Multi-objective Algorithms for the Single Machine Scheduling Problem with Sequence-dependent Family Setups

Abstract

This work treats the single machine scheduling problem in which the setup time depends on the sequence and the job family. The objective is to minimize the makespan and the total weighted tardiness. In order to solve the problem two multi-objective algorithms are analyzed: one based on Multi-objective Variable Neighborhood Search (MOVNS) and another on Pareto Iterated Local Search (PILS). Two literature algorithms based on MOVNS are adapted to solve the problem, resulting in the MOVNS_Ottoni and MOVNS_Arroyo variants. Also, a new perturbation procedure for the PILS is proposed, yielding the PILS1 variant. Computational experiments done over randomly generated instances show that PILS1 is statistically better than all other algorithms in relation to the cardinality, average distance, maximum distance, difference of hypervolume and epsilon metrics.