Operations Research
 
 
Subject Code: EME4066
Aim of Subject: To introduce to the students the techniques of operations research and their applications.
Learning Outcome of Subject: At the completion of the subject, students should be able to :
  • Use operations research techniques for solving decisions problems.
  • Design schedules and sequencing charts for machine and manpower based on the available techniques.
  • Apply and practise linear programming to solve various industrial, business, service application and transportation problems.
  • Use queuing theory and apply the fundamentals in real life problems.
  • Apply decision and game theory in various decision applications.
  • Use simulation concept and have the ability to adapt and use it to real life problem.
  • Use dynamic programming to solve related applications.
Programme Outcomes:
  • Ability to acquire and apply fundamental principles of science and engineering(40%)
  • Capability to communicate effectively(10%)
  • Acquisition of technical competence in specialised areas of engineering discipline(10%)
  • Ability to identify, formulate and model problems and find engineering solutions based on a systems approach(5%)
  • Ability to conduct research in chosen fields of engineering(10%)
  • Understanding of the importance of sustainability and cost-effectiveness in design and development of engineering solutions(5%)
  • Ability to work effectively as an individual, and as a member/leader in a team(5%)
  • Ability to be a multi-skilled engineer with good technical knowledge, management, leadership and entrepreneurship skills(10%)
  • Capability and enthusiasm for self-improvement through continuous professional development and life-long learning(5%)
Assessment Scheme:
  • Tutorial / Assignment - Individual case study assignment,presentation of the assignment,focus group discussion at tutorial,to enhance understanding of basic concepts in lecture(20%)
  • Test Quiz - written exam (20%)
  • Final Exam - written exam (60%)
Teaching and Learning Activities: 48 hours (lectures,tutorials and laboratory experiment)
Credit Hours: 3
Pre-Requisite: EME2036 Manufacturing and Operations Management
References:
  • F.S. Hillier and G. Lieberman, "Introduction to Operations Research", 7th edition, McGraw-Hill, 2007 (Textbook)
  • H.A. Taha, “Operations Research An Introduction” 7th edition, Prentice Hall, 2003.
  • P.A. Jensen and Jonathan F. Bard, “ Operations Research Models and Methods” John Wiley and Sons, 2003

Subject Contents

  • Introduction

    • Origins of operations research. Overview of operation research modeling approach
     
  • Scheduling and Sequencing

    • The role of scheduling. Scheduling as a function in an enterprise. Deterministic scheduling models. Single and parallel machines heuristic: lateness, earliness and tardiness.
     
  • Linear Programming

    • LP model formulation. Theory of simplex method. The revised simplex method. Duality theory and sensitivity analysis. The dual simplex method. Upper method technique. Goal programming and parametric linear programming
     
  • Linear Programming Application

    • Transportation, transshipment and assignment problems. Other examples.
     
  • Integer Linear Programming

    • Modelling concepts of integer programming. Binary variables in model formulation. The branch-and-bound technique and its application to binary integer programming.
     
  • Queuing Models

    • Basic structure of queueing models. The birth-and-death process. Queueing models based on birth-and-death process. Queueing models involving exponential and non-exponential distributions. Applications of queueing theory.
     
  • Game Theory and Decision Analysis

    • The formulation of two-person, zero-sum games. Games with mixed strategies. Decision making under risk. Decisions under uncertainty
     
  • Simulation Modeling

    • The essence of simulation. Generation of random numbers. Elements of discrete-event simulation. Variance-reducing techniques. Regenerative method for statistical analysis. Monte-Carlo simulation modelling.
     
  • Dynamic Programming

    • Characteristics of dynamic programming. Deterministic dynamic programming modeling. Probabilistic dynamic programming modeling.