人工智能在项目调度中的应用:系统综述、文献计量分析及未来研究展望

IF 1.4 Q4 ENGINEERING, INDUSTRIAL Management Systems in Production Engineering Pub Date : 2023-05-03 DOI:10.2478/mspe-2023-0017
Zied Bahroun, Moayad Tanash, R. As'ad, Mohamad Alnajar
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引用次数: 1

摘要

数字基础设施的可用性和伴随的革命性技术的快节奏发展,在理论和实践中引发了对人工智能(AI)技术的前所未有的依赖。在人工智能领域,机器学习(ML)技术作为重要的促进者脱颖而出,在很大程度上使机器拥有类似人类的认知和决策能力。本文重点回顾了新兴机器学习工具在解决各种项目调度问题(psp)方面的应用。特别是,它采用文献计量学和网络分析工具以及系统的文献综述来分析1985年至2021年8月期间发表的104篇论文。通过分析,揭示了贡献最大的作者、最具影响力的论文以及该研究领域的现有研究趋势和专题研究课题。最近相关研究的数量明显增加,截至2018年稳步增长。大多数研究采用人工神经网络、贝叶斯网络和强化学习技术来解决随机环境下的PSPs问题,这些技术经常与经典的元启发式方法相结合。大多数工作(57%)涉及基本的资源受限psp,只有15%致力于项目组合管理问题。此外,本研究清楚地表明,人工智能技术在有效处理psp方面的应用仍处于起步阶段,需要进一步研究这一领域。这项工作还确定了当前的研究差距,并强调了未来研究的众多有希望的途径。
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Artificial Intelligence Applications in Project Scheduling: A Systematic Review, Bibliometric Analysis, and Prospects for Future Research
Abstract The availability of digital infrastructures and the fast-paced development of accompanying revolutionary technologies have triggered an unprecedented reliance on Artificial intelligence (AI) techniques both in theory and practice. Within the AI domain, Machine Learning (ML) techniques stand out as essential facilitator largely enabling machines to possess human-like cognitive and decision making capabilities. This paper provides a focused review of the literature addressing applications of emerging ML tools to solve various Project Scheduling Problems (PSPs). In particular, it employs bibliometric and network analysis tools along with a systematic literature review to analyze a pool of 104 papers published between 1985 and August 2021. The conducted analysis unveiled the top contributing authors, the most influential papers as well as the existing research tendencies and thematic research topics within this field of study. A noticeable growth in the number of relevant studies is seen recently with a steady increase as of the year 2018. Most of the studies adopted Artificial Neural Networks, Bayesian Network and Reinforcement Learning techniques to tackle PSPs under a stochastic environment, where these techniques are frequently hybridized with classical metaheuristics. The majority of works (57%) addressed basic Resource Constrained PSPs and only 15% are devoted to the project portfolio management problem. Furthermore, this study clearly indicates that the application of AI techniques to efficiently handle PSPs is still in its infancy stage bringing out the need for further research in this area. This work also identifies current research gaps and highlights a multitude of promising avenues for future research.
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来源期刊
CiteScore
4.30
自引率
13.30%
发文量
48
审稿时长
10 weeks
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