| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Simulation Techniques | MEN318 | Compulsory | Bachelor's degree | 3 | Spring | 4 |
Name of Lecturer(s)
Assistant Prof. Dr. Atakan ALKAN
Assistant Prof. Dr. Celal ÖZKALE
Learning Outcomes of the Course Unit
6) Explain the system, model and simulation concepts.
7) Describe the concept of random number. Generate numbers with the random number generation methods.
8) Analyze the psuedo-random numbers in terms of randomness.
9) Write the basic elements of the simulation model.
10) Verify, test and analyze the simulation model.
11) Build the simulation model of a system with a software and analyze the system.
Program Competencies-Learning Outcomes Relation
| Program Competencies | ||||||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | ||
| Learning Outcomes | ||||||||||||
| 6 | Low | Low | Low | Low | Middle | Low | Low | Low | High | High | Low | |
| 7 | No relation | No relation | No relation | No relation | No relation | No relation | No relation | No relation | High | Middle | No relation | |
| 8 | No relation | No relation | No relation | No relation | No relation | No relation | No relation | No relation | High | Middle | No relation | |
| 9 | No relation | Low | Low | No relation | Low | Low | Low | Low | High | Middle | Low | |
| 10 | No relation | Low | Low | Low | Middle | Low | Low | Low | High | Low | Middle | |
| 11 | Middle | High | High | Low | High | Middle | Middle | Low | High | Middle | Middle | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Probability and Statistics
Course Contents
This course covers ;simulation concept, system simulation, simulation models, discrete event simulation, random number generation, random variables and probability distributions generation, output analysis, simulation language supports.
Weekly Schedule
1) What is Simulation? Why Simulate? Use of Simulation. When simulation is appropriate?2) System concept, system model types, system complexity, the systems approach, systems analysis techniques, simulation models.
3) Types of simulation: static, dynamic, stochastic, deterministic, discrete-event, continious and mixed (continuous and discrete) simulation models.
4) Random number generation, probability distributions, discrete-event simulation.
5) Monte Carlo and Quasi Monte Carlo simulation techniques, manual simulation and simulation softwares.
6) Simulation with Promodel software and two case study (a manufacturing system case and a service system case).
7) Data collection and analysis for simulation: Tests for independence, tests for identically distributed data, distribution fitting.
8) Midterm examination/Assessment
9) Model building with Promodel software.
10) Model verification and validation.
11) Simulation output analysis: Determining number of replications and warm-up periods, determining run length.
12) Comparing systems: welch confidence interval, paired-t confidence interval, bonferroni approach, factorial design, variance reduction techniques. Optimization.
13) Modeling manufacturing systems .
14) Modeling material handling and storage systems.
15) Modeling service systems .
16) Final examination
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
1) Lecture
2) Lecture
3) Lecture
4) Lecture
5) Question-Answer
6) Question-Answer
7) Question-Answer
8) Question-Answer
9) Modelling
10) Modelling
11) Modelling
12) Modelling
13) Simulation
14) Simulation
15) Simulation
16) Simulation
17) Self Study
18) Self Study
19) Self Study
20) Self Study
Assessment Methods and Criteria
Contribution of Midterm Examination to Course Grade |
40% |
|---|---|
Contribution of Final Examination to Course Grade |
60% |
Total |
100% |
Language of Instruction
Turkish
Work Placement(s)
Not Required