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Design of an efficient genetic algorithm for resource-constrained unrelated parallel machine scheduling problem with machine eligibility restrictions

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  • Mojtaba Afzalirad

    (Mazandaran University of Science and Technology)

  • Masoud Shafipour

    (Mazandaran University of Science and Technology)

Abstract

This study addresses a resource-constrained unrelated parallel machine scheduling problem with machine eligibility restrictions. Majority of the traditional scheduling problems in parallel machine environment deal with machine as the only resource. However, other resources such as labors, tools, jigs, fixtures, pallets, dies, and industrial robots are not only required for processing jobs but also are often restricted. Considering other resources makes the scheduling problems more realistic and practical to implement in manufacturing environments. First, an integer mathematical programming model with the objective of minimizing makespan is developed for this problem. Noteworthy, due to NP-hardness of the considered problem, application of meta-heuristic is avoidable. Furthermore, two new genetic algorithms including a pure genetic algorithm and a genetic algorithm along with a heuristic procedure are proposed to tackle this problem. With regard to the fact that appropriate design of the parameters has a significant effect on the performance of algorithms, hence, we calibrate the parameters of these algorithms by using the response surface method. The performance of the proposed algorithms is evaluated by a number of numerical examples. The computational results demonstrated that the proposed genetic algorithm is an effective and appropriate approach for our investigated problem.

Suggested Citation

  • Mojtaba Afzalirad & Masoud Shafipour, 2018. "Design of an efficient genetic algorithm for resource-constrained unrelated parallel machine scheduling problem with machine eligibility restrictions," Journal of Intelligent Manufacturing, Springer, vol. 29(2), pages 423-437, February.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:2:d:10.1007_s10845-015-1117-6
    DOI: 10.1007/s10845-015-1117-6
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    References listed on IDEAS

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    1. Edis, Emrah B. & Oguz, Ceyda & Ozkarahan, Irem, 2013. "Parallel machine scheduling with additional resources: Notation, classification, models and solution methods," European Journal of Operational Research, Elsevier, vol. 230(3), pages 449-463.
    2. Purushothaman Damodaran & Neal S. Hirani & Mario C. Velez-Gallego, 2009. "Scheduling identical parallel batch processing machines to minimise makespan using genetic algorithms," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 3(2), pages 187-206.
    3. Chen, Jeng-Fung & Wu, Tai-Hsi, 2006. "Total tardiness minimization on unrelated parallel machine scheduling with auxiliary equipment constraints," Omega, Elsevier, vol. 34(1), pages 81-89, January.
    4. Vallada, Eva & Ruiz, Rubén, 2011. "A genetic algorithm for the unrelated parallel machine scheduling problem with sequence dependent setup times," European Journal of Operational Research, Elsevier, vol. 211(3), pages 612-622, June.
    5. Liao, Lu-Wen & Sheen, Gwo-Ji, 2008. "Parallel machine scheduling with machine availability and eligibility constraints," European Journal of Operational Research, Elsevier, vol. 184(2), pages 458-467, January.
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    Cited by:

    1. Hongliang Zhang & Yujuan Wu & Ruilin Pan & Gongjie Xu, 2021. "Two-stage parallel speed-scaling machine scheduling under time-of-use tariffs," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 91-112, January.
    2. Xiaoyun Xiong & Peng Zhou & Yunqiang Yin & T. C. E. Cheng & Dengfeng Li, 2019. "An exact branch‐and‐price algorithm for multitasking scheduling on unrelated parallel machines," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(6), pages 502-516, September.
    3. Maximilian Moser & Nysret Musliu & Andrea Schaerf & Felix Winter, 2022. "Exact and metaheuristic approaches for unrelated parallel machine scheduling," Journal of Scheduling, Springer, vol. 25(5), pages 507-534, October.
    4. Bruno de Athayde Prata & Levi Ribeiro Abreu & José Ytalo Ferreira Lima, 2021. "Heuristic methods for the single-machine scheduling problem with periodical resource constraints," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 524-546, July.
    5. Jesús Isaac Vázquez-Serrano & Leopoldo Eduardo Cárdenas-Barrón & Rodrigo E. Peimbert-García, 2021. "Agent Scheduling in Unrelated Parallel Machines with Sequence- and Agent–Machine–Dependent Setup Time Problem," Mathematics, MDPI, vol. 9(22), pages 1-34, November.
    6. Mohammad Reza Bazargan-Lari & Sharareh Taghipour & Arash Zaretalab & Mani Sharifi, 2022. "Production scheduling optimization for parallel machines subject to physical distancing due to COVID-19 pandemic," Operations Management Research, Springer, vol. 15(1), pages 503-527, June.
    7. Bentao Su & Naiming Xie & Yingjie Yang, 2021. "Hybrid genetic algorithm based on bin packing strategy for the unrelated parallel workgroup scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 957-969, April.

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