A Pareto evolutionary algorithm approach to bi-objective unrelated parallel machine scheduling problems

This paper addresses the unrelated parallel machine scheduling problem with job sequence- and machine-dependent setup times. The preemption of jobs is not permitted, and the optimization criteria are to simultaneously minimize total weighted flow time and total weighted tardiness. The problem has applications in industries such as TFT-LCD, automobile, and textile manufactures. In this study, a Pareto evolutionary approach is proposed to solve the bi-objective scheduling problem. The performance of this approach using different encoding and decoding schemes is evaluated and is compared with that of two multi-objective simulated annealing algorithms via a set of instances generated by a method in the literature. The experimental results indicate that the Pareto evolutionary approach using random key representation and weighted bipartite matching optimization method outperforms the other algorithms in terms of closeness metric, based on similar computation times. Additionally, although the proposed method does not provide the best distribution in terms of diversity metric, it found most of the reference solutions.