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Towards a sustainable manufacturing system design: a multi-objective optimization model

Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Reda Ahmed O Nujoom
  • Qian Wang
  • Ahmed Mohammed
Developments of sustainable manufacturing systems are considered as one of effective solutions to minimize the environmental impact. Design of a sustainable manufacturing system can be partially achieved through the implementation of lean methods to reduce manufacturing wastes and operational costs, and increase the system efficiency and productivity. However, the lean approach does not include environmental wastes in such as energy consumption and CO2 emissions when designing a lean manufacturing system. This paper addresses these issues by evaluating a sustainable manufacturing system design considering energy consumption, CO2 emissions and the total cost using the multi-objective approach. To this aim, a multi-objective mathematical model was developed under economic and ecological constraints in terms of minimization of the total cost, energy consumption and CO2 emissions associated with the number of machines involved in each process in conjunction with a quantity of material flow for processing these products in a manufacturing system. A real case study was used for examining the validation and applicability of the developed sustainable manufacturing system model.
Original languageEnglish
Title of host publicationProceedings of the 23rd International Conference on Automation & Computing
Subtitle of host publicationICAC 2017
PublisherIEEE
ISBN (Electronic)978-0-7017-0260-1
ISBN (Print)978-1-5090-5040-6
DOIs
Publication statusPublished - 26 Oct 2017
Event23rd International Conference on Automation and Computing: ICAC 2017 - Huddersfield, United Kingdom
Duration: 7 Sep 20178 Sep 2017

Conference

Conference23rd International Conference on Automation and Computing
CountryUnited Kingdom
CityHuddersfield
Period7/09/178/09/17

Documents

  • Towards a sustainable (2)

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    Accepted author manuscript (Post-print), 493 KB, PDF document

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