A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance

Liu, Y. , Dong, H., Lohse, N. and Petrovic, S. (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. (doi:10.1016/j.ijpe.2016.06.019)

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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.

Item Type:Articles
Additional Information:The authors acknowledge the support from the EPSRC Centre for Innovative Manufacturing in Intelligent Automation in undertaking this research work under grant reference number EP/ IO33467/1.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Liu, Dr Ying
Authors: Liu, Y., Dong, H., Lohse, N., and Petrovic, S.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:International Journal of Production Economics
Publisher:Elsevier
ISSN:0925-5273
ISSN (Online):1873-7579
Published Online:15 June 2016
Copyright Holders:Copyright © 2016 The Authors
First Published:First published in International Journal of Production Economics 179: 259-272
Publisher Policy:Reproduced under a Creative Commons license

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