Multi-objective Evolutionary Approaches for Solving the Hybrid Flowshop Scheduling Problem

Abstract

In this paper we schedule a set of jobs on a production system with more than one stage and several machines in parallel per stage, considering multiple objectives to be optimized. This problem is known as the Flexible or Hybrid Flowshop Scheduling problem (HFSP), which is NP-hard even for the case of a system with only two processing stages where one stage contains two machines and the other stage contains a single machine. In that sense, it is possible to find an optimal solution for this problem with low computing resources, only for small instances which, in general, do not reflect the industrial reality. For that reason, the use of meta-heuristics as an alternative approach it is proposed with the aim to determine, within a computational reasonable time, the best assignation of the jobs in order to minimize the makespan, total tardiness and the number of tardy jobs simultaneously. In this regard, a Multi-Objective Ant Colony Optimization algorithm (MOACO) and the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) are used for solving this combinatorial optimization problem. Results show the effectiveness of the approaches proposed.