A new heuristic based on local best solution for permutation flow shop scheduling

Abstract

This paper proposes a population-based heuristic based on the local best solution (HLBS) for the minimization of makespan in permutation flow shop scheduling problems. The proposed heuristic operates through three mechanisms: (i) it introduces a new method to produce a trace-model for guiding the search, (ii) it modifies a filter strategy to filter the solution regions that have been reviewed and guide the search to new solution regions in order to keep the search from trapping into local optima, and (iii) it initiates a new jump strategy to help the search escape if the search is trapped at a local optimum. Computational experiments on the well-known Taillard`s benchmark data sets demonstrate that the proposed algorithm generated high quality solutions when compared to existing population-based search algorithms such as genetic algorithms, ant colony optimization, and particle swarm optimization.