Global Journal of Technology and Optimization

ISSN: 2229-8711

Open Access

A GA for the Resource Sharing and Scheduling Problem


Gaby Pinto, Uriel Israelí, Inessa Ainbinder and Gad Rabinowitz

In this paper we consider the resource-sharing and scheduling problem, with makespan minimization as an objective. Although this problem was optimally solved through a customized branch-and-bound algorithm, its complexity motivated the use of heuristics such as genetic algorithms. A previous genetic algorithm used for solving this problem was significantly faster than the branch-andbound algorithm; however, it suffered from a high rate of infeasible offspring. We propose a new genetic approach, which produces only feasible offspring via a much more compact, genotype representation of the solution. While in the previous genetic algorithm the chromosome consisted of all the solution 0-1 variables (genotype=phenotype), in the new algorithm we define a much smaller chromosome (genotype) that stores sufficient information for efficiently generating a solution for the 0-1 variables (phenotype).


Share this article

Google Scholar citation report
Citations: 664

Global Journal of Technology and Optimization received 664 citations as per Google Scholar report

Global Journal of Technology and Optimization peer review process verified at publons

Indexed In

arrow_upward arrow_upward