ENGSCI 760 : Algorithms for Optimisation

Engineering

2024 Semester One (1243) (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, and has often included students studying in areas such as bioengineering and software/electrical/computer systems engineering. Some of the material will draw upon linear (and integer) programming techniques, as taught in ENGSCI 255/391/311; this will be summarised in the handouts for students to work through in their own time.

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). (See below for more on this.) Algorithms will be presented using standard mathematical notation, and so familiarity with such notation is expected.

Course Requirements

Prerequisite: 15 points from COMPSCI 101, ENGGEN 131, MATHS 162, 199, and 15 points from COMPSCI 120, ENGSCI 111, STATS 125

Capabilities Developed in this Course

Capability 3: Knowledge and Practice
Capability 4: Critical Thinking

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 3.1, 3.2, 4.1 and 4.2)
  2. Understand and apply algorithms for modelling under uncertainty: construct analytics models, solve these models and analyse their solution (Capability 3.1, 3.2, 4.1 and 4.2)
  3. Understand and apply heuristic algorithms: devise and apply a meta-heuristic algorithm as part of applying local search to solve a problem (Capability 3.1, 3.2, 4.1 and 4.2)

Assessments

Assessment Type Percentage Classification
3 Assignments 39% Individual Coursework
Final Exam 50% Individual Examination
Test 11% Individual Test
Assessment Type Learning Outcome Addressed
1 2 3
3 Assignments
Final Exam
Test

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, test preparation and exam preparation.

Delivery Mode

Campus Experience

Attendance is expected at scheduled activities including lectures. Lectures will be available as recordings (subject to technology failures), but these are no substitute for lecture attendance. The activities for the course are scheduled as a standard weekly timetable.

Learning 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.

Course notes will be made available through Canvas.

Health & Safety

Students must ensure they are familiar with their Health and Safety responsibilities, as described in the university's Health and Safety policy.

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

This course has a programming pre-requisite; we expect all students to be proficient coders. 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/tools such as Matlab, Python, Excel, 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.

As a minimum, we expect that you can trivially write computer code (eg in Python) for tasks such as the following:
1) Given the set X={A,B,C,D,E,F}, choose a random element from this set, i.e. choose some random y∈X.
2) Generate a vector/list that is a random ordering of X
3) Given a vector/list x=(x[1],x[2],x[3],x[4],..,x[n]), calculate f=∑ d(x[i],x[i+1]) (where i=1, 2, ..., n-1) where d(p,q)=(p-q)^2
4) Given the x above, and some j, k, form a new solution y which is the same as x except that the ordering of elements x[j], x[j+1], ..., x[k] has been reversed in y.

Students will be expected to confidently read and write mathematical expressions, be familiar with mathematical notation such as summations and sets (eg C=A ∪ B , D=A ∩ B , x∈C) and be confident using these to write algorithms.  Student will also 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 optimisation skills.

Students are expected to have a solid background in probability, and so be comfortable with expressions such as: P(A|B)=P(B|A)P(A)/P(B) and other basic concepts of probability.

Policy on Late Assignment Submissions:
For Canvas Quizzes, or assignments given as Canvas Quizzes, no late submissions will be accepted. Once the due date / time is past, all active quizzes will be automatically submitted.
For written assignments, aim to have all files safely uploaded well before the due time. It is highly recommended that you upload draft copies of all files at least one day in advance of the deadline, in case of last-minute technical difficulties. Late submissions will be marked as usual, but will be subject to a linear penalty that scales to 100% deducted after 24 hours past the due date. This is automatically applied by Canvas.

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 for potential plagiarism or other forms of academic misconduct, using computerised detection mechanisms.

Tests will, where possible, be run under exam conditions. Assignments will need to be completed independently, in accordance with relevant University regulations, policies, guidelines and handbooks.

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.

Students who apply for an aegrotat or compassionate consideration for the exam but have not completed the terms test may be required to complete extra work including an oral or written test.

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 course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, 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 students may be asked to submit 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. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.