Closed-loop Supply Chain Network Research Articles

A novel robust fuzzy mean-UPM model for green closed-loop supply chain network design under distribution ambiguity

Green closed-loop supply chain (GCLSC) is a supply chain that encompasses forward and reverse flows of components and products in logistic networks with a focus on economic and environmental performance. In the decision-making process of GCLSC, the presence of uncertainty and risk originating from the size and complexity of network is crucial to consider, and the distribution of uncertain parameter may be ambiguous. To characterize the ambiguity caused by distributional perturbation, a novel ambiguity distribution set is proposed, and further a new upside risk: upper partial moment with power q is introduced to quantify the economic risk in the GCLSC. Subsequently, a distributionally robust fuzzy GCLSC network design model which attempts to optimize the worst-case performance of the network is developed with the perspective of a trade-off between upside risk and expectation of economic cost. To format a sustainable GCLSC paradigm, the policy of carbon cap is adopted to control carbon emissions in terms of environmental constraints. Furthermore, the tractable counterpart of the proposed model is obtained by transforming distributionally robust credibility objective and constraints into their equivalent forms under ambiguous distribution of uncertain parameter. Finally, a case study on Coca-Cola Company in Northeast China is investigated to test and verify the proposed model. The advantage of proposed model is demonstrated through comparative study on distribution ambiguity free and without environmental constraint problem. Computational results reveal that the proposed model has superior capability of immunity against the risk of distribution ambiguity.

The design of a resilient and sustainable maximal covering closed-loop supply chain network under hybrid uncertainties: a case study in tire industry

This paper aims at designing a sustainable and resilient tire closed-loop supply chain network based on a real case study in Iran. To immune the network against disruptions, a new resiliency approach is proposed by extending efficient demand coverage plans. The developed model includes four objectives. The first objective minimizes the total costs of the network along with maximizing the coverage of customers’ demand. As a new concept, the second objective maximizes the operational reliability of facilities to extend a consistent and responsive tire supply chain. Also, to design a sustainable network, the third objective function minimizes CO2 emissions in the strategic and tactical planning horizons. Moreover, the fourth objective maximizes the social responsibility of the network via introducing new social factors. A new mixed fuzzy possibilistic flexible programming method is also proposed to handle constraints’ violations and parameters’ uncertainty. Outputs confirm the accurate performance of the extended model and ensure its applicability in the real-world case study.

A Multi-Objective Fuzzy Approach to Closed-Loop Supply Chain Network Design with Regard to Dynamic Pricing

During the last decade, reverse logistics networks received a considerable attention due to economic importance and environmental regulations and customer awareness. Integration of leading and reverse logistics networks during logistical network design is one of the most important factors in supply chain. In this research, an Integer Linear Programming model is presented to design a multi-layer reverse-leading, multi-product, and multi-period integrated logistics network by considering multi-capacity level for facilities under uncertainty condition. This model included three objectives: maximizing profit, minimizing delay of goods delivering to customer, and minimizing returned raw material from suppliers. This research gives financial incentives to encourage customers in order to return their used product. Considering that the remaining value of used products is the main incentive of a company to buy second-handed goods, a dynamic pricing approach is determined to define purchase price for these types of products, and based on that, the percentage of returned products were collected by customers. In addition, in this study, parameters have uncertainty features and are vague; therefore, at first, they are converted into exact parameters and, then, because model is multi-objective, the fuzzy mathematical programming approach is used to convert multi-objective model into a single objective; finally, the model by version 8 of Lingo is run. In order to solve a large-sized model, a non-dominated sorting genetic algorithm II (NSGA-II) was applied. Computational results indicate the effect of the proposed purchase price on encourage customers to return the used products.

WEEE closed-loop supply chain network management considering the damage levels of returned products.

Due to economic, social, and environmental concerns, managing waste electrical and electronic equipment (WEEE) has become an important research area. The WEEE directive gives responsibility to producers for developing a system for recycling and disposal activities and handle all associated costs. This study proposes a mixed integer programming model for decision-makers to manage their activities on the WEEE closed-loop supply chain network. A decision-maker may be a single producer of any size or a managing body formed by a group of producers and/or third-party companies in the network. The model contributes to the research field by integrating product returns with different quality and damage levels. A set of scenarios was designed to evaluate the effects of the directive and the network design related issues (e.g., the minimum collection rates, the number of producers and stores in the network) on the objective function. The results indicate that the capacity balance among stores, producers, and recovery centers is vital to make the network profitable and sustainable.

