Enhanced memetic search for reducing energy consumption in fuzzy flexible job shops

The flexible job shop is a well-known scheduling problem that has historically attracted much research attention both because of its computational complexity and its importance in manufacturing and engineering processes. Here we consider a variant of the problem where uncertainty in operation processing times is modeled using triangular fuzzy numbers. Our objective is to minimize the total energy consumption, which combines the energy required by resources when they are actively processing an operation and the energy consumed by these resources simply for being switched on. To solve this NP-Hard problem, we propose a memetic algorithm, a hybrid metaheuristic method that combines global search with local search. Our focus has been on obtaining an efficient method, capable of obtaining similar solutions quality-wise to the state of the art using a reduced amount of time. To assess the performance of our algorithm, we present an extensive experimental analysis that compares it with previous proposals and evaluates the effect on the search of its different components.

Automated summary

This article discusses the flexible job shop scheduling problem and its variant where uncertainty in operation processing times is modeled using triangular fuzzy numbers. The objective is to minimize total energy consumption, considering the energy required by resources during operation and the energy consumed when resources are switched on. To solve this NP-Hard problem, a memetic algorithm is proposed, combining global search and local search. The focus is on obtaining an efficient method that can achieve similar solutions to existing state-of-the-art approaches in less time. An extensive experimental analysis compares the algorithm with previous proposals and evaluates the effect of different components on the search.