@article{10902/38207,
year = {2025},
month = {9},
url = {https://hdl.handle.net/10902/38207},
abstract = {This article addresses the flexible job shop problem with uncertain processing times modelled by intervals. Due to climate change and the need for energy efficiency, there is an increasing interest in sustainability in addition to traditional production-related objectives such as makespan. In this work, we tackle a lexicographical goal programming scenario minimising makespan firstly and total energy consumption lately. We propose a hybrid evolutionary algorithm based on a genetic algorithm, incorporating heuristic seeding and a post-processing step using constraint programming. The experimental study shows that the proposed approach is able to meet tighter makespan goals than previously published methods, while offering a 32% improvement in energy consumption when goals are met.},
organization = {This research has been supported by the Spanish Government under research Grants TED2021-131938B-I00 and
PID2022-141746OB-I00.},
publisher = {Springer},
publisher = {Natural Computing, 2025, 24(3), 483-496},
title = {A hybrid evolutionary approach for lexicographic green flexible jobshop with interval uncertainty},
author = {Afsar, Sezin and Puente, Jorge and Palacios, Juan José and González Rodríguez, Inés and Rodríguez Vela, Camino},
}