Dua Weraikat Headshot

Dua Weraikat

Department Coordinator and Assistant Professor of Industrial Engineering

Mechanical and Industrial Engineering
RIT Dubai
Assistant Professor of Industrial Engineering
dxwcad@rit.edu
Office Location
E-E309

Dua Weraikat

Department Coordinator and Assistant Professor of Industrial Engineering

Mechanical and Industrial Engineering
RIT Dubai
Assistant Professor of Industrial Engineering

Education

Doctor of Philosophy, Industrial Engineering from Concordia University, Montreal, Quebec, Canada; Masters of Science, Industrial Engineering- Manufacturing from University of Jordan, Amman, Jordan; Bachelor of Science, Industrial Engineering- Management from Jordan University of Science and Technology, Irbid, Jordan

Bio

Dr. Weraikat teaches courses in operations research, decision analysis, production planning, and production control, and has been actively involved in curriculum development, accreditation, and program coordination. Her research focuses on applying artificial intelligence, machine learning, optimization, and data analytics to address challenges in supply chain resilience, healthcare systems, smart farming, and sustainability.

Dr. Weraikat has published widely in high-impact journals and international conferences, with notable contributions to pharmaceutical supply chain resilience and sustainable industrial practices. She also serves on the IISE Operations Research Division Board (2025–2027) and founded the IISE Student Chapter at RIT Dubai. Her dedication to student engagement and professional service has earned her multiple awards, including the Outstanding Global Faculty Advisor Award (IISE, 2024), the Arabian Peninsula Regional Faculty Advisor Award (IISE, 2024 & 2025), and several Gold Awards in the IISE Chapter Recognition Program (2023–2025).

dxwcad@rit.edu
Personal Links
ORCID
Google Scholar
LinkedIn
Areas of Expertise
Supply Chain
Reverse Supply Management
Circular Economy
Optimization
Data Analytics
AI/ML
Pharmaceutical

Select Scholarship

Some of Recent Publication:

1. Laith, H., Weraikat, D., Khan, S., (2025) The Role of Digitalization Technologies in Enhancing Supply Chain Performance in the Service Industry: Identifying the Current Research Gaps, Journal of Information & Knowledge Management (Q2), 2550077. https://doi.org/10.1142/S0219649225500777. Number of citations 1.

 2. Weraikat, D. and Alhourani, S., (2025) Systematic Review of Machine Learning and Artificial Intelligence in Pharmaceutical Supply Chain (PSC) Resilience: Identifying Gaps and Future Research Directions. Sustainability 17(14), 6591 (Q1). https://doi.org/10.3390/su17146591. Number of citations 11.

3.  O. Al Meheiri and D. Weraikat, (2025). Integration of Renewable Energy Strategies: A Case in Dubai South. Sustainability 17(13) (Q1), 6093. https://doi.org/10.3390/su17136093. Number of citations 1.

4. Weraikat, D.; Šori?, K.; ?agar, M.; Soka?, M. (2024). Data Analytics in Agriculture: Enhancing Decision-Making for Crop Yield Optimization and Sustainable Practices. Sustainability, 16(17) (Q1), 7331. https://doi.org/10.3390/su16177331. Number of citations 10.

5.     Weraikat, D., Zanjani, M. K., & Lehoux, N. (2019). Improving sustainability in a two-level pharmaceutical supply chain through Vendor-Managed Inventory system. Operations Research for Health Care (Q2), 21, 44-55. https://doi.org/10.1016/j.orhc.2019.04.004, Number of citations 106.

6. D. Weraikat, M. Zanjani, N. Lehoux (2016) “Two-echelon Pharmaceutical Reverse Supply Chain Coordination with Customers Incentives,” International Journal of Production Economics (Q1), 176, 41-52. https://doi.org/10.1016/j.ijpe.2016.03.003, Number of citations 118.

