing interrelated problems by focusing on strategic-, tactical-, and operational-level decisions simultaneously. This study introduces an integrated problem that addresses multi-manned DLB, lot sizing, and reverse supply chain (RSC) design problems. To mathematically represent the problem, a generic optimization model is developed to minimize overall costs, including station opening, inventory holding, shortage, transportation, and collection center/facility opening costs. This study addresses several research questions to explore the impact of distribution strategies (centralized and decentralized) on multi-manned DLB and lot size decisions within the context of reverse supply chain design.

A Stochastic Sustainable Closed-Loop Supply Chain Networks for Used Solar Photovoltaic Systems: Meta-Heuristic Comparison and Real Case Study

This research presents a novel approach to setting up a sustainable Closed-Loop Supply Chain (CLSC) network for used solar photovoltaic (PV) systems, addressing end-of-life product waste from solar panel installations and manufacturing centers. The model accounts for uncertainties in PV systems and aims to efficiently collect, refurbish, and recycle used solar PV systems, promoting a circular and environmentally responsible waste management strategy. The supply chain network comprises vendors, collection centers, hybrid centers, distribution centers, and manufacturing centers, with objectives to maximize total profit, minimize environmental risk, and maximize service levels by demonstrating the profitable reuse of used solar photovoltaic systems by manufacturers. The epsilon-constraint method is utilized to handle the model's multi-objectiveness and identify Pareto optimal solutions. A case study in Iran is conducted to validate the methodology's performance, comparing results obtained from three meta-heuristic methods: Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Gray Wolf Optimization (MOGWO). The average error rates are 0.0358 for MOGWO, 0.1248 for MOPSO, and 0.2066 for NSGA-II. Sensitivity analysis highlights the significant impact of demand variations on all objective functions. Lastly, the numerical results are discussed to provide managerial insights for informed decision-making.

Global sustainable closed-loop supply chain considering Incoterms rules and advertisement impacts

Industrial information integration plays a crucial role in modern supply chains by ensuring the smooth flow of data across all stages, including recovery, recycling, and disposal, which is essential for the successful implementation of a closed-loop supply chain (CLSC) model. Building on this, our paper addresses a global CLSC problem by incorporating International Commercial Terms (Incoterms) and international transportation modes, bridging global supply chain operations with sustainability criteria. This innovative approach advances the development of a globally sustainable CLSC by focusing on the integration of economic, environmental, and social factors, i.e., the triple bottom line of sustainability. Specifically, we address environmental concerns through the introduction of carbon taxation and enhance social sustainability by exploring the impact of advertising on customer satisfaction. To further refine this model, we classify customers based on their sustainability engagement and apply a fuzzy programming approach to account for uncertainty in customer demand influenced by advertising. To solve this complex global CLSC model, we conduct a thorough analysis of constraints and develop a robust Lagrangian relaxation reformulation. While the initial solution may result in infeasibility, we propose a heuristic algorithm that ensures feasible solutions. Our efficient Lagrangian-based heuristic, incorporating an adaptive strategy, is capable of solving large-scale networks with an approximate 10% optimality gap. Ultimately, this research provides both a comprehensive framework for practitioners to improve the environmental performance and global operations of their supply chains, as well as significant theoretical contributions to the field of industrial information systems.

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

Towards a Closed-Loop Supply Chain: Assessing Current Practices in Empty Pesticide Container Management in Indonesia

Pesticides are essential to modern agriculture, significantly enhancing crop yields and quality to ensure global food security. Their critical role in preventing hunger is highlighted by the notable increase in global pesticide trade over the past decade. In Indonesia, pesticide usage has surged, with a 24% rise in registered pesticide formulations between 2017 and 2021. However, this increase presents challenges, particularly in the disposal of empty pesticide containers (EPC), which pose substantial environmental and health risks if improperly managed. To address this, a closed-loop supply chain (CLSC) is proposed as an effective, eco-friendly solution for the management of EPC waste. This study evaluates the implementation of a CLSC for EPC in Indonesia, employing rich picture analysis and stakeholder interviews to identify key challenges, opportunities, and strengths. Notable challenges include regulatory gaps, financial and operational feasibility, and limited farmer engagement. However, opportunities exist in enhancing environmental sustainability, generating economic benefits, and gaining support from industry groups. One key strength is the widespread practice of triple rinsing among farmers, with 62.63% of respondents already adopting this method. This study underscores the important of establishing clear and enforceable regulations, introducing financial incentives and supportive policies, promoting public–private partnerships, creating targeted behavior change interventions, and ensuring organizational commitments and training programs. These insights are crucial in developing a sustainable CLSC, ensuring both environmental and economic benefits.

