Courses

Complex decision problems arise throughout society and industry in logistics, healthcare, energy, humanitarian response, public policy and environmental planning. They are characterised by scale, uncertainty, multiple and sometimes conflicting objectives, interdependent decisions, human behaviour and incomplete information. Classical operational research provides powerful tools to model and optimise such problems; artificial intelligence offers complementary capabilities to learn from data, extract insight from unstructured information, represent difficult relationships and guide decision-making. This course provides a structured introduction to the rapidly growing interface between AI and OR, and examines how these approaches can be combined to produce decisions that are not only computationally effective, but also robust, interpretable and useful in practice.

The topics covered include Foundations of Complex Decision Problems, Machine Learning for Optimisation, Contextual Optimisation, Learning-Based Decomposition, Simulation, Optimisation and Generative AI, LLMs in Multi-objective Optimisation Problems, and AI for Evidence Extraction from Text. Lectures are combined with hands-on computational workshops, case studies from warehousing, humanitarian logistics and disaster planning, a practitioner session, and a week-long group challenge based on a real-world Net Zero problem drawn.from

Dr Ramin Raeesi: Course Leader

PRE-REQUISITES
Participants should have a basic familiarity with operational research, probability and quantitative modelling. Some experience of mathematical optimisation and programming will be helpful, but prior specialist knowledge of machine learning or large language models is not required. Guidance on required software and preparatory material will be provided before the course.

AIM
The course aims to equip students with a conceptual framework and a practical toolkit for combining AI and OR to tackle complex decision problems. It provides familiarity with state-of-the-art methods at the interface of machine learning, optimisation, simulation and generative AI, and develops the ability to judge critically when and how AI should be integrated into a decision-support model, and how to assess whether doing so actually leads to better decisions. On completion of the course, participants should be well prepared to understand, and contribute to, the fast-moving research literature in this area.

LEARNING OUTCOMES
On completion of the course, participants will be able to:
• Characterise the sources of complexity in real-world decision problems and explain the complementary roles of AI and operational research.
• Select and apply appropriate machine learning and optimisation approaches, including contextual optimisation, smart predict-then-optimise and learning-based decomposition.
• Explain how simulation, optimisation, generative AI and text-based AI methods can be combined to support complex decisions, and assess the reliability and usefulness of their outputs.
• Critically evaluate AI-supported decision approaches, including their treatment of Pareto-efficient trade-offs, preference elicitation, feasibility, validity, ethics and risk.
• Work collaboratively to frame a real-world complex decision problem and propose a well-justified combined AI-and-OR approach.

PRINCIPAL TOPICS OF STUDY
• Foundations of complex decision problems and the complementary roles of AI and operational research.
• Machine learning for optimisation, including contextual, sequential and smart predict-then-optimise approaches.
• Learning-based decomposition and surrogate modelling for scalable, data-informed optimisation.
• Simulation, optimisation and generative AI for decision support, including agent-based modelling and prompt engineering.
• AI-assisted extraction, measurement and prediction from unstructured text.
• Multi-objective decision support, Pareto-frontier exploration, AI-mediated preference elicitation, and the ethical, practical and interpretive challenges of AI in decision-making.
• Applied case studies and group work on real-world AI-and-OR decision problems.

ASSESSMENT
Assessment will take the form of a group challenge presentation. Participants will develop and present an AI-and-OR response to a complex real-world decision problem, demonstrating their understanding of the methods, assumptions, limitations and practical implications involved.

TIMETABLE
To be confirmed nearer the time.

PRE-REQUISITES
Basic knowledge of mathematical programming.

AIMS OF THE COURSE
The course aims to develop knowledge on different aspects of optimization problems under uncertainty: stochastic optimization, optimization under uncertainty, and its applications.

LEARNING OUTCOMES FOR THE COURSE
On completion of the course, students will be able to:
1. Identify the main elements of a problem in optimization under uncertainty.
2. Understand the suitability of different approaches (for example, stochastic optimization vs robust optimization).
3. Solve independently basic and more advanced problems in optimization under uncertainty.

