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Metaheuristics for drilling operation scheduling in Taiwan PCB industries

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  • Liang, Yun-Chia
  • Hsiao, Yu-Ming
  • Tien, Chia-Yun

Abstract

Among all types of production environment, identical parallel machines are frequently used to increase the manufacturing capacity of the drilling operation in Taiwan printed circuit board (PCB) industries. Additionally, multiple but conflicting objectives are usually considered when a manager plans the production scheduling. Compared to the single objective problem, the multiple-objective version no longer looks for an individual optimal solution, but a Pareto front consisting of a set of non-dominated solutions will be needed and established. The manager then can select one of the alternatives from the set. This research aims at employing a variable neighborhood search (VNS) algorithm and a multiple ant colony optimization (MACO) algorithm to solve the identical parallel-machine scheduling problem with two conflicting objectives: makespan and total tardiness. In VNS, two neighborhoods are defined—insert a job to a different position or swap two jobs in the sequence. To save the computational expense, one of the neighborhoods is randomly selected for the target solution which is also arbitrarily chosen from the current Pareto front. In MACO, a two-phase construction procedure where three colonies are employed in each phase is proposed. These two algorithms are tested on a set of real data collected from a leading PCB factory in Taiwan and their performances are compared. The computational results show that VNS outperforms all competing algorithms—SPGA, MOGA, NSGA-II, SPEA-II, and MACO in terms of solution quality and computational time.

Suggested Citation

  • Liang, Yun-Chia & Hsiao, Yu-Ming & Tien, Chia-Yun, 2013. "Metaheuristics for drilling operation scheduling in Taiwan PCB industries," International Journal of Production Economics, Elsevier, vol. 141(1), pages 189-198.
    • Handle: RePEc:eee:proeco:v:141:y:2013:i:1:p:189-198
    DOI: 10.1016/j.ijpe.2012.04.014
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    References listed on IDEAS

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    Cited by:

    1. Derya Deliktaş, 2022. "Self-adaptive memetic algorithms for multi-objective single machine learning-effect scheduling problems with release times," Flexible Services and Manufacturing Journal, Springer, vol. 34(3), pages 748-784, September.

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