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Dr. Hany Osman

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Dr. Hany Osman has academic and industrial experiences in the areas of data analytics, machine learning, optimization, and operations management.  

He earned his PhD degree in Industrial Engineering from Concordia University, Montreal, QC. He was a former assistant professor at the Department of Industrial Systems Engineering at King Fahd University of Petroleum and Minerals in Saudi Arabia. Hany has been involved in developing and teaching courses related to data mining, machine learning, predictive analytics, operations research, engineering statistics, engineering economics, and industrial costing.  

Hany has several publications focused on both theory and application of data mining and machine learning techniques, manufacturing systems, and supply chain optimization. Hany was a co-director of the Production & Operations Management Research Laboratory at the University of Windsor. He has supervised and conducted research focusing on hybridizing optimization techniques with machine learning models to address challenges in production and operations management, healthcare management, and manufacturing systems.  

He holds the IBM-Mastery Certificate in Predictive Data Analytics. He also holds the professional engineering, PEng, license from Ontario.

Programs/Courses Taught:

Data analytics, statistics, machine learning, operations research, economics, discrete event simulation, operations analytics

Areas of Academic Interest:

  • Data mining
  • Machine learning
  • Operations research
  • Operations management

Areas of Specialization:

Predictive analytics techniques, machine learning, optimization, nature-inspired metaheuristics, supply chain management, production planning, and inventory control  

Professional Certificates and Licenses:

  • PEng. – Professional Engineer Licensee, Ontario
  • IBM – Mastery Certificate in Predictive Data Analytics

Publications

  • Osman, H. Cost-sensitive learning using logical analysis of data. Knowl Inf Syst (2024). https://doi.org/10.1007/s10115-024-02070-1.
  • Osman. H, Azab. A, Bin Hasan. R, Baki. MF. Mass Customization using hybrid manufacturing and smart assembly: An Optimal Configuration and Platform Design Approach. Accepted for publication in Manufacturing Letters.
  • Azab. A, Osman. H, Baki. MF. CAPP-GPT: A Computer-Aided Process Planning-Generative Pretrained Transformer Framework for Smart Manufacturing. Accepted for publication in Manufacturing Letters. 
  • Hasan, R.B, Osman, H., Azab, A., Baki, M.F. Improvement to an existing multi-level capacitated lot sizing problem considering setup carryover, backlogging, and emission control. Manufacturing Letters.Volume 35, Supplement, August 2023, Pages 28-39. 
  • Sakib, M.D., Osman, H., Azab, A., Baki, M.F. Product-platform design and multi-period, multi-platform lot-sizing for hybrid manufacturing considering stochastic demand and processing time. Manufacturing Letters. Volume 35, Supplement, August 2023, Pages 20-27 
  • Osman. H, Azab. A, Baki, M.F. 2023. Optimal Process Planning for Hybrid Additive and Subtractive Manufacturing. J. Manuf. Sci. Eng. Jun 2023, 145(6), 061013 
  • Osman, H., & Yacout, S., 2022. Condition-based monitoring of the rail wheel using logical analysis of data and ant colony optimization. Journal of Quality in Maintenance Engineering, (ahead-of-print). 
  • Osman, H., Ali, A., Mahmoud, A. A., and Elkatatny, S., 2021. Estimation of the Rate of Penetration While Horizontally Drilling Carbonate Formation Using Random Forest. ASME. J. Energy Resour. Technol. 143(9): 093003. https://doi.org/10.1115/1.4050778 
  • Elfar, O., Yacout, S., Osman, H., 2021. Accelerating Logical Analysis of Data Using an Ensemble-Based Technique. Eng. Lett. 29, 1616–1625. 
  • Elfar, O., Yacout, S., Osman, H., 2019. Merging Logical Analysis of Data Models. International Journal of Industrial Engineering and Operations Management Inc.; Emerald Publishing: Bingley, UK; pp. 183–194.
  • Osman, H., Baki, M., F., 2018. A cuckoo search algorithm to solve transfer line balancing problems with different cutting conditions. IEEE Transactions in Engineering Management. 65(3). 505 – 518
  • Ragab, A., Ouali, M.-S., Yacout, S., Osman, H. 2017. Pattern-based prognostic methodology for condition-based maintenance using selected and weighted survival curves. Quality and Reliability Engineering International. DOI: 10.1002/qre.2127 
  • Ragab, A., Ouali, M.-S., Yacout, S., Osman, H. 2016. Prognostics of Multiple Failure Modes in Rotating Machinery Using a Pattern-Based Classifier and Cumulative Incidence Functions. Journal of Intelligent Manufacturing. DOI: 10.1007/s10845-016-1244-8.
  • Ragab, A., Ouali, M.-S., Yacout, S., Osman, H. 2016. Remaining useful life prediction using prognostic methodology based on logical analysis of data and Kaplan–Meier estimation. Journal of Intelligent Manufacturing. 27(5), 943–958
  • Osman, H., Baki, M., F., 2014. Balancing transfer lines using Benders decomposition and ant colony optimization techniques. International Journal of Production Research. 52(5), 1334-1350.
  • Osman, H., Baki , M. F. 2013. A linearization and decomposition based approach to minimize the non-productive time in transfer lines. World Academy of Science, Engineering and Technology 74, 440-445.
  • Osman, H., Demirli, K., 2012. Economic lot and delivery scheduling problem for multi-stage supply chains. International Journal of Production Economics. 136(2), 275–286.
  • Osman, H., Demirli, K., 2012. Integrated safety stock optimization for multiple sourced stockpoints facing variable demand and lead time. International Journal of Production Economics. 135(1), 299-307.
  • Osman, H., Demirli, K., 2010. A bilinear goal programming model and a modified Benders decomposition algorithm for supply chain reconfiguration and supplier selection. International Journal of Production Economics, 124, 97–105.