Quantitative Methods and Applied Statistics Featured at ICAFS 2025 Second Day Session
Within the framework of the Fifth International Scientific Conference on Administrative and Financial Sciences (ICAFS 2025), organized by Cihan University-Erbil in cooperation with the University of Lodz and the University of Mosul, Session 6 was held on Thursday, January 30, 2025. The session, titled Quantitative Methods and Applied Statistics, was part of the second day of the conference.
The session was chaired by Assistant Professor Dr. Ammar Al-Bazi (UK) and co-chaired by Professor Dr. Qusay Hamid Al-Salami from Cihan University-Erbil, Kurdistan Region, Iraq. Academics and experts from various institutions presented innovative research, fostering international collaboration and knowledge exchange. This prestigious conference continues to strengthen academic ties and advance research in administrative and financial sciences.
The session featured several compelling research presentations. One study focused on evaluating power usage patterns in Erbil City from 2015 to 2024, conducted by Azhin Mohammad Khudhur, Dler Hussein Kadir, Sakar Ali Jalala, and Rebaz Othman Yahya. The researchers analyzed power consumption trends using advanced time series forecasting techniques. Their findings highlighted the importance of time series models in addressing resource planning and energy demand forecasting challenges. The study provides a solid foundation for managing Erbil’s seasonal and structural consumption patterns and attracted significant interest from academics and policymakers, emphasizing the importance of data-driven solutions for power distribution issues.
Another presentation explored artificial intelligence-driven approaches to optimize the classic Traveling Salesman Problem while balancing multiple weighted objectives. The research, conducted by Manal Ghassan Ahmed, Faez Hassan Ali, Safanah Faisal Yousif, and Aseel Aboud Jawad, demonstrated that the Exact approach closely matches the performance of BA and PSO algorithms. The presentation sparked engaging discussions on AI’s role in solving complex optimization problems, highlighting its potential for logistics, transportation, and supply chain management.
A subsequent research paper, presented by Faez Hassan Ali, Hanan Ali Chachan, Manal Hashim Ibrahim, and Sajjad Majeed Jasim, explored the use of evolving algorithms to optimize machine scheduling in industrial settings. The study aimed to enhance efficiency, reduce delays, and balance multiple competing objectives. Results demonstrated the efficiency of PSO and BAT algorithms in solving multi-objective machine scheduling problems, offering valuable insights for manufacturing and production environments.
The final presentation focused on optimizing transfer problems involving multiple objectives under uncertainty using Genetic Algorithms. Iraq T. Abbas, Lika’a Diya’a Abd Alzahra, Qusay H. Al-Salami, and Ghufran A. Ghadhban conducted the research. By integrating fuzzy logic, they enhanced decision-making in logistics, transportation, and resource allocation. The findings demonstrated the superiority of the proposed algorithm over traditional methods in handling problems of varying dimensions.
This session underscored the significance of quantitative methods and applied statistics in addressing complex research challenges. The engaging discussions and insightful presentations highlighted the role of innovation and collaboration in advancing academic research and practical applications.
