Real-time dynamic selection algorithm of RCPSP scheduling priority rules based on deep learning

被引：0

作者：

Zhang Y. ^{[1
]}

Bai S. ^{[1
]}

Chen Z. ^{[1
]}

Liu S. ^{[1
]}

Li X. ^{[2
]}

机构：

[1] School of Management, Northwestern Polytechnical University, Xi'an

[2] Department of Civil Engineering, The University of Hong Kong, Hong Kong

来源：

Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | 2023年 / 43卷 / 07期

基金：

中国国家自然科学基金;

关键词：

deep learning; priority rules; project scheduling; real-time dynamic selection;

D O I：

10.12011/SETP2021-3267

中图分类号：

学科分类号：

摘要：

For the resource-constrained scheduling problem, a deep learning-based real-time dynamic selection algorithm of scheduling priority rules is designed to minimize the project’s makespan. Moreover, each scheduling stage selects priority rules in real-time for activity scheduling. Through constructing a deep neural network model, the mapping relationship between the project states and the best priority rule in each scheduling stage of the scheduled project is determined. Then the priority rule is dynamically selected for the scheduled project in real-time. The final scheduling plan is obtained by combining the serial schedule generation scheme. Experimental research shows that the real-time dynamic selection priority rule algorithm outperforms the single priority rule heuristic and the hybrid priority rule heuristic covered in the paper and has better generalizability. In addition, compared with the meta-heuristic algorithm, the algorithm has a higher solution efficiency than the meta-heuristic. © 2023 Systems Engineering Society of China. All rights reserved.

引用

页码：2142 / 2153

页数：11

共 30 条

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