Decision-making in closed-loop supply chains: Effects of government subsidies and risk aversion

Closed-loop supply chains (CLSCs) play a crucial role in promoting sustainable resource management, yet their implementation faces challenges in balancing economic viability with environmental goals. While existing research has explored various aspects of CLSC operations, the interplay between government subsidies and risk-averse behavior of supply chain members remains understudied, particularly in systems involving third-party recyclers. This gap hinders the development of effective policies and management strategies for sustainable CLSCs. Herein, we employ a Stackelberg game-theoretic model to investigate decision-making processes in CLSCs under four distinct subsidy scenarios, incorporating risk aversion through mean-variance utility functions. Our results reveal that subsidy effectiveness significantly depends on remanufacturing costs and risk attitudes of supply chain participants. When remanufacturing costs are high, subsidizing third-party recyclers yields superior environmental and economic outcomes, whereas manufacturer subsidies prove more effective when these costs are low. Moreover, we identify critical thresholds in subsidy levels and risk aversion that alter decision-making patterns. These findings provide a framework for designing targeted subsidy policies and risk management strategies in CLSCs, advancing the field of sustainable supply chain management by offering nuanced insights into the complex dynamics between government interventions, risk preferences, and CLSC performance.

Investigating the effect of circularity index on a closed loop supply chain with multi-shipment policy

Environmental sustainability has emerged as a crucial aspect for every business, as the wastage of used products greatly contributes to greenhouse gas emissions and environmental hazards. Adopting the circular economy concept is essential to address this problem. This approach prioritizes reliability and re-manufacturability, aiming to simultaneously reduce waste and enhance sustainability and profitability. In this context, the proposed model introduces a sustainable closed-loop supply chain model wherein the manufacturer oversees the production process and delivers requested batches to the retailer under the single order and multi-shipment policy. The model incorporates the notion of circularity, quantified by a circularity index representing the degree to which the product's circularity level. The level of product circularity can be enhanced by material choice, supplier selection, and innovative design while manufacturing. The model being presented aims to maximize the total integrated profit of the closed-loop supply chain system by optimizing the number of shipments as well as the circularity index. It is evident from the analysis that the circularity index significantly influences profits and environmental sustainability by increasing product demand and extending product life cycles. Notably, the numerical example provided in the model illustrates that accounting for the effects of the circularity index results in an optimal profit up to 5% higher compared to scenarios where such effects are not considered.

A dynamic bi-objective optimization model for a closed loop supply chain design under environmental policies

Given the increasing focus on sustainability and environmental policy constraints, companies are required to redesign their supply chains. This paper explores the optimization of a closed loop supply chain (CLSC) network under both economic and environmental considerations. To achieve this, a bi-objective mixed integer linear model was developed. The proposed model identifies the optimal selection of CLSC facilities and manages both forward and reverse flows between them. The economic objective is reached by minimizing the total CLSC costs, while the environmental objective is satisfied by reducing CO2 emissions throughout the network. Products can be returned throughout their entire life cycle, which is why our model incorporates a dynamic aspect by considering product life cycle phases as time periods for the decision horizon. The model was tested through numerical experiments using a meta-heuristic approach based on the non-dominated sorting genetic algorithm NSGA-II. This algorithm produces a set of Pareto-optimal solutions that balance both objectives effectively. The results showed good performance in terms of computational time and optimization. Pareto solutions offered various options for managers and decision makers aiming for a sustainable closed loop supply chain design.

