COURSE SYLLABUS

ADVANCED SIMULATION TECHNIQUES

1	Course Title:	ADVANCED SIMULATION TECHNIQUES
2	Course Code:	END 6112
3	Type of Course:	Compulsory
4	Level of Course:	Third Cycle
5	Year of Study:	2
6	Semester:	4
7	ECTS Credits Allocated:	7,5
8	Theoretical (hour/week):	3
9	Practice (hour/week) :	0
10	Laboratory (hour/week) :	0
11	Prerequisites:	None
12	Recommended optional programme components:	None
13	Language:	Turkish
14	Mode of Delivery:	Face to face
15	Course Coordinator:	Prof. Dr. ERDAL EMEL
16	Course Lecturers:
17	Contactinformation of the Course Coordinator:	erdal@uludag.edu.tr Tel: 0224 294 2080 Endüstri Mühendisliği Bölümü, Mühendislik Mimarlık Fakültesi Uludağ Üniversitesi, Görükle, Bursa
18	Website:	http://endustri.uludag.edu.tr
19	Objective of the Course:	Determination of the optimal static and dynamic operating conditions for the stochastic production and service systems and establishment and analysis of their parametric and control simulation models
20	Contribution of the Course to Professional Development

Learning Outcomes:

1	Gain the ability to create, verify, and validate simulation models;
2	Have an understanding the principles of simulation system implementation and have knowledge on advanced simulation methods;
3	Be able to develop new simulation methods and have knowledge on when to apply known methods;
4	Gain the ability to comprehend simulation analyses and interpret outputs from a simulation model;
5	Be able to simulate a complex model on a computer environment and have knowledge on up-to-date simulation software;

22	Course Content:

Week	Theoretical	Practical
1	Simulation optimization: an overview Stochastic parametric optimization Stochastic control optimization
2	Response surfaces and neural nets RSM: an overview RSM: details Neuro-response surface methods
3	Parametric optimization Continuous optimization Discrete optimization (Ranking and Selection, Meta-Heuristics:Simulated Annealing, GeneticAlgorithm)
4	Discrete optimization (Meta-Heuristics: Tabu Search, Learning Automata, vd.) Hybrid solution spaces
5	Dynamic programming Stochastic processes Markov processes, Markov chains and semi-Markov processes Markov decision problems How to solve an MDP using exhaustive enumeration Dynamic programming for average reward
6	Dynamic programming and discounted reward The Bellman equation: an intuitive perspective Semi-Markov decision problems Modified policy iteration Miscellaneous topics related to MDPs and SMDPs
7	Mid Term Reinforcement learning The need for reinforcement learning Generating the TPM through straightforward counting Reinforcement learning: fundamentals Discounted reward reinforcement learning Average reward reinforcement learning
8	Semi-Markov decision problems and RL RL algorithms and their DP counterparts Actor-critic algorithms Model-building algorithms Finite horizon problems Function approximation
9	Case studies A classical inventory control problem Airline yield management Preventive maintenance
10	Transfer line buffer optimization Inventory control in a supply chain.
11	AGV routing Quality control Elevator scheduling
12	Convergence: background Convergence: Parametric optimization
13	Convergence: Control Optimization
14	Course Review

23	Textbooks, References and/or Other Materials:	Simulation-Based Optimization, Abhijit Gosavi, Kluwer Academic Publishers, 2003. Discrete Event System Simulation, 4th ed., J.Banks, J.S. Carson, B.L. Nelson, D.M. Nicol, Prentice Hall, 2005. Simulation Modeling and Analysis, 4th ed., Averill M. Law, McGraw-Hill, Inc., 2007. Simulation Using Promodel with CD-Rom, Charles R. Harrell, Biman K. Ghosh, Royce O. Bowden, McGraw-Hill, 2003. Approximate Dynamic Programming: Solving the Curses of Dimensionality, Warren B. Powell, Wiley-Interscience; 1st edition, 2007 Markov Decision Processes: Discrete Stochastic Dynamic Programming, Martin L. Puterman, Wiley-Interscience; 1st edition, 2005
24	Assesment

TERM LEARNING ACTIVITIES	NUMBER	PERCENT
Midterm Exam	1	10
Quiz	0	0
Homeworks, Performances	3	60
Final Exam	1	30
Total	5	100
Contribution of Term (Year) Learning Activities to Success Grade		70
Contribution of Final Exam to Success Grade		30
Total		100
Measurement and Evaluation Techniques Used in the Course
Information

ECTS / WORK LOAD TABLE

Activites	NUMBER	TIME [Hour]	Total WorkLoad [Hour]
Theoretical	14	3	42
Practicals/Labs	0	0	0
Self Study and Preparation	13	8	104
Homeworks, Performances	3	18	54
Projects	1	24	24
Field Studies	0	0	0
Midtermexams	1	2	2
Others	0	0	0
Final Exams	1	2	2
Total WorkLoad			228
Total workload/ 30 hr			7,6
ECTS Credit of the Course			7,5

CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS

	PQ3	PQ6	PQ7	PQ8	PQ9	PQ10	PQ12
LO1	3	5	0	0	0	0	0
LO2	0	0	5	3	4	0	0
LO3	0	0	0	5	4	0	0
LO4	0	0	0	0	5	0	4
LO5	0	0	0	3	3	5	0

LO: Learning Objectives

PQ: Program Qualifications

Contribution Level:

1 Very Low

2 Low

3 Medium

4 High

5 Very High