High performing evolutionary techniques for solving complex location problems in industrial system design

We propose an overall reconstruction of the traditional genetic algorithm method so that its inherent weaknesses such as slow convergence can be overcome. We explore a number of variations of crossover operators and of the genetic search scheme. The algorithm is also implemented as a partially parallel algorithm on a multi-processors workstation and is capable of handling a large class of real-life location problems. Hub location problems from airline networks and location-allocation problems from the oil industry have been solved successfully.