Simulation with Applications in Logistics (LFM 521)

Content

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

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.

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

Weekly Contents

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)

Assessment System

Contribution to Overall Grade
Activities	Number	Contribution
Contribution of in-term studies to overall grade	2	60
Contribution of final exam to overall grade	1	40
Total	3	100

In-Term Studies
Activities	Number	Contribution
Assignments	0	0
Presentation	0	0
Midterm Examinations (including preparation)	1	30
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
Total	2	60

Relation of Proficiency

No	Program Learning Outcomes	Contribution
No	Program Learning Outcomes	1	2	3	4	5
1
2
3					X
4						X
5
6
7
8						X
9
10
11						X
12						X
13					X
14						X
15

ECTS

Activities	Number	Period	Total Workload
Class Hours	14	3	42
Working Hours out of Class	13	2	26
Assignments	3	7	21
Midterm Examinations (including preparation)	1	15	15
Project	1	40	40
Final Examinations (including preparation)	1	17	17
Total Workload			161
Total Workload / 25			6.44
Credits ECTS			6