Bi-objective group scheduling in hybrid flexible flowshop: A multi-phase approach

This study strives to minimize multi-objective flexible flowshop considering sequence-dependent setup times. The flowshop scheduling problem made up of n jobs that have to be processed on m machine. But a flexible flowshop scheduling problem should have more than one machine in at least one stage. As this problem is proven to be NP-hard, a multi-phase approach is developed to solve it. Both phases two and three improve their previous phase solutions, in order to tackle with the complexity of being multi-objective optimization, Pareto archive concepts have been implemented here. The parameters of the proposed algorithm are calibrated using a design of experiment (DOE) method. We investigate the performance of our algorithm through comparing two last stage of it with a distinguished benchmark, multi-objective genetic algorithm (MOGA). The computational results support the high performance of our innovative algorithm.