IE303 Operations Research I

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Course Code Course Title Weekly Hours* ECTS Weekly Class Schedule
T P
IE303 Operations Research I 3 2 6 Tuesday 11:00-12:50 Thursday 10:00-10:50
Prerequisite MATH201 It is a prerequisite to
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Course Objectives Operations research (OR) has many applications in science, engineering, economics, and industry. The ability to solve OR problems is crucial for both researchers and practitioners. Being able to solve the real life problems and obtaining the right solution requires understanding and modeling the problem correctly and applying appropriate optimization tools and skills to solve the mathematical model. The goal of this course is to teach you to formulate, analyze, and solve mathematical models that represent real-world problems. We will also discuss how to use spreadsheets and other software packages for solving optimization problems.
Textbook Required Textbook: Managerial Decision Modeling with Spreadsheets , Render, B., Stair R. M., and Balakrishnan, N. , 2003, Prentice Hall Recommended Textbook: An Introduction to Management Science: Quantitative Approach , Anderson, Sweeney and Williams , 2001, West Recommended Textbook: Hamdy, Taha; Operations Research: An Introduction, 8th international edition, Pearson International, ISBN: 978-0131889231
Learning Outcomes After successful  completion of the course, the student will be able to:
  1. Formulate a real-world problem as a mathematical programming model
  2. Demonstrate understanding of the theoretical workings of the simplex method for linear programming and perform iterations by hand
  3. Demonstrate understanding of the relationship between a linear program and its dual, including strong duality and complementary slackness
  4. Perform sensitivity analysis to determine the direction and magnitude of change of a model's optimal solution as the data change
  5. Demonstrate understanding of the applications of, basic methods for, and challenges in integer programming
  6. Apply optimality conditions for single- and multiple-variable unconstrained and constrained non-linear models
Teaching Methods The course will be based on the following teaching and learning activities: Lecture discussion and review questions, class presentations, short reports and homework assignments, student presentations and discussions, problem solving in tutorial sessions (every week 2 hours and also before and after the exams)
WEEK TOPIC REFERENCE
Week 1 Introduction to Management Science & Modeling Chapter 1
Week 2 Modeling, cont. Chapter 1
Week 3 Linear Programming (LP): Graphical Solution Methods Chapter 2
Week 4 Linear Programming (LP): Computer Solution Chapter 2
Week 5 Using Excel Solver to solve LP problems MIDTERM EXAM Chapter 2
Week 6 LP Applications Chapter 3
Week 7 LP Applications cont. MIDTERM EXAM Chapter 3
Week 8 LP Sensitivity Analysis Chapter 4
Week 9 Mathematical Programming Models Chapter 6
Week 10 IP, NLP, Goal Programming Models Chapter 6
Week 11 Network models Chapter 5
Week 12 Project Management Chapter 7
Week 13 Simulation Chapter 10
Week 14 Simulation cont Chapter 10
Week 15 Review. LAB EXAM Chapter 1-10
Assessment Methods and Criteria Evaluation Tool Quantity Weight Alignment with LOs
Final: Essay/written 1 25 1,2,3,4,5,6
Semester Evaluation Compenents
Homework 4 15 1,2,3,4,5,6
Midterm: Essay/written 2 30 1,2,3,4,5,6
Project 1 15 1,2,3,4,5,6
Lab exam 1 15 1,4,5,6
***     ECTS Credit Calculation     ***
 Activity Hours Weeks Student Workload Hours Activity Hours Weeks Student Workload Hours
Lecture Hours 3 14 42 Active Tutorials 2 14 28
Homework 3 4 12 Homework 3 4 12
Lab Exam 10 1 10 Final Exam Study 20 1 20
Active Tutorials 2 14 28 Active Tutorials 2 14 28
        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/math201-linear-algebra

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