END321

School of Engineering \ Industrial Engineering

3

6

Compulsory

Turkish

END 213

Endüstri Mühendisliği Lisans Programı

This is an undergraduate level course introducing mathematical modeling of uncertainty using stochastic models and decision making methods under uncertainty.

1. W. Winston, “Operations Research, Applications and Algorithms”, 4th edition, Thomson, 2004.
2. S. Ross, “Introduction to Probability Models”, 13th edition, Academic Press, 2023.

This is an undergraduate level course introducing mathematical modeling of uncertainty using stochastic models and decision making methods under uncertainty. Topics to be covered include a review of probability, markov chains, poisson processes, queueing theory, and decision analysis.

1. Gain fundamental knowledge of stochastic models.
2. Develop modeling skills for decisions made under uncertainty.
3. Model a given process as a Markov chain, calculate system performance measures based on the model, and choose between alternative system configurations and policies.
4. For a given queueing system; calculate the performance measures, make improvements according to these performance measures, and choose between alternative queueing system configurations.
5. Ability to make decisions under uncertainty using decision trees.

Week 1: Probability and Statistics Review - Introduction
Week 2: Probability and Statistics Review – Random Variables
Week 3: Probability and Statistics Review – Expectations, Conditioning
Week 4: Markov Chains - Introduction
Week 5: Markov Chains - Chapman-Kolmogorov Equations
Week 6: Markov Chains - Classification of States
Week 7: Poisson Processes
Week 8: Queueing Theory - Introduction
Week 9: Queueing Theory - Birth-Death Processes
Week 10: Queueing Theory - Queuing Models
Week 11: Queueing Theory - Queuing Networks
Week 12: Decision Theory

Homework Quiz 20 %
Midterm 40 %
Final 40 %