ENGSCI 760 : Algorithms for Optimisation

Engineering

2021 Semester One (1213) (15 POINTS)

Course Prescription

Meta-heuristics and local search techniques such as Genetic Algorithms, Simulated Annealing, Tabu Search and Ant Colony Optimisation for practical optimisation. Introduction to optimisation under uncertainty, including discrete event simulation, decision analysis, Markov chains and Markov decision processes and dynamic programming.

Course Overview

This course gives an introduction to optimisation methods and decision making algorithms. This course is designed to be accessible to students from a wide variety of backgrounds. Some of the material will draw upon linear (and integer) programming techniques, as taught in ENGSCI 255/391/311.

This course has three sections:
Decision Making under Uncertainty (12 lectures)
Heuristics (12 lectures)
Dynamic Programming  (12 lectures)

In the assignments, students will be required to develop code to implement selected algorithms, and so are expected to be proficient in programming and/or spreadsheet development (as required by the assignment). Algorithms will be presented using standard mathematical notation, and so familiarity with such notation is expected.

Course Requirements

Prerequisite: COMPSCI 101 or ENGGEN 131

Capabilities Developed in this Course

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

Learning Outcomes

By the end of this course, students will be able to:
  1. Understand and apply dynamic programming: create a dynamic programming model for an appropriate analytics problem, solve the model and describe the policy it gives (Capability 1, 2 and 3)
  2. Understand and apply algorithms for modelling under uncertainty: construct analytics models, solve these models and analyse their solution (Capability 1, 2 and 3)
  3. Understand and apply heuristic algorithms: devise and apply a meta-heuristic algorithm as part of applying local search to solve a problem (Capability 1, 2 and 3)

Assessments

Assessment Type Percentage Classification
Assignments 30% Individual Coursework
Final Exam 70% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3
Assignments
Final Exam
There will be three assignments.  The final percentage mark cannot exceed the exam percentage mark by more than 10%.

Workload Expectations

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.

This course involves 150 hours of study in total over 15 weeks. You can expect  to spend 36 hours in lectures, another 36 hours reading and thinking about the content and 78 hours of work on assignments and exam preparation.

Delivery Mode

Campus Experience

Attendance is expected at scheduled activities including lectures. Lectures will be available as recordings. 
The activities for the course are scheduled as a standard weekly timetable.

Learning Resources

Course notes will be made available through Canvas.

Health & Safety

This course poses no health and safety risks beyond those experienced in a standard lecture environment, which include tripping, Covid transmission, and similar. All use of a keyboard poses a risk, and so students should acquaint themselves with RSI mitigation strategies.

Student Feedback

At the end of every semester 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 and respond with summaries and actions.

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

Class Representatives in each class can take feedback to the department and faculty staff-student consultative committees.

This course is continually adjusted and improved based on feedback from students, and in response to new technologies becoming available.

Other Information

Students will need to create software programmes and/or develop spreadsheets that implement algorithms covered in this course. In some assignments, example code may be provided using languages such as Matlab, Python, Excel, C#, and Visual Basic for Applications; students will be expected to complete this code, typically in the language provided. Students are expected to draw upon their existing programming expertise to quickly adapt to any of these languages. Knowledge of common data structures such as arrays, lists, and dictionaries will be assumed.

Students will be expected to confidently read and write mathematical expressions, and will need a basic understanding of the two optimisation concepts of branch and bound and the Simplex algorithm. Extra background reading material will be provided for students without these skills. An understanding of probability will also be assumed.

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.

Academic Integrity

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.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.

Note that in Engineering Science, class representatives are appointed for a 'part' (eg Part IV) as a whole, not for individual classes.

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 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.

Learning Continuity

In the event of an unexpected disruption we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and your assessment is fair, and not compromised. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator, and if disruption occurs you should refer to the University Website for information about how to proceed.

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 10/02/2021 05:47 p.m.