IE306 Simulation

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Course Code Course Title Weekly Hours* ECTS Weekly Class Schedule
T P
IE306 Simulation 3 2 6 Tuesday:16.00-16.50 Thursday 14.00-15.50
Prerequisite MATH203 It is a prerequisite to
Lecturer Office Hours / Room / Phone

Currently not available

E-mail
Assistant MSc. Erna Omerasevic Assistant E-mail ekeskinovic@ius.edu.ba
Course Objectives Introduce students to the concept of simulation and modeling. Present them the applications of stochastic simulation as a method for the design and analysis of systems. Enrich their knowledge about statistics and bring them closer to the knowledge of usage of the methods such as Monte Carlo simulation and Random Number Generation. Improve their computer skills and able them to run the simulation in Arena software.
Textbook Simulation,Sheldon M. Ross,Academic Press,2012; Simulation with Arena,W. David Kelton, Randall P. Sadowski, Nancy B. Swets, Mc.Graw Hill, 2001
Learning Outcomes After successful  completion of the course, the student will be able to:
  1. construct logical simulation models and implement computational experiments using current software tools
  2. acquire and reduce input data required to calibrate simulation models
  3. analyze simulation output and correctly infer performance measures, to analyze and compare the performance of alternative designs
  4. use the results of a simulation to verify the appropriateness of the probability model
  5. use a simulation software to perform simulation
Teaching Methods Lecture discussion and review questions; Short reports and homework assignments; Group discussions; Problem solving.
WEEK TOPIC REFERENCE
Week 1 Introduction ch1
Week 2 Element of Probability ch2 (Ross)
Week 3 Fundamental Simulation Concept ch2
Week 4 Fundamental Simulation Concept ch2
Week 5 Guided Thour through Arena ch3
Week 6 Guided Thour through Arena ch3
Week 7 Modeling Basic Operations and Inputs ch4
Week 8 MIDTERM EXAM
Week 9 Random Numbers ch3 (Ross)
Week 10 Generating Discrete Random Variables ch4 (Ross)
Week 11 Generating Continuous Random Variables ch5 (Ross)
Week 12 The Discrete Event Simulation Approach ch7 (Ross)
Week 13 Statistical Analysis of Simulated Data ch8 (Ross)
Week 14 Markov Chain Monte Carlo Methods ch12 (Ross)
Week 15 Markov Chain Monte Carlo Methods ch12 (Ross)
Assessment Methods and Criteria Evaluation Tool Quantity Weight Alignment with LOs
Final Exam 1 40 3,4
Semester Evaluation Compenents
Homeworks 5 10 1,5
In-term Exam 1 30 1,3
Assignment 1 20 2,5
***     ECTS Credit Calculation     ***
 Activity Hours Weeks Student Workload Hours Activity Hours Weeks Student Workload Hours
Lecture Hours 3 15 45 Homework 5 5 25
Assignments 5 3 15 Assignments 5 3 15
Home Study 2 15 30 Final Exam Study 15 1 15
Homework 5 5 25 Homework 5 5 25
        Total Workload Hours = 150
*T= Teaching, P= Practice ECTS Credit = 6
Course Academic Quality Assurance: Semester Student Survey Last Update Date: 04/03/2020
QR Code for https://ecampus.ius.edu.ba/course/math203-introduction-probability-and-statistics

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