Research on Multi-Period Closed-Loop Supply Chain Network Equilibrium Based on Consumers' Preference for Products

This paper comprehensively analyzes the impact of consumer preference for products in a multi-period closed-loop supply chain network (CLSCN) equilibrium. This paper is based on the multi-period CLSCN equilibrium model of remanufacturers' recoveries and multiple planning periods. The consumer preference for products at multiple planning periods are respectively divided into dynamic and static conditions. Moreover, the intervention of government is considered. Meanwhile, the variational inequalities are employed to separately depict the optimal behaviors and equilibrium conditions of the supply chain network. The results show that when the consumer preference for new products is static, the impact of consumer preference for new products has a significant impact on the multi-period CLSCN. If consumer preference for new products is dynamic, at the preliminary planning period, the impact of consumer preference for new products on the multi-period CLSCN will be better defined.

A Closed-loop Supply Chain Network Design Problem in Copper Industry

Undoubtedly, metals are the basis of the sustainable development of all human societies. In the last century, the role of copper, as the third most widely used metal, after steel and aluminum, has been crucial. Copper is a recyclable metal. It has many applications such as industrial electricity, plumping, wiring, electronic equipment, transportation, and infrastructure. Today, with the growth of the industry in societies, the demand for copper has increased. This motivated us to study its supply chain network design firstly. To the best of our knowledge, there is no research reported about copper supply chain network design. This paper aims to maximize the profit of the copper closed-loop supply chain. We formulate this network design problem as a Mixed Integer Programming model. The model is considered as single-objective and multi-product. The exact solution of the model is found by using GAMS software. Sensitivity analysis results provide useful results that managers can use them in decisions.

Green closed-loop supply chain network design with stochastic demand: a new accelerated benders decomposition method

Changing the structure of supply chains to move towards less polluting industries and better performance has attracted many researchers in recent studies. Design of such networks is a process associated with uncertainties and control of the uncertainties during decision-making is of particular importance. In this paper, a two-stage stochastic programming model was presented for the design of a green closed-loop supply chain network. In order to reach the environmental goals, an upper bound of emission capability that helps governments and industries to control greenhouse gas emissions was considered. During the reverse logistics of this supply chain, waste materials are returned to the forward flow by the disassembly centers. To control the uncertainty of strategic decisions, demand and the upper bound of emission capacity with three possible scenarios is considered. To solve the model, a new accelerated Benders decomposition algorithm along with Pareto-Optimal-Cut was used. The efficiency of the proposed algorithm was compared with the regular Benders algorithm. The effect of different numerical values of parameters and probabilities of scenarios on the total cost was also examined.

Designing a data-driven leagile sustainable closed-loop supply chain network

ABSTRACT Nowadays, there is a great deal of interest in applying sustainability concepts for logistics and supply chain management. This paper proposes a new multi objective model in the area of closed loop supply chain problem integrated with lot sizing by considering lean, agility and sustainability factors simultaneously. In this regard, responsiveness, environmental, social and economic aspects are regarded in the model in addition to the capacity and service-level constraints. Most importantly, strategic and operational backup decisions are developed to increase the resiliency of the system against disruption of the facilities and routes simultaneously. In the following, a new hybrid metaheuristic algorithm comprised a parallel Multi-Objective Particle Swarm Optimization (PMOPSO) algorithm and a multi objective social engineering optimizer (MOSEO) is developed to deal with large size problems efficiency. To ensure about the effectiveness of the proposed hybrid algorithm, the results of this algorithm are compared with a Non-dominated Sorting Genetic Algorithm (NSGA-II).

