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EP30.FSF1123 — Operations Research | Chương trình đào tạo NEU

EP30.FSF1123 — Operations Research

Credits
3
Self-study hours
90
Showing intake
K68-2026

Course description

This course introduces students to core models and methods in Operations Research (OR), with applications in economics and quantitative decision-making. Hillier-Lieberman provides the theoretical foundation of OR modeling, while Taha supplements detailed algorithms and computational procedures. Topics include: linear programming, simplex method, duality, sensitivity, transportation models, network optimization, decision analysis, integer programming, dynamic programming, game theory, Markov chains, queuing theory, and simulation. Students learn to formulate, solve, and interpret optimization models using Excel Solver, TORA, and Python (PuLP).

Course requirements

Requirements for taking final /terminal exams

Students are allowed to take the final exam if their attendance score reaches 5 points or more (on the scale of 10).

Requirements for attending classes

Students are responsible for attending all classes. In case of absence from school due to force majeure reasons, sufficient and reasonable proofs must be provided. For each absence, 1 point will be deducted 1 point. Students who miss any classes more than 3 times, with or without reason, will be considered as failing to complete the course and have to re-register.

Students will be awarded points for constructive comments, these points are added to attendance points and group assignments.

Groups who do not submit the group work will receive a score of 0 (zero). Late submissions will be deducted for each day of late submission.

Requirements for in-class behaviour

The course is conducted on the principle of respecting learners and lecturers. Any behaviour that affects the teaching and learning process is strictly prohibited.

Students must come to class on time. Students who are late more than 10 minutes after class starts will not be allowed to attend the class. Do not make noise and affect others during the learning process.

Laptops and tablets are only used for the purpose of taking notes for and calculating for lectures and exercises, absolutely forbidden to use for other purposes.

Detailed content

The course content includes

  • Introduction to Operational Research 
  • Linear Programming (LP) Modeling
  • Advanced LP solving
  • Transportation, Assignment, Transshipment
  • Network Optimization
  • Decision Analysis 
  • Integer Programming
  • Dynamic Programming
  • Multiobjective optimization
  • Queuing Theory
  • Markov Decision Process

Lecturers

  • , PhD. Ha Minh Hoang
  • Le Thi Anh
  • Phung Minh Duc
  • Nguyen Van Quy
  • Tong Thanh Trung

Teaching plan

Week 1

Introduction to Operational Research (OR) What is OR? Types of OR models Structure of OR modeling process: Examples from economics: supply chain, portfolio optimization, pricing, scheduling
References
Syllabus, [1] Chapter 1 [2] Chapter 1
Activities
Lectures: Introduce course syllabus, textbooks, expectations. Present the concept of Operational Research, OR modeling cycle, and OR problem structure. Provide economic examples (supply chain, portfolio, scheduling). In-class discussions: Students discuss what types of economic/management problems may require optimization. Students share personal expectations and prior understanding of quantitative modeling. In-class exercises: Identify decision variables, constraints, and objectives for simple real-world problems. Short group task: classify OR problems into deterministic/stochastic/network.
Assessment
Criteria: Attendance, Responding & Behavior Tools: Appendix 2, Rubric 1 Method: Attendance Check & Work Assignment

Week 3

Chapter 1. Linear Programming Modeling 1.1 Decision variables, objective, constraints. Economic interpretations of LP: Cost minimization Profit maximization Portfolio optimization 1.3 Graphical LP (2 variables). 1.4 LP modeling pitfalls & modeling judgment.
References
[1] Chapter 2 [2] Chapter 2
Activities
Lectures: Review LP structure: decision variables, constraints, objective functions. Introduce graphical solutions and economic interpretation (profit, cost, portfolio). In-class discussions: Discussion of modeling choices and pitfalls in LP formulation. Students critique sample LP models and propose improvements. In-class exercises: Formulate LP models from short case descriptions. Solve a 2-variable LP graphically.

