Topic outline

  • Course Description

    In this course, we will learn how to:

    1. Develop mathematical models to analyse complex business situations.
    2. Implement mathematical models in appropriate software, e.g., Excel, GAMS, or MATLAB, to solve business cases.
    3. Test the validity of the model’s assumptions by performing sensitivity analysis and interpreting the findings.

    Application areas include defense, energy, environment, finance, human resource management, IT planning, logistics, manufacturing, marketing, and transportation. Quantitative methods such as linear programming (LP), integer programming, network flows, multi-criteria analysis, and simulation will be applied as appropriate with an emphasis on problem formulation and computational implementation with realistic data. This course is designed to develop students’ problem-solving skills and expertise in state-of-the-art decision tools. The emphasis will be on understanding the models thoroughly so that they may be applied to analyse real-world business decisions. As always, while a mathematical approach is encouraged throughout, the main concepts will be illustrated through extensive case studies. Furthermore, emphasis is placed on the intuition behind the concepts to enable more profound understanding. Students registering for this course would benefit from a solid grounding in mathematics, statistics, or programming.


    The course is given in period I such that the first four (recorded) lectures are given in 21-24 September and the second four in 5-8 October (9:00-12:00). The course is assessed through a group assignment and a final examination (both graded on an 1-5 scale). The assignment will consist of case studies that groups of students will solve together. It will cover topics from the first four sessions and will be available on 24 September with a deadline of 4 October. The final exam will be a take-home assessment during 19-23 October. Grading is based 25% on the assignment and 75% on the exam. 

    Both the lecturer and the teaching assistant will have remote drop-in help sessions for the group assignment, more information on these later.

    Lecturer: Afzal Siddiqui

    Course Assistant: Olli Herrala

    E-mail addresses: firstname.surname (at)

    Recommended Reading:

    Ragsdale, Cliff T. (2015), Spreadsheet Modeling & Decision Analysis: a Practical Introduction to Business Analytics, Cengage Learning, Stamford, CT, USA (ISBN: 9781285418681)

    Course schedule (will be updated with links as lectures etc. are published):
    Content Recommended reading (Ragsdale, 2015)
    Lecture 1: Linear Programming (LP)

    • LP problem formulation
    • Guiding decisions via LPs
    • Graphical solution approach
    • Simplex solution method
    • Implementation of LPs in Excel
    • Sensitivity analysis

    Chapter 2, Sections 3.0 to 3.8, Sections 4.0 to 4.6
    22.9. Lecture 2: LP Applications in Business

    • Finance
    • Marketing
    • Production management
    • Interpretation of results in Excel

    Sections 3.9, 3.10, and 3.12
    23.9. Lecture 3: Network Models

    • Taxonomy
    • Minimum cost flow
    • Shortest path
    • Maximum flow
    • Transportation
    • Assignment

    Chapter 5
    24.9. Lecture 4: Integer Programming (IP) and Its Business Applications

    • IP problem formulation
    • Branch-and-bound solution approach
    • Implementation of IPs in Excel
    • Binary variables: logical conditions
    • Binary variables: fixed costs
    • Binary variables: disconnected intervals
    • Binary variables: piecewise linear functions
    • Application: supplier selection

    Group assignment available
    Chapter 6
    4.10. Group assignment deadline
    5.10. Lecture 5: Multi-Period Planning

    • Fundamentals
    • Cash flows
    • Application: hydropower scheduling

    Sections 3.13-3.14
    6.10. Lecture 6: Multi-Criteria Decision Making

    • Soft constraints
    • Goal programming
    • Application: hotel expansion
    • Pareto optimality and efficient frontier
    • Applications: environment and logistics

    Chapter 7
    7.10. Lecture 7: Simulation

    • Generating discrete random variables
    • Generating continuous random variables
    • Statistical analysis of results
    • Application: newsvendor
    • Application: manufacturing

    Chapter 12
    8.10. Lecture 8: Project Management

    • Critical path method (CPM)
    • LP resolution
    • Project crashing
    • Project management under uncertainty via simulation

    Chapter 15
    19.-23.10. Take-home exam