Closed-loop supply chain network equilibrium strategy model with environmental protection objectives

With the increasing popularity of ecological civilization and sustainable development, enterprises should consider environmental protection measures in their operations in addition to pursue their economic interests. This paper establsihes a closed-loop supply chain network model composed of multiple suppliers, manufacturers, retailers, recyclers, and demand markets—regarding their dual goals of the profit maximization and the minimization of carbon emissions. The conditions necessary for establishing overall equilibrium and an equilibrium model of the entire closed-loop supply chain network are determined by applying variational inequality and dual theory. A modified projection contraction algorithm is used to design a model-solving program. Finally, using numerical examples, the paper conducts a comparative static analysis on important parameters such as the weight coefficients of environmental protection objectives and consumers' awareness of low-carbon environmental protection and attains some beneficial enlightenment on management. The results indicate that when the environmental protection objectives of a certain type of enterprise increases, both the economic benefits and environmental protection performance will improve; when the environmental protection objectives of all enterprises increases simultaneously, environmental protection performance improves significantly, but the changes in economic benefits of different enterprises are inconsistent and profit coordination is more complex. Although consumers’ awareness of low-carbon preference could improve environmental performance, it reduces the overall profits of network members and the entire closed-loop supply chain network as a whole. The above conclusions can be used as a reference for the government in designing low-carbon environmental protection policy and in closed-loop supply chain research.

A Trade-off Analysis of Economic and Environmental Aspects of a Disruption Based Closed-Loop Supply Chain Network

Closed-loop supply chain networks are gaining research popularity due to environmental, economic and social concerns. Such networks are primarily designed to overcome carbon footprints and to retrieve end of life products from customers. This study considers a multi echelon closed-loop supply chain in the presence of machine disruption. A multi-objective model is presented to optimize the total cost, the total time and emissions in a closed-loop supply chain network. The aim is to analyze the trade-off between the objectives of cost, time, and emissions and how these decisions are impacted by the selection of different available machines. A number of solution approaches are tested on a case study from the tire industry. The results suggest the improved performance of the hybrid heuristic and the importance of controlling disruption in a closed-loop supply chain network. Furthermore, there is a trade-off between the different objective functions which can help the decision maker to choose a particular solution according to the preference of an organization. Finally, conclusion and future research avenues are provided.

A robust optimization model for sustainable and resilient closed-loop supply chain network design considering conditional value at risk

<p style='text-indent:20px;'>One of the challenges facing supply chain designers is designing a sustainable and resilient supply chain network. The present study considers a closed-loop supply chain by taking into account sustainability, resilience, robustness, and risk aversion for the first time. The study suggests a two-stage mixed-integer linear programming model for the problem. Further, the robust counterpart model is used to handle uncertainties. Furthermore, conditional value at risk criterion in the model is considered in order to create real-life conditions. The sustainability goals addressed in the present study include minimizing the costs, <inline-formula><tex-math id="M1">\begin{document}$ \text{CO}_2 $\end{document}</tex-math></inline-formula> emission, and energy, along with maximizing employment. In addition, effective environmental and social life-cycle evaluations are provided to assess the associated effects of the model on society, environment, and energy consumption. The model aims to answer the questions regarding the establishment of facilities and amount of transported goods between facilities. The model is implemented in a car assembler company in Iran. Based on the results, several managerial insights are offered to the decision-makers. Due to the complexity of the problem, a constraint relaxation is applied to produce quality upper and lower bounds in medium and large-scale models. Moreover, the LP-Metric method is used to merge the objectives to attain an optimal solution. The results revealed that the robust counterpart provides a better estimation of the total cost, pollution, energy consumption, and employment level compared to the basic model.

Sustainable closed-loop supply chain network for an integrated water supply and wastewater collection system under uncertainty

Today, an increase in the drought and water shortage all around the world is a challengeable threat for different governments and international committees. Water supply chain aims to manage the water consumption and to control the water shortage. Contrary to most of previous studies focused on the forward directions of the water supply chain, this paper also considers the reverse logistics known as a Closed-Loop Supply Chain (CLSC). Hence, an integrated Water Supply and Wastewater Collection System (WSWCS) under uncertainty is proposed. Furthermore, regarding the trend of sustainable development, the environmental dimensions and social benefits of the integrated WSWCS are contributed. To meet the standards of the sustainable development in developing countries, a case study in Iran is applied to a novel multi-objective stochastic optimization model based on triple bottom lines of sustainability. Having already been employed to similar optimization problems, the Social Engineering Optimizer (SEO) has been never applied in this research area. Another innovation of this study is to introduce an improved multi-objective SEO to solve this complicated model. Eventually, with regards to an extensive comparison contributing to the Pareto-based metrics and different sensitivities, some managerial implications are concluded as the main findings.

