A hybrid method to solve reliability-cost-oriented bi-objective machine configuration problem for a flow shop system

Cheng-Ta Yeh (), Louis Cheng-Lu Yeng, Yi-Kuei Lin () and Yu-Lun Chao
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Cheng-Ta Yeh: Fu Jen Catholic University
Louis Cheng-Lu Yeng: National Yang Ming Chiao Tung University
Yi-Kuei Lin: National Yang Ming Chiao Tung University
Yu-Lun Chao: National Yang Ming Chiao Tung University

Annals of Operations Research, 2024, vol. 340, issue 1, No 27, 643-669

Abstract: Abstract Machine configuration is a crucial strategic decision in designing a flow shop system (FSS) and directly affects its performance. This involves selecting device suppliers and determining the number of machines to be configured. This study addresses a bi-objective optimization problem for an FSS that considers repair actions and aims to determine the most suitable machine configuration that balances the production reliability and purchase cost. A nondominated sorting genetic algorithm II (NSGA-II) is used to determine all the Pareto solutions. The technique for order preference by similarity to an ideal solution is then used to identify a compromise alternative. It is necessary to assess the production reliability of any machine configuration identified by the NSGA-II. The FSS under the machine configuration is modeled as a multistate flow shop network, and Absorbing Markov Chain and Recursive Sum of Disjoint Products are integrated into the NSGA-II for reliability evaluation. The experimental results of solar cell manufacturing demonstrate the applicability of the proposed hybrid method and validate the efficiency of the NSGA-II compared with an improved strength Pareto evolutionary algorithm.

Keywords: Multistate flow shop network; Production reliability; Bi-objective; Absorptive Markov Chain; Nondominated sorting genetic algorithm II (NSGA-II); Technique for order preference by similarity to an ideal solution (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10479-023-05813-5

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