Reducing energy consumption in fuzzy flexible job shops using memetic search

Abstract

The flexible job shop is a problem that has attracted much research attention both because of its importance in manufacturing processes and its computational complexity. However, industry is a highly complex environment that is constantly changing, and models and solving methods need to evolve and become richer to stay relevant. A source of complexity is the uncertainty in some parameters, in this work it is incorporated by modeling processing time using triangular fuzzy numbers. We also introduce the objective of reducing energy consumption, motivated by the fight against global warming. To solve the problem, we propose a memetic algorithm, a hybrid method that combines global search with local search. We have put a special focus on the neighborhood functions used to guide the local search since they are key for correct intensification. To assess the performance of the proposed method, we present an experimental analysis that compares the memetic algorithm to a powerful constraint programming solver, and we analyze how the proposed neighborhood functions contribute to increasing the search power of our method.