Simulation with Applications in Logistics(LFM 521)
| Course Code | Course Name | Semester | Theory | Practice | Lab | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| LFM 521 | Simulation with Applications in Logistics | 2 | 3 | 0 | 0 | 3 | 6 |
| Prerequisites | |
| Admission Requirements |
| Language of Instruction | English |
| Course Type | Compulsory |
| Course Level | Masters Degree |
| Course Instructor(s) | EBRU ANGÜN ebru.angun@gmail.com (Email) |
| Assistant | |
| Objective |
Simulation is a statistical computer-based technique to model and analyze complex, real-life problems. This course aims at enabling students to model real-life logistics problems through simulation models, estimate several performance measures through statistical analysis, analyze the sensitivity of the system to various parameters, and find the optimum operating conditions. The objectives of the course are determined as follows: • Introduce how to build simulation models of real-life problems • Enable students to use the statistical techniques efficiently • Enable students to use softwares such ARENA and MATLAB efficiently and effectively • Enable students to use sensitivity analysis and simulation-based optimization techniques |
| Content | |
| Course Learning Outcomes |
Upon successful completion of this course, the student will be able to LO 1: Build ARENA model of a real-life logistics problem. LO 2: Distinguish different types of simulation and how they are used. LO 3: Perform input analysis through various statistical procedures. LO 4: Interpret the results of statistical packages. LO 5: Explain how computers generate uniform pseudo-variates. LO 6: Compare and test different pseudo-random number generators in different softwares (e.g., MATLAB) LO 7: Define several methods to generate random variates. LO 8: Define different techniques for the output analysis for different types of simulation. LO 9: Define classic and new methods for design of experiments. LO 10: Perform simulation-optimization techniques and obtain optimum operating conditions. LO 11: Perform sensitivity analysis and find the most important parameters fort he system. |
| Teaching and Learning Methods | |
| References |
Law, A.M., “Simulation Modeling and Analysis”, 4. Edition, McGraw-Hill, New York, 2007 Kelton, W.D., Sadowski, R.P., Sturrock, D.T., “Simulation with ARENA”, 3. Edition, McGraw-Hill, New York, 2003 Kleijnen, J.P.C., “Design and Analysis of Simulation Experiments”, Springer, New York, 2008 Alexopoulos, C., Seila, A., “Output data analysis”, Chapter 7 in Handbook of Simulation, Wiley, New York, 1998 |
Theory Topics
| Week | Weekly Contents |
|---|---|
| 1 | Introduction to simulation, different simulation techniques, applications of simulation with ARENA (Law, Chapter 1, Kelton, Sadowski & Sturrock) |
| 2 | Introduction to simulation, different simulation techniques, applications of simulation with ARENA (Law, Chapter 1, Kelton, Sadowski & Sturrock) |
| 3 | Basic concepts in probability and statistics (Law, Chapter 4) |
| 4 | Input analysis (Law, Chapter 6) with MATLAB applications |
| 5 | Input analysis (Law, Chapter 6) with MATLAB applications |
| 6 | Pseudo-random number generators (Law, Chapter 7), testing the pseudo-random numbers generators in MATLAB |
| 7 | Generating random variates (Law, Chapter 8) |
| 8 | Generating random variates (Law, Chapter 8) |
| 9 | Midterm I |
| 10 | Output analysis (Law, Chapter 9, Alexopoulos & Seila) |
| 11 | Output analysis (Law, Chapter 9, Alexopoulos & Seila) |
| 12 | Design of experiments, sensitivity analysis and Response Surface Methodology (Kleijnen, Chapters 2, 3, 4 and 5) |
| 13 | Design of experiments, sensitivity analysis and Response Surface Methodology (Kleijnen, Chapters 2, 3, 4 and 5) |
| 14 | Design of experiments, sensitivity analysis and Response Surface Methodology (Kleijnen, Chapters 2, 3, 4 and 5) |
Practice Topics
| Week | Weekly Contents |
|---|
Contribution to Overall Grade
| Number | Contribution | |
|---|---|---|
| Contribution of in-term studies to overall grade | 5 | 60 |
| Contribution of final exam to overall grade | 1 | 40 |
| Toplam | 6 | 100 |
In-Term Studies
| Number | Contribution | |
|---|---|---|
| Assignments | 3 | 10 |
| Presentation | 0 | 0 |
| Midterm Examinations (including preparation) | 1 | 20 |
| Project | 1 | 30 |
| Laboratory | 0 | 0 |
| Other Applications | 0 | 0 |
| Quiz | 0 | 0 |
| Term Paper/ Project | 0 | 0 |
| Portfolio Study | 0 | 0 |
| Reports | 0 | 0 |
| Learning Diary | 0 | 0 |
| Thesis/ Project | 0 | 0 |
| Seminar | 0 | 0 |
| Other | 0 | 0 |
| Toplam | 5 | 60 |
| No | Program Learning Outcomes | Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
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| Activities | Number | Period | Total Workload |
|---|---|---|---|
| Class Hours | 14 | 3 | 42 |
| Working Hours out of Class | 13 | 3 | 39 |
| Assignments | 3 | 7 | 21 |
| Midterm Examinations (including preparation) | 1 | 10 | 10 |
| Project | 1 | 25 | 25 |
| Final Examinations (including preparation) | 1 | 17 | 17 |
| Total Workload | 154 | ||
| Total Workload / 25 | 6,16 | ||
| Credits ECTS | 6 | ||