Designing a multi-echelon closed-loop sustainable supply chain network with demand uncertainty

PurposeThis 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/approachIn 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.FindingsThe 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/implicationsThis 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/valueThis 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.

PurposeThe purpose of this paper is to assist a manufacturing firm in designing the closed-loop supply chain network under risks that are affecting its supply quality and logistics operations. The modeling approach adopted aims at the embedding supply chain risks in a closed-loop supply chain (CLSC) network design process and suggests optimal supply chain configuration and risk mitigation strategies.Design/methodology/approachThe method proposes a closed-loop supply chain network and identifies the network parameter and variables required for closing the loop. Mixed-integer-linear-programming-based mathematical modeling approach is used to formulate the research problem. The solutions and test results are obtained from CPLEX solver.FindingsThe outcomes of the proposed model were demonstrated through a case study conducted in an Indian hospital furniture manufacturing firm. The modern supply chain is mapped to make it closed loop, and potential risks in its supply chain are identified. The supply chain network of the firm is redesigned through embedding risk in the modeling process. It was found that companies can be in great profit if they follow closed-loop practices and simultaneously keep a check on risks as well. The cost of making the supply chain risk averse was found to be insignificant.Practical implicationsAlthough the study was conducted in a practical case situation, the obtained results are not indiscriminate to the other circumstances. However, the approach followed and proposed methodology can be applied to many industries once a firm decides to redesign its supply chain for closing its loop or model under risks.Originality/valueBy using the identified CLSC parameters and applying the proposed network design methodology, a firm can design/redesign their supply chain network to counter the risk and accordingly come up with planned mitigation strategies to achieve a certain degree of robustness.

ABSTRACTOne of the basic requirements of the companies to survive in real-world competitive environments is to make their supply chains as efficient as possible. Due to recent governmental regulations, environmental issues, and the development of the concept of social responsibility, the closed-loop supply chain management has been focused by many researchers. A closed-loop supply chain includes both forward and reverse supply chain networks with the purpose of combining environmental considerations with the traditional supply chain network designs through the collection of used products and activities related to their reuse. In this paper, a bi-objective, multi-period, multi-product, closed-loop supply chain network is designed under environmental considerations, discounts, and uncertainties. The deterministic model of the chain is first solved by three multi-objective decision-making methods. Then, based on real-world uncertainties involved in some of the parameters, a robust optimization model is proposed and solved using decision-making methods. At the end, the best deterministic and robust models are selected based on the displaced ideal solution.

PurposeContinuous benchmarking of a closed-loop supply chain (CLSC) network is required to achieve circular economic viability for end-of-life vehicle (ELV) recovery programs for original equipment manufacturers (OEMs). This study develops a framework to assess and benchmark CLSC networks in ELV recovery programs, addressing the absence of a standard index and enabling circular economic viability for OEMs.Design/methodology/approachThe study uses a Bayesian evidential reasoning approach (BERA) that helps decision-makers develop a reintegration index (RI) for the automobile CLSC network. To develop the index, a total of 15 factors related to the automobile CLSC are identified from the literature. Bayesian belief network (BBN) is used on those factors to generate conditional probabilities for different nodes of the BBN. With the opinion of 12 domain experts, the ERA is used to generate a score for each node. Finally, the Markovian tree is used on the scores to generate the RI for a particular CLSC network.FindingsThe analysis demonstrates that both operational and strategic actions aimed at ensuring customer satisfaction and retaining core components are quantified using a standardized index value for each alternative amidst uncertainty. Leveraging the BERA model, decision-makers can calculate RI values, providing them with the means to assess and regulate ratings for CLSC networks. This capability serves to bolster circular economic sustainability by facilitating informed decision-making within the automotive industry.Practical implicationsThis framework offers a structured approach for decision-makers to evaluate CLSC networks in ELV recovery programs, allowing for adaptability to specific organizational objectives and facilitating informed decision-making in the automotive industry.Originality/valueThe study’s integration of expert insights and probabilistic modeling fills the gap in the literature by providing a comprehensive framework for assessing CLSC networks in ELV recovery programs, contributing to circular economic viability and strategic decision-making for OEMs.

