A hybrid metaheuristic for the generalized quadratic assignment problem

Abstract

The generalized quadratic assignment problem (GQAP) concerns with assigning m facilities to n locations. Multiple facilities can be assigned to a single location as long as the space allows. The aim is to find a way of assignment such that the total cost involved is minimized while respecting the space constraints. The total cost composes of installation and transportation costs, which is obtained by multiplying the facility flows and distances. GQAP is the generalization of the well-known quadratic assignment problem, which requires the number of facilities and locations to be the same and exactly one facility is assigned to one location. This paper presents a hybrid metaheuristic for solving the GQAP. The proposed algorithm hybridizes biogeography-based optimization with tabu search. Experimental results show that the hybrid metaheuristic is able to solve problem instances obtained from a manufacturing company, within reasonable computational times.