Artificial intelligence applications in project scheduling: a systematic review, bibliometric analysis, and prospects for future research

• Autorzy: Bahroun Zied , Tanash Moayad , Ad Rami As , Alnajar Mohamad

Identyfikatory

DOI

10.2478/mspe-2023-0017

• Języki publikacji: EN

Abstrakty

EN

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 toolsto 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.

Słowa kluczowe

EN

artificial intelligence; machine learning; project scheduling; bibliometric analysis; network analysis; review

• Wydawca: STE GROUP
• Czasopismo: Management Systems in Production Engineering
• Rocznik: 2023
• Tom: nr 2 (31)
• Strony: 144--161
• Opis fizyczny: Bibliogr. 147 poz., rys., tab.

Twórcy

autor

Bahroun Zied

• Department of Industrial Engineering American University of Sharjah P.O. Box 26666, Sharjah, United Arab Emirates; tel: [phone]

• https://orcid.org/0000-0003-2832-3672

autor

Tanash Moayad

• Department of Industrial Engineering The Hashemite University P.O. Box 330127, Zarqa, Jordan

• https://orcid.org/0000-0002-3016-7122

autor

Ad Rami As

• Department of Industrial Engineering American University of Sharjah P.O. Box 26666, Sharjah, United Arab Emirates

• https://orcid.org/0000-0002-4747-9579

autor

Alnajar Mohamad

• Department of Industrial Engineering American University of Sharjah P.O. Box 26666, Sharjah, United Arab Emirates

• https://orcid.org/0000-0002-9441-9771

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu „Społeczna odpowiedzialność nauki” - moduł: Popularyzacja nauki i promocja sportu (2022-2023).