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Title: A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance
Authors: Liu, Ying
Dong, Haibo
Lohse, Niels
Petrovic, Sanja
Issue Date: 2016
Publisher: © The Authors. Published by Elsevier B.V.
Citation: LIU, Y. ... et al, 2016. A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance. International Journal of Production Economics, 179, pp. 259 - 272.
Abstract: Increasing energy price and requirements to reduce emission are new challenges faced by manufacturing enterprises. A considerable amount of energy is wasted by machines due to their underutilisation. Consequently, energy saving can be achieved by turning off the machines when they lay idle for a comparatively long period. Otherwise, turning the machine off and back on will consume more energy than leave it stay idle. Thus, an effective way to reduce energy consumption at the system level is by employing intelligent scheduling techniques which are capable of integrating fragmented short idle periods on the machines into large ones. Such scheduling will create opportunities for switching off underutilised resources while at the same time maintaining the production performance. This paper introduces a model for the bi-objective optimisation problem that minimises the total non-processing electricity consumption and total weighted tardiness in a job shop. The Turn off/Turn on is applied as one of the electricity saving approaches. A novel multi-objective genetic algorithm based on NSGA-II is developed. Two new steps are introduced for the purpose of expanding the solution pool and then selecting the elite solutions. The research presented in this paper is focused on the classical job shop environment, which is widely used in the manufacturing industry and provides considerable opportunities for energy saving. The algorithm is validated on job shop problem instances to show its effectiveness.
Description: This is an Open Access article published by Elsevier and distributed under the terms of the Creative Commons Attribution Licence (CC-BY 4.0), https://creativecommons.org/licenses/by/4.0/
Sponsor: The authors acknowledge the support from the EPSRC Centre for Innovative Manufacturing in Intelligent Automation in under-taking this research work under grant reference number EP/IO33467/1.
Version: Published
DOI: 10.1016/j.ijpe.2016.06.019
URI: https://dspace.lboro.ac.uk/2134/21968
Publisher Link: http://dx.doi.org/10.1016/j.ijpe.2016.06.019
ISSN: 0925-5273
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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