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A Two-Level Metaheuristic Algorithm for the Job-Shop Scheduling Problem

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  • Pisut Pongchairerks

Abstract

This paper proposes a novel two-level metaheuristic algorithm, consisting of an upper-level algorithm and a lower-level algorithm, for the job-shop scheduling problem (JSP). The upper-level algorithm is a novel population-based algorithm developed to be a parameter controller for the lower-level algorithm, while the lower-level algorithm is a local search algorithm searching for an optimal schedule in the solution space of parameterized-active schedules. The lower-level algorithm’s parameters controlled by the upper-level algorithm consist of the maximum allowed length of idle time, the scheduling direction, the perturbation method to generate an initial solution, and the neighborhood structure. The proposed two-level metaheuristic algorithm, as the combination of the upper-level algorithm and the lower-level algorithm, thus can adapt itself for every single JSP instance.

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  • Pisut Pongchairerks, 2019. "A Two-Level Metaheuristic Algorithm for the Job-Shop Scheduling Problem," Complexity, Hindawi, vol. 2019, pages 1-11, March.
    • Handle: RePEc:hin:complx:8683472
    DOI: 10.1155/2019/8683472
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    References listed on IDEAS

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    8. Pisut Pongchairerks, 2016. "Efficient local search algorithms for job-shop scheduling problems," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 9(2), pages 258-277.
    9. Pisut Pongchairerks & Voratas Kachitvichyanukul, 2009. "A Particle Swarm Optimization Algorithm On Job-Shop Scheduling Problems With Multi-Purpose Machines," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 26(02), pages 161-184.
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