ISE4002
Subject Description Form
Subject Code
ISE4002
Subject Title
Operations Research II
Credit Value
3
Level
4
Pre-requisite/Co-requisite/Exclusion
ISE3001 Operations Research I
Objectives
This subject will provide students with
1. ability to understand and analyze managerial problems in industry so that they are able to use resources (capitals, materials, staffing, and machines) more effectively;
2. knowledge of formulating mathematical models for quantitative analysis of managerial problems in industry;
3. skills in the use of Operations Research approaches and computer tools in solving real problems in industry;
4. mathematical models for analysis of real problems in Operations Research.
Intended Learning Outcomes
Upon completion of the subject, students will be able to
a. recognize the importance and value of Operations Research and mathematical modeling in solving practical problems in industry;
b. formulate a managerial decision problem into a mathematical model;
c. understand Operations Research models and apply them to real-life problems;
d. use computer tools to solve a mathematical model for a practical problem.
Subject Synopsis/ Indicative Syllabus
1. Introduction
Review of Linear Programming. Multiple-objectives.
2. Decision Analysis
Analytic Hierarchy Process (AHP), and Concepts in Game Theory.
3. Queuing Theory
Analytic Hierarchy Process (AHP), and Concepts in Game Theory.
4. Markovian Decision Process
Concepts in stochastic processes, Markov Chains.
5. Non-linear Programming
Non-linear programming.
Teaching/Learning Methodology
A mixture of lectures, tutorial exercises, and case studies will be used to deliver the various topics in this subject. Some of them will be covered in a problem-based format which enhances the learning objectives. Others will be covered through directed study in order to enhance the students’ ability of “learning to learn”. Some case studies will be used to integrate these topics and thus demonstrate to students how the
Subject Code
ISE4002
Subject Title
Operations Research II
Credit Value
3
Level
4
Pre-requisite/Co-requisite/Exclusion
ISE3001 Operations Research I
Objectives
This subject will provide students with
1. ability to understand and analyze managerial problems in industry so that they are able to use resources (capitals, materials, staffing, and machines) more effectively;
2. knowledge of formulating mathematical models for quantitative analysis of managerial problems in industry;
3. skills in the use of Operations Research approaches and computer tools in solving real problems in industry;
4. mathematical models for analysis of real problems in Operations Research.
Intended Learning Outcomes
Upon completion of the subject, students will be able to
a. recognize the importance and value of Operations Research and mathematical modeling in solving practical problems in industry;
b. formulate a managerial decision problem into a mathematical model;
c. understand Operations Research models and apply them to real-life problems;
d. use computer tools to solve a mathematical model for a practical problem.
Subject Synopsis/ Indicative Syllabus
1. Introduction
Review of Linear Programming. Multiple-objectives.
2. Decision Analysis
Analytic Hierarchy Process (AHP), and Concepts in Game Theory.
3. Queuing Theory
Analytic Hierarchy Process (AHP), and Concepts in Game Theory.
4. Markovian Decision Process
Concepts in stochastic processes, Markov Chains.
5. Non-linear Programming
Non-linear programming.
Teaching/Learning Methodology
A mixture of lectures, tutorial exercises, and case studies will be used to deliver the various topics in this subject. Some of them will be covered in a problem-based format which enhances the learning objectives. Others will be covered through directed study in order to enhance the students’ ability of “learning to learn”. Some case studies will be used to integrate these topics and thus demonstrate to students how the
References: 1. Murty, K. G. 1995, Operations Research: Deterministic Optimization Models, Prentice Hall 2. Taha, H. A. 2007, Operations Research, 8th Edition, Pearson 3. Taylor, B. W. III 1999, Introduction to Management Science, 6th edn, Prentice Hall 4. Schrage, L. 1997, Optimization Modeling with LINDO, 5th edn, Thomson. 5. Winston, W. L. 2004, Operations Research: Applications and Algorithms, 4th edn, Thomson 6. Williams, H. P., 1993, Model Building in Mathematical Programming, 3rd edn, Wiley 7. Hillier, F. S. and Lieberman, G. J. 2010, Introduction to Operations Research, 9th edn, McGraw-Hill 8. Ravindran, R. 2009, Operations Research, CRC Press