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American Journal of Business and Operations Research
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Title

Evaluating the Role of Artificial Intelligence in Operational Decision-Making

  Abedallah Z. Abualkishik 1 * ,   Rasha Almajed 2

1  American University in the Emirates, Dubai, UAE
    (abedallah.abualkishik@aue.ae)

2  American University in the Emirates, Dubai, UAE
    (rasha.almajed@aue.ae)

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Doi   :   https://doi.org/10.54216/AJBOR.080106

https://doi.org/10.54216/AJBOR.080106

Abstract :

In today’s paced and data centric world the integration of Artificial Intelligence (AI) technologies has become a game changer, in industries. However effectively utilizing AI to make informed decisions is still a task due to the complexities of datasets and the need for predictive models. This study aims to explore and evaluate Machine Learning (ML) classifiers such as Gradient Boosting, Light Gradient Boosting Machine (LightGBM) Extreme Gradient Boosting (XGBoost) and stacking classifiers within decision making scenarios. The objective is to assess their effectiveness in handling datasets and gain insights into their performance metrics for improving decision making processes. Comparative analysis of these classifiers reveals strengths and capabilities when applied in decision making contexts. The experimental findings highlight the potential of classifiers Gradient Boosting, in optimizing decision making even in complex situations.

Keywords :

Artificial Intelligence; Business operations; Machine Learning; Decision Making Operation Research.

References :

[1]    Al-Surmi, A., Bashiri, M., & Koliousis, I. (2022). AI based decision making: combining strategies to improve operational performance. International Journal of Production Research, 60(14), 4464-4486.

[2]    Pomerol, J. C. (1997). Artificial intelligence and human decision making. European Journal of Operational Research, 99(1), 3-25.

[3]    Shrestha, Y. R., Ben-Menahem, S. M., & Von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California management review, 61(4), 66-83.

[4]    Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International journal of information management, 48, 63-71.

[5]    Xu, B., Song, X., Cai, Z., Lim, E., Tan, C. W., & Yu, J. (2020). Artificial Intelligence or Augmented Intelligence: A Case Study of Human-AI Collaboration in Operational Decision Making.

[6]    Belhadi, A., Kamble, S., Fosso Wamba, S., & Queiroz, M. M. (2022). Building supply-chain resilience: an artificial intelligence-based technique and decision-making framework. International Journal of Production Research, 60(14), 4487-4507.

[7]    Cao, G., Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2021). Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making. Technovation, 106, 102312.

[8]    Sen, D., Fashokun, A., Angelotti, R., Brooks, M., Bhaumik, H., Card, C., ... & Chung, C. (2018). An Artificial Intelligence Platform for Asset Management Contributes to Better Decision‐making Tools for Operations, Maintenance, and Utility Management: Sen et al. Water Environment Research, 90(4), 355-375.

[9]    Magrabi, F., Ammenwerth, E., McNair, J. B., De Keizer, N. F., Hyppönen, H., Nykänen, P., ... & Georgiou, A. (2019). Artificial intelligence in clinical decision support: challenges for evaluating AI and practical implications. Yearbook of medical informatics, 28(01), 128-134.

[10] Dhamija, P., & Bag, S. (2020). Role of artificial intelligence in operations environment: a review and bibliometric analysis. The TQM Journal, 32(4), 869-896.

[11] Dewhurst, F. W., & Gwinnett, E. A. (1992). Artificial intelligence and decision analysis. Artificial Intelligence in Operational Research, 277-285.

[12] Lawrence, T. (1991). Impacts of artificial intelligence on organizational decision making. Journal of Behavioral Decision Making, 4(3), 195-214.

[13] Doumpos, M., & Grigoroudis, E. (2013). Multicriteria decision aid and artificial intelligence: links, theory and applications. John Wiley & Sons.

[14] Di Vaio, A., Hassan, R., & Alavoine, C. (2022). Data intelligence and analytics: A bibliometric analysis of human–Artificial intelligence in public sector decision-making effectiveness. Technological Forecasting and Social Change, 174, 121201.

[15] Pournader, M., Ghaderi, H., Hassanzadegan, A., & Fahimnia, B. (2021). Artificial intelligence applications in supply chain management. International Journal of Production Economics, 241, 108250.

[16] Abedallah Z. Abualkishik,Rasha Almajed,William Thompson, An Innovative Multi-Criteria Decision-Making (MCDM) Framework for Picking the Right Used Chemical Tankers: A Classified Model-Based Discussion, American Journal of Business and Operations Research, Vol. 7 , No. 2 , (2022) : 08-18 (Doi   :  https://doi.org/10.54216/AJBOR.070201).

[17] Min, H. (2010). Artificial intelligence in supply chain management: theory and applications. International Journal of Logistics: Research and Applications, 13(1), 13-39.

[18] Bag, S., Gupta, S., Kumar, A., & Sivarajah, U. (2021). An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance. Industrial marketing management, 92, 178-189.

[19] Budhwar, P., Malik, A., De Silva, M. T., & Thevisuthan, P. (2022). Artificial intelligence–challenges and opportunities for international HRM: a review and research agenda. The International Journal of Human Resource Management, 33(6), 1065-1097.

[20] Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. Ieee Access, 8, 75264-75278.

Cite this Article as :
Style #
MLA Abedallah Z. Abualkishik, Rasha Almajed. "Evaluating the Role of Artificial Intelligence in Operational Decision-Making." American Journal of Business and Operations Research, Vol. 8, No. 1, 2022 ,PP. 60-70 (Doi   :  https://doi.org/10.54216/AJBOR.080106)
APA Abedallah Z. Abualkishik, Rasha Almajed. (2022). Evaluating the Role of Artificial Intelligence in Operational Decision-Making. Journal of American Journal of Business and Operations Research, 8 ( 1 ), 60-70 (Doi   :  https://doi.org/10.54216/AJBOR.080106)
Chicago Abedallah Z. Abualkishik, Rasha Almajed. "Evaluating the Role of Artificial Intelligence in Operational Decision-Making." Journal of American Journal of Business and Operations Research, 8 no. 1 (2022): 60-70 (Doi   :  https://doi.org/10.54216/AJBOR.080106)
Harvard Abedallah Z. Abualkishik, Rasha Almajed. (2022). Evaluating the Role of Artificial Intelligence in Operational Decision-Making. Journal of American Journal of Business and Operations Research, 8 ( 1 ), 60-70 (Doi   :  https://doi.org/10.54216/AJBOR.080106)
Vancouver Abedallah Z. Abualkishik, Rasha Almajed. Evaluating the Role of Artificial Intelligence in Operational Decision-Making. Journal of American Journal of Business and Operations Research, (2022); 8 ( 1 ): 60-70 (Doi   :  https://doi.org/10.54216/AJBOR.080106)
IEEE Abedallah Z. Abualkishik, Rasha Almajed, Evaluating the Role of Artificial Intelligence in Operational Decision-Making, Journal of American Journal of Business and Operations Research, Vol. 8 , No. 1 , (2022) : 60-70 (Doi   :  https://doi.org/10.54216/AJBOR.080106)
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