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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 9
2752-2756

Stochastic resource-constrained multi-project dynamic scheduling strategy based on deep reinforcement learning

Guo Xiaojian
Hu Fangyong
School of Economics & Management, Jiangxi University of Science & Technology, Ganzhou Jiangxi 341000, China
DOI: 10.19734/j.issn.1001-3695.2022.03.0065

Abstract

There are few studies on the problem of stochastic resource-constrained distributed multi-project scheduling(SDRCMPSP) and most of them are static scheduling schemes, which cannot adjust and optimize the strategy in real time according to changes in the environment and respond to frequent dynamic factors in a timely manner. Therefore, this paper established a stochastic resource-constrained multi-project dynamic scheduling DRL model with the goal of minimizing the total drag cost, designed the corresponding agent interaction environment, and used the DDDQN algorithm in reinforcement learning to solve the model. The experiment firstly analyzed the hyperparameters of the algorithm, and then trained and tested the model under two different conditions of variable activity duration and uncertain arrival time, and the results show that the deep reinforcement learning algorithm can obtain scheduling results that are better than any single rule, effectively reduce the total drag-off cost of random resources limited multi-project expectations, and provide a good basis for multi-project scheduling decision optimization.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.03.0065
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 9
Section: Algorithm Research & Explore
Pages: 2752-2756
Serial Number: 1001-3695(2022)09-029-2752-05

Publish History

[2022-04-29] Accepted Paper
[2022-09-05] Printed Article

Cite This Article

郭晓剑, 胡方勇. 基于深度强化学习的随机资源受限多项目动态调度策略 [J]. 计算机应用研究, 2022, 39 (9): 2752-2756. (Guo Xiaojian, Hu Fangyong. Stochastic resource-constrained multi-project dynamic scheduling strategy based on deep reinforcement learning [J]. Application Research of Computers, 2022, 39 (9): 2752-2756. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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