7. D. Weraikat, M. Zanjani, N. Lehoux (2016) “Coordinating a Green Reverse Supply Chain in Pharmaceutical Sector by Negotiation,” Computer and Industrial Engineering Journal (Q1), 93, 67-77. https://doi.org/10.1016/j.cie.2015.12.026 , Number of citations 96.

Currently Teaching

ISEE-120
Fundamentals of Industrial Engineering
3 Credits
This course introduces students to industrial engineering and provides students with foundational tools used in the profession. The course is intended to prepare students for their first co-op experience in industrial engineering by exposing them to tools and concepts that are often encountered during early co-op assignments. The course covers specific tools and their applications, including systems design and integration. The course uses a combination of lecture and laboratory activities to cover hands-on applications and problem-solving related to topics examined in lectures.
ISEE-301
Operations Research
4 Credits
An introduction to optimization through mathematical programming and stochastic modeling techniques. Course topics include linear programming, and transportation and assignment algorithms. Special attention is placed on sensitivity analysis and the need of optimization in decision-making. The course is delivered through lectures and a weekly laboratory where students learn to use state-of-the-art software packages for modeling large discrete optimization problems.
ISEE-420
Production Planning/Scheduling
3 Credits
A first course in mathematical modeling of production-inventory systems. Topics included: Inventory: Deterministic Models, Inventory: Stochastic Models, Push v. Pull Production Control Systems, Factory Physics, and Operations Scheduling. Modern aspects such as lean manufacturing are included in the context of the course.
ISEE-499
Co-op
0 Credits
One semester of paid work experience in industrial engineering.
ISEE-561
Data Analytics and Predictive Modeling
3 Credits
In systems where parameters can vary, we often want to understand the effects that some variables exert on others and their impact on system performance. “Data Analytics and Predictive Modeling” describes a variety of machine learning and data analysis techniques that can be used to describe the interrelationships among such variables. In this course, we will examine these techniques in detail, including data cleansing processes, data clustering, associate analysis, linear regression analysis, classification methods, naïve Bayes, neural networks, random forests, variable screening methods, and variable transformations. Cases illustrating the use of these techniques in engineering applications will be developed and analyzed throughout the course.
ISEE-720
Production Control
3 Credits
This course covers the process and the analysis methods used to produce goods and services to support of the production and operations management functions. Topics include: forecasting, inventory policies and models, job shop scheduling, aggregate production planning, and ERP systems. Students will understand the importance of production control and its relationship to other functions within the organization, and the role of mathematical optimization to support production planning. The course emphasizes how a production process can be characterized by a process that requires answering a sequence of decision-making problems. The course will show how the production functions integrate with each other and how their coordination can be automated through mathematical programming. Identifying opportunities for improvement through optimization is also highlighted.
ISEE-752
Decision Analysis
3 Credits
This course presents the primary concepts of decision analysis. Topics important to the practical assessment of probability and preference information needed to implement decision analysis are considered. Decision models represented by a sequence of interrelated decisions, stochastic processes, and multiple criteria are also addressed. We cover EMV and Non-EMV decision-making concepts. Finally, the organizational use of decision analysis and its application in real-world case studies is presented.
ISEE-790
Thesis
1 - 6 Credits
In conference with a faculty adviser, an independent engineering project or research problem is selected. The work may be of a theoretical and/or computational nature. A state-of-the-art literature search in the area is normally expected. A formal written thesis and an oral defense with a faculty thesis committee are required. Submission of bound copies of the thesis to the library and to the department and preparation of a written paper in a short format suitable for submission for publication in a refereed journal are also required. Approval of department head and faculty adviser needed to enroll.
ISEE-792
Engineering Capstone
3 Credits
Students must investigate a discipline-related topic in industrial and systems engineering. The general intent of the engineering capstone is to demonstrate the students' knowledge of the integrative aspects of a particular area. The capstone should draw upon skills and knowledge acquired in the program.

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Website last updated: December 4, 2025