Machine Learning and Inverse Optimization for Estimation of Weighting Factors in Multi-Objective Production Scheduling Problems
In recent years, scheduling optimization has been utilized in production systems. To construct a suitable mathematical model of a production scheduling problem, modeling techniques that can automatically select an appropriate objective function from historical data are necessary. This paper presents...
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| Format: | Article |
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MDPI AG
2022-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/19/9472 |
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| author | Hidetoshi Togo Kohei Asanuma Tatsushi Nishi Ziang Liu |
| author_facet | Hidetoshi Togo Kohei Asanuma Tatsushi Nishi Ziang Liu |
| author_sort | Hidetoshi Togo |
| collection | DOAJ |
| description | In recent years, scheduling optimization has been utilized in production systems. To construct a suitable mathematical model of a production scheduling problem, modeling techniques that can automatically select an appropriate objective function from historical data are necessary. This paper presents two methods to estimate weighting factors of the objective function in the scheduling problem from historical data, given the information of operation time and setup costs. We propose a machine learning-based method, and an inverse optimization-based method using the input/output data of the scheduling problems when the weighting factors of the objective function are unknown. These two methods are applied to a multi-objective parallel machine scheduling problem and a real-world chemical batch plant scheduling problem. The results of the estimation accuracy evaluation show that the proposed methods for estimating the weighting factors of the objective function are effective. |
| first_indexed | 2024-03-09T22:06:36Z |
| format | Article |
| id | doaj.art-754949ae7f734331bd88a9e26e70dc6c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:06:36Z |
| publishDate | 2022-09-01 |
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| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-754949ae7f734331bd88a9e26e70dc6c2023-11-23T19:40:07ZengMDPI AGApplied Sciences2076-34172022-09-011219947210.3390/app12199472Machine Learning and Inverse Optimization for Estimation of Weighting Factors in Multi-Objective Production Scheduling ProblemsHidetoshi Togo0Kohei Asanuma1Tatsushi Nishi2Ziang Liu3Graduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushima-naka, Kita-ku, Okayama City 700-8530, Okayama, JapanGraduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-Cho, Toyonaka City 560-8531, Osaka, JapanGraduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushima-naka, Kita-ku, Okayama City 700-8530, Okayama, JapanGraduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushima-naka, Kita-ku, Okayama City 700-8530, Okayama, JapanIn recent years, scheduling optimization has been utilized in production systems. To construct a suitable mathematical model of a production scheduling problem, modeling techniques that can automatically select an appropriate objective function from historical data are necessary. This paper presents two methods to estimate weighting factors of the objective function in the scheduling problem from historical data, given the information of operation time and setup costs. We propose a machine learning-based method, and an inverse optimization-based method using the input/output data of the scheduling problems when the weighting factors of the objective function are unknown. These two methods are applied to a multi-objective parallel machine scheduling problem and a real-world chemical batch plant scheduling problem. The results of the estimation accuracy evaluation show that the proposed methods for estimating the weighting factors of the objective function are effective.https://www.mdpi.com/2076-3417/12/19/9472multi-objective schedulingestimationweighting factorsmachine learningsimulated annealinginverse optimization |
| spellingShingle | Hidetoshi Togo Kohei Asanuma Tatsushi Nishi Ziang Liu Machine Learning and Inverse Optimization for Estimation of Weighting Factors in Multi-Objective Production Scheduling Problems Applied Sciences multi-objective scheduling estimation weighting factors machine learning simulated annealing inverse optimization |
| title | Machine Learning and Inverse Optimization for Estimation of Weighting Factors in Multi-Objective Production Scheduling Problems |
| title_full | Machine Learning and Inverse Optimization for Estimation of Weighting Factors in Multi-Objective Production Scheduling Problems |
| title_fullStr | Machine Learning and Inverse Optimization for Estimation of Weighting Factors in Multi-Objective Production Scheduling Problems |
| title_full_unstemmed | Machine Learning and Inverse Optimization for Estimation of Weighting Factors in Multi-Objective Production Scheduling Problems |
| title_short | Machine Learning and Inverse Optimization for Estimation of Weighting Factors in Multi-Objective Production Scheduling Problems |
| title_sort | machine learning and inverse optimization for estimation of weighting factors in multi objective production scheduling problems |
| topic | multi-objective scheduling estimation weighting factors machine learning simulated annealing inverse optimization |
| url | https://www.mdpi.com/2076-3417/12/19/9472 |
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