Review on learning-based methods for shop scheduling problems

Xiaoxia Li, Xin Lu, Wei Wang, Yanguo Jing

Research output: Contribution to conferencePaperpeer-review

Abstract

Shop scheduling is an effective way for manufacturers to improve their manufacturing performances. However, due to its complexity, it is difficult to deal with shop scheduling problems (SSP). Thus, SSP has received a lot of attention from industry and academia.
Various kinds of methods have been proposed to solve SSP. Learning-based method is just one of the most representative methods for SSP. This paper focuses on reviewing the learning-based methods for SSP. Firstly, the methods for SSP are briefly introduced. Then, its description and model are provided and its classification is discussed. Next, the learning-based methods for SSP are classified according to the machine learning technique used in the methods. Based on the classification, the related work on each type of learning-based methods for SSP is summarized and further analyzed and compared with other traditional methods. Finally, the future research opportunities and challenges of the learning-based methods for SSP are summarized.
Original languageEnglish
Pages294-298
Number of pages5
DOIs
Publication statusPublished - 1 Feb 2023
Event2022 IEEE International Conference on e-Business Engineering - Bournemouth, United Kingdom
Duration: 14 Oct 202216 Oct 2022

Academic conference

Academic conference2022 IEEE International Conference on e-Business Engineering
Abbreviated titleICEBE
Country/TerritoryUnited Kingdom
CityBournemouth
Period14/10/2216/10/22

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