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author:

Zhu, Guangyu (Zhu, Guangyu.) [1] | Jia, Haibin (Jia, Haibin.) [2]

Indexed by:

EI PKU CSCD

Abstract:

In semiconductor manufacturing workshopthe machine states are often inconsistent when the products re-enter the machine The traditional reentrant scheduling methods are unsuitable for semiconductor workshop scheduling because of the characteristic of inconsistentAccording to the characteristics of semiconductor workshopthe multi-objective reentrant hybrid flow shop scheduling problem for the semiconductor manufacturing workshop was proposedA mathematical model of this problem was built based on minimizing the maximum completion timereducing product failure rate and decreasing machine process switching timesAn optimal foraging algorithm based on substantial uncertainty factor (SUF_OFA) was proposedIn proposed algorithmthe grey correlation analyzing and the Pythagorean fuzzy set of MYCIN uncertainty factor were used to build the multi-objective processing strategyThe substantial uncertainty factors of Pareto solutions were adopted as the fitness value of the optimal foraging algorithmThe workpiece number was used as the coding schemeand the feasible scheduling solution was decoded by the three-stage decoding methodThrough different experiments and a semiconductor workshop casethe proposed algorithm was compared with four other algorithmsThe proposed model was verified and the performance of the proposed algorithm was analyzedThe results show that SUF_OFA has significant advantages in solving the multi-objective reentrant hybrid flow shop scheduling problem. © 2023 Huazhong University of Science and Technology. All rights reserved.

Keyword:

Decoding Failure analysis Machine shop practice Semiconductor device manufacture Uncertainty analysis

Community:

  • [ 1 ] [Zhu, Guangyu]School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China
  • [ 2 ] [Jia, Haibin]School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou; 350108, China

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Source :

华中科技大学学报(自然科学版)

ISSN: 1671-4512

Year: 2023

Issue: 2

Volume: 51

Page: 122-130

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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