A Hybrid Machine Learning and Population Knowledge Mining Method to Minimize Makespan and Total Tardiness of Multi-Variety Products
1
School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China
2
Key Laboratory of Advanced Manufacturing Equipment Technology, Jiangnan University, Wuxi 214122, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(24), 5286; https://doi.org/10.3390/app9245286
Received: 6 November 2019
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Revised: 30 November 2019
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Accepted: 2 December 2019
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Published: 4 December 2019
(This article belongs to the Special Issue Special Issue of the Manufacturing Engineering Society 2019 (SIMES-2019))
Nowadays, the production model of many enterprises is multi-variety customized production, and the makespan and total tardiness are the main metrics for enterprises to make production plans. This requires us to develop a more effective production plan promptly with limited resources. Previous research focuses on dispatching rules and algorithms, but the application of the knowledge mining method for multi-variety products is limited. In this paper, a hybrid machine learning and population knowledge mining method to minimize makespan and total tardiness for multi-variety products is proposed. First, through offline machine learning and data mining, attributes of operations are selected to mine the initial population knowledge. Second, an addition–deletion sorting method (ADSM) is proposed to reprioritize operations and then form the rule-based initial population. Finally, the nondominated sorting genetic algorithm II (NSGA-II) hybrid with simulated annealing is used to obtain the Pareto solutions. To evaluate the effectiveness of the proposed method, three other types of initial populations were considered under different iterations and population sizes. The experimental results demonstrate that the new approach has a good performance in solving the multi-variety production planning problems, whether it is the function value or the performance metric of the acquired Pareto solutions.
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MDPI and ACS Style
Qiu, Y.; Ji, W.; Zhang, C. A Hybrid Machine Learning and Population Knowledge Mining Method to Minimize Makespan and Total Tardiness of Multi-Variety Products. Appl. Sci. 2019, 9, 5286. https://doi.org/10.3390/app9245286
AMA Style
Qiu Y, Ji W, Zhang C. A Hybrid Machine Learning and Population Knowledge Mining Method to Minimize Makespan and Total Tardiness of Multi-Variety Products. Applied Sciences. 2019; 9(24):5286. https://doi.org/10.3390/app9245286
Chicago/Turabian StyleQiu, Yongtao, Weixi Ji, and Chaoyang Zhang. 2019. "A Hybrid Machine Learning and Population Knowledge Mining Method to Minimize Makespan and Total Tardiness of Multi-Variety Products" Applied Sciences 9, no. 24: 5286. https://doi.org/10.3390/app9245286
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