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

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