Multi-period Closed-loop Supply Chain Research Articles

A new approach for reliability modeling in green closed-loop supply chain design under post-pandemic conditions: A case study

Climate change, pandemics, and economic crises have created complex challenges for supply chains. Managing such situations requires the development of reliable decision-making frameworks. In this paper, a multi-level, multi-product, and multi-period closed-loop supply chain is studied with environmental considerations. A bi-objective mixed-integer linear programming model is presented for facility location, flow allocation, and transportation mode determination. The objectives of the model are to minimize the total cost and maximize the reliability of suppliers to meet the needs of factories. In the area of reliability engineering, a new approach is defined for modeling the probability of supplier availability considering catastrophic failures caused by pandemics, economic sanctions, and other failure modes. Furthermore, the decision-maker can handle the emission of greenhouse gases by an upper-bound constraint. In order to face the simultaneous uncertainty of demand and the maximum CO2 emission allowed, a scenario-based two-stage stochastic programming approach is proposed. The improved version of the augmented ε-constraint method, known as AUGMECON2, is used to solve the proposed model. The efficiency of the model and the proposed solution approach are investigated through a real-world case study of a battery manufacturing company in Iran.

Designing a multi-objective closed-loop supply chain for multi-period multi-generational products with social impacts considerations

This paper assesses a multi-period closed-loop supply chain (CLSC) for making strategic and operational decisions in multi-generation applications. For this purpose, joint decisions on network design, inventory control, production planning, pricing, supplier selection, and service level requirements are determined along with the forward flows of the supply chain. Besides, a segmentation policy is proposed for disassembling and recovering the different quality grades of returned products. All the raised issues have become more challengeable when an inner competition on coexistence goes up between the incumbent and new generations. Accordingly, this paper attempts to bridge the gaps in the literature by proposing a bi-objective mathematical model that involves a demand function with the integration of price drop effects, marketing efforts, and technology desirability to tackle trade-offs decisions between multiple generations. This research aims to simultaneously optimize the total profit and social benefits. To solve the problem, first, the ε-constraint method has been applied to transform the bi-objective problem into a single objective. Then, as a great deal to solve the complex mixed-integer non-linear program model, an outer approximation algorithm is applied based on approximation, relaxation, and decomposition. Finally, a case example in a mobile phone CLSC problem has been investigated to validate the proposed model and provide managerial insights.

An optimal pricing and quality assessment model for consumer returns in a multi-period closed-loop supply chain

An optimal pricing and quality assessment model for consumer returns in a multi-period closed-loop supply chain

A multi-period closed-loop supply chain network design with circular route planning

A multi-period closed-loop supply chain network design with circular route planning

Design a Multi Period Closed-Loop Supply Chain Program to Supply Recycled Products

Abstract Over the course of the last decades, closed-loop supply chains (CLSC) and reverse logistics issues have attracted increasing attention owing to strict environmental laws, social responsibilities, economic interests, and customer awareness. Hence, the issue of closed-loop supply chain and reverse logistics has emerged as a field of research in the new era. This issue has received much attention because it allows recyclable products to return to their original cycle. Therefore, this study primarily intends to present a mathematical model for designing a supply chain network for recycled products. The multi-stage and multi-period objective function of the closed-loop supply chain is presented to meet that aim. In this chain, dismantling, recycling, and disposal centers are considered. The objective function is to reduce the total cost of the closed-loop supply chain. The results of optimizing the mathematical model demonstrate that this model has the necessary efficiency for use in recycled products.

Designing a new multi-echelon multi-period closed-loop supply chain network by forecasting demand using time series model: a genetic algorithm.

Demand plays a vital role in designing every closed-loop supply chain network in today's world. The flow of materials and commodities in the opposite direction of the standard supply chain is inevitable. In this way, this study addresses a new multi-echelon multi-period closed-loop supply chain network to minimize the total costs of the network. The echelons include suppliers, manufacturers, distribution centers, customers, and recycling and recovery units of components in the proposed network. Also, a Mixed Integer Linear Programming (MILP) model considering factories' vehicles and rental cars of transportation companies is formulated for the proposed problem. Moreover, for the first time, the demand for the products is estimated using an Auto-Regressive Integrated Moving Average (ARIMA) time series model to decrease the shortage that may happen in the whole supply chain network. Conversely, for solving the proposed model, the GAMS software is utilized in small and medium-size problems, and also, genetic algorithm is applied for large-size problems to obtain initial results of the model. Numerical results show that the proposed model is closer to the actual situation and could reach a reasonable solution in terms of service level, shortage, etc. Accordingly, sensitivity analysis is performed on essential parameters to show the performance of the proposed model. Finally, some potential topics are discussed for future study.

