Closed-loop Supply Chain Design Research Articles

Designing a sustainable supply chain for battery PVC cases: A multi-objective optimization approach

Nowadays, Polyvinyl Chloride (PVC) and its derivatives are being used in a wide range of products, including car battery cases, due to their corrosion resistance and exceptional durability. As PVC production has grown, the amount of non-hazardous waste generated from PVC products has dramatically increased. In this context, incorporating PVC waste management into the PVC supply chain can effectively address the PVC waste generation issue. This study investigates the design of the Polyvinyl Chloride Closed-Loop Supply Chain (PVCCLSC) based on a triple-objective model for Abadan Petrochemical Company. The first objective aims to minimize the total cost of PVCCLSC, the second objective focuses on minimizing environmental impact, and the third objective maximizes the improvement of Occupational Health and Safety (OHS). In this study, an integrated solution approach is developed encompassing four main phases: (i) Developing an accelerated K-means clustering algorithm, along with the Elbow Method (EM) and Average Silhouette Method (ASM), to estimate the quality of returned PVC products, (ii) Employing a robust optimization method to capture the uncertainty, (iii) Implementing an augmented ε-constraint method to solve the proposed multi-objective model, and (iv) Applying a Bender’s Decomposition Algorithm (BDA) to solve the model and determine optimal solutions. As a result of the proposed BDA, relative optimality gaps have been reduced from 4.56 % in the conventional solution to zeros, and runtime has been reduced from 55 min to 20 min. Furthermore, as a result of this study, more than 44 % of returned PVC products should be recycled using the tertiary method to fulfill environmental requirements.

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.

Pricing strategies in a dual-channel green closed-loop supply chain considering incentivized recycling and circular economy

In recent years, significant attention has been dedicated to the design of closed-loop supply chains. However, opportunities exist to refine modeling approaches in alignment with circular economy principles. This refinement could foster the collaborative involvement of customers and retailers, resulting in a more sustainable operational framework. This study aims to address this gap, fostering waste reduction, resource conservation, and collaborative engagement in closed-loop supply chains. Thus, a multi-level, dual-channel green closed-loop supply chain that integrates circular economy principles is examined. We focus on implementing an incentive-based recycling program and examining various closed-loop supply chain settings: centralized, decentralized, and collaborative. We aim to determine optimal equilibrium prices, greening levels, and advertising strategies while considering the concurrent aspects of greening and incentivized recycling in today environmentally conscious era. By developing a demand function that integrates price elasticity, cross-price impacts, sustainability, and advertising, we evaluate performance through numerical examples and a case study in the clothing industry. The findings indicate that a collaborative approach surpasses a decentralized one regarding sustainability, guaranteeing profitability across the supply chain. The study also underscores the trade-offs between centralization and decentralization, with the former failing to ensure maximum individual profitability despite augmenting overall chain profits. Our findings can guide managers in reconciling profitability with sustainability when choosing supply chain structures, accentuating the value of collaborative strategies and green cost-sharing contracts. Furthermore, our results highlight that targeted advertising can stimulate demand for green products, and incentivizing circular economy practices through collaboration can yield positive environmental and economic outcomes.

Toward circular economy for pomegranate fruit supply chain under dynamic uncertainty: A case study

Food security is a fundamental prerequisite of human survival. This study focuses on providing a novel bi-objective model for the design of a green closed-loop pomegranate supply chain based on the value chain. To maximize the benefits of circular economy, we propose a thermochemical conversion process and develop a novel hybrid risk-neutral and risk-averse multi-stage stochastic programming (RNRAMSSP) approach to cope with supply uncertainty. We employ the expected value of perfect information (EVPI) and value of stochastic solution (VSS) to validate the proposed approach. For the VSS metric, we define a novel measure to compare the worst-case cost of the stochastic and deterministic model solutions. To demonstrate the applicability of the proposed model, we provide a real case study in Iran. Based on the VSS metric, the risk-neutral and risk-averse stochastic model solutions save up to 1% and 1.76% in the worst-case cost, respectively, compared to the deterministic model.

