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A Hybrid Genetic Algorithm Based on Intelligent Encoding for Project Scheduling

In: Perspectives in Modern Project Scheduling

Author

Listed:
  • Javier Alcaraz

    (Universidad Politécnica de Valencia)

  • Concepción Maroto

    (Universidad Politécnica de Valencia)

Abstract

In the last few years several heuristic, metaheuristic and hybrid techniques have been developed to solve the Resource-Constrained Project Scheduling Problem (RCPSP). Most of them use the standard activity list representation, given that it seems to perform best in solving the RCPSP independently of the paradigm employed (genetic algorithms, tabu search, simulated annealing, ...). However, we have designed an innovative representation, one which has not been used before and which includes a lot of problem-specific knowledge. Based on that representation we have developed a new competitive and robust hybrid genetic algorithm, which uses genetic operators and an improvement mechanism specially designed to work on that representation and exploit, in a very efficient way, the information contained in it. We have compared this algorithm with the best algorithms published so far, using the standard benchmark of PSPLIB. The results show the excellent performance of our algorithm.

Suggested Citation

  • Javier Alcaraz & Concepción Maroto, 2006. "A Hybrid Genetic Algorithm Based on Intelligent Encoding for Project Scheduling," International Series in Operations Research & Management Science, in: Joanna Józefowska & Jan Weglarz (ed.), Perspectives in Modern Project Scheduling, chapter 0, pages 249-274, Springer.
    • Handle: RePEc:spr:isochp:978-0-387-33768-5_10
    DOI: 10.1007/978-0-387-33768-5_10
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    Cited by:

    1. Bernardo F. Almeida & Isabel Correia & Francisco Saldanha-da-Gama, 2018. "A biased random-key genetic algorithm for the project scheduling problem with flexible resources," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(2), pages 283-308, July.

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