@article{10902/32755,
year = {2023},
month = {3},
url = {https://hdl.handle.net/10902/32755},
abstract = {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.},
organization = {Supported by the Spanish Government under research grants PID2019-106263RB-I00 and TED2021-131938B-I00 and by Universidad de Cantabria and the Government of Cantabria under Grant Concepción Arenal UC-20-20.},
publisher = {IOS Press},
publisher = {Integrated Computer-Aided Engineering, 2023, 30(2), 151-167},
title = {Enhanced memetic search for reducing energy consumption in fuzzy flexible job shops},
author = {García Gómez, Pablo and González Rodríguez, Inés and Vela, Camino R.},
}