Parallel Genetic Algorithm for a Flow-Shop Problem with Multiprocessor Tasks

摘要

Machine scheduling problems belong to the most difficult deterministic combinatorial optimization problems. Hence, most scheduling problems are NP-hard and it is impossible to find the optimal schedule in reasonable time. In this paper, we consider a flow-shop scheduling problem with multiprocessor tasks. A parallel genetic algorithm using multithreaded programming technique is developed to obtain a quick but good solution to the problem. The performance of the parallel genetic algorithm under various conditions and parameters are studied and presented.