A coevolutionary algorithm based on the auxiliary population for constrained large-scale multi-objective supply chain network

Xin Zhang; Zhaobin Ma; Bowen Ding; Wei Fang; Pengjiang Qian; Xin Zhang; Zhaobin Ma; Bowen Ding; Wei Fang; Pengjiang Qian

doi:10.3934/mbe.2022014

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 1: 271-286. doi: 10.3934/mbe.2022014

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A coevolutionary algorithm based on the auxiliary population for constrained large-scale multi-objective supply chain network

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: Xiangtao Li

Received: 15 September 2021 Accepted: 02 November 2021 Published: 12 November 2021

Supply chain network is important for the enterprise to improve the operation and management, but has become more complicated to optimize in reality. With the consideration of multiple objectives and constraints, this paper proposes a constrained large-scale multi-objective supply chain network (CLMSCN) optimization model. This model is to minimize the total operation cost (including the costs of production, transportation, and inventory) and to maximize the customer satisfaction under the capacity constraints. Besides, a coevolutionary algorithm based on the auxiliary population (CAAP) is proposed, which uses two populations to solve the CLMSCN problem. One population is to solve the original complex problem, and the other population is to solve the problem without any constraints. If the infeasible solutions are generated in the first population, a linear repair operator will be used to improve the feasibility of these solutions. To validate the effectivity of the CAAP algorithm, the experiment is conducted on the randomly generated instances with three different problem scales. The results show that the CAAP algorithm can outperform other compared algorithms, especially on the large-scale instances.
- supply chain network,
- large-scale optimization,
- multi-objective optimization,
- constrained optimization,
- coevolutionary algorithm
Citation: Xin Zhang, Zhaobin Ma, Bowen Ding, Wei Fang, Pengjiang Qian. A coevolutionary algorithm based on the auxiliary population for constrained large-scale multi-objective supply chain network[J]. Mathematical Biosciences and Engineering, 2022, 19(1): 271-286. doi: 10.3934/mbe.2022014

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Abstract

Supply chain network is important for the enterprise to improve the operation and management, but has become more complicated to optimize in reality. With the consideration of multiple objectives and constraints, this paper proposes a constrained large-scale multi-objective supply chain network (CLMSCN) optimization model. This model is to minimize the total operation cost (including the costs of production, transportation, and inventory) and to maximize the customer satisfaction under the capacity constraints. Besides, a coevolutionary algorithm based on the auxiliary population (CAAP) is proposed, which uses two populations to solve the CLMSCN problem. One population is to solve the original complex problem, and the other population is to solve the problem without any constraints. If the infeasible solutions are generated in the first population, a linear repair operator will be used to improve the feasibility of these solutions. To validate the effectivity of the CAAP algorithm, the experiment is conducted on the randomly generated instances with three different problem scales. The results show that the CAAP algorithm can outperform other compared algorithms, especially on the large-scale instances.

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