A genetic algorithm with two-step rank-based encoding for closed-loop supply chain network design

Bowen Ding; Zhaobin Ma; Shuoyan Ren; Yi Gu; Pengjiang Qian; Xin Zhang; Bowen Ding; Zhaobin Ma; Shuoyan Ren; Yi Gu; Pengjiang Qian; Xin Zhang

doi:10.3934/mbe.2022277

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 6: 5925-5956. doi: 10.3934/mbe.2022277

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A genetic algorithm with two-step rank-based encoding for closed-loop supply chain network design

1.
School of Artificial Intelligence and Computer Science, and Jiangsu Key Laboratory of Media Design and Software Technology, Jiangnan University, Wuxi 214122, China
2.
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China

Academic Editor: Wenqiang Zhang

Received: 14 February 2022 Revised: 23 March 2022 Accepted: 27 March 2022 Published: 11 April 2022

The closed-loop supply chain (CLSC) plays an important role in sustainable development and can help to increase the economic benefits of enterprises. The optimization for the CLSC network is a complicated problem, since it often has a large problem scale and involves multiple constraints. This paper proposes a general CLSC model to maximize the profits of enterprises by determining the transportation route and delivery volume. Due to the complexity of the multi-constrained and large-scale model, a genetic algorithm with two-step rank-based encoding (GA-TRE) is developed to solve the problem. Firstly, a two-step rank-based encoding is designed to handle the constraints and increase the algorithm efficiency, and the encoding scheme is also used to improve the genetic operators, including crossover and mutation. The first step of encoding is to plan the routes and predict their feasibility according to relevant constraints, and the second step is to set the delivery volume based on the feasible routes using a rank-based method to achieve greedy solutions. Besides, a new mutation operator and an adaptive population disturbance mechanism are designed to increase the diversity of the population. To validate the efficiency of the proposed algorithm, six heuristic algorithms are compared with GA-TRE by using different instances with three problem scales. The results show that GA-TRE can obtain better solutions than the competitors, especially on large-scale instances.
- closed-loop supply chain network design,
- genetic algorithm,
- constrained optimization,
- large-scale optimization
Citation: Bowen Ding, Zhaobin Ma, Shuoyan Ren, Yi Gu, Pengjiang Qian, Xin Zhang. A genetic algorithm with two-step rank-based encoding for closed-loop supply chain network design[J]. Mathematical Biosciences and Engineering, 2022, 19(6): 5925-5956. doi: 10.3934/mbe.2022277

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Abstract

The closed-loop supply chain (CLSC) plays an important role in sustainable development and can help to increase the economic benefits of enterprises. The optimization for the CLSC network is a complicated problem, since it often has a large problem scale and involves multiple constraints. This paper proposes a general CLSC model to maximize the profits of enterprises by determining the transportation route and delivery volume. Due to the complexity of the multi-constrained and large-scale model, a genetic algorithm with two-step rank-based encoding (GA-TRE) is developed to solve the problem. Firstly, a two-step rank-based encoding is designed to handle the constraints and increase the algorithm efficiency, and the encoding scheme is also used to improve the genetic operators, including crossover and mutation. The first step of encoding is to plan the routes and predict their feasibility according to relevant constraints, and the second step is to set the delivery volume based on the feasible routes using a rank-based method to achieve greedy solutions. Besides, a new mutation operator and an adaptive population disturbance mechanism are designed to increase the diversity of the population. To validate the efficiency of the proposed algorithm, six heuristic algorithms are compared with GA-TRE by using different instances with three problem scales. The results show that GA-TRE can obtain better solutions than the competitors, especially on large-scale instances.

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