Course Information:
Course Title: Operations Research
Type: Methodological
Credit: 3
Coefficient: 2
VHH: Course: 1h.30, TD: 1h.30, TP: 1h30
Personal Weekly Workload: 6

Lecturer's Information:

Lecturer: Dr. Khadra Bouanane

TD/TP Teacher: Ms. Chaima Ayachi Amar

Contact: bouanane.khadra@univ-ouargla.dz
Office Hours: Wednesday: 8 am-12 am, Thursday: 9 am-12 am.

Course Description:

The Operations Research course introduces students to mathematical modeling and optimization techniques for solving complex decision-making problems. It covers fundamental concepts of linear, integer, and nonlinear programming, as well as scheduling and optimization methods used in real-world applications. The course emphasizes the formulation of optimization models, the use of exact and heuristic solution techniques, and the interpretation of results.

Particular attention is given to the role of operations research in artificial intelligence, highlighting how optimization methods support learning, inference, and decision processes in intelligent systems. Through theoretical foundations, practical case studies, and the use of professional optimization software, students develop the ability to model, analyze, and solve complex problems encountered in engineering, industry, and AI-driven applications. 

Targeted Audience

This course is dedicated to AI enginnering students.

 Prerequisites:
Linear Programming
Graph Theory.
 
Learning Objectives:
  • Understand the foundations, scope, and objectives of operations research

  • Formulate linear, integer, and nonlinear optimization models

  • Solve integer and Boolean programming problems using exact methods such as branch-and-bound and dynamic programming

  • Understand the principles and theory underpinning constrained and unconstrained optimization

  • Understand and apply gradient-based methods

  • Understand and apply the Lagrangian method for constrained optimization problems

  • Model and solve real-world optimization problems

  • Understand the role of optimization techniques in artificial intelligence and intelligent decision-making systems


Assessment 
  1. Continuous Grading: 40%

- Assessments: 85%
- Class Participation: 15%

     2. Exam: 60%