On the Neutrality of Flowshop Scheduling Fitness Landscapes
This work addresses optimization challenges in scheduling for operations research, but it is incremental as it builds on known neutrality concepts.
The paper tackles the permutation flowshop scheduling problem by analyzing the neutrality property, where multiple solutions share the same fitness value, and proposes methods to use this neutrality to guide search more efficiently.
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.