文献类型：期刊文献

英文题名：High-Dimensional Robust Multi-Objective Optimization for Order Scheduling: A Decision Variable Classification Approach

作者：Du, Wei[1];Zhong, Weimin[1];Tang, Yang[1];Du, Wenli[1];Jin, Yaochu[1,2]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Univ Surrey, Dept Comp Sci, Guildford GU2 7XH, Surrey, England

年份：2019

卷号：15

期号：1

起止页码：293

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：;EI(收录号：20182105217156);WOS:【SCI-EXPANDED(收录号:WOS:000455726700028)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61703163, in part by Shanghai Sailing Program under Grant 17YF1427700, in part by the China Postdoctoral Science Foundation under Grant 2016M601525, in part by the Fundamental Research Funds for the Central Universities under Grant 222201714028, and in part by the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017. The work of Y. Jin was supported by EPSRC under Grant EP/M017869/1.

语种：英文

外文关键词：Decision variable classification (DVC); evolutionary multi-objective optimization; high-dimensional optimization; robust evolutionary optimization; robust order scheduling

摘要：This paper tackles the high-dimensional robust order scheduling problem. A multi-objective evolutionary algorithm called constrained nondominated sorting differential evolution based on decision variable classification is developed to search for robust order schedules. The decision variables are classified into highly and weakly robustness-related variables according to their contributions to the robustness of candidate solutions. The experimental results reveal that the performance of robust evolutionary optimization can be greatly improved via analyzing the properties of decision variables and then decomposing the high-dimensional robust optimization problem. It is also unveiled that the order scheduling is greatly affected by the uncertain daily production quantities. The robust order schedules are able to provide more information on earliness/tardiness of the orders, which enhances the flexibility of the production.