# STU44005 – Decision Analysis

**Source**: https://teaching.scss.tcd.ie/module/stu44005-decision-analysis/
**Parent**: https://www.maths.tcd.ie/undergraduate/modules/minor-stats.php

|  |  |
| --- | --- |
| **Module Code** | STU44005 |
| **Module Name** | Decision Analysis |
| **ECTS Weighting [**[1]**](https://teaching.scss.tcd.ie/wp-admin/post.php?post=372&action=edit#_ftn1)** | 5 ECTS |
| **Semester Taught** | Semester 1 |
| **Module Coordinator/s** | Athanasios G. Georgiadis |

## Module Learning Outcomes

On successful completion of this module, students will be able to:

LO1. Model problems and extract decisions in operations research, using\
deterministic dynamic programming;\
LO2. Decide under stochastic procedures about celebrated problems in\
operations research;\
LO3. Employee Markov chains for obtaining decisionsin several problems that\
depend on time evolutions governed by probabilities.

## Module Content

To introduce Students to the field of Operations Research. The Students will model\
and solve problems popping up from Operations Research. The powerful tools of\
Dynamic Programming (both deterministic and stochastic) as well as Markov chains\
will be studied in depth.\
• Deterministic Dynamic Programming: Optimal route problem, Equipment\
replacement, Resource allocation, Optimal load problem;\
• Stochastic Dynamic Programming: The preceding problems in a stochastic\
form;\
• Markov Chains in Operations Research;\
The module contains decisive knowledge for Students of MSISS.At the same time, it\
consists a precise field ofapplication of the mathematical knowledges of Math\
Students.

## Teaching and Learning Methods

Two lectures and one tutorial per week.

## Assessment Details

|  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- |
| **Assessment Component** | **Brief Description** | **Learning Outcomes Addressed** | **% of Total** | **Week Set** | **Week Due** |
| Examination | In person written examination, 2.5-hours | LO1, LO2, LO3 | 100% | N/A | N/A |

## Reassessment Details

In person written examination, 2.5 hours, 100%.

## Contact Hours and Indicative Student Workload

|  |  |
| --- | --- |
| **Contact Hours (scheduled hours per student over full module), broken down by**: | **33 hours** |
| Lecture | 22 hours |
| Laboratory | 0 hours |
| Tutorial or seminar | 11 hours |
| Other | 0 hours |
| **Independent Study (outside scheduled contact hours), broken down by:** | **83 hours** |
| Preparation for classes and review of material (including preparation for examination, if applicable) | 41 hours |
| Completion of assessments (including examination, if applicable) | 42 hours |
| **Total Hours** | **116 hours** |

## Recommended Reading List

Full manuscripts and videos as well as corresponding exercises, will be provided by\
the instructor to Students. Some auxiliary literature that deals for the mainstream\
Operations Research follows.\
Dimitri P. Bertsekas, Dynamic Programming and Optimal Control, Vol. I, 4TH EDITION,\
2017.\
Dimitri P. Bertsekas, Dynamic Programming and Optimal Control, Vol. II, 4TH EDITION:\
APPROXIMATE DYNAMIC PROGRAMMING 2012.\
Wintson, Operations Research: Applications and Algorithms, 2003.

## Module Pre-requisites

**Prerequisite modules:** The module is designed to be self-contained.

**Other/alternative non-module prerequisites:** knowledge of elementary probability.

## Module Co-requisites

None

## Module Website

[Blackboard](https://tcd.blackboard.com/webapps/login/)