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Evolving ant colony system for large-sized integrated process planning and scheduling problem considering sequence-dependent setup times

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  • Chunghun Ha

    (Hongik University)

Abstract

This paper proposes a new ant colony optimization (ACO) algorithm suitable for integrated process planning and scheduling (IPPS) that optimizes both process planning and scheduling simultaneously. The IPPS covered in this study, when compared to the conventional IPPS, is more flexible and complicated because sequence-dependent setups and tool-related capacity constraints are additionally considered. Traditional ACOs have limitations in improving the solution quality and computation time for IPPS. The high flexibility and complexity of IPPS requires a large size of repository for pheromone trails and it causes the long computation time for updating them, excessive evaporation of pheromones, and unbalancing between pheromones and desirability. In the proposed ACO, each ant agent improves their own incumbent solution or finds a new solution using the pheromone trails that is composed of the experience information of the colony. Therefore, the proposed ACO conducts individual and cooperative evolving at the same time. Furthermore, we propose a simplified updating rule for pheromone trails and standardization of the transition probability to increase efficiency of the algorithm. Experimental results show that the proposed ACO is superior to recently proposed meta-heuristics for benchmark problems of different sizes in terms of both solution quality and computation time.

Suggested Citation

  • Chunghun Ha, 2020. "Evolving ant colony system for large-sized integrated process planning and scheduling problem considering sequence-dependent setup times," Flexible Services and Manufacturing Journal, Springer, vol. 32(3), pages 523-560, September.
  • Handle: RePEc:spr:flsman:v:32:y:2020:i:3:d:10.1007_s10696-019-09360-9
    DOI: 10.1007/s10696-019-09360-9
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    References listed on IDEAS

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    1. Allahverdi, Ali, 2015. "The third comprehensive survey on scheduling problems with setup times/costs," European Journal of Operational Research, Elsevier, vol. 246(2), pages 345-378.
    2. S. Zhang & T. N. Wong, 2018. "Integrated process planning and scheduling: an enhanced ant colony optimization heuristic with parameter tuning," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 585-601, March.
    3. Liu, Jiyin & MacCarthy, B. L., 1997. "A global MILP model for FMS scheduling," European Journal of Operational Research, Elsevier, vol. 100(3), pages 441-453, August.
    4. Allahverdi, Ali & Ng, C.T. & Cheng, T.C.E. & Kovalyov, Mikhail Y., 2008. "A survey of scheduling problems with setup times or costs," European Journal of Operational Research, Elsevier, vol. 187(3), pages 985-1032, June.
    5. Jianping Dou & Jun Li & Chun Su, 2018. "A discrete particle swarm optimisation for operation sequencing in CAPP," International Journal of Production Research, Taylor & Francis Journals, vol. 56(11), pages 3795-3814, June.
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