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Journal of Zhejiang University SCIENCE A 2005 Vol.6 No.10 P.1040-1046

http://doi.org/10.1631/jzus.2005.A1040


Flow shop rescheduling problem under rush orders


Author(s):  HU Yan-hai, YAN Jun-qi, YE Fei-fan, YU Jun-he

Affiliation(s):  Faculty of Engineering, Ningbo University, Ningbo 315211, China; more

Corresponding email(s):   huyanhai@nbu.edu.cn, huyanhai@sjtu.edu.cn

Key Words:  Flow shop rescheduling, Dynamic scheduling, Rush order, Ant colony optimization, Mutation operation


HU Yan-hai, YAN Jun-qi, YE Fei-fan, YU Jun-he. Flow shop rescheduling problem under rush orders[J]. Journal of Zhejiang University Science A, 2005, 6(10): 1040-1046.

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author="HU Yan-hai, YAN Jun-qi, YE Fei-fan, YU Jun-he",
journal="Journal of Zhejiang University Science A",
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doi="10.1631/jzus.2005.A1040"
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%DOI 10.1631/jzus.2005.A1040

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T1 - Flow shop rescheduling problem under rush orders
A1 - HU Yan-hai
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2005.A1040


Abstract: 
In the environment of customization, disturbances such as rush orders and material shortages often occur in the manufacturing system, so rescheduling is necessary for the manufacturing system. The rescheduling methodology should be able to dispose of the disturbance efficiently so as to keep production going smoothly. This aims researching flow shop rescheduling problem (FSRP) necessitated by rush orders. Disjunctive graph is employed to demonstrate the FSRP. For a flow shop processing n jobs, after the original schedule has been made, and z out of n jobs have been processed in the flow shop, x rush orders come, so the original n jobs together with x rush orders should be rescheduled immediately so that the rush orders would be processed in the shortest time and the original jobs could be processed subject to some optimized criteria. The weighted mean flow time of both original jobs and rush orders is used as objective function. The weight for rush orders is much bigger than that of the original jobs, so the rush orders should be processed early in the new schedule. The ant colony optimization (ACO) algorithm used to solve the rescheduling problem has a weakness in that the search may fall into a local optimum. mutation operation is employed to enhance the ACO performance. Numerical experiments demonstrated that the proposed algorithm has high computation repeatability and efficiency.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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