Multiobjective scheduling of a reentrant hybrid flowshop

This paper deals with the multiobjective scheduling of a two stages reentrant hybrid flow shop. The system studied here is reentrant: jobs have to be processed more than once at each stage which is made of several identical parallel machines. Furthermore, the sequence is the same on each stage. In this study the two objectives are the minimization of both the maximum completion time and the sum of the tardiness. Two evolutionary algorithms are proposed : our Lorenz non dominated sorting genetic algorithm (L-NSGA) and the strength Pareto evolutionary algorithm version 2 (SPEA2). Several configurations of the system are tested and the results of the two algorithms are compared with the Pareto optimal front with respect to two different measures. The results show that our L-NSGA is more efficient than SPEA2 in 81% of the configurations. Furthermore the L-NSGA reaches optimal solutions for some instances.