Optimizing a multi-product closed-loop supply chain using NSGA-II, MOSA, and MOPSO meta-heuristic algorithms

Designing the supply chain network is one of the significant areas in e-commerce business management. This concept plays a crucial role in e-commerce systems. For example, location-inventory-pricing-routing of an e-commerce supply chain is considered a crucial issue in this field. This field established many severe challenges in the modern world, like maintaining the supply chain for returned items, preserving customers' trust and satisfaction, and developing an applicable supply chain with cost considerations. The research proposes a multi-objective mixed integer nonlinear programming model to design a closed-loop supply chain network based on the e-commerce context. The proposed model incorporates two objectives that optimize the business's total profits and the customers' satisfaction. Then, numerous numerical examples are generated and solved using the epsilon constraint method in GAMS optimization software. The validation of the given model has been tested for the large problems via a hybrid two-level non-dominated sort genetic algorithm. Finally, some sensitivity analysis has been performed to provide some managerial insights.

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Designing a Bi-Objective Closed-Loop Supply Chain Problem with Shortage and All Unit Discount: “Nondominated Sorting Genetic Algorithm II” and “Multi-Objective Particle Swarm Optimization”

Journal of Advanced Manufacturing Systems ◽

10.1142/s0219686720500316 ◽

2020 ◽

Vol 19 (04) ◽

pp. 701-736

Author(s):

Masoomeh Vazifeh Pirnagh ◽

Hamed Davari-Ardakani ◽

Seyed Hamid Reza Pasandideh

Keyword(s):

Genetic Algorithm ◽

Mathematical Model ◽

Particle Swarm Optimization ◽

Supply Chain ◽

Closed Loop ◽

Nsga Ii ◽

Swarm Optimization ◽

Closed Loop Supply Chain ◽

The Real ◽

Multi Objective

Nowadays, due to environmental issues, government rules and economic interests have increased attention to the collection and recovery of products, which has led to the formation of new concepts such as reverse and closed-loop supply chains. The implementation of the closed-loop supply chain as a solution to sustainable development is expanding from one hand and increasing the profitability of companies on the other. For this purpose, a mathematical model was developed to design an integrated closed-loop supply chain network, which is a combination of two-problem localization problems and flow optimization. The proposed model was designed to minimize network costs and to maximize the level of responsiveness to customers. The cost parameters of establishing centers in this model are uncertain; to overcome the model’s uncertainties, stochastic programming is used. In the mathematical model, supplier, manufacturer, distributor and customer in the direct supply chain and collection/rehabilitation, destruction, recycling centers and, second-type distribution center for sale of second-hand products as well as second-hand products customers in the reverse flow are considered, to be closer to the real today world. This model is multi-periodic mix integer nonlinear programming where the shortage has allowed. To motivate and encourage customers to buy more, in addition to getting closer to the real world and it happens more in practice, is considered all units of discount for transportation cost in the forward flow. To solve this model Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) is using. The parameter tuning was done using the Taguchi method. Then, the important criteria for measurement and comparison of performance algorithms have used, including the Mean Ideal Distance, Diversification Metric, Number of Pareto-optimal Solutions, and the Quality Metric. Results of the Comparative metrics show that NSGA-II outperforms MOPSO in almost all cases in achieving the best trade-off solutions.

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Analyzing a Closed-Loop Supply Chain Considering Environmental Pollution Using the NSGA-II

IEEE Transactions on Fuzzy Systems ◽

10.1109/tfuzz.2018.2870693 ◽

2019 ◽

Vol 27 (5) ◽

pp. 1066-1074 ◽

Cited By ~ 16

Author(s):

Yi Su ◽

Wei Sun

Keyword(s):

Supply Chain ◽

Environmental Pollution ◽

Closed Loop ◽

Nsga Ii ◽

Closed Loop Supply Chain

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Multi-objective multi-facility green manufacturing closed-loop supply chain under uncertain environment

Assembly Automation ◽

10.1108/aa-09-2018-0138 ◽

2019 ◽

Vol 39 (1) ◽

pp. 58-76 ◽

Cited By ~ 4

Author(s):

