Multi-Objective for a Partial Flexible Open Shop Scheduling Problem Using a Priority-Based Evolutionary Algorithm

In this study, an evolutionary algorithm (EA) with priority-based representation has been applied to get a partial flexible, open shop scheduling problem (PFOSP) which happens to be one of the hardest combinatorial and operations research problems. The priority for each operation is represented by a genetic on a best chromosome by a constructive algorithm performed for the decoding method on all active schedules, which will constitute for a subset of partial feasible schedules including the optimal solution. To obtain improved solutions, iterative local search algorithm (ILSA) is applied to the best chromosome to obtain at the end of each reproduction process. The most widely used PFOSP data sets are generated in the literature which are used for benchmarking and evaluating the performance of the proposed EA methodology. The computational results show that the proposed EA performed at the same level or at a better level with respect to the minimization of make span for some data sets when compared with the results based from the literature.