**[2022 Spring : Operations Research I (POSTECH IMEN 260)]**

Objective: This course introduces the concept of operations research and applications. It mainly covers a variety of topics in the linear programming; formulation, solution method, theoretical analysis and applications

Course outline:

1. Overview of Operations Research

2. Introduction to LP

3. Solving LP problems : Simplex Method

4. Theory of Simplex Method

5. Duality Theory

6. Transportation and Assignment Problems

7. Network Optimization Problems

8. Dynamic Programming

9. Integer Programming

**[2018 -2019 Spring : Simulation (POSTECH IMEN 481)]**

Objective: To provide basic concepts of system modeling and simulation, characteristics of the discrete systems, simulation techniques, statistical methods to analyze the results, and application to real-life problems.

Course outline:

1. Fundamental simulation concepts

2. Simulation language

3. Verification & validation

4. Data collection and selecting input probability distribution

5. Random number generation

6. Output data analysis

7. Variance reduction techniques

**[2017 Fall/2019 Fall/2021Fall : Dynamic Programming and Reinforcement Learning Applications (POSTECH IMEN 764)]**

Objective: To provide the student with the art of formulating recursive equations and the method that can solve many of optimization problems involving sequential decision making.

Course outline:

1. Introduction (Sequential Decision Process, Principle of Optimality)

2. Deterministic DP : DP Algorithm

3. Deterministic DP : Shortest Path Problem and Other Applications

4. Deterministic DP : DP and Hamilton-Jacobi-Bellman (HJB) Eq.

5. Stochastic DP : Introduction to Markov Decision Process (MDP)

6. Stochastic DP : Short Review on Discrete Time Markov Chain (DTMC)

7. Stochastic DP : MDP Applications

8. Stochastic DP : Finite-Horizon MDP

9. Stochastic DP : Infinite-Horizon MDP

10. Stochastic DP : Discounted MDP and Policy/Value Iterations

11. Approximate Dynamic Programming

12. Reinforcement Learning : Temporal Difference Learning, On-Policy/Off-Policy Learning

13. Reinforcement Learning : Value Function Approximation, Basics of Deep Q Learning

**[2016 Fall /2018 Fall/2021Spring : Game Theory and Business Applications (POSTECH IMEN 811)]**

– Analyze game theory models: Computing equilibria

– Understanding the classifications of interactions among stakeholders

– Using above models and concepts into some applications in various disciplines

Course outline:

1. Introduction of Game Theory

2. Static game of complete information : Cournot competition, Betrand competition, etc.

3. Dynamic game of complete information : Stakelberg game, Repeated game, Imperfect information, etc.

4. Static game of incomplete information : Bayesian game, Asymmetric information game, Auction, etc.

5. Dynamic game of incomplete information : Signaling game, Principal-Agent problem etc.

6. Applications in sypply chain coordination

**[2016-2020 Spring : Probability & Statistics for Engineers (POSTECH IMEN 272)]**

Objective: This course introduces students to standard methods of describing and analyzing data, probability theory, statistical inference, and ordinary least squares. The understanding of fundamental concepts and rules of probability and statistics is applied to engineering problems

Course outline:

1. Chapter 1: Introduction

2. Chapter 2: Probability

3. Chapter 3: Random Variables and Probability Distributions

4. Chapter 4: Mathematical Expectation

5. Chapter 5: Some Discrete Probability Distributions

6. Chapter 6: Some Continuous Probability Distributions

7. Chapter 7: Functions of Random Variables

8. Chapter 8: Fundamental Sampling Distributions and Data Descriptions

9. Chapter 9: One- and Two-Sample Estimation Problems

10. Chapter 10: One- and Two-Sample Tests of Hypotheses

11. Chapter 11: Simple Linear Regression and Correlation

**[2012 Summer : Supply Chain Economics (Gatech ISyE 4301)]**

– Developing models that incorporate strategic competitor/consumer behavior

– Understanding how market structure affects decisions

– Demonstrating the benefits of coordination and collaboration in the supply chain along with techniques for achieving and maintaining it

– Developing mathematical models for pricing, revenue management and auctions and understand how these strategies can improve profitability Applications include pricing strategies, revenue management, differentiation strategies, and incentives

3. Pricing Models and Strategies: components of price optimization, bundling, market segmentation strategies, and price discrimination strategies

4. Revenue Management: Pricing Models and Strategies: components of price optimization, bundling, market segmentation strategies, and price discrimination strategies

5. Games, Strategy and Equilibria: strategic interaction between firms with particular emphasis on Bertrand, Cournot, and Stackelberg games; Nash equilibrium; Pareto efficiency

6. Supply Chain Coordination: introduction to coordination strategies such as revenue sharing and transfer payments

7. Value of Information: importance of information asymmetries and agency issues; introduction to moral hazard, adverse selection and developing principal agent models

8. Business Auction: basic design of auctions and bidding strategies