Learning resources

  • Introduction to Operations Research — Hillier, F. & Lieberman, G. J., 10th Edition, 2025
  • Operations Research: An Introduction — Hamdy A. Taha, 10th Edition, 2017
  • An Introduction to Optimization — Chong, E. K. P., & Zak, S. H., 4th ed, 2013
  • Lecture notes and slides
Assessment
Criteria: Attendance, Responding & Behavior Tools: Appendix 2, Rubric 1 Method: Attendance Check

Week 3

Chapter 2. Advanced LP solving 2.1. Simplex method 2.1.1 Simplex tableau. 2.1.2 Pivot operations 2.1.3 Degeneracy, cycling, unboundedness 2.2. Duality 2.2.1 Constructing primal–dual pairs 2.2.2 Complementary slackness 2.2.3 Shadow prices and marginal analysis 2.3. Non-simplex method 2.4 Parametric Linear Programming
References
[1] Chapter 3, 4 [2] Chapter 3, 4, 7 [3] Chapter 18
Activities
Lectures: Explain Simplex tableau, pivot rules, degeneracy, unboundedness. Introduce dual problems, complementary slackness, shadow prices. In-class discussions: Students discuss the meaning of dual variables in economic optimization. Compare Simplex vs. dual interpretation. In-class exercises: Perform step-by-step Simplex iterations on a small problem. Solve primal–dual pairs (students derive dual constraints).
Assessment
Criteria: Attendance, Responding & Behavior Tools: Appendix 2, Rubric 1 Method: Attendance Check & Work Assignment

Week 5

Chapter 3. Transportation, Assignment, Transshipment 3.1 Balanced vs. unbalanced models. 3.2 Initial feasible solutions (NW, VAM). 3.3 Optimizing transportation using MODI. 3.4 Assignment with Hungarian method. 3.5 Economic contexts
References
[1] Chapter 7 [2] Chapter 5
Activities
Lectures: Explain balanced/unbalanced transportation models. Present NW, VAM, MODI methods and Hungarian algorithm. In-class discussions: Students evaluate the efficiency of different initial solution methods. Discussion: real-life applications in supply chain and logistics. In-class exercises: Use NW and VAM to generate initial feasible solutions. Apply MODI and Hungarian method to sample datasets.
Assessment
Criteria: Attendance, Responding & Behavior Tools: Appendix 2, Rubric 1 Method: Attendance Check & Work Assignment

Week 6

Chapter 4. Network Optimization 4.1 Shortest path (Dijkstra, Bellman). 4.2 Minimum spanning tree (Prim, Kruskal). 4.3 Maximum flow and minimum cut theory. 4.4 CPM/PERT network planning. Applications: Logistics Project management Telecom and utility networks
References
[1] Chapter 8 [2] Chapter 6
Activities
Lectures: Present shortest path, minimum spanning tree, maximum flow, CPM/PERT. Explain algorithm intuition (Dijkstra, Prim, Kruskal, Ford-Fulkerson). In-class discussions: Discuss differences between path, flow, and spanning-tree problems. Debate real-world network optimization (traffic, telecom, project scheduling). In-class exercises: Build networks from diagrams and solve shortest path & MST problems. Compute earliest start/finish times and critical path on a PERT network.
Assessment
Criteria: Attendance, Responding & Behavior Tools: Appendix 2, Rubric 1 Method: Attendance Check & Work Assignment

Week 7

Review Midterm Test Assignment Giving
Activities
Midterm examination (calculation + conceptual questions). Instructor reviews key mistakes afterward.
Assessment
Criteria: Calculation problems, Discussion questions Tools: Appendix 2, Rubric 2 Method: Written Exam

Week 8

Chapter 5. Decision Analysis 5.1 Decision trees. 5.2 Expected monetary value. 5.3 Utility theory & risk preferences. 5.4 Multi-stage decision processes
References
[1] Chapter 11 [2] Chapter 15
Activities
Lectures: Introduce decision trees, EMV, utility theory, risk attitudes. Cover multi-stage decisions with uncertainty. In-class discussions: Students debate rational vs. risk-averse behavior. Evaluate decision strategies in uncertain economic environments. In-class exercises: Draw decision trees for sample problems. Compute EMV and expected utility for several alternatives.
Assessment
Criteria: Attendance, Responding & Behavior Tools: Appendix 2, Rubric 1 Method: Attendance Check & Work Assignment

