END6112 İleri Simulasyon Teknikleri

END 6112 İLERİ SİMÜLASYON TEKNİKLERİ
Dönem : 2012-2013 Bahar
Kredisi : 3-0-0
Ders Saati : Çarşamba 09:30 - 12:00
Derslik : Y320
Öğretim Üyesi : Prof. Dr. Erdal EMEL, ([email protected])
Dersin Amacı : Stokastik üretim ve hizmet sistemlerinin statik ve dinamik en uygun işletim koşullarının
belirlenmesi için parametrik ve kontrol simülasyon modellerinin kurulması ve
çözümlenmesi
Ödevler : Üç adet dönem içi ödev, bir adet dönem projesi
Sınavlar : Kısa Sınav, Final sınavı
Değerlendirme :
1. Ödev 2. Ödev 3. Ödev Dönem Projesi Kısa Sınav Final Sınavı
%10
%10
%10
%20
%20
%30
Kaynak Yayınlar
1.
Simulation-Based Optimization, Abhijit Gosavi, Kluwer Academic Publishers, 2003.
2.
Discrete Event System Simulation, 4th ed., J.Banks, J.S. Carson, B.L. Nelson, D.M. Nicol, Prentice
Hall, 2005.
3.
Simulation Modeling and Analysis, 4th ed., Averill M. Law, McGraw-Hill, Inc., 2007.
4.
Simulation Using Promodel with CD-Rom, Charles R. Harrell, Biman K. Ghosh, Royce O. Bowden,
McGraw-Hill, 2003.
5.
Approximate Dynamic Programming: Solving the Curses of Dimensionality, Warren B. Powell,
Wiley-Interscience; 1st edition, 2007
6.
Markov Decision Processes: Discrete Stochastic Dynamic Programming, Martin L. Puterman,
Wiley-Interscience; 1st edition, 2005
7.
Introducton to Operations Research, F.S. Hillier, G.J. Lieberman, McGrawHill , 8th, ed., 2005
Dersin Kapsamı
Hafta
Tarih
Konu
Kaynak
1
27.02
5. Simulation optimization: an overview. 5.2 Stochastic parametric
optimization. 5.3. Stochastic control optimization.
1:5
2
06.03
6. Response surfaces and neural nets. 6.2. RSM: an overview. 6.3. RSM:
details. 6.4. Neuro-response surface methods.
1:6
3
13.03
7. Parametric optimization. 7.2. Continuous optimization. 7.3. Discrete
optimization (7.3.2.1 Ranking and Selection, Meta-Heuristics:Simulated
Annealing, GeneticAlgorithm)
1:7
2:12
3:12
4
27.03
7.3.2. Discrete optimization (Meta-Heuristics: Tabu Search, Learning
Automata, vd.) 7.4. Hybrid solution spaces.
1:7
4:11
5
03.04
8. Dynamic programming. 8.2. Stochastic processes. 8.3. Markov processes,
Markov chains and semi-Markov processes. 8.4. Markov decision problems.
8.5. How to solve an MDP using exhaustive enumeration. 8.6. Dynamic
programming for average reward
1:8
7:10
6
10.04
8.7. Dynamic programming and discounted reward. 8.8. The Bellman
equation: an intuitive perspective. 8.9. Semi-Markov decision problems. 8.10.
Modified policy iteration. 8.11. Miscellaneous topics related to MDPs and
SMDPs.
1:8
7:19
Hafta
Tarih
Konu
Kaynak
7
17.04
KISA SINAV
9. Reinforcement learning. 9.2. The need for reinforcement learning. 9.3.
Generating the TPM through straightforward counting. 9.4. Reinforcement
learning: fundamentals. 9.5. Discounted reward reinforcement learning. 9.6.
Average reward reinforcement learning.
1:5-8
1:9
8
24.04
9.7. Semi-Markov decision problems and RL. 9.8. RL algorithms and their DP
counterparts. 9.9. Actor-critic algorithms. 9.10. Model-building algorithms.
9.11. Finite horizon problems. 9.12. Function approximation.
1:9
9
01.05
14. Case studies. 14.2. A classical inventory control problem. 14.3. Airline
yield management. 14.4. Preventive maintenance.
1:14
10
08.05
14.5. Transfer line buffer optimization. 14.6. Inventory control in a supply
chain.
1:14
11
15.05
14.7. AGV routing. 14.8. Quality control. 14.9. Elevator scheduling.
1:14
12
22.05
11. Convergence: background.
12. Convergence: Parametric optimization
1:11
1:12
13
29.05
13. Convergence: Control Optimization
1:13
14
05.06
Proje Sunumları
Ödev Kapsamı
Veriliş Tarihi Konu
Teslim Tarihi
1. ödev
2. hafta
27.02
Parametric Optimization
5. hafta
03.04
2. ödev
5. hafta
03.04
Dynamic Programming
7. hafta
17.04
3. ödev
7. hafta
17.04
Reinforcement Learning
9. hafta
01.05
Dönem Projesi
9. hafta
01.05
Application to real problems 14. hafta
05.06