Multi-Objective Discrete Brainstorming Optimizer to Solve the Stochastic Multiple-Product Robotic Disassembly Line Balancing Problem Subject to Disassembly Failures

Robots are now widely used in product disassembly lines, which significantly improves end-of-life (EOL) product disassembly efficiency. Most of the current research on disassembly line balancing problems focuses on decomposing one product. More than one product can be disassembled concurrently, whic...

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Vydané v:Mathematics (Basel) Ročník 11; číslo 6; s. 1557
Hlavní autori: Xu, Gongdan, Zhang, Zhiwei, Li, Zhiwu, Guo, Xiwang, Qi, Liang, Liu, Xianzhao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.03.2023
Predmet:
Analysis
Assembly-line methods
Balancing
Brainstorming
Decomposition
Depreciation
Design and construction
disassembly failure
Dismantling
End of life
Energy consumption
Environmental impact
Evolutionary algorithms
Failure
Food science
Genetic algorithms
Group problem solving
machine learning
Mathematical optimization
Methods
Multiple objective analysis
multiple product disassembly
Optimization
Pareto optimization
robotic disassembly line balancing problem
Robotics
Robots
Robots, Industrial
Search algorithms
Sorting algorithms
Stochastic processes
Swarm intelligence
Work stations
China
ISSN:2227-7390, 2227-7390
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Popis
Shrnutí:Robots are now widely used in product disassembly lines, which significantly improves end-of-life (EOL) product disassembly efficiency. Most of the current research on disassembly line balancing problems focuses on decomposing one product. More than one product can be disassembled concurrently, which can further improve the efficiency. Moreover, uncertainty such as the depreciation of EOL products, may result in disassembly failure. In this research, a stochastic multi-product robotic disassembly line balancing model is established using an AND/OR graph. It takes the precedence relationship, cycle constraint, and disassembly failure into consideration to maximize the profit and minimize the energy consumption for disassembling multiple products. A Pareto-improved multi-objective brainstorming optimization algorithm combined with stochastic simulation is proposed to solve the problem. Furthermore, by conducting experiments on some real cases and comparing with four state-of-the-art multi-objective optimization algorithms, i.e., the multi-objective discrete gray wolf optimizer, artificial bee colony algorithm, nondominated sorting genetic algorithm II, and multi-objective evolutionary algorithm based on decomposition, this paper validates its excellent performance in solving the concerned problem.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math11061557