Module manager: Antonino Sgalambro
Email: a.sgalambro@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2026/27
LUBS2029 Optimisation and AI for Business
This module is approved as a discovery module
In today's global economy, the number of businesses and organizations exploring and relying upon the adoption of intelligent technologies to gain a competitive advantage and enhance efficiency is growing exponentially. A good understanding and appropriate utilisation of intelligent technologies has therefore become an essential skill in the workplace at all levels of responsibilities. This module is aimed at providing students with the fundamental knowledge pillars around methods and tools from optimisation and artificial intelligence, which underpin data-driven intelligent technologies. Drawing upon real-world success cases in a range of productive sectors — including manufacturing, healthcare, logistics and service management — students will learn how to lead and manage the adoption of optimisation and artificial intelligence tools in the context of businesses and organisations, securing awareness around opportunities and limitations. The module will allow students to acquire a critical understanding and hands-on experiences on how optimisation and AI can be successfully adopted in Business Management.
The module aims to:
Provide an introduction to basic concepts in optimisation modelling, exact, heuristic and multi-objective solution algorithms, machine learning, and language processing tools.
Develop practical skills in optimisation and artificial intelligence tools and data-driven business analysis.
Utilise an array of real-world success cases to explore different approaches to building, solving, applying and calibrating optimisation and AI models for business management.
Encourage critical understanding and thinking about the broad impact of adopting technologies based on optimisation and AI, including ethical considerations and future trends.
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
LO1: Understand the basic concepts underpinning prescriptive and predictive modelling in optimisation and artificial intelligence
LO2: Critically evaluate an appropriate adoption of optimisation and AI tools in business management, and assess their effectiveness.
LO3: Select and utilise a range of tools from optimisation and AI for increasing efficiency in data-analysis and problem-solving in business management.
LO4: Actively and creatively design and apply optimisation and AI solutions to specific real-world business problems.
LO5: Undertake knowledgeable reflections on the role of optimisation and AI and related emerging technologies in business applications.
On successful completion of the module students will have demonstrated the following skills learning outcomes:
SLO1 Academic Skills: Critical engagement with relevant literature to effectively understand and articulate optimisation and artificial intelligence concepts.
SLO2 Work-ready skills 1: Problem Solving & Analytic skills achieved through a critical understanding and flexible approach to modelling and solving business management problems, adopting tools from optimisation and artificial intelligence.
SLO3 Work-ready skills 2: Commercial awareness in order to assess real-world decision-making challenges at different levels (strategic, tactical and operational) and develop data-driven approaches to provide insightful and actionable recommendations, understanding impact and benefits.
SLO4 Digital skills: Digital creation, problem solving & innovation to be able to analyse and solve specific digital challenges in business operations, decision-making and strategic planning.
SLO5 Technical skills: Acquire technical skills of optimisation modelling languages and tools, spreadsheet-based optimisation solvers, AI oriented programming languages, tools and resources.
Indicative content:
Introduction to Decision Optimisation modelling
Linear programming: product mix problem
The Knapsack Problem and its applications
Location problems
Multiple-objective Optimisation and Pareto Optimality
Valuing subjectivity: estimating impact with linear regression
Classification with logistics regression
Decision tree and random forest
Neural networks
From GenAI to AGI
Optimisation and AI: emerging joint perspectives
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lectures | 11 | 1.5 | 16.5 |
| Practicals | 8 | 2 | 16 |
| Private study hours | 167.5 | ||
| Total Contact hours | 32.5 | ||
| Total hours (100hr per 10 credits) | 200 | ||
Formative feedback will be offered through a series of introductory pieces of work related to the core concepts covered in lectures and seminars, thus stimulating the students to engage immediately with an autonomous reflection on the impact of those concept on real-world business management. Formative feedback exercises represent a fundamental part of the module and are instrumental at allowing the students to get ready for practical sessions, where they apply the knowledge to solve realistic business problems. This approach supports a smooth and progressive preparation for the final assessment. Students will receive individual tailored formative feedback from the lecturers based on the implementation of each proposed piece of work.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Coursework | Individual, 3,500 word coursework | 100 |
| Total percentage (Assessment Coursework) | 100 | |
The resit for this module will be 100% by 3,500 word coursework.
Check the module area in Minerva for your reading list
Last updated: 30/04/2026
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