Environmental and social equity in network design of sustainable closed-loop supply chains

While the whole society is meant to benefit from sustainable development; environmental and social fairness considerations are often overlooked in the design of supply chain networks. The literature acknowledges that a reductionist interpretation of sustainability is often found in existing mathematical formulations due to lack of evaluation of the social dimension. In this paper, we suggest new indicators based on Gini index to measure environmental and social inequalities between regions concerned with installation of new product recovery facilities for the reverse part of a closed-loop supply chain. The initial levels of pollution and unemployment rate in each region are impacted by facility location decisions and corresponding industrial activities. We show through numerical experiments that inequalities between the regions can be diminished as a result of the use of deliberate objectives aiming to reduce environmental and social inequity. Managerial insights and future research directions are also discussed.

A scenario-based sustainable dual-channel closed-loop supply chain design with pickup and delivery considering social conditions in a natural disaster under uncertainty: a real-life case study.

In the context of the COVID-19 pandemic, utilizing the potential of online markets to sell products has become increasingly important for creating competitive advantages and ensuring the growth and survival of businesses. The pandemic has disrupted traditional business practices, and with social distancing measures in place, consumers have turned to online channels to meet their needs. As a result, businesses that have adapted quickly to online markets have been able to maintain their customer base and revenue streams. Thus, considering the potential of online markets is of utmost importance in the current pandemic situation. In this regard, the present research aims to provide a practical framework for creating a green and sustainable closed-loop supply chain network (SCLSCN), including the integration of online markets, to assist managers in making decisions that support economic, environmental, and social goals. Accordingly, a multi-objective mixed integer linear programming (MOMILP) optimization model was designed under uncertain demand and disruption caused by natural disasters in Iran's home appliance industry. The study also considered changes in the capacities of online and marketplace sales channels, revealing a significant reduction in costs at each stage. The results show that the increase in demand has a direct impact on the production level, warehousing, and transportation costs, leading to social impacts on the model. However, the current system cannot handle an increase in demand of more than 20%, requiring managers to make decisions to increase production capacity or build new factories. Thus, the study highlights the importance of considering online markets as a means to adapt to disruptions caused by the pandemic and maintain a competitive edge.

Integration of blockchain-enabled closed-loop supply chain and robust product portfolio design

Blockchain technology is one of the latest financial concepts influencing international trades, which contributes to speeding the supply chains by managing faster and more accurate financial transactions. Moreover, the product portfolio is highly effective on the financial flow of the supply chain. Accordingly, in this research, a comprehensive framework is proposed to design a blockchain-enabled closed-loop supply chain (BCSC), considering the role of the product portfolio. First, a mathematical model for robust product portfolio design is proposed. Next, a closed-loop supply chain network is designed where the financial flow via blockchain is of concern. To optimize this supply chain, the change in equity, as well as satisfaction from the blockchain network, is maximized. An exact solution method using GAMS software is applied to optimize the proposed mathematical model. The results obtained by implementation and analysis of this proposed framework indicate that by designing a product portfolio based on a robust model, BCSC would achieve the ideal value for its financial indexes, the change in equity in specific. Comparison of this newly proposed framework with the integrated portfolio-supply chain model reveals a 0.33% average error and a 1.5% reduction in lost revenue. It is concluded that this proposed framework assists supply chain managers in reducing their inaccurate decisions in both the physical and financial flow considerably.

Impact of reverse channel competition, individual rationality, and information asymmetry on multi-channel closed-loop supply chain design

Original equipment manufacturers (OEM) are focusing on remanufactured product business to improve their profitability and are using a multi-channel recollection strategy to improve recollection efficiency. Motivated by this phenomenon, we study a closed-loop supply chain consisting of an OEM, a retailer and a third-party vendor (3V) where the OEM designs an efficient multi-channel recollection structure when recollection agents competitively recollect used products. We analyze three different return channel structures under simultaneous influence of competition, collection efficiencies, individual rationality and information asymmetry. Our key findings are as follows: (i) OEM employs multiple recollection agents only if the recollection competition is low, (ii) OEM’s profit level is maximum when it recollects used products with one external agent (either a 3V or a retailer), (iii) with individual rationality constraint, there exist threshold cost values for both the OEM and retailer below which they do not trade and these values increase in consumer’s valuation of remanufactured products, (iv) OEM improves profitability by gathering information about external recollection agent’s cost coefficient; if the OEM engages only external agents for recollection, then the value of this information is zero, (v) when both competition and recollection cost are low, OEM prefers to recollect with one external agent and when recollection cost is high, OEM completely outsources recollection. We test the robustness of our findings by studying the influence of various channel coordinating contracts on the forward and reverse channels and the influence of quality and quantity dependent transfer pricing on the design of the multi-channel recollection.

