Multi-layered coding-based study on optimization algorithms for automobile production logistics scheduling

作     者：Yue, Guo Guo Tailai Dan, Wei 

作者机构：Ningbo Inst Technol Ningbo 315211 Peoples R China SHU UTS SILC Business Sch Shanghai 200444 Peoples R China Changan Univ Xian 710064 Peoples R China 

出 版 物：《TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE》 (技术预测与社会变革)

年 卷 期：2021年第170卷

页      面：120889-120889页

核心收录：

学科分类：12[管理学] 1202[管理学-工商管理] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0833[工学-城乡规划学] 

基　　金：Zhejiang Province Postdoctoral Project Foundation [BSH1502022] Ningbo Soft Science Fund [2017A10013] 

主　　题：Automobile production Logistics scheduling Multi-layered coding Disruptive technologies Optimization algorithm 

摘      要：With the acceleration of economic globalization, competition among manufacturing industries has become increasingly fierce. Automobile manufacturing has always been a critical investment and development industry in different countries. For the automobile manufacturing industry, the logistics scheduling problem of automobile production is affects automobile manufacturing enterprises ability to compete. This paper discusses disruptive technologies, such as AI, IoT, Big data, etc., to solve production problems. Therefore, production logistics systems research is essential to automobile manufacturing enterprises, to improve production efficiency, reduce production costs, and increase enterprises economic benefits. We present three kinds of mathematical models designed and calculated by a genetic algorithm, aimed at the Pareto solution set to solve multi-objective optimization, as well as designs for a new contrast flow, which can quickly find the optimal solution and simulate the algorithm.