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

Dries Bredael and Mario Vanhoucke

European Journal of Operational Research, 2024, vol. 315, issue 1, 19-34

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.

Keywords: Project scheduling; Genetic algorithms; Metaheuristics; Multi-project (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:315:y:2024:i:1:p:19-34

DOI: 10.1016/j.ejor.2023.11.009

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