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

Liu, Ying; Dong, Haibo; Lohse, Niels; Petrovic, Sanja; Gindy, Nabil

Authors

Ying Liu

Haibo Dong

Niels Lohse

SANJA PETROVIC SANJA.PETROVIC@NOTTINGHAM.AC.UK
Professor of Operational Research

Nabil Gindy



Abstract

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.

Citation

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

Journal Article Type Article
Acceptance Date Jul 26, 2013
Online Publication Date Aug 17, 2013
Publication Date Feb 15, 2014
Deposit Date Apr 29, 2014
Publicly Available Date Apr 29, 2014
Journal Journal of Cleaner Production
Print ISSN 0959-6526
Electronic ISSN 0959-6526
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 65
DOI https://doi.org/10.1016/j.jclepro.2013.07.060
Keywords Energy efficient production planning, Sustainable manufacturing, Job shop scheduling
Public URL http://eprints.nottingham.ac.uk/id/eprint/3064
Publisher URL http://www.sciencedirect.com/science/article/pii/S0959652613005258#
Copyright Statement Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0





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