PRINCIPAL TOPICS OF STUDY
• Two-stage stochastic programming.
• Multi-stage stochastic programming.
• Robust optimization.
• Applications of stochastic programming and robust optimization

ASSESSMENT
Multiple-choice test

DESCRIPTION
Multiple Criteria Decision Making (MCDM) is a subfield of Operational Research that focuses on methods and tools to support decision-making in problems involving multiple, often conflicting, criteria. It has broad applications across domains such as business, supply chain management, energy, healthcare, sustainability, and logistics. This course introduces the theory and principles of MCDM, including preference modelling and goal programming, and explores various approaches for analysing multi-criteria decision problems and solving multi-objective optimisation models.

Main contributors:
• Dr Banu Lokman (Course Leader), University of Portsmouth (banu.lokman@port.ac.uk)
• Professor Dylan Jones, University of Portsmouth (dylan.jones@port.ac.uk)
• Dr Nikolaos Argyris, Loughborough University (N.Argyris@lboro.ac.uk)

PRE-REQUISITES
The basics of mathematical programming

AIMS OF THE COURSE
The course aims to develop knowledge of the theory of MCDM and develop skills in building and solving optimisation problems with multiple objectives.

LEARNING OUTCOMES
On completion of the course, students will be expected to:
• Understand the properties of efficient solution alternatives in decision problems with multiple
objectives
• Develop and solve mathematical models to obtain nondominated solutions of multi-objective optimisation
problems
• Understand and apply the goal programming method to solve decision problems with multiple goals
• Understand and apply preference modelling and interactive methods in MCDM

PRINCIPAL TOPICS OF STUDY

• Introduction to Multiple criteria decision making (MCDM)
• Efficiency and Nondominance
• Scalarization Techniques in Multi-objective Optimisation
• Exact & Approximate Solution Methods
• Goal Programming
• Preferences in MCDM: Foundations
• Preferences in MCDM: Interactive Methods

ASSESSMENTS
Assessment, a summative assessment exercise, will be held in the last day. It will typically last about an-hour. Feedback will be provided at the end of the course.

The course is led by Martin Kunc who chairs the special interest group of the OR Society on OR and Strategy, has published numerous papers and edited books on System Dynamics and is an Editor-in-Chief of JORS. The course will run from 0900-1300 hrs each day between 6th – 10th July 2026.

System Dynamics will focus on behavioural modelling with a systems perspective. Modelling is a common core for both parts, with behavioural modelling following the field of Behavioural OR by focusing on behavioural aspects of operations, and the systems perspective will emphasise modelling and simulation of systems. In addition to sessions by the two course staff, there will also be sessions by guest academics who are leaders in their respective fields.

PRE-REQUISITES
• None

AIMS OF THE COURSE
• To learn System Dynamics modelling
• To understand the publication aspects of System Dynamics modelling
• To integrate System Dynamics with other tools and methods in Operational Research (including Behavioural aspects)

LEARNING OUTCOMES
• Use of System Dynamics professional software
• Use of programming software to develop System Dynamics models
• How to publish System Dynamics papers
• Incorporate System Dynamics within the broad Operational Research are.

PRINCIPAL TOPICS OF STUDY
• Introduction to System Dynamics and Vensim
• System Dynamics within Operational Research (hybrid simulation, optimisation and behavioural aspects)
• System Dynamics Modelling Using Python
• System Dynamics Modelling Using R

TIMETABLE

Day 1
The initial half-day is devoted to learning the most used System Dynamics software: Vensim. Initially a simple model to familiarise with the existing components of System Dynamics is offered. Then, rules for modelling and basic structures in System Dynamics are taught.

Day 2
This second half-day is devoted to introducing system dynamics (SD) within the field of operational research through a set of generic models related to current topics of interest. Methodological aspects of using Optimisation and System Dynamics.

