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An evolutionary simulation-optimization approach for the problem of order allocation with flexible splitting rule in semiconductor assembly

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Abstract

In this study, we propose an evolutionary simulation-optimization algorithm for the order allocation problem with flexible splitting rule in semiconductor assembly (SA). There are numerous complex production constraints associated with the operational problems in SA, such as identical and unrelated machines, flexible order lot split, and stochastic processing time, which hinder the decision-making process (i.e., which order allocates which machines and the most efficient lot-split size for a production system). To address complex production constraints in SA, this study constructed a simulation model to evaluate the system performance of each design alternative with minimization of the expected flow time of all orders. Due to the large design alternatives, this study proposes a simulation optimization algorithm to efficiently determine the design alternative. Owing to the high time consumption involved in using a high-fidelity simulation model to evaluate system performance, this algorithm employed a ranking and selection method known as the optimal replication allocation strategy (ORAS), to efficiently allocate computing resources. The ORAS reduced the additional computing cost of non-critical solutions and generated an elite set, which contained elite members not significantly different compared to the global best (Gbest), in each generation of the search algorithm. As this problem is complex and involves numerous local and global optima, an enhanced genetic algorithm (EGA) is proposed to utilize the elite set to enhance the diversity and further improve the solution quality. The proposed algorithm was validated by comparing its performance using statistical methods on 12 instances with those of several state-of-the-art algorithms. The results demonstrated the superior solution quality and search efficiency of the proposed algorithm compared to those of the competitors.

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Acknowledgements

We thank the Ministry of Science and Technology, Taiwan, ROC, for funding this project under contract no. MOST 109-2222-E-167-004.

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Authors and Affiliations

  1. Department of Industrial Engineering and Management, National Chin-Yi University of Technology, No.57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung, 41170, Taiwan (Republic of China)

    Chun-Chih Chiu

  2. Institute of Resources Management and Decision Science, Management College, National Defense University, No.70, Sec. 2, Zhongyang N. Rd., Beitou Dist, Taipei, 112, Taiwan (Republic of China)

    Chyh-Ming Lai

  3. Department of Industrial Management, National Formosa University, No. 64, Wunhua Road, Huwei Township, Yunlin County, 632, Taiwan (Republic of China)

    Chien-Ming Chen

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  1. Chun-Chih Chiu
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Correspondence to Chun-Chih Chiu.

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The original online version of this article was revised: There were errors found in Tables 10, 12 and 14.

Appendix

Appendix

Complexity analysis of the EGA ORAS

The following parameter settings were used for the EGAORAS: a population size (P) of 30, a maximum generations number (G) of 60, a crossover rate of 0.8 (fixed), a mutation probability of 0.4 (fixed), and the n0 and Δ were set as 5 and 50, respectively. In addition, the maximum total computing budget for each solution budget, T, and P(CS)* of OCBA and ORAS were set at P × t and 0.9, respectively, where t is usually set at 30. In addition, there were two cut-point crossover (the crossover point was randomly selected), four-point mutation (randomly changed four genes), the termination criterion (maximum generation, G). Thus, the worst-case computational complexity of the proposed EGAORAS for simulation optimization of the problem of order allocation with flexible splitting rule was given as: O(P × G × O(t) × (crossover rate ×O(crossover)) + (mutation rate × O(mutation))). In the evaluation procedure of ORAS or OCBA, the best case computational complexity was given as: O(P × G × O(n0) × (crossover rate ×O(crossover)) + (mutation rate × O(mutation))) as P(CS) is always larger than P(CS)*.

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Chiu, CC., Lai, CM. & Chen, CM. An evolutionary simulation-optimization approach for the problem of order allocation with flexible splitting rule in semiconductor assembly. Appl Intell 53, 2593–2615 (2023). https://doi.org/10.1007/s10489-022-03701-2

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