An efficient genetic algorithm for the uncapacitated multiple allocation p-hub median problem

Abstract

In this paper the Uncapacitated Multiple Allocation p-hub Median Problem (the UMApHMP) is considered. A new heuristic method based on a genetic algorithm approach (GA) for solving UMApHMP is proposed. The described GA uses binary representation of the solutions. Genetic operators which keep the feasibility of individuals in the population are designed and implemented. The mutation operator with frozen bits is used to increase the diversibility of the genetic material. The running time of the GA is improved by caching technique. Proposed GA approach is bench-marked on the well known CAB and AP data sets and compared with the existing methods for solving the UMApHMP. Computational results show that the GA quickly reaches all previously known optimal solutions, and also gives results on large scale AP instances (up to n=200, p=20) that were not considered in the literature so far.