Green Closed-Loop Supply Chain Networks’ Response to Various Carbon Policies during COVID-19

As concerns about the environment continue to increase and restrictions become tougher, professionals in business and legislators are being compelled to investigate the environmental effects of the activities associated with their supply chains. The control of carbon emissions by governments all over the world has involved the adoption of a variety of strategies to lower such emissions. This research optimizes COVID-19 pandemic logistics management as well as a green closed-loop supply chain design (GCLSCD) by basing it on carbon regulatory rules. This research looks at three of the most common types of normal CO2 restrictions. In the models that have been proposed, both costs and emissions are optimized. When it comes to supply chain (SC) activities, there is a delicate balance to strike between location selection, the many shipment alternatives, and the fees and releases. The models illustrate these tensions between competing priorities. Based on the numerical experiment, we illustrate the impact that a variety of policies have on costs in addition to the efficiency with which they reduce emissions. By analyzing the results of the models, managers can make predictions concerning how regulatory changes may affect overall emissions from SC operations.

Responsibility sharing strategy of product ecological design and collection in manufacturer-retailer closed-loop supply chain

In order to enhance the product ecological design and collection efficiency of closed-loop supply chain (CLSC), the manufacturer and the retailer cooperate with each other by responsibility sharing. Finding a more effective cooperative sharing model has always been a research hotspot. This paper proposes three kinds of responsibility sharing models, namely, the retailer sharing the manufacturer’s product ecological design responsibility (SD), the manufacturer sharing the retailer’s collection responsibility (SC), and the manufacturer and retailer respectively sharing each other’s collection and product ecological design responsibility (SDC). A single-period multi-stage Stackelberg model consisting of the manufacturer and the retailer is constructed. The impacts of the responsibility sharing ratios on the optimal decisions and profits of the closed-loop supply chain are investigated. In addition, the optimal product ecological design and collection rate decisions and profits of the two-echelon supply chain under responsibility sharing models are compared and the optimal sharing ratios are identified. Last, based on the optimal sharing ratios, the environmental impacts of the models with and without responsibility sharing are compared. The results show that responsibility sharing can improve the collection efficiency of the supply chain. From the perspective of the product design, Model SD can always incentivize the manufacturer to improve the product ecological design. In addition, model SDC and SC has same optimal collection responsibility sharing ratio whereas model SDC has higher optimal product ecological design responsibility sharing ratio than model SD. Last, the optimal choice of responsibility sharing models from the viewpoint of the environment depends on the relative environmental impact of the product using recycled materials.

Design of a robust closed-loop supply chain with backup suppliers under disruption scenarios

Design of a robust closed-loop supply chain with backup suppliers under disruption scenarios

An optimization model for a sustainable closed-loop supply chain considering efficient supplier selection and total quantity discount policies

This paper addresses the sustainable closed-loop supply chain (SCLSC) design problem regarding selecting a supplier under total quantity discount with demand uncertainty and logistic flow uncertainty. The proposed model considers the three pillars of sustainability: the economic, environmental, and social realms. The model deals with the costs incurred by products-related manufacturing and minimizes the carbon dioxide emissions resulting from different manufacturing processes, as well as the attendant rate of injuries among the workers. Python edition 2019-07 software with the SCIPY solver was used to solve the model, using a sequential least squares programming algorithm (SLSQP) to obtain optimal solutions. A numerical study was conducted to validate the model. A sensitivity analysis was conducted to address the effects of both types of uncertainty on the optimal solution. It was found that the effect of a high rate of demand uncertainty is more severe than the effect of the uncertainty of the flow logistics in the reverse direction since the former generated a lower value of the optimal solution than the worst-case scenario generated by the uncertainty budget. Moreover, the higher the weight of environmental and social objectives, the higher the proportion of recycled products from the total production. This study proposes a robust optimization model for an SCLSC that considers two types of uncertainty: the uncertainty budget that is used for the logistics flow in the reverse direction for refurbished and redesigned products and the box of uncertainty that is used to address the demand uncertainty.

