An investigation into minimising total energy consumption and total weighted tardiness in job shops

Liu, Y. , Dong, H., Lohse, N., Petrovic, S. and Gindy, N. (2014) An investigation into minimising total energy consumption and total weighted tardiness in job shops. Journal of Cleaner Production, 65, pp. 87-96. (doi: 10.1016/j.jclepro.2013.07.060)

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Manufacturing enterprises nowadays face the challenge of increasing energy prices and requirements to reduce their emissions. Most reported work on reducing manufacturing energy consumption today focuses on the need to improve the efficiency of resources (machines) largely ignoring the potential for energy reducing on the system-level where the operational method can be employed as the energy saving approach. The advantage is clearly that the scheduling and planning approach can also be applied across existing legacy systems and does not require large investment. Therefore, a multi-objective scheduling method is developed in this paper with reducing energy consumption as one of the objectives. This research focuses on classical job shop environment which is widely used in the manufacturing industry. A model for the bi-objectives problem that minimises total electricity consumption and total weighted tardiness is developed and the Non-dominant Sorting Genetic Algorithm is employed as the solution to obtain the Pareto front. A case study based on a modified 10 × 10 job shop is presented to show the effectiveness of the algorithm and to prove the feasibility of the model.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Liu, Dr Ying
Authors: Liu, Y., Dong, H., Lohse, N., Petrovic, S., and Gindy, N.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Journal of Cleaner Production
ISSN (Online):1879-1786
Published Online:17 August 2013
Copyright Holders:Copyright © 2013 The Authors
First Published:First published in Journal of Cleaner Production 65: 87-96
Publisher Policy:Reproduced under a Creative Commons license

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