Sustainable Closed-loop Supply Chain Research Articles

A comprehensive framework for sustainable closed-loop supply chain network design

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 Stochastic Multi-Objective Model for a Sustainable Closed-Loop Supply Chain Network Design in the Automotive Industry

A Stochastic Multi-Objective Model for a Sustainable Closed-Loop Supply Chain Network Design in the Automotive Industry

A Neutrosophic Fuzzy Optimisation Model for Optimal Sustainable Closed-Loop Supply Chain Network during COVID-19

In this paper, a sustainable closed-loop supply chain problem is modelled in conditions of uncertainty. Due to the COVID-19 pandemic situation, the designed supply chain network seeks to deliver medical equipment to hospitals on time within a defined time window to prevent overcrowding and virus transmission. In order to achieve a suitable model for designing a sustainable closed-loop supply chain network, important decisions such as locating potential facilities, optimal flow allocation, and vehicle routing have been made to prevent the congestion of vehicles and transmission of the COVID-19 virus. Since the amount of demand in hospitals for medical equipment is unknown, the fuzzy programming method is used to control uncertain demand, and to achieve an efficient solution to the decision-making problem, the neutrosophic fuzzy method is used. The results show that the designed model and the selected solution method (the neutrosophic fuzzy method) have led to a reduction in vehicle traffic by meeting the uncertain demand of hospitals in different time windows. In this way, both the chain network costs have been reduced and medical equipment has been transferred to hospitals with social distancing.

A sustainable closed loop supply chain design problem in intuitionistic fuzzy environment for dairy products

A sustainable closed loop supply chain design problem in intuitionistic fuzzy environment for dairy products

A fuzzy multi-objective optimization model for sustainable closed-loop supply chain network design in food industries

A fuzzy multi-objective optimization model for sustainable closed-loop supply chain network design in food industries

Sustainable closed-loop supply chain network under uncertainty: a response to the COVID-19 pandemic.

This study proposes a sustainable closed-loop supply chain under uncertainty to create a response to the COVID-19 pandemic. In this paper, a novel stochastic optimization model integrating strategic and tactical decision-making is presented for the sustainable closed-loop supply chain network design problem. This paper for the first time implements the concept of sustainable closed-loop supply chain for the application of ventilators using a stochastic optimization model. To make the problem more realistic, most of the parameters are considered to be uncertain along with the normal probability distribution. Since the proposed model is more complex than majority of previous studies, a hybrid whale optimization algorithm as an enhanced metaheuristic is proposed to solve the proposed model. The efficiency of the proposed model is tested in an Iranian medical ventilator production and distribution network in the case of the COVID-19 pandemic. The results confirm the performance of the proposed algorithm in comparison with two other similar algorithms based on different multi-objective criteria. To show the impact of sustainability dimensions and COVID-19 pandemic for our proposed model, some sensitivity analyses are done. Generally, the findings confirm the performance of the proposed sustainable closed-loop supply chain for the pandemic cases like COVID-19.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-021-16077-6.

Multi-objective optimization for multi-echelon, multi-product, stochastic sustainable closed-loop supply chain

ABSTRACT The closed-loop supply chain (CLSC) is the supply chain that includes various recovery plans for used products to be reused in the industry. Most of the previous stochastic CLSC studies considered the effect of uncertain parameter changes on the economic aspect only, while the other sustainability aspects were neglected. The purpose of this study is to develop a realistic mathematical model that represents and analyzes the impact of uncertainty in demand and recovery rate of products on the economic, environmental, and social sustainability aspects in the CLSC. The objective functions were optimized using the constrained optimization by linear approximation (COBYLA) algorithm along with preference-based Pareto optimal solution set algorithm at multiple computations to optimize various objectives simultaneously and efficiently. The results show a significant correlation between demand uncertainty, rate of return uncertainty, and the sustainability objectives in the CLSC using optimal inventory management settings.

Multi-objective mixed-integer linear optimization model for sustainable closed-loop supply chain network: a case study on remanufacturing steering column

Multi-objective mixed-integer linear optimization model for sustainable closed-loop supply chain network: a case study on remanufacturing steering column

A multi-objective optimization model to sustainable closed-loop solar photovoltaic supply chain network design: A case study in Iran

Along with the increasing rate of using renewable energies, following the finite amount of fossil fuel resources, and the associated pollution of these non-renewable energies, photovoltaic systems have recently achieved widespread acceptance to harness solar energy in the last decades. Since photovoltaic systems have a limited lifespan and cannot be used after a while, it seems imperative to pay attention to recycling and sustainability in the supply chain network design of these systems. In this respect, this paper first reviews the photovoltaic systems literature systematically. Thereinafter, a multi-objective, mixed-integer non-linear optimization model is deployed to design a photovoltaic supply chain network addressing the system's associated costs, environmental impacts, and social values. The concerned model also considers a reverse flow in addition to the typical forward flow in the proposed supply chain for special environmental and social issues. Likewise, to optimize the presented multi-objective model, the augmented ε-constraint method is employed, being capable of warranting the Pareto optimal solutions. Eventually, the model's efficiency is illustrated by discussing a real-world case in Iran, through which prominent managerial results are acquired.