Modeling and optimizing a multi-period closed-loop supply chain for pricing, warranty period, and quality management

Nowadays, most of the researchers have focused on collecting the used products to carry out the recovery process. This paper deals with the repair process to improve the virtual age of used products and integrate to forward flow as a closed-loop supply chain (CLSC). The products can be returned to the chain several times until they have the required quality to be repaired. Here the optimal number of returning and repairing the used products for maximization of the profits are calculated. Also, the price of selling the products, the acquisition cost, and the warranty period are determined to motivate the customers to bring back their used products and increase the demand for products. For our proposed multi-period problem, an appropriate inventory control policy is taken, and in case of increasing the production amount, additional capacity can be installed by extra cost. The proposed mixed-integer non-linear model has been solved by three metaheuristic algorithms: Particle Swarm Optimization Algorithm (PSO), Genetic Algorithm (GA), Invasive Weeds Optimization algorithm (IWO). Numerical problems depicted model efficiency and by the use of the Taguchi method, qualitative parameters of proposed algorithms are calibrated. Then, the performance comparison of the methods has been done by Relative Performance Deviation.

Multi-Period Closed-Loop Supply Chain Network Equilibrium: Perspective of Marketing and Corporate Social Responsibility

Marketing and corporate social responsibility (CSR) are crucial factors that affect closed-loop supply chain network (CLSCN) equilibrium. And the environment of enterprises’ behaviors often occurs during durations and dynamics. To explore the optimal marketing and CSR strategies in a dynamic multi-period CLSCN system, this paper investigates the dynamic multi-period CLSCN equilibrium problem considering marketing and CSR. A multi-period CLSCN system includes manufacturers, retailers, recyclers, and demand markets. Based on Nash non-cooperative game theory and variational inequality, we design the optimal behavior and equilibrium conditions of members. Then, a new equilibrium model of a multi-period CLSCN is constructed. In this model, marketing is the responsibility of manufacturers and retailers, and CSR is the responsibility of manufacturers. Numerical examples are provided to verify the effectiveness of the model and analyzed to describe how marketing and CSR affect equilibrium results. We find that the retailers are responsible for marketing, and the level of CSR activities of the manufacturers is higher during the early period, which is most beneficial to the multi-period CLSCN system and social welfare. Based on the conclusions of this paper, the management enlightenments from the perspectives of enterprises and government are also proposed.

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.

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.

An optimal pricing and quality assessment model for consumer returns in a multi-period closed-loop supply chain

An optimal pricing and quality assessment model for consumer returns in a multi-period closed-loop supply chain

A bi-objective multi-period closed-loop supply chain network under uncertain demand

Due to the competitive environment, supply chain management is an important subject in the world of economy. It affects all of the activities including products manufacturing, flow between facilities and costs. In this research, a mixed-integer linear programming model is considered which included supplier, plants, demand markets, collection centres, and disposal centre. The closed-loop supply chain model is bi-objective. So, it is solved by the e-constraint method and non-dominated sorting genetic algorithm-II. In order to improve the meta-heuristic algorithm's efficiency, its parameters are tuned by Taguchi method. Afterward, the different dimensions of the model are considered and the problem is rewritten as a single-objective model and solved by LINGO software and the genetic algorithm using MATLAB software to compare the efficiency of the LINGO and meta-heuristic algorithm. In small-scale problems, solving by LINGO software and in large-scale problems, solving by meta-heuristic algorithms are more efficient.

A bi-objective multi-period closed-loop supply chain network under uncertain demand

Due to the competitive environment, supply chain management is an important subject in the world of economy. It affects all of the activities including products manufacturing, flow between facilities and costs. In this research, a mixed-integer linear programming model is considered which included supplier, plants, demand markets, collection centres, and disposal centre. The closed-loop supply chain model is bi-objective. So, it is solved by the e-constraint method and non-dominated sorting genetic algorithm-II. In order to improve the meta-heuristic algorithm's efficiency, its parameters are tuned by Taguchi method. Afterward, the different dimensions of the model are considered and the problem is rewritten as a single-objective model and solved by LINGO software and the genetic algorithm using MATLAB software to compare the efficiency of the LINGO and meta-heuristic algorithm. In small-scale problems, solving by LINGO software and in large-scale problems, solving by meta-heuristic algorithms are more efficient.

