Hybrid Metaheuristics for Solving the Blocking Flowshop Scheduling Problem

Abstract

This paper suggests two evolutionary optimization approaches for solving the blocking flow shop scheduling problem with the maximum completion time (makespan) criterion, namely the genetic algorithm (GA) and the simulated annealing genetic algorithms (SAGA) that combines the simulated annealing (SA) with the (GA), respectively. The considered problem and the proposed algorithms have some parameters to be adjusted through a design of experiments with exorbitant runs. In fact, a Taguchi method is presented to study the parameterization problem empirically. The performance of the proposed algorithms is evaluated by applying it to Taillard’s well-known benchmark problem, the experiment results show that the SA combined with GA method is advanced to the GA and to the compared algorithms proposed in the literature in minimizing makespan criterion. Ultimately, new known upper bounds for Taillard’s instances are reported for this problem, which can be used thereafter as a basis of benchmark in eventual investigations.