PurposeThe purpose of this paper is to give an up-to-date and structured insight into the literature published during the past decade on sustainable green supply chain management. It also suggests trends for future research based on the research issues identified through systematic and comprehensive analysis of previous studies in the area of green and sustainable supply chain management.Design/methodology/approachA state-of-the-art literature review is carried out by systematically collecting the existing literature over a period of 10 years (2005-2014) and categorizing it on the basis of attributes such as stages in supply chain, methodology and the industries/sectors under consideration. The classification of literature is also done according to the geographic region and year of publication.FindingsThere has been an increased interest among researchers and practitioners in the area of sustainable green supply chain management in the past decade. A need for achieving sustainability through adoption of greener practices has been universally felt, owing to an increasing environmental and ecological complexity. The review reveals that there exists a need to address behavioural issues like human resource management and supply chain partner relationship management. Moreover, reverse logistics, closed-loop supply chain management and waste management are areas that need special focus to achieve environmental sustainability.Research limitations/implicationsThe current review focuses on research trends in the past 10 years only. Moreover, papers from only good quality, peer-reviewed journals are considered in the study.Originality/valueMost of the previous reviews have either focused on specific issues related to sustainable supply chains only or green supply chains. The present study collectively takes into consideration papers both from green supply chain management as well as from sustainable supply chain literature that have a prime focus on environmental sustainability.

Supply chain network design is an important decision-making problem affecting the long-term profitability of firms. Evaluating the performance of supply chain network designs can help decision-makers to select the network configuration that meets the business specifications while operating at a reasonable cost. In this study, Social Network Analysis (SNA) metrics are used to evaluate the performance of closed-loop supply chain (CLSC) Network designs in terms of resilience when exposed to disruptions and the balance of flows. CLSC Networks accommodate the flow of returned products from the customers for recycling, remanufacturing, or disposal, increasing the design complexity compared to traditional supply chain networks. The proposed approach involves custom-designed network-level SNA metrics and random forest (RF) feature selection which are computationally low-cost approaches. The proposed metrics are implemented in an R package titled NetworkSNA and shared on GitHub, and RF feature selection method is performed in python. The optimal and near-optimal network designs from a CLSC Network based on real data are used as a case study. The metric values are interpreted into practical recommendations to compare the alternative CLSC Networks.

Many companies face challenges in reducing their supply chain costs while increasing sustainability and customer service levels. A comprehensive framework for a sustainable closed-loop supply chain (CLSC) network is a practical solution to these challenges. Hence, for the first time, this study considers an integrated multi-objective mixed-integer linear programming (MOMILP) model to design sustainable CLSC networks with cross-docking, location-inventory-routing, time window, supplier selection, order allocation, transportation modes with simultaneous pickup, and delivery under uncertainty. An intelligent simulation algorithm is proposed to produce CLSC network data with probabilistic distribution functions and feasible solution space. In addition, a fuzzy goal programming approach is proposed to solve the MOMILP model under uncertainty. Eight small and medium-size test problems are used to evaluate the performance of the proposed model with the simulated data in GAMS software. The results obtained from test problems and sensitivity analysis show the efficacy of the proposed model.

