Modeling and multi-objective optimization for energy-aware scheduling of distributed hybrid flow-shop

被引:8
|
作者
Lu, Chao [1 ]
Zhou, Jiajun [1 ]
Gao, Liang [2 ]
Li, Xinyu [2 ]
Wang, Junliang [3 ]
机构
[1] China Univ Geosci, Sch Comp Sci, Wuhan 430074, Hubei, Peoples R China
[2] Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Hubei, Peoples R China
[3] Donghua Univ, Coll Mech Engn, Shanghai 201620, Peoples R China
基金
美国国家科学基金会;
关键词
Distributed hybrid flow-shop scheduling; Iterated greedy; Multi-objective optimization; Energy-aware scheduling; SEARCH ALGORITHM; SHOP; MAKESPAN; CONSUMPTION;
D O I
10.1016/j.asoc.2024.111508
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With the development of economic globalization and sustainable manufacturing, energy -aware scheduling of distributed manufacturing systems has become a research hot topic. However, energy -aware scheduling of distributed hybrid flow -shop is rarely explored. Thus, this paper is the first attempt to study an energy -aware distributed hybrid flow -shop scheduling problem (DHFSP). We formulate a novel mathematical model of the DHFSP with minimizing makespan and total energy consumption ( TEC ) criteria. A hybrid multi -objective iterated greedy (HMOIG) approach is proposed to address this energy -aware DHFSP. In this proposed HMOIG, firstly, a new energy -saving method is presented and introduced into the model for reducing TEC criterion. Secondly, an integration initialization scheme is devised to produce initial solutions with high quality. Thirdly, two properties of DHFSP are used to invent a knowledge -based local search operator. Finally, we validate the effectiveness of each improvement component of HMOIG and compare it with other well-known multi -objective evolutionary algorithms on instances and a real -world case. Experimental results manifest that HMOIG is a promising method to solve this energy -aware DHFSP.
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页数:14
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