  The generalized quadratic assignment problem (GQAP) is one of the hardest
problems to solve in the operations research area. The GQAP addressed in this
work is defined as the task of minimizing the assignment and transportation
costs of assigning a set of facilities to a set of locations. The facilities
have different space requirements, and the locations have different space
capacities. Multiple facilities can be assigned to each location if the space
capacity is not violated. In this work, three instances of GQAP in different
situations are presented. Then, a genetic algorithm is developed to solve the
GQAP instances. Finally, the local neighborhood search with the steepest
descend strategy is constructed and applied to the final solution obtained by
the GA, and the final solution is compared with the best solution found by
MPL/CPLEX software and reference papers. The results show that the developed GA
heuristic is effective for solving the GQAP.
