# Prescriptive Analytics for Decision Making - BUSN9970

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## Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2024 to 2025
Canterbury
Autumn Term 7 15 (7.5) Vedat Bayram

## Overview

The aim of this module is to introduce students to optimisation modelling and solution techniques, typical applications areas within strategic/operation business planning, and the use of commercial optimisation software.

The module covers the following indicative topics:
• Linear Programming: Students will be introduced to the building blocks of optimisation (i.e. decision variables, objectives, constraints), how to mathematically formulate linear programming (LP) models, LP solution techniques, sensitivity analysis (e.g. range of optimality reduced costs, dual prices), and typical applications like production planning, scheduling, and portfolio selection.
• Network Models: This topic includes a range of concepts and modelling techniques for formulating classic network models, including transportation and assignment, shortest path, maximum flow, and minimum spanning tree problems, and common solution approaches.
• Integer Programming: This will cover integer linear programming (ILP) models, including binary integer models, classic exact and heuristic solution methods (e.g. branch and bound, greedy heuristics), and typical application areas of ILP, including capital budgeting, fixed charge production, and facility location.

## Details

### Contact hours

Total contact hours: 42
Private study hours: 108
Total study hours: 150

## Method of assessment

Main assessment methods:
Individual Written Assessment on Mathematical Modelling and Computation (2000 words): 30%.
Exam (2 hours): 70%

Reassessment methods
Reassessment Instrument: 100% Exam

See the library reading list for this module (Canterbury)

## Learning outcomes

The intended subject specific learning outcomes.
On successfully completing the module students will be able to:
- Demonstrate a comprehensive understanding of quantitative models for decision making.
- Demonstrate conceptual understanding of how complex real-world systems can be represented in mathematical form.
- Exhibit a systematic knowledge of some classic business, management, and industry problems, formulate them mathematically, and solve them.
- Demonstrate an ability to deal with various real-world complexities and incorporate these into the modelling framework in order to prescribe actionable recommendations.
- Implement such models using industry-standard software and perform analyses to support business planning and management.

The intended generic learning outcomes.
On successfully completing the module students will be able to:
- Independently apply their model building, problem-solving and numerical skills to solve complex business/management/industry problems.
- Demonstrate an ability to select the most appropriate technique for a particular business/management/industrial problem.
- Independently analyse the outcome of a model and present their findings in a clear yet rigorous manner.