حسن محمد عيسى هجري
HASSAN HIJRY is an Assistant Professor of Industrial Engineering at the University of Tabuk in Saudi Arabia With an MS in Industrial Engineering from Lawrence Technological University (LTU), and a PhD in Systems Engineering from Oakland University, USA He brings industry experience as a former front-line manager at PEPSICO, Al-Riyadh His teaching expertise spans Work Study, Production Planning, and Control, Facilities Planning, Materials Handling, and Industry 40 Technologies and Engineering Management Hassans research focus encompasses Industry 40, AI, process optimization, systems analysis, and data-driven decision within diverse sectors like workplaces, manufacturing industries, and healthcare systems
المؤهلات العلمية
- دكتوراه في هندسة الأنظمة الصناعية PhD in Industrial Systems Engineering
- ماجستير في الهندسة الصناعية
- بكالوريوس هندسه صناعية
الاهتمامات البحثية
The primary research interests include Industry 40, AI, and ML The research area, such as manufacturing industries and healthcare
- Hijry, H, & Olawoyin, R, "Simulation Model for Patient workflow at Selected Emergency Room during Hajj," 2022 International Journal of Simulation: Systems, Science & Technology (IJSSST) V22, N4, https://edasinfo/doi/105013/IJSSSTa220408, Jan 22202
- H Hijry, R Olawoyin, G McDonald, D Debnath, W Edward, Y Al-Hejri,"Predicting Average Wait-Time of COVID-19 Test Results and Efficacy Using Machine Learning Algorithms,"- 2021 International Journal of Industrial Engineering and Operations Management (IJIEOM ), 2021, pp 75-88, doiorg/1046254/jieom20210202
- H Hijry and R Olawoyin,"Predicting Patient Waiting Time in Queuing System Using Multi Deep Learning Optimization Algorithms in the Emergency Room, " International Journal of Industrial Engineering and Operations Management (IJIEOM), 2021, pp 33-45, doiorg/1046254/jieom20210103
- H Hijry and R Olawoyin," Application of Machine Learning Algorithms for Patient Length of Stay Prediction in Emergency Department During Hajj," 2020 IEEE International Conference on Prognostics and Health Management (ICPHM), 2020, pp 1-8, doi: 101109/ICPHM4902220209187055
- Hijry, H, Naqvi, S M R, Javed, K, Albalawi, O H, Olawoyin, R, Varnier, C, & Zerhouni, N (2024) Real Time Worker Stress Prediction in a Smart Factory Assembly Line IEEE Access
- Hijry, H (2024, June) Proposed Model for NEOM City Based on Internet of Things (IoT) and MLC at ED System In 2024 IEEE International Conference on Prognostics and Health Management (ICPHM) (pp 23-32) IEEE