期刊
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
卷 19, 期 3, 页码 1941-1959出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TASE.2021.3062994
关键词
Tutorials; Job shop scheduling; Search methods; Optimization; Linear programming; Heuristic algorithms; Greedy algorithms; Flow-shop scheduling problem (FSP); heuristic algorithm; iterated greedy algorithm (IGA); review; tutorial
资金
- China Scholarship Council
- National Key Research and Development Program of China [2017YFB0306400]
- National Natural Science Foundation of China [62073069]
Iterated greedy algorithm (IGA), developed in 2007, is widely used for flow-shop scheduling problems (FSPs) in production scheduling. Various FSPs have been solved using IGA-based methods, including basic IGA, variants, and hybrid algorithms. Over 100 articles related to IGA and FSPs have been published, highlighting the significance and potential of this algorithm in optimization.
An iterated greedy algorithm (IGA) is a simple and powerful heuristic algorithm. It is widely used to solve flow-shop scheduling problems (FSPs), an important branch of production scheduling problems. IGA was first developed to solve an FSP in 2007. Since then, various FSPs have been tackled by using IGA-based methods, including basic IGA, its variants, and hybrid algorithms with IGA integrated. Up until now, over 100 articles related to this field have been published. However, to the best of our knowledge, there is no existing tutorial or review paper of IGA. Thus, we focus on FSPs and provide a tutorial and comprehensive literature review of IGA-based methods. First, we introduce a framework of basic IGA and give an example to clearly show its procedure. To help researchers and engineers learn and apply IGA to their FSPs, we provide an open platform to collect and share related materials. Then, we make classifications of the solved FSPs according to their scheduling scenarios, objective functions, and constraints. Next, we classify and introduce the specific methods and strategies used in each phase of IGA for FSPs. Besides, we summarize IGA variants and hybrid algorithms with IGA integrated, respectively. Finally, we discuss the current IGA-based methods and already-solved FSP instances, as well as some important future research directions according to their deficiency and open issues.
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