Behzad Karimi ◽

Amir Hossein Niknamfar ◽

Babak Hassan Gavyar ◽

Majid Barzegar ◽

Ali Mohtashami

Keyword(s):

Genetic Algorithm ◽

Supply Chain ◽

Closed Loop ◽

Distribution Centers ◽

Nsga Ii ◽

Closed Loop Supply Chain ◽

Content Type ◽

Uncertain Environment ◽

Multi Objective ◽

Hierarchy Process

Purpose Today’s, supply chain production and distribution of products to improve the customer satisfaction in the shortest possible time by paying the minimum cost, has become the most important challenge in global market. On the other hand, minimizing the total cost of the transportation and distribution is one of the critical items for companies. To handle this challenge, this paper aims to present a multi-objective multi-facility model of green closed-loop supply chain (GCLSC) under uncertain environment. In this model, the proposed GCLSC considers three classes in case of the leading chain and three classes in terms of the recursive chain. The objectives are to maximize the total profit of the GCLSC, satisfaction of demand, the satisfactions of the customers and getting to the proper cost of the consumers, distribution centers and recursive centers. Design/methodology/approach Then, this model is designed by considering several products under several periods regarding the recovery possibility of products. Finally, to evaluate the proposed model, several numerical examples are randomly designed and then solved using non-dominated sorting genetic algorithm and non-dominated ranking genetic algorithm. Then, they are ranked by TOPSIS along with analytical hierarchy process so-called analytic hierarchy process-technique for order of preference by similarity to ideal solution (AHP-TOPSIS). Findings The results indicated that non-dominated ranked genetic algorithm (NRGA) algorithm outperforms non-dominated sorting genetic algorithm (NSGA-II) algorithm in terms of computation times. However, in other metrics, any significant difference was not seen. At the end, to rank the algorithms, a multi-criterion decision technique was used. The obtained results of this method indicated that NSGA-II had better performance than ones obtained by NRGA. Originality/value This study is motivated by the need of integrating the leading supply chain and retrogressive supply chain. In short, the highlights of the differences of this research with the mentioned studies are as follows: developing multi-objective multi-facility model of fuzzy GCLSC under uncertain environment and integrating the leading supply chain and retrogressive supply chain.

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A novel algorithm to allocate customers and retailers in a closed-loop supply chain under probabilistic demand

10.21203/rs.3.rs-1242518/v1 ◽

2022 ◽

Author(s):

Omid Keramatlou ◽

Nikbakhsh Javadian ◽

Hosein Didehkhani ◽

Mohammad Amirkhan

Keyword(s):

Supply Chain ◽

Closed Loop ◽

Heuristic Algorithms ◽

Programming Model ◽

Solution Process ◽

Closed Loop Supply Chain ◽

Optimization Software ◽

Minimum Value ◽

Proposed Model ◽

Nonlinear Programming Model

Abstract In this paper, a closed-loop supply chain (CLSC) is modeled to obtain the best location of retailers and allocate them to other utilities. The structure of CLSC includes production centers, retailers’ centers, probabilistic customers, collection, and disposal centers. In this research, two strategies are considered to find the best location for retailers by focusing on 1- the type of expected movement 2- expected coverage (distance and time) for minimizing the costs and maximizing the profit by considering the probabilistic customer and uncertainly demand. First of all, the expected distances between customers and retailers are calculated per movement method. These values are compared with the Maximum expected coverage distance of retailers, which is displayed in algorithm 1 heuristically, and the minimum value is picked. Also, to allocate customers to retailers, considering the customer's movement methods and comparing it with Maximum expected coverage time, which is presented in Algorithm 2 heuristically, the minimum value is chosen to this end, a bi-objective nonlinear programming model is proposed. This model concurrently compares Strategies 1 and 2 to select the best competitor. Based on the chosen strategy, the best allocation is determined by employing two heuristic algorithms, and the locations of the best retailers are determined. As the proposed model is NP-hard, a meta-heuristics (non-dominated sorting genetic) algorithm is employed for the solution process. Afterward, the effectiveness of the proposed model is validated and confirmed, and the obtained results are analyzed. For this purpose, a numerical example is given and solved through the optimization software.

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