# STATS 255 : Optimisation and Data-driven Decision Making

## Science

### Course Prescription

Explores methods for using data to assist in decision making in business and industrial applications. Software packages will be used to solve practical problems. Topics such as linear programming, transportation and assignment models, network algorithms, queues, Markov chains, inventory models, simulation, analytics and visualisation will be considered.

### Course Overview

This course covers three aspects of data-driven optimisation and decision-making:

• linear programming models, including transportation and transshipment models.
• decision trees and classification trees
• simulation
There is an emphasis on the use of software, namely Excel and R throughout the course. We do not assume that you are able to program, but we expect that you will be comfortable using a command-line to run code to analyse and tidy data.

### Course Requirements

Prerequisite: ENGSCI 211 or STATS 201 or 208, or a B+ or higher in either MATHS 120 or 130 or 150 or 153 or STATS 101 or 108, or a concurrent enrolment in either ENGSCI 211 or STATS 201 or 208 Restriction: ENGSCI 255

### Capabilities Developed in this Course

 Capability 1: Disciplinary Knowledge and Practice Capability 2: Critical Thinking Capability 3: Solution Seeking Capability 4: Communication and Engagement

### Learning Outcomes

By the end of this course, students will be able to:
1. Define linear or integer programs, using mathematics (including identifying decision variables, constraints, and the objective function) based on a written problem description. (Capability 1 and 2)
2. Implement optimisation models in Excel, and solve them. (Capability 3)
3. Interpret solution and sensitivity analysis output from optimisation models. (Capability 2)
4. Model a decision problem (given as a paragraph + some tables) using a decision tree, and carry out calculations. (Capability 1 and 2)
5. Apply R functions for tidying and visualising data (Capability 1 and 4)
6. Apply probability distributions in the context of simulation of a situation, and in modelling inter-arrival times and service times in simulations of queues. (Capability 1)
7. Interpret output from simulation and produce confidence intervals around key statistics. (Capability 2)
8. Apply classification tree and random forest methods to model classification problems. (Capability 1 and 3)

### Assessments

Assessment Type Percentage Classification
Assignments 30% Individual Coursework
Test 20% Individual Test
Exam 50% Individual Examination
1 2 3 4 5 6 7 8
Assignments
Test
Exam
Plussage can be applied: Assignments 30% + Test 10% + Exam 60%. The best of the two marks is taken as the final mark.

Students must score at least 45% in the exam to pass the course.

### Key Topics

• Linear Programming (Weeks 1-4) - covers the formulation of linear programs, both algebraically and in Excel, and interpretation of the solutions. Integer programming models, transportation and transshipment problems are considered.
• Decision Making and Data Analytics (Weeks 5-8) - covers decision trees, data manipulation and visualisation, and classification trees, random forests and naive Bayes for classifying data.
• Simulation (Weeks 9-12) - covers modelling random processes in R, random number generation, with applications to queues, inventory management, revenue management and optimisation.

### Learning Resources

A coursebook is available to purchase from the Science Student Resource Centre.

### Special Requirements

Some lectures and tutorials will require students to bring a laptop or tablet running RStudio or RStudio.cloud.

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, you can expect 3 hours of lectures, a 1 hour tutorial, 3 hours of reviewing the course content and doing tutorial problems, and 3 hours of working on assignments / test preparation, each week.

### Digital Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

### Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website at http://disability.auckland.ac.nz

### Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page: https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.

This should be done as soon as possible and no later than seven days after the affected test or exam date.

### Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

### Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter (https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html).

### Disclaimer

Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.

Published on 20/12/2019 01:17 p.m.