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

• Luis F. Machado Domínguez ^[1] ; Carlos D. Paternina Arboleda ^[1] ; Jorge I. Vélez ^[1] ; Agustín Barrios Sarmiento ^[1]
  1. [1] Universidad del Norte

     Universidad del Norte

     Colombia

     
• Localización: Top, ISSN-e 1863-8279, ISSN 1134-5764, Vol. 30, Nº. 2, 2022, págs. 221-248
• Idioma: inglés
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• Resumen

  • In this paper, a metaheuristic solution algorithm for solving the multi-mode resource-constrained project scheduling problem (MRCPSP) with discounted cash fows (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 justifcation of the activities given the cash fow. 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.