A bi-population based genetic algorithm for the resource-constrained project scheduling problem

Authors:
Dieter Debels;Mario Vanhoucke
Affiliations:
Faculty of Economics and Business Administration, Ghent University, Ghent, Belgium;Faculty of Economics and Business Administration, Ghent University, Ghent, Belgium
Venue:
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
Year:
2005

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Abstract

The resource-constrained project scheduling problem (RCP- SP) is one of the most challenging problems in project scheduling. During the last couple of years many heuristic procedures have been developed for this problem, but still these procedures often fail in finding near-optimal solutions for more challenging problem instances. In this paper, we present a new genetic algorithm (GA) that, in contrast of a conventional GA, makes use of two separate populations. This bi-population genetic algorithm (BPGA) operates on both a population of left-justified schedules and a population of right-justified schedules in order to fully exploit the features of the iterative forward/backward scheduling technique. Comparative computational results reveal that this procedure can be considered as today's best performing RCPSP heuristic.