Bi-objective scheduling for the re-entrant hybrid flow shop with learning effect and setup times

Abstract

The production scheduling problem in hybrid flow shops is a complex combinatorial optimization problem observed in many real-world applications. The standard hybrid flow shop problem often involves unrealistic assumptions. In order to address the realistic assumptions, four additional traits were added to the proposed problem. These include the re-entrant line, setup times, position-dependent learning effects, and consideration of maximum completion time together with total tardiness as an objective function. Since the proposed problem is NP-hard, a meta-heuristic algorithm is proposed as the solution procedure. The solution procedure is categorized as an a priori approach. To show the efficiency and effectiveness of the proposed algorithm, computational experiments were carried out on various test problems. Computational results show that the proposed algorithm can obtain an effective and appropriate solution quality for our investigated problem.