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International Standard Serial Number:
ISSN 1001-4551
Sponsor:
Zhejiang University;
Zhejiang Machinery and Electrical Group
Edited by:
Editorial of Journal of Mechanical & Electrical Engineering
Chief Editor:
ZHAO Qun
Vice Chief Editor:
TANG ren-zhong,
LUO Xiang-yang
Tel:
86-571-87041360,87239525
Fax:
86-571-87239571
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No.9 Gaoguannong,Daxue Road,Hangzhou,China
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310009
E-mail:
meem_contribute@163.com
Abstract: Aiming at the problems of inaccuracy and untimeliness in the material distribution process of discrete manufacturing assembly workshop, a workshop material scheduling optimization research based on vehicle number optimization preference under space loading constraint was proposed. Firstly, a path-loading mathematical model with the dual optimization objectives of vehicle dispatch number and delivery cost was constructed. Then,the whale optimization algorithm (WOA) was improved by introducing random exchange operator, random insertion operator and 2-opt operator combined with neighborhood search strategy and simulated annealing (SA) algorithm to accept inferior solution criterion. A two-stage hybrid algorithm was proposed to integrate the improved whale optimization algorithm with the packing inspection algorithm. Finally, MATLAB was used to analyze the production data of a part processing workshop, so as to verify the performance of the improved whale optimization algorithm. The results show that the improved whale optimization algorithm is superior to other hybrid algorithms, and the algorithm has fast convergence speed and is not easy to fall into local optimum. The optimal total distribution cost obtained by the improved two-stage hybrid algorithm is 2 526 yuan, which reduces the total distribution cost by 6 % and improves the station service satisfaction by 5 %. The research can provide material scheduling optimization scheme for intelligent workshop.
Key words: flexible job shop scheduling; space loading constraints; path optimization; whale optimization algorithm (WOA); simulated annealing (SA) algorithm; material demand urgency
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