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A genetic algorithm with resource buffers for the resource-constrained multi-project scheduling problem

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  • Bredael, Dries
  • Vanhoucke, Mario

Abstract

In this study, we compose a new metaheuristic algorithm for solving the resource-constrained multi-project scheduling problem. Our approach is based on a general metaheuristic strategy which incorporates two resource-buffered scheduling tactics. We build on the most effective evolutionary operators and other well-known scheduling methods to create a novel genetic algorithm with resource buffers. We test our algorithm on a large benchmark dataset and compare its performance to ten existing metaheuristic algorithms. Our results show that our algorithm can generate new best-known solutions for about 20% of the test instances, depending on the optimisation criterion and due date. In some cases, our algorithm outperforms all other available methods combined. Finally, we introduce a new schedule metric that can quantitatively measure the dominant structure of a solution, and use it to analyse the differences between the best solutions for different objectives, due dates, and instance parameters.

Suggested Citation

  • Bredael, Dries & Vanhoucke, Mario, 2024. "A genetic algorithm with resource buffers for the resource-constrained multi-project scheduling problem," European Journal of Operational Research, Elsevier, vol. 315(1), pages 19-34.
    • Handle: RePEc:eee:ejores:v:315:y:2024:i:1:p:19-34
    DOI: 10.1016/j.ejor.2023.11.009
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    References listed on IDEAS

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    1. Li, K. Y. & Willis, R. J., 1992. "An iterative scheduling technique for resource-constrained project scheduling," European Journal of Operational Research, Elsevier, vol. 56(3), pages 370-379, February.
    2. Browning, Tyson R. & Yassine, Ali A., 2010. "Resource-constrained multi-project scheduling: Priority rule performance revisited," International Journal of Production Economics, Elsevier, vol. 126(2), pages 212-228, August.
    3. Bredael, Dries & Vanhoucke, Mario, 2023. "Multi-project scheduling: A benchmark analysis of metaheuristic algorithms on various optimisation criteria and due dates," European Journal of Operational Research, Elsevier, vol. 308(1), pages 54-75.
    4. M.L. Mittal & Arun Kanda, 2009. "Two-phase heuristics for scheduling of multiple projects," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 4(2), pages 159-177.
    5. Rob Eynde & Mario Vanhoucke, 2020. "Resource-constrained multi-project scheduling: benchmark datasets and decoupled scheduling," Journal of Scheduling, Springer, vol. 23(3), pages 301-325, June.
    6. Debels, Dieter & De Reyck, Bert & Leus, Roel & Vanhoucke, Mario, 2006. "A hybrid scatter search/electromagnetism meta-heuristic for project scheduling," European Journal of Operational Research, Elsevier, vol. 169(2), pages 638-653, March.
    Full references (including those not matched with items on IDEAS)

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