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A worm optimization algorithm to minimize the makespan on unrelated parallel machines with sequence-dependent setup times

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  • Jean-Paul Arnaout

    (Gulf University for Science and Technology)

Abstract

This paper addresses the unrelated parallel machine scheduling problem with setup times, with an objective of minimizing the makespan. The machines are unrelated in the sense that the processing speed depends on the job being executed and not the machine. Each job will have different processing times for each of the available machines, is available at the beginning of the scheduling horizon, and can be processed on any of the machines but needs to be processed by one machine only. Sequence-dependent and machine-dependent setup times are also considered. A Worm Optimization (WO) algorithm is introduced and is applied to this NP-hard problem. The novel WO is based on the behaviors of the worm, which is a nematode with only 302 neurons. Nevertheless, these neurons allow worms to achieve several intricate behaviors including finding food, interchanging between solitary and social foraging styles, alternating between dwelling and roaming, and entering a type of stasis/declining stage. WO’s performance is evaluated by comparing its solutions to solutions of six other known metaheuristics for the problem under study, and the extensive computational results indicated that the proposed WO performs best.

Suggested Citation

  • Jean-Paul Arnaout, 2020. "A worm optimization algorithm to minimize the makespan on unrelated parallel machines with sequence-dependent setup times," Annals of Operations Research, Springer, vol. 285(1), pages 273-293, February.
    • Handle: RePEc:spr:annopr:v:285:y:2020:i:1:d:10.1007_s10479-019-03138-w
    DOI: 10.1007/s10479-019-03138-w
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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. Jean-Paul Arnaout, 2016. "Worm Optimization for the Traveling Salesman Problem," International Series in Operations Research & Management Science, in: Ghaith Rabadi (ed.), Heuristics, Metaheuristics and Approximate Methods in Planning and Scheduling, edition 1, chapter 0, pages 209-224, Springer.
    3. Absalom E Ezugwu & Olawale J Adeleke & Serestina Viriri, 2018. "Symbiotic organisms search algorithm for the unrelated parallel machines scheduling with sequence-dependent setup times," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-23, July.
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    Cited by:

    1. Rakesh Prakash & Jitamitra Desai & Rajesh Piplani, 2022. "An optimal data-splitting algorithm for aircraft sequencing on a single runway," Annals of Operations Research, Springer, vol. 309(2), pages 587-610, February.
    2. Jovanovic, Raka & Sanfilippo, Antonio P. & Voß, Stefan, 2023. "Fixed set search applied to the clique partitioning problem," European Journal of Operational Research, Elsevier, vol. 309(1), pages 65-81.
    3. Jean-Paul Arnaout & John Khoury, 2022. "Adaptation of WO to the Euclidean location-allocation with unknown number of facilities," Annals of Operations Research, Springer, vol. 315(1), pages 57-72, August.
    4. J. Adan, 2022. "A hybrid genetic algorithm for parallel machine scheduling with setup times," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 2059-2073, October.

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