Week 9

Chapter 6. Integer Programming 6.1 Binary, mixed integer decisions. 6.2 Branch-and-bound tree. Cutting plane intuition. Applications: Capital budgeting Facility location Production planning
References
[1] Chapter 12 [2] Chapter 9
Activities
Lectures: Introduce binary/mixed-integer models and key applications. Explain branch-and-bound algorithm and cutting-plane insights. In-class discussions: Discuss when integer constraints are necessary (logical constraints). Students critique IP model feasibility. In-class exercises: Build integer models for facility location or capital budgeting. Perform first steps of a B&B tree manually.
Assessment
Criteria: Attendance, Responding & Behavior Tools: Appendix 2, Rubric 1 Method: Attendance Check & Work Assignment

Week 10

Chapter 7. Dynamic Programming 7.1 Recursive structure of DP. 7.2 Forward and backward recursion. 7.3 Applications: 7.3.1 Inventory planning 7.3.2 Resource allocation 7.3.3 Equipment replacement
References
[1] Chapter 10 [2] Chapter 12
Activities
Lectures: Explain recursive structure, forward/backward recursion. Present DP applications: inventory, allocation, replacement. In-class discussions: Students discuss when DP is better than LP/IP. Explore dimensionality issues and state-space growth. In-class exercises: Solve a knapsack DP manually (tabular method). Perform backward recursion for an inventory example.
Assessment
Criteria: Attendance, Responding & Behavior Tools: Appendix 2, Rubric 1 Method: Attendance Check & Work Assignment

Week 11

Chapter 8. Multiobjective optimization 8.1 Global criterion method. 8.2 Utility function method. 8.3 Inverted utility method. 8.4 Bounded objective function method. 8.5 Lexicographic model
References
[3] Chapter 24 [4] Chapter 9
Activities
Lectures: Introduce multiobjective problems and explain in detail several methods (global, utilitym, lexicographic). In-class discussions: Strategic behavior discussion: what is rational strategy? Students compare pure vs. mixed strategy outcomes. In-class exercises: Solve game matrices for saddle points. Apply dominance reduction to simplify a game.
Assessment
Criteria: Attendance, Responding & Behavior Tools: Appendix 2, Rubric 1 Method: Attendance Check & Work Assignment

Week 12

Chapter 9. Queuing Theory 9.1 M/M/1, M/M/s systems. 9.2 Performance measures: L, Lq, W, Wq. 9.3 Economic trade-offs: service cost vs waiting cost. 9.4 Applications: banks, hospitals, call centers.
References
[1] Chapter 17 [2] Chapter 15
Activities
Lectures: Present M/M/1, M/M/s models and steady-state measures. Explain economic trade-offs: service cost vs. waiting cost. In-class discussions. Discuss real queuing systems (banks, hospitals, call centers). Students analyze cases with different service configurations. In-class exercises: Compute L, Lq, W, Wq for multiple queue scenarios. Compare alternative service rates and their costs.
Assessment
Criteria: Attendance, Responding & Behavior Tools: Appendix 2, Rubric 1 Method: Attendance Check & Work Assignment

Week 13

Chapter 10. Markov Decision Process 10.1 Transition matrices. 10.2 n-step transitions. 10.3 Steady-state probabilities. 10.4 Absorbing chains. 10.5 Applications: brand switching, credit risk, inventory
References
[1] Chapter 16 [2] Chapter 17
Activities
Lectures: Introduce structure of Markov chains, transition matrices. Explain n-step transitions and steady-state behavior. In-class discussions: Discuss Markov assumptions and limitations. Explore examples in credit scoring, brand switching. In-class exercises: Compute 2-step transitions for a small matrix. Solve steady state for a 3-state system.
Assessment
Criteria: Attendance, Responding & Behavior Tools: Appendix 2, Rubric 1 Method: Attendance Check & Work Assignment

Week 15

Summary and Revision Report Submission
Activities
Homework 6 discussion (TU) Revision (TU) Groups present projects and answer questions Lecturer and class members comments
Assessment
Appendix 2, Rubrics 1, 3
Final exam
Activities
Students take exams according to give schedule
Assessment
According to the exam requirement Appendix 2, Rubric 2