Open AccessAn s-metric selection evolutionary multi-objective optimization algorithm solving u-shaped disassembly line balancing problem
Author(s) -
Wei Bai,
Xi Guo,
Fangjie Peng,
Liang Qi,
Qin Shu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2024/1/012057
Subject(s) - mathematical optimization , computer science , reuse , evolutionary algorithm , selection (genetic algorithm) , metric (unit) , genetic algorithm , algorithm , mathematics , engineering , artificial intelligence , operations management , waste management
With the continuous consumption of commodities, the number of waste products is also increasing, and its impact on the environment and resources has also attracted great attention. Therefore, the reuse of waste products is one of the ways to solve the problem of increasing waste products at present. In this work, the cycle time constraint of disassembly components is considered in a multi-product partial U-shaped disassembly-line-balancing problem. Moreover, the maximum profit and the minimum idle time are taken as the optimization objectives, and a mathematical model of multi-objective optimization under the cycle time constraints is established. To optimize this problem, an S-metric selection evolutionary multi-objective optimization algorithm (SMS-EMOA) is proposed. The SMS-EMOA is compared with the multi-objective evolutionary algorithm based on decomposition and the indicator-based evolutionary algorithm. The experimental results show the practicability and feasibility of the SMS-EMOA algorithm.