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: | Bhunia Asoke Kumar, Majumdar Jayanta |
Keywords: | heuristics: genetic algorithms, combinatorial optimization |
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.