Multi-period multi-scenario optimal design for closed-loop supply chain network of hazardous products with consideration of facility expansion

Increased concern for the environment has led to urgent need to design the closed-loop supply chain network of hazardous products economically and ecologically. In this paper, we focus on the optimal design problem of multi-period closed-loop supply chain network of hazardous products considering uncertain demands and returns, expandable facility capacities and social acceptable risk simultaneously. In each period, the built facilities have the ability to expand within a certain scope. The problem is formulated as a mixed-integer nonlinear programming model, which can determine the number, location and expansion scale of the facilities and the forward and reverse logistics quantities between the facilities in each period simultaneously. The goal is to minimize the expected cost over the multi-period planning horizon, including the facilities building, operating and expansion costs and the costs related to manufacturing, collection, processing, recovery and transportation. In order to solve the proposed model, two classes of dummy variables are introduced to equivalently transform it into a mixed-integer linear programming, which can be optimally solved by LINGO. A case study is presented to illustrate the validity of the proposed model. The dynamic design with expansion strategy addressed in this paper is compared with two different strategies of static design and dynamic design without expansion. The results highlight that the dynamic design with expansion strategy has the advantages in saving expenses and raising the average expected collection rate of hazardous wastes.

Multi-Period E-Closed-Loop Supply Chain Network Considering Consumers’ Preference for Products and AI-Push

The remanufacturing industry plays a significant role to improve the utilization of resources and contribute to environmental protection and sustainable development. E-commerce channel (e-channel) has become an important retail channel. The multi-period closed-loop supply chain network of electronic channels (E-CLSCN) is studied. Moreover, this study considers the influence of consumers’ preference for products and artificial intelligence push (AI-push) on network balance under the conditions of dynamic and static. The network consists of manufacturers, remanufacturers, and demand markets. Remanufacturers are responsible for collecting and remanufacturing. The multi-period E-CLSCN model is established through variational inequalities. The study finds that whether dynamic or static, consumers’ preference for new products (NP) has positively correlated with the profits of manufacturers and the whole E-CLSCN. Consumers’ preference for remanufacturer products (RP) has only positively correlated with the profits of remanufactures. In the aspect of AI-push, the manufacturers’ AI-push has positively correlated with the profits of the entire players of E-CLSCN, while the remanufacturers’ AI-push has only positively correlated with the profits of the remanufacturers. Also, considering the multi-period, the sensitivity of the demand markets for the investment of manufacturers’ and remanufacturers’ AI-push gradually reduces, and the marginal benefit of AI-push gradually decreases. Finally, this paper addresses interesting managerial insights from the perspective of government and enterprises. Under the dual objectives of environmental protection and economic interests of enterprises, this study provides a reference for governments and enterprises to develop relevant dynamic policies. Namely, the government formulates dynamic environmental protection measures, and enterprises formulate dynamic strategic plans and implement government policies. While promoting the management level of E-CLSCN, it effectively boosts the development of the remanufacturing industry and recycling sustainable economy.

Novel model and kernel search heuristic for multi-period closed-loop food supply chain planning with returnable transport items

Closed-loop supply chain (CLSC) is of utmost importance to sustainable development and has received increasing attention in recent decades. However, food CLSC with returnable transport items (RTIs) has been rarely studied although its growing applications in practice. This paper aims to investigate a multi-period CLSC planning problem that coordinates the flows of perishable food products and RTIs considering food quality. The objective is to maximise the total profit of the holistic supply chain over a finite planning horizon. To this end, a novel mixed integer linear programming model is first formulated. As the problem is proven NP-hard, an improved kernel search-based heuristic is then developed. A real case study deriving from a food manufacturer in China shows the applicability of the proposed model and method. The results indicate that the manufacturer’s profit can be improved by more than 10% with our method. Numerical experiments on randomly generated instances demonstrate that the proposed heuristic can yield high-quality solutions with much less computation time compared with the commercial solver CPLEX and an existing heuristic.