Fuzzy Bi-Objective Closed-Loop Supply Chain Network Design Problem with Multiple Recovery Options

A network design of a closed-loop supply chain (CLSC) with multiple recovery modes under fuzzy environments is studied in this article, in which all the cost coefficients (e.g., for facility establishment, transportation, manufacturing and recovery), customer demands, delivery time, recovery rates and some other factors that cannot be precisely estimated while designing are modeled as triangular fuzzy numbers. To handle these uncertain factors and achieve a compromise between the two conflicting objectives of maximizing company profit and improving customer satisfaction, a fuzzy bi-objective programming model and a corresponding two-stage fuzzy interactive solution method are presented. Applying the fuzzy expected value operator and fuzzy ranking method, the fuzzy model is transformed into a deterministic counterpart. Subsequently, Pareto optimal solutions are determined by employing the fuzzy interactive solution method to deal with the conflicting objectives. Numerical experiments address the efficiency of the proposed model and its solution approach. Furthermore, by comparing these results with the CLSC network design in deterministic environments, the benefits of modeling the CLSC network design problem with fuzzy information are highlighted.

Multi-objective fuzzy robust optimization approach to sustainable closed-loop supply chain network design

Nowadays, the importance of sustainable development has persuaded the supply chain managers to shift their network to sustainable supply. This research develops a multi-objective mathematical model to configure a Sustainable Closed-Loop Supply Chain (SCLSC) network for a water tank considering sustainability measures. The goals of the proposed model are optimizing financial, environmental and social impacts of the SCLSC. Generally, the uncertainty exists in configuring the SCLSC network problem according to the changes in the business environment (like transportation costs and demand). As a result, a Fuzzy Robust Optimization (FRO) is applied to cope with uncertainty in this study. Then, the proposed model is solved using the goal programming approach. The numerical results showed some insightful observations regarding planning and strategic decisions for the SCLSC network design. Finally, several sensitivity analyses are carried out on some important parameters, the changes in objective functions are investigated and the robustness of the proposed model is examined.

A review of closed-loop supply chain models

Closed-loop supply chain (CLSC) networks have been studied extensively in the literature due to the cost and environmental optimization in operations. A CLSC consists of forward and reverse supply chains. This paper reviews the literature of CLSC networks. 225 peer-reviewed articles that have been published in international journals from 1997 to 2020 are gathered and analysed. The aim is to answer the following main questions: (i) What is the problem domain in CLSC? (ii) Which techniques are utilized frequently in CLSC? (iii) What elements, variables, and objectives are the main focus in the field? (iv) What are the gaps in the literature? After reviewing the literature, observations are provided. Finally, the evaluations conclude suggested improvements and potential future directions in this research field.

Scenario-based design of a steel sustainable closed-loop supply chain network considering production technology

Nowadays, many organizations are trying to balance the social, environmental, and economic aspects of their supply chains to gain competitive advantages against their rivals and have a sustainable supply chain. Steel is one of the most critical raw materials practically used in every aspect of our life, directly or indirectly influencing the industry and economy of a country. The final products of the steel industry can be reused at the end of their lifecycle, making the principles of reverse logistics applicable in the supply chain and turning it to a closed-loop supply chain. In this research, a multi-objective linear mathematical model under uncertainty is developed to optimize a steel sustainable closed-loop supply chain. The existed uncertainty is modeled through a scenario-based method in the stochastic environment, and the proposed multi-objective model is developed following a fuzzy goal programming approach. A real case study is explored in one of the active steel supply chains in Iran to validate the model. The model optimizes total profit, energy, and water consumption, CO2 emission, job opportunity created, and lost working days through determining optimal production technology, whether retailers get changed to hybrid centers or not, and the flow of material and products. The final results show that a mere 1% decrease in the profit can alleviate the harmful environmental effects by 5%. Finally, several managerial implications derived from final results and sensitivity analysis are discussed to provide insights for industry leaders who desire to increase their profits with regard to environmental and social effects.