Robust multiobjective scheme for closed-loop supply chains by considering financial criteria and scenarios

This paper considers the closed-loop supply chain design problem by examining financial criteria and uncertainty in the parameters. A robust multiobjective optimization methodology is proposed by considering financial measures such as maximizing the net present value (NPV) and minimizing the financial risk (FR). The proposed methodology integrates various multiobjective optimization elements based on epsilon constraints and robustness measurements through the FePIA (named after the four steps of the procedure: Feature–Perturbation–Impact–Analysis) methodology. Similarly, an analysis of the parameter variability using scenarios was considered. The proposed method's efficiency was tested with real information from a multinational company operating in Colombia. The results show the effectiveness of the methodology in addressing real problems associated with supply chain design.

A robust fuzzy stochastic multi-objective model for stone paper closed-loop supply chain design considering the flexibility of soft constraints based on Me measure

The Closed-Loop Supply Chain Network Design (CLSCND) encourages attention to the economic and environmental issues concerning returned products. CLSCND is a significant decision issue and it is accompanied by uncertainty. Hybrid uncertainties and the flexibility of soft constraints are some of the key issues in CLSCND. There is a research gap surrounding the utilization of the uncertainties of randomness, epistemic, and soft constraints simultaneously with a robust procedure. This study focuses on the Me measure and proposes a novel flexible, probabilistic, and stochastic programming based on robust optimization. In this approach, a convex mixture of the pessimistic–optimistic views based on the Me measure is implemented. The least level of satisfaction with soft constraints is determined Optimally that the need for repetitive reviews by Decision-Maker (DM) is obviated in the proposed model. Additionally, the model controls violations of soft constraints as well as stochastic deviations, probabilistic deviations, unfulfilled demand, and capacity. A case study is conducted on the development of a stone paper considering economic, environmental, and responsive objectives by minimizing total cost and carbon emissions as well as transportation and processing time. In addition, to estimate the weight of the objectives and handle the multi-objective model, the Best–Worst method (BWM) and the interactive fuzzy programming approach are employed, respectively. Robustness analysis and sensitivity analysis for the case study are then performed and the results are simulated and evaluated using the realization model. The findings revealed that the suggested model performed well.

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

Considering supply chain efficiency during the network design process significantly affect chain performance improvement. In this paper, the design process of a sustainable lead-acid battery supply chain network was addressed. Because the design of such networks always involves great computational complexity, in the present study, a two-stage model was proposed to overcome this issue. In the first stage, candidate sites of recycling centers were identified using data envelopment analysis (DEA) and based on their efficiency scores. Unlike the previous studies, not only economic criteria but also technical and geographical criteria were employed to select these locations. In the second stage, a bi-objective programming model was developed to simultaneously determine the tactical and strategic decisions of the chain. Since some data was subject to uncertainty, a robust possibilistic approach was presented. The model ensures that the resulting structure for the chain will be robust to noise and disturbance in parameters. A life cycle assessment model based on the ReCiPe 2008 method was developed in SimaPro software. To evaluate the applicability of the presented method, a case study in the automotive industry was used. The results of implementing the DEA method showed that from among 23 available locations, 11 potential places were selected for construct recycling centers. The final results showed that the inappropriate potential locations of recycling centers were eliminated, and the complexity of the mathematical model proposed in the second stage was reduced. The obtained results of environmental protection costs revealed that this criterion changed from 0 to 8,333,874,332. Moreover, the first objective function resulted in a centralized network to minimize costs, and in contrast, the second objective function tended to decentralize the network to minimize environmental impacts.

Developing a Mathematical Model for a Green Closed-Loop Supply Chain with a Multi-Objective Gray Wolf Optimization Algorithm

Abstract Intense competition in today’s market and quick change in customer preferences, along with the rapid development of technology and globalization, have forced companies to work as members of a supply chain instead of individual companies. The success of the supply chain depends on the integration and coordination of all its institutions to form an efficient network structure. An efficient network leads to cost savings throughout the supply chain and helps it respond to customer needs faster. Accordingly, and with respect to the importance of the supply chain, in this study a developed mathematical model for the design of a green closed-loop supply chain is presented. In this mathematical model, the economic and environmental objectives are simultaneously optimized. In order to tackle this mathematical model, two methods of epsilon constraint and multi-objective gray wolf optimization (MOGWO) algorithm have been applied. The results of comparisons between the two mentioned methods show that MOGWO reduce the average solving time from about 1300 seconds to 88 seconds. In the last step of this research, in order to show the application of the proposed mathematical model and the method of solving the research problem, it was implemented in the supply chain of Dalan Kouh diary product and the Pareto optimal solutions were analyzed.

