Solving problems of the flexible scheduling machines

Abstract

Flexible job scheduling problem (JSP) as an optimization problem, tends to find solution for allowing different operations to be processed faster. This problem could be solved by genetic algorithm, as we have proven in another experiment. Now, we have tried to outperform state of the art, by using parallel genetic algorithm. Parallel genetic algorithm has two types and we have chosen the most popular one coarsed grained genetic algorithm, for our specific case. The results have improved time wise and are promising in some of the datasets, while a need exists for improving on other ones. In the future, we will compare both versions of parallel genetic algorithms but also compare the results to another algorithm.