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Robust Order Scheduling in the Fashion Industry: a Multi-Objective Optimization Approach
Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai , Institute of Textiles and Clothing, The Hong Kong Polytechnic University.
Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai.
Institute of Textile and Clothing, The Hong Kong Polytechnic University.
Institute of Textile and Clothing, The Hong Kong Polytechnic University.
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2017 (English)In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050Article in journal (Refereed) Epub ahead of print
Abstract [en]

In the fashion industry, order scheduling focuses on the assignment of production orders to appropriate production lines. In reality, before a new order can be put into production, a series of activities known as pre-production events need to be completed. In addition, in real production process, owing to various uncertainties, the daily production quantity of each order is not always as expected. In this research, by considering the pre-production events and the uncertainties in the daily production quantity, robust order scheduling problems in the fashion industry are investigated with the aid of a multi-objective evolutionary algorithm (MOEA) called nondominated sorting adaptive differential evolution (NSJADE). The experimental results illustrate that it is of paramount importance to consider pre-production events in order scheduling problems in the fashion industry. We also unveil that the existence of the uncertainties in the daily production quantity heavily affects the order scheduling.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017.
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-64513DOI: 10.1109/TII.2017.2664080OAI: oai:DiVA.org:ltu-64513DiVA: diva2:1115251
Available from: 2017-06-26 Created: 2017-06-26 Last updated: 2017-06-26

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