Sustainable closed-loop supply chain with energy efficiency: Lagrangian relaxation, reformulations and heuristics

Research on the development of sustainable supply chain models is highly active nowadays. Merging the concept of supply chain management with sustainable development goals, leads to simultaneous consideration of all economic, environmental and social factors. This paper addresses the design of a sustainable closed-loop supply chain including suppliers, manufacturers, distribution centers, customer zones, and disposal centers considering the consumption of energy. In addition, the distribution centers play the roles of warehouse and collection centers. The problem involves three choices of remanufacturing, recycling, and disposing the returned items. The objectives are including the total profit, energy consumption and the number of created job opportunities. As far as we know, these objectives are rarely considered in a sustainable closed-loop supply chain model. The proposed model also responds to the customer demand and also addresses the real-life constraints for location, allocation and inventory decisions in a closed-loop supply chain framework. Another novelty of this research is to develop a set of efficient Lagrangian relaxation reformulations and fast heuristics for solving a real-world numerical example. The results have revealed that the obtained solution is feasible and the developed solution algorithm is highly efficient for solving supply chain models. Finally, a comprehensive discussion is provided to highlight our findings and managerial insights from our results.

A NOVEL BI-OBJECTIVE MODEL FOR A MULTI-PERIOD MULTI-PRODUCT CLOSED-LOOP SUPPLY CHAIN

Closed-loop supply chain (CLSC) is a kind of supply chain which contains forward and backward flows of commodities within a logistics network. In the decision-making process of CLSC, locational, inventory control and transportation issues are addressed to deal with strategic, tactical and operational decisions. This paper utilizes a novel bi-objective mixed-integer linear programming (MILP) model to formulate a multi-period multi-product CLSC design problem considering aggregate cost minimization and service level maximization at the same time. To tackle the bi-objectiveness of the model, goal attainment method (GAM) is applied which is then executed by Gurobi Python API to test the applicability of the suggested model for three different scales (small, medium and large). It is demonstrated that the proposed methodology can find the optimal solutions for different problems in a maximum of 500 seconds. Finally, a set of sensitivity analyses is carried out on the main parameters in order to test the behaviors of the objective functions and suggest managerial insights as well as decision aids. The results reveal that the model is highly dependent on the demand parameter, that is, an increase in demand is closely related to an increase in the aggregate cost and a simultaneous downward trend in the service level.

A flexible closed loop supply chain design considering multi-stage manufacturing and queuing based inventory optimization

In this study, a multi-objective nonlinear model is used to design a closed-loop supply chain network. We consider a multi-period, multi-product and multi-stage manufacturing model by employing a queuing system for inventory management of finished and return products under demand uncertainty limitations. The purpose of this paper is to simultaneously reduce the waiting times of queues in warehouses and the costs of production, transportation, inventory and queuing systems. The flexibility of the chain is formulated at three concepts: product flexibility, the flexibility of supply against demand fluctuations, and the flexibility of time to satisfy all demand. The proposed non-linear model is solved by a robust genetic algorithm. A sensitivity analysis of the flexibility ratios reveals several insights for practitioners and academics.

Optimization of production batches in a circular supply chain under uncertainty

During the covid-19 outbreak, millions of people are required to use disposable masks to protect themselves from this infectious disease. Under an exponential spread tendency of this pandemic, the conventional end of life of these plastic products is to dispose of them in landfills or to incinerate them. Actually, this scenario is the most optimistic one since millions of tonnes of masks are ended up in oceans. Therefore, recycling which is one of the circular economy concepts remains the key solution aiding countries to mitigate masks pollution, in addition to its role in decreasing the widespread of the disease. In this paper, we will exanimate several studies dealing with reverse logistics and circular economy, then we will develop a closed-loop supply chain design during the pandemic and translate it mathematically as a MILP model under certain and uncertain client demand. Finally, the problem will be solved using IBM ILOG Cplex Optimization Studio and the obtained results will be discussed.