MN4043 Decision Modeling and Analysis

This course introduces mathematical modeling for a sound conceptual understanding of the decision-making process. This course familiarizes the students with applications, assumptions, and limitations of the quantitative methods in modeling. It focuses on the development of mathematical and spreadsheet models, the verification of those models, sensitivity analysis of the solutions generated from a model, and the implementation of those solutions. Some of the topics covered include linear programming, non-linear and integer programming, simulation, and forecasting. The process of modeling and particular modeling tools are applied to business problems in finance, acquisition, logistics and manpower planning.

Prerequisite

MN3041 or MN3911 or MN3040

Lecture Hours

4

Lab Hours

0

Course Learning Outcomes

• Explain the role of mathematical modeling in analyzing complex decision-making problems across diverse domains, including resource allocation, scheduling, logistics, and financial planning.

• Formulate and solve linear programming models using Microsoft Excel Solver, including defining objective functions, constraints, and interpreting solutions.
• Apply optimization concepts such as feasible regions, optimality, and sensitivity analysis to assess the robustness and implications of model solutions.
• Formulate and analyze integer programming models, and recognize the computational challenges and practical considerations associated with discrete decision variables.
• Employ decision analysis techniques to support decision-making under conditions of risk and uncertainty, incorporating tools such as decision trees and expected value analysis.
• Use time series forecasting methods to predict trends and behaviors from historical data, applying appropriate techniques based on the data context.