Exploring the knowledge base and trajectories of knowledge dissemination in closed loop supply chain

Recently, the closed-loop supply chain (CLSC) domain has garnered considerable attention from scholars and practitioners. The increasing number of publications makes it necessary to study this domain comprehensively. To achieve this goal, this paper makes the first attempt to investigate the knowledge base and knowledge diffusion paths of the whole area systematically from both static and dynamic perspectives. Based on 1041 papers published in 2001–2020 from Web of Science, the characteristics of publications, productive authors, institutions, countries/territories and journals, as well as the co-authorship relationships are investigated by using bibliometrics. In addition, the evolution of hot topics and the historical development of CLSC domain are vividly described by keyword analysis and main path analysis respectively. The results show that: (1) Van Wassenhove, LN, Guide, VDR and Govindan, K play an important role in this domain and in addition Govindan, K has established extensive cooperative relationships; (2) INSEAD and Penn State Univ are more influential and they cooperate closely; (3) Several countries such as Italy, UK, Iran and China have begun to pay more attention to CLSC research in the past five years; (4) the hot topics revolve around the three aspects of network design, coordination and circular economy, and the trajectories of knowledge diffusion of this domain show that the focus has shifted to the green and sustainable CLSC recently. This study is able for researchers to grasp the general information, identify hot topics and capture history, development status and possible directions for the future research in the CLSC domain.

Optimizing price, quality and CSR investment under competing dual recycling channels in a sustainable closed-loop supply chain

This paper deals with pricing, product quality, Corporate Social Responsibility (CSR) investment, and used products collection strategy in a closed-loop supply chain (CLSC) in which both the manufacturer and the retailer contribute to CSR. The market demand is affected by the retail price, product quality and CSR investments. We develop the centralized model (C-Model) and three decentralized models viz. MR-Model, MT-Model, and RT-Model depending on competition between any two of the manufacturer, the retailer and the third-party collector for collecting used products in the reverse channel. A joint revenue-and-cost sharing contract is proposed to address the channel coordination issue. Optimal decisions are derived and compared analytically to determine the most efficient decentralized model, and verified with the help of a real case study. Our study reveals that due to lower product quality, CSR investments, collection rate of used products and higher selling price, the MT-Model is disadvantageous to all channel individuals while the MR-Model gives the best performance under comparatively less competition. The proposed contract can coordinate the supply chain and provide more profit to both the manufacturer and the retailer than their decentralized profits. It is additionally noticed that higher CSR investment, cost savings and collection rate of used products lead to sustainable development.

Sustainable closed-loop supply chain network: Mathematical modeling and Lagrangian relaxation

This paper addresses a novel two-stage model for a Sustainable Closed-Loop Supply Chain (SCLSC). This model, as a contribution, provides a balance among economic aims, environmental concerns, and social responsibilities based on price, green quality, and advertising level. Therefore, in the first stage, the optimal values of price are derived by considering the optimal level of advertising and greening. After that, in the second stage, multi-objective Mixed-Integer Linear Programming (MOMILP) is extended to calculate Pareto solutions. The objectives are include maximizing the profit of the whole chain, minimizing the environmental impacts due to CO2 emissions, and maximizing employee safety. Besides, a Lagrangian relaxation algorithm is developed based on the weighted-sum method to solve the MOMILP model. The findings demonstrate that the proposed two-stage model can simultaneously cope with coordination decisions and sustainable objectives. The results show that the optimal price of the recovered product equals 75% of the new product price which considerably encourages customers to buy it. Moreover, to solve the MOMILP model, the proposed algorithm can reach to exact bound with an efficiency gap of 0.17% compared to the optimal solution. Due to the use of this algorithm, the solution time of large-scale instances is reduced and simplified by an average of 49% in comparison with the GUROBI solver.

