文献类型：期刊文献

英文题名：An improved gravitational search algorithm to the hybrid flowshop with unrelated parallel machines scheduling problem

作者：Cao, Cuiwen[1];Zhang, Yao[1];Gu, Xingsheng[1];Li, Dan[2];Li, Jie[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 Manchester, Ctr Proc Integrat, Dept Chem Engn & Analyt Sci, Manchester M13 9PL, Lancs, England

年份：2021

卷号：59

期号：18

起止页码：5592

外文期刊名：INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000547156400001)】；

基金：This work was supported by the National Nature Science Foundation of China [grant numbers 61673175, 61973120, 61773165, 61603139]; and the Fundamental Research Funds for the Central Universities [grant number 222201717006].

语种：英文

外文关键词：Improved gravitational search algorithm; hybrid flowshop; unrelated parallel machines; water-meter manufacturing enterprise

摘要：The hybrid flowshop scheduling problem with unrelated parallel machines exists in many industrial manufacturers, which is an NP-hard combinatorial optimisation problem. To solve this problem more effectively, an improved gravitational search (IGS) algorithm is proposed which combines three strategies: generate new individuals using the mutation strategy of the standard differential evolution (DE) algorithm and preserve the optimal solution via a greedy strategy; substitute the exponential gravitational constant of the standard gravitational search (GS) algorithm with a linear function; improve the velocity update formula of the standard GS algorithm by mixing an adaptive weight and the global search strategy of the standard particle swarm optimisation (PSO) algorithm. Benchmark examples are solved to demonstrate the proposed IGS algorithm is superior to the standard genetic algorithm, DE, GS, DE with local search, estimation of distribution algorithm and artificial bee colony algorithms. Two more examples from a real-world water-meter manufacturing enterprise are effectively solved.