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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

13 Scopus Citations
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Author(s)

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

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)48-73
Journal / PublicationFlexible Services and Manufacturing Journal
Volume25
Issue number1-2
Publication statusPublished - Jun 2013

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.

Research Area(s)

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

Citation Format(s)

New meta-heuristics for the resource-constrained project scheduling problem. / Lim, Andrew; Ma, Hong; Rodrigues, Brian; Tan, Sun Teck; Xiao, Fei.

In: Flexible Services and Manufacturing Journal, Vol. 25, No. 1-2, 06.2013, p. 48-73.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review