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Multi-objective enhanced memetic algorithm for green job shop scheduling with uncertain times

Abstract: The quest for sustainability has arrived to the manufacturing world, with the emergence of a research field known as green scheduling. Traditional performance objectives now co-exist with energy-saving ones. In this work, we tackle a job shop scheduling problem with the double goal of minimising energy consumption during machine idle time and minimising the project?s makespan. We also consider uncertainty in processing times, modelled with fuzzy numbers. We present a multi-objective optimisation model of the problem and we propose a new enhanced memetic algorithm that combines a multiobjective evolutionary algorithm with three procedures that exploit the problem-specific available knowledge. Experimental results validate the proposed method with respect to hypervolume, -indicator and empirical attaintment functions.

Otras publicaciones de la misma revista o congreso con autores/as de la Universidad de Cantabria

 Autoría: Afsar S., Palacios J.J., Puente J., Vela C.R., González-Rodríguez I.,

 Fuente: Swarm and Evolutionary Computation, 2022, 68, 101016

Editorial: Elsevier

 Año de publicación: 2022

Nº de páginas: 14

Tipo de publicación: Artículo de Revista

 DOI: 10.1016/j.swevo.2021.101016

ISSN: 2210-6502,2210-6510

 Proyecto español: TIN2016-79190-R

Url de la publicación: https://doi.org/10.1016/j.swevo.2021.101016

Autoría

AFSAR, SEZIN

PALACIOS, JUAN JOSÉ

PUENTE, JORGE

VELA, CAMINO R.