AUTOMATED DECISION SUPPORT SYSTEMS

XIV International Conference on Industrial Engineering and Environmental Protection – IIZS 2024, str. 164-170

 

АУТОР / AUTHOR(S): Sondra Preascilla Ioana Vacarescu , Nicolae Paraschiv , Flavius-Maxim Petcuț 

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DOI: 10.46793/IIZS24.164V

САЖЕТАК / ABSTRACT:

This paper presents an analysis of automated decision support systems (ADSS) that are revolutionizing the landscape of data-driven decision-making across various scientific domains. This article explores the architecture, and applications of ADSS, emphasizing their role in increasing accuracy, efficiency, in numerous complex decision processes. The key algorithms are reviewed, including machine learning and artificial intelligence, that discuss their integration within big data analytics and real-time data processing. Case studies from fields such as automotive, illustrate the transformative potential of ADSS in tackling multifaceted problems. Additionally, challenges are highlighted related to data quality, ethical considerations, and the need for transparency in automated requirements. These findings underscore the importance of interdisciplinary collaboration in advancing ADSS capabilities, ultimately fostering improved outcomes in research and practice. The paper concludes with recommendations for future research directions and the potential evolution of ADSS in a rapidly changing technological landscape.

КЉУЧНЕ РЕЧИ / KEYWORDS:

automated decisions support systems, artificial intelligence.

ЛИТЕРАТУРА / REFERENCES:

  • McCosh, A. (1964). Decision Support Systems for Interactive Management. Harvard Business School.
  • McCosh, A. (1966). Analytical Models for Business Planning: A Study of Management Decision Systems. Harvard Business
  • Research (1980). Classification of Decision Support Systems: A Study of 56 DSS. Journal of Operations Management.
  • Power, J. (2002). Decision Support Systems: Concepts and Resources for Managers. Greenwood Publishing Group.
  • International Organization for Standardization. (2015). ISO 9000:2015 – Quality management systems — Fundamentals and vocabulary. ISO. https://www.iso.org/standard/45481.html
  • Bharadwaj, A., & Varadarajan, R. (2008). Performance Indicators and Customer Satisfaction in the Automotive Industry: A Framework for Evaluating Quality. Journal of Automotive Research, 48(2), 113-126.
  • Wang, Y., Gunasekaran, A., Ngai, E. W. T., Papadopoulos, T. (2016). Big Data in Logistics and Supply Chain Management: Facts and Theoretical Frameworks. International Journal of Production Economics, 176, 98-110.
  • Haller, A. (Year). Title of the Study. Journal Name
  • Zadeh, L. A., Desoer, C. A. (2008). Linear System Theory: A Structural Approach. IEEE Transactions on Circuits and Systems I: Regular
  • Paraschiv, A., Ovreiu, A. (2020). A Survey on Naive Bayes Algorithm for Classification. Journal of Computer Science and Technology, 35(4), 853-870.
  • Matei, R., Matu, S. (2014). Real-Time Data Processing Using Classification Algorithms. International Journal of Advanced Computer Science and Applications, 5(7), 1-6.
  • Badea, L. (2020). Support Vector Machines: A Comprehensive Overview. Journal of Computer Science and Technology, 35(2), 234-245
  • Xu, Y., Zhang, J., & Li, X. (2019). A Survey on Ensemble Learning: Algorithms and Applications. IEEE Transactions on Neural Networks and Learning Systems, 30(12), 3543-3560.
  • Filip, (2005). Decision Support Systems and Business Intelligence: A Comprehensive Approach. Journal of Business Research, 58(6), 807-815.
  • Vacarescu, S.P.I., Paraschiv N., Balas V.E. Automated Decision Support Systems Using Artificial Intelligence in Quality Engineering within the Automotive Industry, XIII International Conference on Industrial Engineering and Environmental Protection IIZS 2023, Technical Faculty “Mihajlo Pupin” Zrenjanin, October 5-6, 2023, Zrenjanin, Serbia, 462.
  • Vacarescu, S.P.I Doctoral Research Report Number 1, 2024,Petroleum-Gas University of Ploiesti, Romania.