文献类型：期刊文献

英文题名：Hybrid Genetic Simulated Annealing Algorithm for Improved Flow Shop Scheduling with Makespan Criterion

作者：Wei, Hongjing Li, Shaobo Jiang, Houmin Hu, Jie Hu, Jianjun

第一作者：Wei, Hongjing;魏宏静

通信作者：Li, SB[1];Hu, JJ[1];Li, SB[2];Hu, JJ[3]

机构：[1]Guizhou Univ, Minist Educ, Key Lab Adv Mfg Technol, Guiyang 550025, Guizhou, Peoples R China;[2]Guizhou Inst Technol, Sch Mech Engn, Guiyang 550003, Guizhou, Peoples R China;[3]Guizhou Univ, Sch Mech Engn, Guiyang 550025, Guizhou, Peoples R China;[4]Guizhou Univ, Guizhou Prov Key Lab Publ Big Data, Guiyang 550025, Guizhou, Peoples R China;[5]Guizhou Univ, Coll Comp Sci & Technol, Guiyang 550025, Guizhou, Peoples R China;[6]GuiZhou Univ Finance & Econ, Coll Big Data Stat, Guiyang 550025, Guizhou, Peoples R China;[7]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, Guizhou, Peoples R China

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

年份：2018

卷号：8

期号：12

外文期刊名：APPLIED SCIENCES-BASEL

收录：;Scopus(收录号：2-s2.0-85058501015);WOS:【SCI-EXPANDED(收录号:WOS:000455145000289)】；

基金：This work is supported by the National Natural Science Foundation of China under Grant No. 51475097, 91746116, and 51741101; and Science and Technology Foundation of Guizhou Province under Grant No. [2015]4011, [2016]5013, [2015]02, and [2017]239.

语种：英文

外文关键词：flow shop scheduling; makespan; hybrid algorithm; genetic algorithms; simulated annealing

摘要：Flow shop scheduling problems have a wide range of real-world applications in intelligent manufacturing. Since they are known to be NP-hard for more than two machines, we propose a hybrid genetic simulated annealing (HGSA) algorithm for flow shop scheduling problems. In the HGSA algorithm, in order to obtain high-quality initial solutions, an MME algorithm, combined with the MinMax (MM) and Nawaz-Enscore-Ham (NEH) algorithms, was used to generate the initial population. Meanwhile, a hormone regulation mechanism for a simulated annealing (SA) schedule was introduced as a cooling scheme. Using MME initialization, random crossover and mutation, and the cooling scheme, we improved the algorithm's quality and performance. Extensive experiments have been carried out to verify the effectiveness of the combination approach of MME initialization, random crossover and mutation, and the cooling scheme for SA. The result on the Taillard benchmark showed that our HGSA algorithm achieved better performance relative to the best-known upper bounds on the makespan compared with five state-of-the-art algorithms in the literature. Ultimately, 109 out of 120 problem instances were further improved on makespan criterion.