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The ‘Internet of Things’ enabled real-time scheduling for remanufacturing of automobile engines
KTH, School of Industrial Engineering and Management (ITM), Production Engineering. Northwestern Polytechnical University, Shaanxi, China.
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2018 (English)In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 185, p. 562-575Article in journal (Refereed) Published
Abstract [en]

Typical challenges that managers of remanufacturing face are composed of the lack of timely, accurate, and consistent information of remanufacturing resources. Therefore, it is difficult to implement real-time production scheduling for the shop floor. To address this problem, the authors applied the concept of the ‘Internet of Things’ to the remanufacturing of automobile engines to form an Internet of Manufacturing Things environment. Under the Internet of Manufacturing Things, an identification technology for disassembled engine parts was designed, and the real-time status of the remanufacturable resources can be monitored. Based on the captured remanufacturing information, a real-time production scheduling method was developed, and a mathematical model was developed to achieve cost reduction, dynamic management of remanufacturable resources, and energy consumption decrease. To obtain an optimal solution, a Pareto-based optimization method was used. Finally, a case study was performed to analyze the effectivity of the proposed method. The results showed that the remanufacturing cost and energy consumption were reduced by 34% and 34% respectively, and the worker load rate was more balanced. These improvements can contribute to more sustainable development and greener production within the remanufacturing industry, especially for remanufacturing of automobile engines.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 185, p. 562-575
Keywords [en]
Internet of Things, Production optimization, Real-time scheduling, Remanufacturing
National Category
Other Engineering and Technologies
Identifiers
URN: urn:nbn:se:kth:diva-227567DOI: 10.1016/j.jclepro.2018.02.061ISI: 000430772400050Scopus ID: 2-s2.0-85044869744OAI: oai:DiVA.org:kth-227567DiVA, id: diva2:1205712
Funder
VINNOVA, 2017-01649
Note

QC 20180515

Available from: 2018-05-15 Created: 2018-05-15 Last updated: 2018-05-15Bibliographically approved

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Wang, Lihui

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