Day 3
A discussion of using System Dynamics as part of hybrid modelling will be offered. Presentation of recent works and explanation of the methods to develop hybrid models will be offered. Additionally, we will discuss System Dynamics modelling using Python.

Day 4
System dynamics models using R will be discussed in terms of fundamentals and examples.

Day 5
Additional presentation of System dynamics models using R. Finally, the assessment for the course is going to be held.

PURPOSES
The Nottingham NATCOR course covers the main techniques for heuristic optimisation algorithms (local search, meta-heuristics, multi-objective heuristics, hyper-heuristics, evolutionary algorithms) as well as an insight into complexity theory, automated configuration of heuristics, automated algorithm design, big data science and machine learning in the context of heuristic optimisation. The course is delivered by experts in the field with strong publication records and experience in the design and deployment of these methods on real-world problems in various business and industry scenarios.

DESCRIPTION
Heuristic Optimisation and Learning are cornerstone methodologies from the disciplines of Operational Research and Computer Science. These methods have been very successful in providing solutions to real-world problems across a wide range of application areas. Heuristic algorithms include a range of techniques from simple ‘rules of thumb’ to more sophisticated methods inspired on physical and natural processes like energy flow, evolution, and swarm intelligence. Heuristics can provide good-quality solutions (although not necessarily optimal) in practical computational time to otherwise intractable problems. Heuristics can be applied and tailored to a wide range of optimisation problems. Heuristic algorithms can also be combined with exact optimisation algorithms to form a rich variety of hybrid methodologies capable of performing with high effectiveness and efficiency when tackling complex problems. Big data usually refers to data that has large volume, variety, and complexity so that specialised modern techniques are required to analyse it and extract valuable knowledge from it. With the widespread availability of big data in many research problems and real-world applications, learning from it presents more challenges than traditional data science. There are many facets of big data science including acquisition, storage, computing infrastructure, visualisation, security, privacy, analysis, mining, machine learning etc. There are exciting interactions between big data science, machine learning and optimisation, and one of these is learning from evolutionary algorithms which is one of the topics covered in this course.

The course is suitable for participants from a wide range of backgrounds, from those that are new to optimisation to those that already have knowledge in some of the main topics (optimisation, heuristics, evolutionary computation, big data science, machine learning) but want to learn about the other topics and their interactions. The course is also effective to provide a full picture of heuristic optimisation and its interactions to big data science and machine learning.

In terms of techniques, the course covers a good number of them, giving a comprehensive view of heuristic optimisation and learning. The course starts with a gentle introduction to the fundamentals of optimisation and complexity theory. It then moves to cover key concepts and design principles of constructive and local search heuristics. Then, the course studies some metaheuristic techniques and evolutionary algorithms focusing on the main mechanisms from the single and multi-objective perspectives. This is followed by sessions on hyper-heuristic techniques, their origins, classification, and insights into variants of these methods. The automated configuration of heuristics and automated design of algorithms are also covered. The course also includes sessions on big data science and machine learning as well as some of the links to optimisation with evolutionary algorithms.

In terms of practice, the course describes the application of some of the optimisation and learning techniques covered in the course and others, to tackle real-world problems in areas like air transportation, airport operations, vehicle and personnel operational logistics, energy consumption prediction and others. Practical work with some of the techniques covered is also a feature of the course. Participants are organised in teams to work of various practical tasks where optimisation and learning are applied following the concepts and techniques covered in the course. There are plenty of opportunities for course participants to network and discuss interests and ideas.

DELIVERY METHOD
This residential course is planned to be delivered in-person. However, online tools like Microsoft Teams will also be used to enrich the learning experience and communication for course participants. Some course materials will be made available for downloading during the course.

The course will be delivered using a mix of lectures, discussions, and practical work. For some parts of the course, participants will be asked to undertake some tasks individually or in groups and use some freely available or trial software. Emphasis will be put on making the course a rich experience where theory and practical work are blended. In addition to the instructors, there will be teaching assistants to help with managing the course and its logistics.

