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An improved imperialist competitive algorithm based photolithography machines scheduling

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  • Peng Zhang
  • Youlong Lv
  • Jie Zhang

Abstract

Abstract: Photolithography machine is one of the most expensive equipment in semiconductor manufacturing system, and as such is often the bottleneck for processing wafers. This paper focuses on photolithography machines scheduling with the objective of total completion time minimisation. In contrast to classic parallel machines scheduling, it is characterised by dynamical arrival wafers, re-entrant process flows, dedicated machine constraints and auxiliary resources constraints. We propose an improved imperialist competitive algorithm (ICA) within the framework of a rolling horizon strategy for the problem. We develop a variable time interval-based rolling horizon strategy to decide the scheduling point. We address the global optimisation in every local scheduling by proposing a mixed cost function. Moreover, an adaptive assimilation operator and a sociopolitical competition operator are used to prevent premature convergence of ICA to local optima. A chaotic sequence-based local search method is presented to accelerate the rate of convergence. Computational experiments are carried out comparing the proposed algorithm with ILOG CPLEX, dispatching rules and meta-heuristic algorithms in the literature. It is observed that the algorithm proposed shows an excellent behaviour on cycle time minimisation while with a good on time delivery rate and machine utilisation rate.

Suggested Citation

  • Peng Zhang & Youlong Lv & Jie Zhang, 2018. "An improved imperialist competitive algorithm based photolithography machines scheduling," International Journal of Production Research, Taylor & Francis Journals, vol. 56(3), pages 1017-1029, February.
  • Handle: RePEc:taf:tprsxx:v:56:y:2018:i:3:p:1017-1029
    DOI: 10.1080/00207543.2017.1346320
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    Cited by:

    1. Jianxin Fang & Brenda Cheang & Andrew Lim, 2023. "Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey," Sustainability, MDPI, vol. 15(17), pages 1-44, August.
    2. Qingyun Yu & Haolin Yang & Kuo-Yi Lin & Li Li, 2021. "A self-organized approach for scheduling semiconductor manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 689-706, March.
    3. Donghun Lee & Hyeongwon Kang & Dongjin Lee & Jeonwoo Lee & Kwanho Kim, 2023. "Deep Reinforcement Learning-Based Scheduler on Parallel Dedicated Machine Scheduling Problem towards Minimizing Total Tardiness," Sustainability, MDPI, vol. 15(4), pages 1-14, February.

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