CS5111 Discrete Optimisation

Academic year

2025 to 2026 Summer after graduation

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

15

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 11

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Availability restrictions

This module is only available to MSc AI students who started their programme in January.

Module coordinator

Dr A D Barwell

Dr A D Barwell
This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module Staff

Dr Joan Espasa Arxer, Dr Nguyen Dang

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

Discrete optimisation problems are central to many real-world applications, ranging from timetabling to optimising transportation networks. These problems often require making decisions over large, discrete sets of possibilities, where finding high-quality solutions can be computationally demanding.This module provides an introduction to the theory and practice of discrete optimisation. Students will gain a solid foundation in formulating, solving, and evaluating discrete optimisation problems using modern tools and techniques.We first introduce how to model real-world problems declaratively, using modelling languages that abstract away from algorithmic details. We then explore generic solving techniques that can be applied across a wide range of problems. To tackle large-scale or particularly hard instances, we introduce various metaheuristic approaches. Finally, we explore how machine learning can be integrated into the optimisation process to enhance solving performance.

Assessment pattern

Coursework = 100%

Re-assessment

Coursework = 100%

Learning and teaching methods and delivery

Weekly contact

2 hr x 10 weeks in-person seminars/tutorials.

Scheduled learning hours

20

The number of compulsory student:staff contact hours over the period of the module.

Guided independent study hours

130

The number of hours that students are expected to invest in independent study over the period of the module.

Intended learning outcomes

  • Understand core concepts in the field of Discrete Optimisation.
  • Learn about declarative modelling and generic solving approaches for discrete optimisation problems.
  • Learn about state-of-the-art (meta-)heuristic solving approaches for solving large-scale discrete optimisation problems.
  • Integrate machine learning into the solving process to improve performance and robustness.