A machine learning algorithm for scheduling a burn-in oven problem

被引：0
作者：
Mathirajan M. [1 ]
Sujan R. [2 ]
Rani M.V. [3 ]
Dhaval P. [1 ]
机构：
[1] Department of Management Studies, Indian Institute of Science, Bangalore
[2] Department of Information Technology, National Institute of Technology, Suratkal, Karnataka
[3] Vinod Gupta School of Management, Indian Institute of Technology, Kharagpur
来源：
International Journal of Industrial and Systems Engineering | 2023年 / 43卷 / 01期
关键词：
ANN; artificial neural network; dispatching rule-based greedy heuristic algorithm; dispatching rules; DR-GHA; estimated optimal solution; GHA; greedy heuristic algorithm; HNN; hybrid neural network; optimal solution; semiconductor manufacturing;
D O I：
10.1504/IJISE.2021.10042607
中图分类号：
学科分类号：
摘要：
This study applies artificial neural network (ANN) to achieve more accurate parameter estimations in calculating job-priority-data of jobs and the same is applied in a proposed dispatching rule-based greedy heuristic algorithm (DR-GHA) for efficiently scheduling a burn-in oven (BO) problem. The integration of ANN and DR-GHA is called as a hybrid neural network (HNN) algorithm. Accordingly, this study proposed eight variants of HNN algorithms by proposing eight variants of DR-GHA for scheduling a BO. The series of computational analyses (empirical and statistical) indicated that each of the variants of proposed HNN is significantly enhancing the performance of the respective proposed variants of DR-GHA for scheduling a BO. That is, more accurate parameter estimations in calculating job-priority-data for DR-GHA via back-propagation ANN leads to high-quality schedules w.r.t. total weighted tardiness. Further, proposed HNN variant: HNN-ODD is outperforming relatively with other HNN variants and provides very near optimal/estimated solution. Copyright © 2023 Inderscience Enterprises Ltd.
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页码：20 / 58
页数：38
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