Retraction Note: A hybrid model for robust design of sustainable closed-loop supply chain in lead-acid battery industry.

Retraction Note: A hybrid model for robust design of sustainable closed-loop supply chain in lead-acid battery industry.

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.

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

Creating a sustainable closed-loop supply chain: An incentive-based contract with third-party E-waste collector

This paper proposes a closed-loop supply chain model to study the impact of product returns and awareness program on e-waste supply chain. The study focuses on the sustainability aspects of e-waste management and the effects such initiatives bring about to the environment at large. Reusing and recycling of collected products in manufacturing will improve the supply of scarce resources and reduce reliance on virgin raw materials. Engaging in CLSC can enhance brand reputation and competitiveness through sustainability while reducing the health risks to informal recycling workers by formalizing practices. The proposed model consists of a manufacturer, retailer, and third-party collector (TPC) under wholesale price and incentive contracts with deterministic demand. From the analysis it is observed that integrating a third-party collector with higher recycling rate boosts the total profit of the decentralized setting with a TPC. The centralized model, with a single decision maker, results in lower retail prices and higher collection rates compared to the decentralized models thereby commanding a higher overall profit.

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.

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.

Fuzzy-random robust flexible programming on sustainable closed-loop renewable energy supply chain

Photovoltaic (PV) systems have become a widely accepted method of harnessing solar energy in recent decades. In addition to the growing use of renewable energies are being used due to the depletion of fossil fuel supplies and the corresponding pollution generated by these non-renewable fuels. Since PV systems possess a limited lifespan, and are predicted to eventually become outdated, sustainability and recycling that should be incorporated into the structure of the solar system supply chain. The management of high and low moisture waste (H&LMW) is crucial due to its harmful environmental impacts, including sludge waste, carbon dioxide (CO2), and acid compounds. This study explores the integration of research on H&LMW and PV systems, focusing on energy generation from biogas, incineration power plants, and PV systems. In this respect, we propose a multi-objective mixed-integer programming model to construct a sustainable closed-loop renewable energy supply chain by considering forward and reverse flow through solar, biogas, and incineration power plants. Reducing CO2 with power-to-gas technology is thought to be an environmentally friendly approach. A novel fuzzy-random robust flexible programming approach based on Me measure is proposed, which overcomes the limitations of addressing the uncertainty of the parameters. Next, in order to solve the suggested multi-objective model, a novel strategy known as utility function based multi-choice conic goal programming is presented. A case study explores the sustainable challenges of a closed-loop renewable energy supply network in India’s renewable energy sector. The experimental results indicate that 9 candidate locations are selected for solar wafer production facilities, while 10, 9, and 15 locations are chosen for solar cell production, module manufacturing, and solar power plants, respectively. A sensitivity analysis reveals that the proportion of air pollution limitations possessively rises from 100% to 80% corresponding to the aggregate cost increases from INR 8021452 to INR 8056542.

A Multi-Objective Model for Designing a Sustainable Closed-Loop Supply Chain Logistics Network

Background: The growing concern for environmental and social issues has led to a focus on designing sustainable supply chains and increasing industrial responsibility towards society. In this paper, a multi-objective mixed-integer programming model is presented for designing a sustainable closed-loop supply chain. The model is aimed at the minimization of the total cost with the total used facilities, the negative environmental impacts, and the maximization of the positive social impacts. Methods: The epsilon-constraint method is utilized for solving the model and further extracting the Pareto solutions. Results: The result of the research clearly shows an optimal trade-off between the conflicting objectives, where, by paying more attention to the social and environmental aspects of sustainability, the total costs are increased or by optimizing the number of facilities, a better balance between the dynamics associated with the short-term and long-term goals is reached. The results of the sensitivity analysis also show that increasing the demand of the supply chain has the greatest impact on the supply chain costs compared to other objectives. Conclusions: Consequently, investigating such comprehensive sustainable objectives provides better insights into the impact of design variables on the expectations of stakeholders.