A robust fuzzy mathematical programming model for the closed-loop supply chain network design and a whale optimization solution algorithm

The closed-loop supply chain (CLSC) management as one of the most significant management issues has been increasingly spotlighted by the government, companies and customers, over the past years. The primary reasons for this growing attention mainly down to the governments-driven and environmental-related regulations which has caused the overall supply cost to reduce while enhancing the customer satisfaction. Thereby, in the present study, efforts have been made to propose a facility location/allocation model for a multi-echelon multi-product multi-period CLSC network under shortage, uncertainty, and discount on the purchase of raw materials. To design the network, a mixed-integer nonlinear programming (MINLP) model capable of reducing total costs of network is proposed. Moreover, the model is developed using a robust fuzzy programming (RFP) to investigate the effects of uncertainty parameters including customer demand, fraction of returned products, transportation costs, the price of raw materials, and shortage costs. As the developed model was NP-hard, a novel whale optimization algorithm (WOA) aimed at minimizing the network total costs with application of a modified priority-based encoding procedure is proposed. To validate the model and effectiveness of the proposed algorithm, some quantitative experiments were designed and solved by an optimization solver package and the proposed algorithm. Comparison of the outcomes provided by the proposed algorithm and exact solution is indicative of high quality performance of the applied algorithm to find a near-optimal solution within the reasonable computational time.

New mathematical modeling for a location\u2013routing\u2013inventory problem in a multi-period closed-loop supply chain in a car industry

This paper studies a location–routing–inventory problem in a multi-period closed-loop supply chain with multiple suppliers, producers, distribution centers, customers, collection centers, recovery, and recycling centers. In this supply chain, centers are multiple levels, a price increase factor is considered for operational costs at centers, inventory and shortage (including lost sales and backlog) are allowed at production centers, arrival time of vehicles of each plant to its dedicated distribution centers and also departure from them are considered, in such a way that the sum of system costs and the sum of maximum time at each level should be minimized. The aforementioned problem is formulated in the form of a bi-objective nonlinear integer programming model. Due to the NP-hard nature of the problem, two meta-heuristics, namely, non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO), are used in large sizes. In addition, a Taguchi method is used to set the parameters of these algorithms to enhance their performance. To evaluate the efficiency of the proposed algorithms, the results for small-sized problems are compared with the results of the ε-constraint method. Finally, four measuring metrics, namely, the number of Pareto solutions, mean ideal distance, spacing metric, and quality metric, are used to compare NSGA-II and MOPSO.

Design and planning of a closed-loop supply chain with three way recovery and buy-back offer

Due to strict environmental legislations and competitive economics worldwide, closed-loop supply chain (CLSC) management has been receiving increasing attention. In order to improve the return rate of used products, in this paper, a recovery framework is proposed by employing buy-back offer at retailer level. The proposed recovery framework is integrated with an optimization model for a multi-period CLSC under demand and capacity uncertainty to determine optimal buy-back price that needs to be offered to consumers so that the minimum collection limit set by the legislators is fulfilled as well as overall cost of the integrated system is minimized. The developed model addresses the possibility of three way recovery options, namely; product remanufacturing, component remanufacturing, and raw material recovery to determine the optimal manufacturing, remanufacturing and recycling quantity. Sensitivity analysis suggests that there is a trade-off between the extra benefit generated through remanufacturing and the cost of acquisition of used products due to the employment of buy-back offer. A condition for the buy-back offer to be simultaneously advantageous for both manufacturer and retailer under revenue sharing contract is derived. A comparative study is also undertaken to examine the performance of the CLSC when collection is made through buy-back offer vs. third-party collection. The proposed CLSC model is further extended by adding a separate demand for remanufactured products and the performance of the initial model with the extended model is compared.

Advanced cross-entropy in closed-loop supply chain planning

Developing new methodologies for nondeterministic polynomial (NP-hard) problems such as supply chain network design is always a major consideration for academia and practitioners. In this paper a cross-entropy (CE) based solution methodology is developed in order to cope with complex combinatorial problems. The NP-hard problem of designing and planning a closed-loop supply chain (CLSC) is considered. Furthermore, a multi-product multi-period CLSC network in a mixed-integer programming structure is regarded. On the other side, cross-entropy is one of the newly developed and successful metaheuristic algorithms. Thus, in order to achieve better solutions in comparison with current solution methodologies, a cross-entropy algorithm is developed for the first time in CLSC design and planning. Then, the capabilities of the cross-entropy algorithm are elevated, in order to achieve solutions that are more robust. Therefore, an algorithm, which is called “advanced cross-entropy” (ACE) is proposed. Finally, two presented CE-based algorithms are compared with a developed genetic algorithm (GA) for the same problem. GA is the most well-known metaheuristic algorithm, which has been abundantly developed in CLSC. Results prove that both of proposed CE-based algorithms dominate current methodologies. Both can find acceptable solutions in comparison with GA. Furthermore, the proposed advanced cross-entropy performs even better than CE in the quality of solutions and time.