Credits: 4
Duration: One Semester (12–14 weeks)
Level: Undergraduate (Core)
This course builds on the foundations of Operations Research I and focuses on dynamic and stochastic optimization techniques. The emphasis is on modeling, analytical solution methods, and interpretation of results for decision-making under uncertainty and strategic interaction.
To enable students to:
| Week | Topics | No. of Lectures | Assignments / Materials |
|---|---|---|---|
| 1 |
Review of Optimization Concepts Convexity, Optimality Conditions |
3 | Reading: Bazaraa et al., Ch. 1–2 |
| 2 |
Unconstrained Nonlinear Programming First and Second Order Conditions |
4 | Problem Set 1 |
| 3 |
Constrained Nonlinear Programming Lagrange Multipliers, KKT Conditions |
4 | Numerical examples |
| 4 |
Introduction to Dynamic Programming Principle of Optimality |
3 | Reading: Bertsekas, Ch. 1 |
| 5 |
Deterministic Dynamic Programming Resource Allocation, Shortest Path |
4 | Problem Set 2 |
| 6 |
Dynamic Programming Applications Inventory and Replacement Models |
3 | Test 1 |
| 7 |
Stochastic Processes Review Discrete-Time Markov Chains |
3 | Reading: Ross, Ch. 4 |
| 8 |
Markov Chains Classification, Steady-State Analysis |
4 | Problem Set 3 |
| 9 |
Markov Decision Processes (MDPs) Policy Evaluation and Optimization |
4 | Numerical examples |
| Bonus |
Infinite Horizon MDPs Discounted and Average Reward Criteria |
2 | Reading notes |
| Optional |
Game Theory Basics Static Games, Nash Equilibrium |
3 | Ref: Osborne & Rubinstein |
| Optional |
Dynamic and Evolutionary Games Repeated Games, Replicator Dynamics |
3 | Ref: L.C. Thomas |
| Component | Weight |
|---|---|
| Continuous Evaluation (Assignments, Quiz) | 40% (20+20) |
| Midterm Exam | 30% |
| Final Exam | 30% |
Lecture notes, problem sets, etc. are provided. Check the course moodle page for more recent updates.