New meta-heuristics for the resource-constrained project scheduling problem

Andrew Lim, Hong Ma, Brian Rodrigues, Sun Teck Tan, Fei Xiao

    Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

    24 Citations (Scopus)

    Abstract

    In this paper, we study the resource-constrained project scheduling problem and introduce an annealing-like search heuristic which simulates the cooling process of a gas into a highly-ordered crystal. To achieve this, we develop diversification procedures that simulate the motion of high energy molecules as well as a local refinement procedure that simulates the motion of low energy molecules. We further improve the heuristic by incorporating a genetic algorithm framework. The meta-heuristic algorithms are applied to Kolisch's PSPLIB J30, J60 and J120 RCPSP instances. Experimental results show that they are effective and are among the best performing algorithms for the RCPSP. © 2012 Springer Science+Business Media, LLC.
    Original languageEnglish
    Pages (from-to)48-73
    JournalFlexible Services and Manufacturing Journal
    Volume25
    Issue number1-2
    DOIs
    Publication statusPublished - Jun 2013

    Research Keywords

    • Genetic algorithms
    • Meta-heuristics
    • Resource-constrained project scheduling problem

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