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ACO-based method for single machine scheduling with sequence-dependent setup time and limited capacity warehouse

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  • Shijin Wang

Abstract

Much of the research on operations scheduling problems has ignored setup times and also assumes that output warehouse (or buffer) is infinite. While in many real-world production scheduling systems, it requires explicit consideration of sequence-dependent setup times and limited capacity output warehouse. This paper studies a single machine scheduling (SMS) problem considering sequence-dependent setup times and limited capacity output warehouse simultaneously, with the objective of minimising the total tardiness. A mathematical model is constructed to depict the problem. As the problem is NP-hard, a modified ant colony optimisation (ACO) method based on ant system meta-heuristic is presented to solve the problem. Incorporated with different state transition rules due to different combinations of heuristic information, several versions of the ACO method are generated. For each method, parameters are tuned with design of experiments (DOE). Then, based on different settings of experimental simulation, the performance of the methods is discussed and also compared with those of genetic algorithm (GA) and dispatching rules. The results show the feasibility and effectiveness of the proposed method for the considered problem.

Suggested Citation

  • Shijin Wang, 2014. "ACO-based method for single machine scheduling with sequence-dependent setup time and limited capacity warehouse," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 16(3), pages 334-364.
    • Handle: RePEc:ids:ijisen:v:16:y:2014:i:3:p:334-364
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    Cited by:

    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. Sicheng Zhang & T.N. Wong, 2016. "Studying the impact of sequence-dependent set-up times in integrated process planning and scheduling with E-ACO heuristic," International Journal of Production Research, Taylor & Francis Journals, vol. 54(16), pages 4815-4838, August.

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