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An adaptative bacterial foraging optimization algorithm for solving the MRCPSP with discounted cash flows

Author

Listed:
  • Luis F. Machado-Domínguez

    (Universidad del Norte)

  • Carlos D. Paternina-Arboleda

    (Universidad del Norte)

  • Jorge I. Vélez

    (Universidad del Norte)

  • Agustín Barrios-Sarmiento

    (Universidad del Norte)

Abstract

In this paper, a metaheuristic solution algorithm for solving the multi-mode resource-constrained project scheduling problem (MRCPSP) with discounted cash flows (MRCPSPDC) is proposed. This problem consists of determining a schedule such that the project is completed, maximizing the project’s net present value (NPV) while complying with the delivery deadline. The adaptative bacterial foraging optimization (ABFO) algorithm is a variation of the original bacterial foraging optimization (BFO), which is a nature-inspired metaheuristic optimization algorithm. We implement a version of the chemotactic operator based on a double justification of the activities given the cash flow. This metaheuristic has been tested in the PSPLIB and MMLIB benchmark datasets available in the literature with promising results. Our ABFO algorithm shows excellent performance in all tested instances and provides suitable solutions for the MRCPSP maximizing the NPV.

Suggested Citation

  • Luis F. Machado-Domínguez & Carlos D. Paternina-Arboleda & Jorge I. Vélez & Agustín Barrios-Sarmiento, 2022. "An adaptative bacterial foraging optimization algorithm for solving the MRCPSP with discounted cash flows," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 221-248, July.
    • Handle: RePEc:spr:topjnl:v:30:y:2022:i:2:d:10.1007_s11750-021-00612-2
    DOI: 10.1007/s11750-021-00612-2
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