Multi-product and multi-period closed loop supply chain network design under take-back legislation

This paper addresses the closed loop supply chain (CLSC) network design problem for durable products with consideration of take-back legislation. Each returned end-of-life product can be dismantled into different components. A recovery option is adopted for each component based on its quality level, its economic value, and its environmental impact. First, a multi-product multi-period mixed integer linear programming (MIP) model is proposed while assuming a 100% recovery target. The latter is then extended in order to incorporate other take-back legislations: namely, regulation based on other recovery targets and regulation based on reward-penalty (RP) mechanisms. Sensitivity analysis is then carried out in order to capture how some model parameters influence the take-back decision, the integration of the reverse supply chain, and its structure when there are no regulatory restrictions on the take-back of returns. In addition, the CLSC performance, in terms of environment and economic efficiency, is investigated under different take-back legislations. For the considered case study, a comparative study shows that a higher reverse service level and CLSC profit can be achieved when a regulation based on RP mechanism is implemented.

Robust optimization model for sustainable supply chain for production and distribution of polyethylene pipe

PurposeIn this study, the authors consider the key decisions in the design of the green closed-loop supply chain (CSLC) network. These decisions include considering the optimal location of suppliers, production facilities, distribution, customers, recycling centers and disposal of non-recyclable goods. In the proposed model, the level of technology used in recycling and production centers is taken into account. Moreover, in this paper is the environmental impacts of production and distribution of products based on the eco-indicator 99 are considered.Design/methodology/approachIn this study, the author consider the key decisions in the design of the green CLSC network. These decisions include considering the optimal location of suppliers, production facilities, distribution, customers, recycling centers and disposal of non-recyclable goods. In the proposed model, the level of technology used in recycling and production centers is taken into account. Moreover, the environmental impacts of production and distribution of products based on the eco-indicator 99 are considered.FindingsThe results indicate that the results obtained from the colonial competition algorithm have higher quality than the genetic algorithm. This quality of results includes relative percentage deviation and computational time of the algorithm and it is shown that the computational time of the colonial competition algorithm is significantly lower than the computational time of the genetic algorithm. Furthermore, the limit test and sensitivity analysis results show that the proposed model has sufficient accuracy.Originality/valueSolid modeling of the green supply chain of the closed loop using the solid optimized method by Bertsimas and Sim. Development of models that considered environmental impacts to the closed loop supply chain. Considering the impact of the technology type in the manufacture of products and the recycling of waste that will reduce emissions of environmental pollutants. Another innovation of the model is the multi-cycle modeling of the closed loop of supply chain by considering the uncertainty and the fixed and variable cost of transport.

A closed loop supply chain network design problem with multi-mode demand satisfaction in fuzzy environment

A closed loop supply chain network design problem with multi-mode demand satisfaction in fuzzy environment

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

Today, due to the increasing environmental hazards and governmental regulations, as well as the limitation of sources of production, researchers have paid special attention to the design of closed-loop green supply chain networks. The closed-loop supply chain networks (CLSCN) include the returns processes and the producers aim to capturing additional value considering further integration of all supply chain activities. Therefore, all return processes need to be optimized as well as considering environmental impacts leading to form a closed-loop green supply chain network (CLGSCN). For decision making purposes, operational and tactical decision making levels are integrated to configure a coordinated supply chain network aiming to maximize profit while keeping environmental-friendly policies. The case is more sophisticated in melting industries where the collection and categorization in return process and different environmental challenges should be considered at the same time. Thus, in this paper, a CLGSCN of a melting industry is modeled with respect to environmental hazards to optimiza overall profits. Since real-world demand in melting industry under study is uncertain, the robust optimization has been employed, and while the optimization of the proposed mathematical model is time consuming, an improved version of the genetic algorithm has been implemented as a solution method. This study has been carried out at Melting Imen Tabarestan (MIT) company in Iran. The proposed model along with the solution method are investigated in the case study. The results imply the effectiveness and applicability of the model and provide tactical considerations for the managers and practitioners.