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An automatic algorithm selection approach for the multi-mode resource-constrained project scheduling problem

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  • Messelis, Tommy
  • De Causmaecker, Patrick

Abstract

This paper investigates the construction of an automatic algorithm selection tool for the multi-mode resource-constrained project scheduling problem (MRCPSP). The research described relies on the notion of empirical hardness models. These models map problem instance features onto the performance of an algorithm. Using such models, the performance of a set of algorithms can be predicted. Based on these predictions, one can automatically select the algorithm that is expected to perform best given the available computing resources. The idea is to combine different algorithms in a super-algorithm that performs better than any of the components individually. We apply this strategy to the classic problem of project scheduling with multiple execution modes. We show that we can indeed significantly improve on the performance of state-of-the-art algorithms when evaluated on a set of unseen instances. This becomes important when lots of instances have to be solved consecutively. Many state-of-the-art algorithms perform very well on a majority of benchmark instances, while performing worse on a smaller set of instances. The performance of one algorithm can be very different on a set of instances while another algorithm sees no difference in performance at all. Knowing in advance, without using scarce computational resources, which algorithm to run on a certain problem instance, can significantly improve the total overall performance.

Suggested Citation

  • Messelis, Tommy & De Causmaecker, Patrick, 2014. "An automatic algorithm selection approach for the multi-mode resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 233(3), pages 511-528.
  • Handle: RePEc:eee:ejores:v:233:y:2014:i:3:p:511-528
    DOI: 10.1016/j.ejor.2013.08.021
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    References listed on IDEAS

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    Cited by:

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    2. Hartmann, Sönke & Briskorn, Dirk, 2022. "An updated survey of variants and extensions of the resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 297(1), pages 1-14.
    3. Osman Hürol Türkakın & David Arditi & Ekrem Manisalı, 2021. "Comparison of Heuristic Priority Rules in the Solution of the Resource-Constrained Project Scheduling Problem," Sustainability, MDPI, vol. 13(17), pages 1-17, September.
    4. Felipe Rosa-Rivera & Jose I. Nunez-Varela & Cesar A. Puente-Montejano & Sandra E. Nava-Muñoz, 2021. "Measuring the complexity of university timetabling instances," Journal of Scheduling, Springer, vol. 24(1), pages 103-121, February.
    5. Nataliia Dotsenko & Dmytro Chumachenko & Igor Chumachenko & Andrii Galkin & Tomasz Lis & Marek Lis, 2021. "Conceptual Framework of Sustainable Management of the Process of Forming a Project Team with Functional Redundancy," Energies, MDPI, vol. 14(24), pages 1-22, December.
    6. Patoghi, Amirhosein & Mousavi, Seyed Meysam, 2021. "A new approach for material ordering and multi-mode resource constraint project scheduling problem in a multi-site context under interval-valued fuzzy uncertainty," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    7. Karimi-Mamaghan, Maryam & Mohammadi, Mehrdad & Meyer, Patrick & Karimi-Mamaghan, Amir Mohammad & Talbi, El-Ghazali, 2022. "Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art," European Journal of Operational Research, Elsevier, vol. 296(2), pages 393-422.
    8. Müller, David & Müller, Marcus G. & Kress, Dominik & Pesch, Erwin, 2022. "An algorithm selection approach for the flexible job shop scheduling problem: Choosing constraint programming solvers through machine learning," European Journal of Operational Research, Elsevier, vol. 302(3), pages 874-891.
    9. Mohammad Rostami & Morteza Bagherpour, 2020. "A lagrangian relaxation algorithm for facility location of resource-constrained decentralized multi-project scheduling problems," Operational Research, Springer, vol. 20(2), pages 857-897, June.
    10. Wang, Yan & Hu, Hejuan & Sun, Xiaoyan & Zhang, Yong & Gong, Dunwei, 2022. "Unified operation optimization model of integrated coal mine energy systems and its solutions based on autonomous intelligence," Applied Energy, Elsevier, vol. 328(C).
    11. Ripon K. Chakrabortty & Ruhul A. Sarker & Daryl L. Essam, 2020. "Single mode resource constrained project scheduling with unreliable resources," Operational Research, Springer, vol. 20(3), pages 1369-1403, September.

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