Master Thesis Competition
The IEOM Master Thesis Presentation Award recognizes outstanding research in industrial engineering, manufacturing engineering, systems engineering, operations research, engineering management, operations management and related areas. Students must enroll in the masters’ program during the current academic year. These awards are presented at the 2022 Rome Conference.
- The submitter must enroll in a masters’ program or be a recent graduate
- A synopsis of the thesis should be submitted
- Masters’ research must be presented at the 2022 Rome Conference.
Deadline for submission of abstract / paper /synopsis: March 15, 2022.
Submit a synopsis of your thesis online and present at the conference event: https://www.xcdsystem.com/IEOM/abstract/index.cfm?ID=a4uiLxh
A group of academics / professionals will serve as judges and evaluate the papers and presentations. Scoring rubrics will be used for both parts of the judging.
1st, 2nd and 3rd place winners will receive award certificates and a monetary prize based the sponsorship.
If you have any question for the IEOM Student Competition, please contact: firstname.lastname@example.org
WINNERS – Master Thesis Competition Award
ID 687 Information systems in Industry 4.0: Mechanisms to support the shift from data to knowledge in Lean environments
Juliana Salvadorinho, Master’s degree student in Industrial Engineering and Management, Department of Economics, Management, Industrial Engineering and Thourism, University of Aveiro, Aveiro, Portugal
Leonor Teixeira, Advisor, Institute of Electronics and Informatics Engineering of Aveiro (IEETA), Department of Economics, Management, Industrial Engineering and Thourism, University of Aveiro, Aveiro, Portugal
ID 236 Knowledge assessment for radiation protection practices among dental professionals- A literature review
Fatma Eltarabishi, Hamad Rashid, and Walid A. Metwally, Industrial Engineering and Engineering Management Dept., University of Sharjah, UAE
ID 605 Computer Vision and Internet of Things Application to Enhance Pedestrian Safety
Ujjwal Khanna, Concordia Uniersity, Montreal, Quebec, Canada
ID 566 A Parallel Randomized Approximation Algorithm for Single Machine Scheduling With Applications to Flow Shop Scheduling
HOSSEIN BADRI, Department of Computer Science, Wayne State University, Detroit, MI 48202, USA
ID 106 A Review on Comparative Study to Detect Fraud Financial Statement using Data Mining and Machine Learning Algorithms
Swati Srivastava, Jaipur, IN, India