文献类型：期刊文献

英文题名：Unified Multi-Objective Genetic Algorithm for Energy Efficient Job Shop Scheduling

作者：Wei, Hongjing Li, Shaobo Quan, Huafeng Liu, Dacheng Rao, Shu Li, Chuanjiang Hu, Jianjun

第一作者：魏宏静;Wei, Hongjing

通信作者：Li, SB[1];Hu, JJ[2]

机构：[1]Guizhou Univ, Minist Educ, Key Lab Adv Mfg Technol, Guiyang 550025, Peoples R China;[2]Guizhou Inst Technol, Sch Mech Engn, Guiyang 550003, Peoples R China;[3]Guizhou Univ, Sch Mech Engn, Guiyang 550025, Peoples R China;[4]Guizhou Univ Finance & Econ, Coll Big Data Stat, Guiyang 550025, Peoples R China;[5]Guizhou Financial Dev Serv Ctr, Guiyang 550003, Peoples R China;[6]Univ South Carolina, Dept Comp Sci & Engn, Columbia, SC 29208 USA

第一机构：Guizhou Univ, Minist Educ, Key Lab Adv Mfg Technol, Guiyang 550025, Peoples R China

通信机构：corresponding author), Guizhou Univ, Sch Mech Engn, Guiyang 550025, Peoples R China;corresponding author), Univ South Carolina, Dept Comp Sci & Engn, Columbia, SC 29208 USA.

年份：2021

卷号：9

起止页码：54542-54557

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20211510209607);Scopus(收录号：2-s2.0-85103905270);WOS:【SCI-EXPANDED(收录号:WOS:000641014400001)】；

基金：This work was supported in part by the Science and Technology Innovation 2030-"New Generation of Artificial Intelligence'' Major Project under Grant 2018AAA0101803, in part by the National Science and Technology Major Project of Guizhou Province under Grant [2019]3003 and Grant [2017]3001, in part by the Science and Technology Project of Guizhou Province under Grant [2015]4011 and Grant [2017]5788, and in part by the Major Platform Project for Integration of Institutions of Higher Learning in Guizhou Province under Grant [2020]005.

语种：英文

外文关键词：Job shop scheduling; Energy consumption; Manufacturing; Production; Optimization; Genetic algorithms; Energy efficiency; Job shop scheduling; energy efficiency; unified multi-objective genetic algorithm; machine status switching

摘要：In recent years, people have paid more and more attention to traditional manufacturing's environmental impact, especially in terms of energy consumption and related emissions of carbon dioxide. Except for adopting new equipment, production scheduling could play an important role in reducing the total energy consumption of a manufacturing plant. Machine tools waste a considerable amount of energy because of their underutilization. Consequently, energy saving can be achieved by switching machines to standby or off when they lay idle for a comparatively long period. Herein, we first introduce the objectives of minimizing non-processing energy consumption, total weighted tardiness and earliness, and makespan into a typical production scheduling model-the job shop scheduling problem, based on a machine status switching framework. The multi-objective genetic algorithm U-NSGA-III combined with MME (a heuristic algorithm combined with the MinMax (MM) and Nawaz-Enscore-Ham (NEH) algorithms) population initialization method is used to solve the problem. The multi-objective optimization algorithm can generate a Pareto set of solutions so that production managers can flexibly select a schedule from these non-dominated schedules based on their priorities. Three sets of numerical experiments have been carried out on the extended Taillard benchmark to verify this three-objective model's effectiveness and the multi-objective optimization algorithm. The results show that U-NSGA-III has obtained better Pareto solutions in most test problem instances than NSGA-II and NSGA-III. Furthermore, the non-processing energy consumption is reduced by 46%-69%, which is 13-83% of the total energy consumption.