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

Author

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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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