Uniform Parallel Machines Scheduling Using a Genetic Algorithm

Scheduling problems are very important for many (research) fields like management, industrial engineering, operations research and computer science. However many instances of scheduling problems were proven to be NP-hard, therefore the use of heuristics is the de-facto approach in order to cope in practice with its difficulty. In this paper we consider instance problems from the class of uniform parallel machines and present a genetic algorithm for this class of scheduling problems (GASP). We report some preliminary results and the performance of the presented approach are compared with other optimization techniques. Empirical results indicate that GASP approach is more efficient.