Mathematical Model of Location, Multi-Commodity and Multi-Period in Sustainable Closed-Loop Supply Chain Considering Risk and Demand and Quality Uncertainty (A case Study)

زنجیره‌­های تأمین پایدار به دنبال ایجاد تعادل بین اهداف اقتصادی، زیست‌محیطی و اجتماعی هستند. شرکت‌­ها نیز به‌منظور کاهش هزینه‌­ها و افزایش کارایی زنجیره تأمین مجبور به استفاده از زنجیره تأمین حلقه‌بسته هستند. در­نظرگرفتن ریسک در زنجیره‌های تأمین به‌خصوص زنجیره‌­های تأمین بازگشتی یکی از موضوع­‌هایی است که مطالعات زیادی در خصوص آن انجام نشده است؛ بنابراین در این پژوهش به مکان‌یابی اجزای یک زنجیره ‌تأمین سه‌­هدفه، حلقه­‌بسته پایدار، چند‌کالایی، چند­دوره‌ای با درنظرگرفتن عدم‌­قطعیت و سناریوهای بازار برای با رویکرد ریسک پرداخته می‌­شود. نوآوری‌های پژوهش عبارت‌اند از: در­نظر­گرفتن ریسک در زنجیره تأمین حلقه‌­بسته پایدار به‌عنوان بخشی از تابع هدف؛ در­نظر­گرفتن عدم‌قطعیت تقاضا در زنجیره تأمین با استفاده از سناریوهای تعریف­‌شده؛ توجه به کیفیت محصولات بازگشتی؛ چند­دوره‌ای­‌بودن و چند­محصولی‌­بودن مدل و سفارشی‌کردن مدل پیشنهادی برای یک مطالعه موردی واقعی. با توجه به NP-Hard­ بودن مسئله، مدل پیشنهادی با استفاده از رویکرد فراابتکاری ژنتیک رتبه‌بندی نامغلوب NSGA-II حل شده است. تحلیل حساسیت بر روی پارامترهای مسئله انجام شده است و کارایی روش‌های موردمطالعه بررسی شده‌اند. میانگین نقاط پارتو حاصل از تابع هدف اول برابر 9/56789، میانگین نقاط پارتو برای تابع هدف دوم برابر 8/1828و برای تابع هدف سوم برابر 32/77365 و همچنین میانگین زمان حل مدل برابر 9/15 ثانیه است.

Developing a two-stage model for a sustainable closed-loop supply chain with pricing and advertising decisions

Closed-Loop Supply Chain (CLSC) has become a critical problem due to its effects on various factors including economic motivations, environmental concerns, and social impacts. Moreover, there are coordination tools, such as pricing and advertising, which impact its performance. In this paper, we offer a two-stage approach to model and solve a sustainable CLSC, taking into account pricing, green quality, and advertising. In the first stage, optimal decisions on pricing, greening, and advertising are made, while in the second stage, a fuzzy multi-objective Mixed Integer Linear Programming (MILP) model is used to maximize the total profit, reduce CO2 emissions, and improve social impacts. Suitable solution methods are introduced according to the scale of the problem. For small-scale instances, an augmented ϵ-constraint method is used to solve the problem. For large-scale instances, approximations are required, and a Lagrangian relaxation algorithm solves the problem in polynomial time. The performance of the proposed model is evaluated through various numerical examples. The results illustrate the applicability and efficiency of the model, while confirming significant improvements in sustainable objectives under optimal pricing, green quality, and advertising. Besides, the proposed Lagrangian relaxation method significantly reduces the computational time for large-scale instances, with only a 2.308% deviation from the optimal results.

Efficient and sustainable closed-loop supply chain network design: A two-stage stochastic formulation with a hybrid solution methodology

In recent years, consumers and legislators have pushed companies to design their supply chain networks to consider environmental and social impacts as an important performance outcome. Due to the role of resource utilization as a key component of logistics network design, another primary goal of design is ensuring available scarce resources are used as efficiently as possible across all facilities. To address efficiency issues in a sustainable closed-loop supply chain network, a stochastic integrated multi-objective mixed integer nonlinear programming model is developed in this paper, in which sustainability outcomes as well as efficiency of facility resource utilization are considered in the design of a sustainable supply chain network. In doing so, efficiency is assessed using a bi-objective output-oriented data envelopment analysis model. A hybrid three-step solution methodology is presented that creates a linear form of the original mixed integer nonlinear programming problem using piecewise McCormick envelopes approach. In the second step, an aggregated single objective programming model is derived by exploiting the multi-choice goal programming. Finally, a Lagrangian relaxation algorithm is developed to effectively solve the latter stochastic single objective mixed integer linear programming problem. The application of the proposed approach is investigated with data drawn from a case study in the electronics industry. This case study illustrates how firms may balance sustainability and efficiency in the supply chain network design problem. Further, it demonstrates the integration of efficiency results in improving economic aspects of sustainability as well as social responsibility outcomes, but also highlights the trade-offs that exist between efficiency and environmental impacts.

