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The Joint Load Balancing and Parallel Machine Scheduling Problem

In: Operations Research Proceedings 2010

Author

Listed:
  • Yassine Ouazene

    (University of Technology of Troyes)

  • Faicel Hnaien

    (University of Technology of Troyes)

  • Farouk Yalaoui

    (University of Technology of Troyes)

  • Lionel Amodeo

    (University of Technology of Troyes)

Abstract

The addressed problem in this paper considers the joint load balancing and parallel machines scheduling problem. Two decisions are taken at once: to build the best schedule of n jobs on m identical parallel machines in order to minimize the total tardiness and to find the equitable distribution of the machine’s time activity. To our knowledge, these two criteria have never been simultaneously studied for the case of parallel machines. The considered problem is NP-hard since the problem with only the total tardiness minimization is NP-hard. We propose an exact and an approached resolution. The first method is based on the mixed integer linear programming method solved by Cplex solver. The second one is an adapted genetic algorithm. The test examples were generated using the schema proposed by Koulamas [3]for the problem of total tardiness minimization. The obtained results are promising.

Suggested Citation

  • Yassine Ouazene & Faicel Hnaien & Farouk Yalaoui & Lionel Amodeo, 2011. "The Joint Load Balancing and Parallel Machine Scheduling Problem," Operations Research Proceedings, in: Bo Hu & Karl Morasch & Stefan Pickl & Markus Siegle (ed.), Operations Research Proceedings 2010, pages 497-502, Springer.
    • Handle: RePEc:spr:oprchp:978-3-642-20009-0_79
    DOI: 10.1007/978-3-642-20009-0_79
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    Cited by:

    1. Rujapa Nanthapodej & Cheng-Hsiang Liu & Krisanarach Nitisiri & Sirorat Pattanapairoj, 2021. "Hybrid Differential Evolution Algorithm and Adaptive Large Neighborhood Search to Solve Parallel Machine Scheduling to Minimize Energy Consumption in Consideration of Machine-Load Balance Problems," Sustainability, MDPI, vol. 13(10), pages 1-25, May.

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