MSc in Engineering Management

Course Information Package

Course Unit CodeMEM501
Course Unit DetailsMSc Engineering Management (Required Courses) -
Number of ECTS credits allocated7
Learning Outcomes of the course unitBy the end of the course, the students should be able to:
  1. Develop the insight to notice when quantitative tools are appropriate for solving an engineering management problem.
  2. Develop an understanding of the use of quantitative techniques in engineering management.
  3. Develop the expertise to build mathematical models for engineering management problems.
  4. Select the suitable approach for solving the problem at hand.
  5. Employ optimization software to solve the mathematical models.
  6. Analyze and interpreting the solutions.
  7. Modify optimization models and performing sensitivity analysis.
Mode of DeliveryFace-to-face
Recommended optional program componentsNONE
Course Contents

Data Management:

Mathematical Modelling in Engineering Management:

Modelling examples, the modelling process, certainty and uncertainty in models


Confidence intervals, distribution fit, hypothesis testing, regression analysis


Modeling & Optimization:

Linear programming:

Decision variables, objectives, constraints, linear programming solver, sensitivity analysis

Linear programming in Production Models:

Blending models, product mix models, multi period process models, process based cost models

Network Models:

Assignment, shortest path problems

Mathematical Programming with Integer Variables:

Effect of Integer Variables in Mathematical Programming

Integer Programming and Mixed Linear-Integer Programming

Transportation and Transhipment problems,

Location allocation, multistage logistic models


Decision Analysis & Simulation

Decision analysis:

Decision criterion, probabilistic decision models, decision trees


Monte Carlo simulation, input and output analysis

Queuing analysis:

Design and analysis of production and service system using queuing models to predict and mitigate delay and congestion

Recommended and/or required reading:
  • Render, B. R. M. Stair, Quantitative analysis for management, Prentice Hall
  • Baker, K.R., Optimization Modeling with Spreadsheets. Duxbury Applied Series, 2006
  • A. Ravindran, K. M. Ragsdell, G. V. Reklaitis, Engineering Optimization: Methods and Applications, 2006
  • D. Montgomery, Applied Statistics and Probability for Engineers, 2007
Planned learning activities and teaching methods

The course is delivered through three hours of lectures per week, which include presentation of new material and demonstration of concepts and methods. Lectures also include in-class exercises to enhance the material learning process and to assess the student level of understanding and provide feedback accordingly.

Practical demonstrations and labwork are conducted in computer laboratories using various software (Microsoft XL) as well as specialised tools (e.g. AIMMS modelling package).

Assessment methods and criteria
Final Exam50%
Language of instructionEnglish
Work placement(s)NO