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A genetic algorithm for the robust resource leveling problem

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  • Hongbo Li

    (Shanghai University
    KU Leuven
    Beihang University)

  • Erik Demeulemeester

    (KU Leuven)

Abstract

The resource leveling problem (RLP) involves the determination of a project baseline schedule that specifies the planned activity starting times while satisfying both the precedence constraints and the project deadline constraint under the objective of minimizing the variation in the resource utilization. However, uncertainty is inevitable during project execution. The baseline schedule generated by the deterministic RLP model tends to fail to achieve the desired objective when durations are uncertain. We study the robust resource leveling problem in which the activity durations are stochastic and the objective is to obtain a robust baseline schedule that minimizes the expected positive deviation of both resource utilizations and activity starting times. We present a genetic algorithm for the robust RLP. In order to demonstrate the effectiveness of our genetic algorithm, we conduct extensive computational experiments on a large number of randomly generated test instances and investigate the impact of different factors (the marginal cost of resource usage deviations, the marginal cost of activity starting time deviations, the activity duration variability, the due date, the order strength, the resource factor and the resource constrainedness).

Suggested Citation

  • Hongbo Li & Erik Demeulemeester, 2016. "A genetic algorithm for the robust resource leveling problem," Journal of Scheduling, Springer, vol. 19(1), pages 43-60, February.
    • Handle: RePEc:spr:jsched:v:19:y:2016:i:1:d:10.1007_s10951-015-0457-6
    DOI: 10.1007/s10951-015-0457-6
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    References listed on IDEAS

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    Cited by:

    1. Öncü Hazir & Gündüz Ulusoy, 2020. "A classification and review of approaches and methods for modeling uncertainty in projects," Post-Print hal-02898162, HAL.
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    3. Claudio Szwarcfiter & Yale T. Herer & Avraham Shtub, 2022. "Project scheduling in a lean environment to maximize value and minimize overruns," Journal of Scheduling, Springer, vol. 25(2), pages 177-190, April.
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    5. Hazır, Öncü & Ulusoy, Gündüz, 2020. "A classification and review of approaches and methods for modeling uncertainty in projects," International Journal of Production Economics, Elsevier, vol. 223(C).

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