An alternative approach for unbalanced assignment problem via genetic algorithm

An alternative approach for unbalanced assignment problem via genetic algorithm

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Article ID: iaor20121904
Volume: 218
Issue: 12
Start Page Number: 6934
End Page Number: 6941
Publication Date: Feb 2012
Journal: Applied Mathematics and Computation
Authors: ,
Keywords: heuristics: genetic algorithms, combinatorial optimization
Abstract:

This paper presents an alternative approach using genetic algorithm to a new variant of the unbalanced assignment problem that dealing with an additional constraint on the maximum number of jobs that can be assigned to some agent(s). In this approach, genetic algorithm is also improved by introducing newly proposed initialization, crossover and mutation in such a way that the developed algorithm is capable to assign optimally all the jobs to agents. Computational results with comparative performance of the algorithm are reported for four test problems.

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