Modules

Core Module Information
Module title: AI for Transportation and Logistics Optimisation
SCQF level: 11:
SCQF credit value: 20.00
ECTS credit value: 10
Module code: SET11130
Module leader: Neil Urquhart
School School of Computing, Engineering and the Built Environment
Subject area group: Computer Science
Prerequisites

There are no pre-requisites for this module to be added
Description of module content:

After studying this module you will have a clear understanding as to the potential applications of a range of AI techniques in the domain of transportation and logistics. You will study a range of techniques in a Python environment, the emphasis will be on minimal coding, working with existing libraries and examples. We will cover fundamentals of geospatial data science, including the use of graphs and path finding algorithms, and problem solving algorithms that can be applied to range of real-world problems.
Learning Outcomes for module:

Upon completion of this module you will be able to

LO1: Critically analyse a real-world transportation problem and determine suitable optimisation techniques to find solutions

LO2: Compare the performance of multiple optimisation techiques when applied to a problem using critical analysis of the solutions generated and the algorithmic performance

LO3: Demonstrate an in-depth knowledge of algorthmic techniques and data structures which may be applied to transportation problems

LO4: Critically analyse and reflect on the suitability of AI techniques that have been used to optimisae a problem
Full Details of Teaching and Assessment
2026/7, Trimester 2, In Person,
VIEW FULL DETAILS
Occurrence: 001
Primary mode of delivery: In Person
Location of delivery: MERCHISTON
Partner:
Member of staff responsible for delivering module: Neil Urquhart
Module Organiser:


Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)NESH Description
Face To Face Lecture 20 Each lecture will cover a specific topic: 1 Python Revision 2 Travelling Salesman Problem 3 Heuristics for the Vehicle Routing Problem 4 Applying Evolution to the Vehicle Routing Problem 5 Solving Problems that have dual solution characteristics 6 Solving Problems that have multiple solution characteristics 7 Illuminating Problems 8 Geospatial Data & Data Sources 9 Routing algorithms 10 Case Study Each lecture will cover theory and demonstration of practical aspects. The practical demonstrations will feed directly into the lab sessions.
Face To Face Practical classes and workshops 20 The lab materials will be based on Jupyter Notebook documents, with the emphasis being on experimentation within frameworks to solve problems that have a real-world basis. Some coding will be undertaken, withe emphasis being on the application and evaluation of heuristics to range of problems.
Online Guided independent study 160 Students will be encouraged to undertake the following: 1. Self-study to increase their Python skills 2. Read background chapters from the course text 3. Read further papers, book chapters as directed.
Total Study Hours200
Expected Total Study Hours for Module200


Assessment
Type of Assessment Weighting % LOs covered Week due Length in Hours/Words Description
Centrally Time Tabled Examination 40 1~3~4 Exam Period HOURS= 2 hrs This exam will assess the students' knowledge and understanding of the fundamental geospatial data science and AI techniques. Some questions will be purely knowledge and understanding, whilst others will require a limited amount of problem solving and analysis.
Practical Skills Assessment 60 1~2~3~4 Week 12 HOURS= 2500 words The student will be presented with a problem scenario (including data) and required to optimise the problem using AI techniques. Boilerplate code will be provided, the emphasis being on applying and evaluating techniques rather than coding. A 5 page report (within a template) will be required, this will describe critical analysis of the problem, justify the selection of AI techniques and then critically appraise the solution developed by the student.
Component 1 subtotal: 100
Component 2 subtotal: 0
Module subtotal: 100

Indicative References and Reading List - URL:
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