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

The production scheduling problem in hybrid flow shops is a complex combinatorial optimization problem observed in many realworld applications. The standard hybrid flow shop problem involves often unrealistic assumptions. In order to address the realistic assumptions, four additional traits were added to the proposed problem. These include reentrant line, setup times, positiondependent learning effects, and the consideration of maximum completion time together with total tardiness as objective function. Since the proposed problem is nondeterministic polynomialtime (NP)hard, a metaheuristic 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 done on various test problems. Computational results show that the proposed algorithm can obtain an effective and appropriate solution quality for our investigated problem.