Sustainable closed-loop supply chain for dairy industry with robust and heuristic optimization

This paper supplements the augmented ε-constraint approach with linearization using robust optimization and heuristics with an improved algorithm to maximize the total profit and minimize the environmental effects of a sustainable closed-loop supply chain (CLSC) in the dairy industry. The resultant mixed-integer linear programming (MILP) model is applied to a case from the dairy industry and evaluated against several test problems. The pessimistic, optimistic, and worst-case scenarios are considered along with the sensitivity analysis on the profitability of the CLSC concerning the product lifetimes. Our results inform that applying the heuristic on large-scale problems yields a 25% improvement in runtime. Furthermore, products with a longer lifetime under the worst-case scenario yield greater profit than those products with a shorter lifetime under an optimistic scenario.

A hybrid modeling approach for green and sustainable closed-loop supply chain considering price, advertisement and uncertain demands

The closed-loop supply chain network design (CLSCND) has garnered a lot of attention since it can handle economic and environmental issues. Likewise, supply chain coordination (SCC) tools can play an important role in enhancing the performance of the supply chains. This paper proposes a new hybrid method, in which SCC decisions and CLSCND objectives are simultaneously involved. First, this approach makes price, greenness, and advertisement decisions, and then it aims at maximizing profit and minimizing CO2 emission. A new nonlinear programming (NLP) model is developed based on the sensitivity of the return rate to green quality and the customers’ maximum tolerance, while the demands are uncertain. In order to overcome the uncertain demands, a robust optimization (RO) model is used. A Lagrangian relaxation algorithm is also employed to solve large-scale instances in a logical running time. The applicability of the proposed approach is corroborated through several examples. The results indicate an improvement in the performance of economic and environmental objectives under greening and advertising decisions. Furthermore, the proposed RO model outperforms the model that does not consider a robust approach.

Developing and solving an integrated model for production routing in sustainable closed-loop supply chain

Social and environmental sustainability has gained increasing importance in today’s complex supply chains. Accordingly, an integrated model for production routing in the sustainable closed-loop supply chain is presented in the current study. A three-objective mathematical model is also proposed to minimize supply chain costs, maximize social responsibility or social benefits, and finally, minimize environmental emissions. Sample trial problems are solved in three groups of the small, medium, and large size using the BCO algorithm. To prove the efficiency of this algorithm, its results are compared with the results using the NSGA-II algorithm in terms of comparative metrics such as quality, diversity, and spacing, as well as the runtime to the solution. According to the results, in all cases, the BCO algorithm outperformed the NSGA-II algorithm as it achieved more qualitative and near-optimal solutions. Also, the diversity metric values showed that the BCO algorithm is stronger in the exploration and extraction of the solution feasible region. The results of the metric of spacing and runtime to solution also showed that the NSGA-II algorithm achieves the solution in lower runtime than the BCO algorithm and searches solutions space in a more uniform manner.

Hybridization of an interactive fuzzy methodology with a lexicographic min-max approach for optimizing a multi-period multi-product multi-echelon sustainable closed-loop supply chain network

Here, a fuzzy multi-period multi-echelon multi-objective mixed-integer non-linear programming (MOMINLP) model is considered for a sustainable multi-product multi-site multi-distribution multi-customer supply chain in forward flow having multi-centers for collecting, checking, repairing and decomposing and multi-disposal centers in the reverse flow. Minimizing the total cost of the closed-loop supply chain (CLSC), elevating the customer satisfaction degrees, minimizing the total waiting time, minimizing the manufacturing site greenhouse gases and minimizing the CO2 emissions from vehicles are considered as the objective functions. Furthermore, integration of strategic decisions of flow allocations and vehicle routing with tactical and operational decisions such as production and workforce planning and improving upon customer satisfaction are considered. Also, we are concerned with the stability of the model and the accuracy of the obtained solution. We consider uncertainty and propose an appropriate method to develop a fair optimization of the distribution of raw materials and products to the supply chain participants. The presented solution method initially considers an adaptation of the lexicographic min–max fairness approach to finding a short delay for the delivery time of all the existing flows between every two consecutive echelons of the CLSC network. Then, a service quality measure is introduced to evaluate the uncertain delivery time considering the delay unpleasantness measure (DUM) index and measure the delay unpleasantness of all the existing delivery time delays. The model is converted to an auxiliary crisp MOMINLP problem by taking appropriate strategies, and a novel interactive fuzzy approach is proposed to find a compromised solution. The effectiveness of the algorithm is illustrated through a generated case study. The validity of the proposed method is confirmed by comparing the obtained results with the ones obtained by some other valid approaches, making use of distance and dispersion measure functions. Computational results show the proposed fuzzy method to be more efficient than other approaches. The encouraging results provide motivations for the use of our proposed fuzzy approach to solving other kinds of multi-objective mixed-integer models.

A sustainable closed-loop supply chain in a two-period: a game theory approach

A sustainable closed-loop supply chain in a two-period: a game theory approach