Sustainable closed-loop supply chains and their optimization models: a review of the literature

Sustainable closed-loop supply chains and their optimization models: a review of the literature

Impact of prices and collection rivalry in a sustainable closed-loop dual-channel supply chain under different power configurations

Market competitiveness and waste pollution are the two biggest problems facing the modern world. The first one may be handled with appropriate production and marketing tactics, while the last one can be reclaimed by recycling any unused products and employing disposable items that are environmentally friendly. This study employs both integrated and decentralised frameworks to evaluate forward and reverse supply chain rivalry in a closed-loop dual-channel supply chain for sustainable products. Competition on buy-back prices is also studied beyond price competition in forward supply chain. Two collectors collect used goods under buyback competition, then screen them, and ship them back to the manufacturer. When they generate their goods using both recycled and new raw materials, manufacturers impose the proper environmental requirements. The manufacturer and retailer, respectively, use online and conventional distribution channels to market the new sustainable products. The primary goal of the article is to investigate pricing, green innovation level, and buy-back price strategies for players' optimal gains under various scenarios. The article reveals that if the acceptance rate surpasses certain thresholds following the screening of the acquired usable items, then all participants benefit. Manufacturer-collector partnerships reduce environmental pollution more effectively than retailer-collector alliances, however, collectors' profits may suffer.

Optimization Of a Sustainable Closed Loop Supply Chain Network (SCLSCN): A Modified NSGA-III Method

For a generic closed-loop supply chain network (CLSCN), a mixed-integer Linear programing (MILP) Model was developed that simultaneously optimizes three of the fundamental elements of sustainability: cost, social impact, and environmental effect. Many real-world features of SC operations, which includes multiple products types, multiple echelons, multiple production technologies, and multiple modes of transportation, were all incorporated into the model during its development. As a result of the complexity of the model and need to provide Pareto front for decision maker, a novel hybrid NSGA III (HNSGA-III) was developed to solve the model. The Relative Gap was used result of the model was validated using the ε-constraint method. the algorithm parameters were tuned using Taguchi design then the performance of HNSGA III as compared to NSGA III, HypE and MOEA/D was assessed using RNS, SNS, IGD, CPU times and HC index. The result clearly shows the HNSGA III outperforms the other meta-heuristic algorithms. Keywords: Closed loop supply chain network, Sustainability, CO2 Emission, Multi-objective model, HNSGA III, NSGA III, MOEA/D, HypE Victor Jinn, Harold Ugochukwu Nwosu & Matthew Uzoma Shadrach (2023): Optimization Of a Sustainable Closed Loop Supply Chain Network (SCLSCN): A Modified NSGA-III Method. Journal of Advances in Mathematical & Computational Science. Vol. 10, No. 4. Pp 45-70. dx.doi.org/10.22624/AIMS/MATHS/V11N4P4. Available online at www.isteams.net/mathematics-computationaljournal.

A triple bottom line approach for designing a sustainable closed-loop supply chain network in fruit industry: A metaheuristic solution approach

In this study, the design of a sustainable closed-loop supply chain network for agricultural products with the goals of minimizing the cost and emission of greenhouse gases and maximizing the response to customer demand, and creating justice-based job opportunities, simultaneously, are aimed. The intended chain is based on the fruit supply chain study, which includes fresh fruits, concentrate, and vermicompost fertilizer. A mixed-integer linear programming (MILP) model has been developed to achieve the triple bottom line. Due to the nature of the NP-hard problem, the proposed model is solved using metaheuristic approaches consisting of two renowned algorithms, NSGA-II and NRGA, and a relatively new algorithm called NSGA-III. It is worth noting that the parameters of the algorithms are adjusted to achieve the best performance in small, medium, and large-size problems exerting the Taguchi method. After comparing the results of the three algorithms based on the well-known criteria, NRGA is introduced as the superior algorithm. Ultimately, the results of sensitivity analysis indicate that appending the possibility of using vermicompost in gardens and considering several vehicles in the proposed sustainable supply chain boosts the values of economic and environmental objective functions to about 6.4 and 8.2%, respectively.