Multi-objective PSO algorithm for a bi-criteria permutation hybrid flow shop scheduling problem with sequence dependent setup times

This paper deals with the problem of sequence-dependent setup time hybrid flowshop scheduling with the objectives of minimizing the weighted mean completion time and weighted mean tardiness. Because a flow shop scheduling problem has been proved to be NP-hard, an effective multi-objective particle swarm optimization (MOPSO) is used for finding Pareto-optimal frontier of the problem. To generate an initial swarm NEH heuristic and EDD heuristic are incorporated into the initialization of population. Results show that NEH method generates better initial swarms than EDD method. To prove the efficiency of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison metrics, is compared with multi-objective genetic algorithms, i.e. NSGA2, SPEA2 and PESA2. The computational tests show that MOPSO provides better solutions than NSGA2, SPEA2 and PESA2 especially for the large-sized problems.