On the Neutrality of Flowshop Scheduling Fitness Landscapes

TL;DR

This paper analyzes the neutrality property in permutation flowshop scheduling landscapes and proposes ways to leverage this property to improve local search efficiency.

Contribution

It provides a detailed landscape analysis of neutrality in flowshop problems and suggests methods to utilize neutrality for more effective search strategies.

Findings

Neutrality affects the structure of the fitness landscape.

Proposed methods to exploit neutrality in local search.

Enhanced understanding of flowshop landscape properties.

Abstract

Solving efficiently complex problems using metaheuristics, and in particular local searches, requires incorporating knowledge about the problem to solve. In this paper, the permutation flowshop problem is studied. It is well known that in such problems, several solutions may have the same fitness value. As this neutrality property is an important one, it should be taken into account during the design of optimization methods. Then in the context of the permutation flowshop, a deep landscape analysis focused on the neutrality property is driven and propositions on the way to use this neutrality to guide efficiently the search are given.