A Genetic Algorithm for the Capacity and Distance Constrained Plant Location Problem1

In this work we consider the capacity and distance constrained plant location problem (CDCPLP). This problem is a type of location and routing problem, in which a set of customers has to be assigned cost efficiently to plants that can be opened at discrete locations. Customers are then served from the associated plants by round-trips of plant’s vehicles. Since the capacity of the plant and the total maximum driving distance is limited the CDCPLP is a hard combinatorial optimization problem. We propose a genetic algorithm with an embedded local search based on an incomplete solution representation. Computational results show that the presented algorithm is able to quickly find optimal or near-optimal solutions and to outperform a previous state-of-the-art tabu search on many instances.