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**An alternative approach for unbalanced assignment problem via genetic algorithm.**
*(English)*
Zbl 1241.90073

Summary: 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.

### MSC:

90B80 | Discrete location and assignment |

90C59 | Approximation methods and heuristics in mathematical programming |

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\textit{J. Majumdar} and \textit{A. K. Bhunia}, Appl. Math. Comput. 218, No. 12, 6934--6941 (2012; Zbl 1241.90073)

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### References:

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[3] | () |

[4] | Majumdar, J.; Bhunia, A.K., Elitist genetic algorithm approach for assignment problem, AMO - adv. mod. optimiz., 8, 2, 135-149, (2006) · Zbl 1157.90511 |

[5] | Kumar, A., A modified method for solving the unbalanced assignment problems, Appl. math. comput., 176, 76-82, (2006) · Zbl 1131.90408 |

[6] | Kagade, K.L.; Bajaj, V.H., A fuzzy method for solving unbalanced assignment problems with interval valued coefficients, Int. J. comm. buss. manag., 3, 1, 82-87, (2010) |

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