A Green Dual-Channel Closed-Loop Supply Chain Network Design Model

The adoption of closed-loop supply chain (CLSC) network is one of the effective approaches to reduce carbon emissions. In current globalization, inherent uncertainty exists in business environment so there is a need to be design robust supply chains. This paper proposes a deterministic mixed integer linear programming (MILP) model integrating economics and carbon emission considerations including selection of production technologies and transportation mode as a part of CLSC network strategic and tactical decisions. The robust counterpart of the proposed deterministic model is developed based on three alternative uncertainty sets to represent the imprecise input parameters. The robust counterpart is used to study the supply chain performance by considering the two most globally practiced carbon regulatory policies; carbon tax policy and carbon trading policy. Numerical results show that total cost of the proposed robust optimization model under each uncertainty set is greater than the total cost of deterministic model. The additional cost is due to solution space of each uncertainty set to accommodate any uncertainty level. As uncertainty level increases the overall supply chain cost worsen. Moreover, the results suggest that carbon tax rate has direct relation with overall supply chain cost whereas having carbon market trading flexibility in carbon trading policy, this policy is more efficient policy as compared to carbon tax policy. Furthermore, the proposed robust optimization model is useful for mangers to achieve not only a robust supply chain network design which can withstand any possible uncertainty level but also significant reduction in carbon emissions by choosing suitable carbon-efficient policy.

A novel multi-objective optimization model for integrated problem of green closed loop supply chain network design and quantity discount

Designing a dual-channel closed loop supply chain network using advertising rate and price-dependent demand: Case study in tea industry

Robust optimization and modified genetic algorithm for a closed loop green supply chain under uncertainty: Case study in melting industry

PurposeThe purpose of this study is to develop a holistic optimization model for an integrated sustainable fleet planning and closed-loop supply chain (CLSC) network design problem under uncertainty.Design/methodology/approachA novel mixed-integer programming model that is able to consider interactions between vehicle fleet planning and CLSC network design problems is first developed. Uncertainties of the product demand and return fractions of the end-of-life products are handled by a chance-constrained stochastic program. Several Pareto optimal solutions are generated for the conflicting sustainability objectives via compromise and fuzzy goal programming (FGP) approaches.FindingsThe proposed model is tested on a real-life lead/acid battery recovery system. By using the proposed model, sustainable fleet plans that provide a smaller fleet size, fewer empty vehicle repositions, minimal CO2 emissions, maximal vehicle safety ratings and minimal injury/illness incidence rate of transport accidents are generated. Furthermore, an environmentally and socially conscious CLSC network with maximal job creation in the less developed regions, minimal lost days resulting from the work's damages during manufacturing/recycling operations and maximal collection/recovery of end-of-life products is also designed.Originality/valueUnlike the classical network design models, vehicle fleet planning decisions such as fleet sizing/composition, fleet assignment, vehicle inventory control, empty repositioning, etc. are also considered while designing a sustainable CLSC network. In addition to sustainability indicators in the network design, sustainability factors in fleet management are also handled. To the best of the authors' knowledge, there is no similar paper in the literature that proposes such a holistic optimization model for integrated sustainable fleet planning and CLSC network design.

A closed-loop supply chain (CLSC) network consists of both forward and reverse supply chains. In this paper, a CLSC network is investigated which includes multiple plants, collection centres, demand markets, and products. To this aim, a mixed-integer linear programming model is proposed that minimizes the total cost. Besides, two test problems are examined. The model is extended to consider environmental factors by weighed sums and ε-constraint methods. In addition, we investigate the impact of demand and return uncertainties on the network configuration by stochastic programming (scenario-based). Computational results show that the model can handle demand and return uncertainties, simultaneously.

ABSTRACTThe increase in the development of proper channels for recycling and disposal of the manufactured products have motivated the study of closed loop supply chains. The closed loop supply chain networks can be considered as a strong tool for attaining the goals of sustainable development. The customers are not the terminating destination for the products in closed loop supply chain networks. However, after some recycling or refurbishing processes the product once again enters the supply chain networks. The involvement of forward and reverse flow of the products makes the closed loop supply chain networks very complex. As, there may be several conflicting objectives related to the closed loop supply chains, in this paper, we have proposed a bi-level multi-objective programming model for these networks. Bi-level programming problems deal with the situations where decisions are to be taken at two different hierarchical levels. We have considered three objective functions which are distributed among these two levels in such a manner that the decision makers solves for a single objective at each level. A solution procedure is also discussed for solving the proposed model.