PRE-REQUISITES
Basics of complexity and optimisation theory as well as computer algorithms and data structures. Some reading material is provided to students a couple of weeks in advance to the start of the course.

AIMS OF THE COURSE
On completion of the course, students should have a working knowledge of the theory, design, implementation, configuration, and application of the main heuristic methods, as well as an insight into their interplay with data science and machine learning in the context of optimisation scenarios.

LEARNING OUTCOMES
Understanding of the fundamental theory underlying the main heuristic optimisation methods (e.g. local search, metaheuristics, hyper-heuristics, evolutionary algorithms, etc.). Awareness of the strengths and limitations of different heuristic optimisation methods. Ability to critically evaluate the applicability and quality of different heuristic optimisation methods. Understanding the fundamentals of automated algorithm design in the continuous and discrete spaces. Capability for designing and developing heuristic methods for some optimisation problems. Awareness of some software tools for the rapid prototyping of heuristic optimisation methods. Understanding the fundamentals of data science and machine learning in the context of heuristic optimisation. Awareness of the key concepts in the application of some optimisation techniques to real-world airport and air transportation operations.

ASSESSMENT
A few formative assessments throughout the course in the form of quizzes and practical exercises, in addition to a simple summative test at the end of the course.

The summative test at the end of the course is in the format of multiple-choice questions spanning the various topics covered in the course. Undertaking this assessment is a requisite given the mechanism by which the delivery of the course is funded. However, it is emphasised that the degree of difficulty of this summative assessment in moderate. Engaging with the sessions and activities of the course should be sufficient to be able to do well in this assessment.

PREVIOUS COURSES
The Nottingham NATCOR course has been delivered every two years since 2010. Each time as a residential course, except in 2020 when it was delivered online using Microsoft Teams due to the covid-19 pandemic. In past runs of the course, the number of participants has been between 50 and 90 and from several countries around the world. The course has attracted participants from diverse academic backgrounds including mathematics, business, computer science, industrial engineering, economics, statistics, operations management, management science, transportation logistics, psychology, etc. This is a course with a strong track record of success and being able to deliver it again in-person is a great excitement!

FEEDBACK
Some comments from previous participants in the course include the following:

• “I appreciate the overview of different techniques given in a very organised manner.”
• “Well organised and loved the real-life applications and case studies addressed; these were my favourite.”
• “The lectures included a lot of interesting topics and case examples. Lecturers were very experienced and keen to respond our questions.”
• “The environment between attendees was really good and allowed an understanding of the wide applicability of these methods.”
• “I have really enjoyed the course. It made me feel a lot more confidence concerning the approach to my research work.”
• “Overall thoroughly enjoyed, very interesting; most enjoyed the case studies and looking at the research that the lecturers have performed using what we are learning.”
• “Exceptional organization, sufficient related courses, high level professors and lecturers, valuable, amazing and interesting experience, nice to meet new people and exchange ideas.”
• “Well-organised in a logical way to connect all the ‘knowledge dots’ I had before into a clearer picture.”
• “I really liked the course. It was very well organized and overall, very interesting. I liked how we were going deeper into the topics throughout the week, and mainly how the topics were so connected to the research of the lecturers.”

PRE-REQUISITES
Undergraduate basic knowledge of linear algebra and calculus.

AIMS OF THE COURSE
The course aims to develop knowledge of different aspects of convex optimization and its applications, provide familiarity with state-of-the-art open-source convex optimization software, and develop the theoretical and practical foundations of different aspects of interior point methods, semi-definite programming, and its applications.

LEARNING OUTCOMES FOR THE COURSE
On completion of the course, students will be able to:
1. Identify the main elements of a problem in convex optimization.
2. Understand the main theory behind linear programming methods, matrix sparsity, interior pointed methods, and semi-definite programming.
3. Identify the suitability of state-of-the-art open-source convex optimization software to solve convex optimization problems.

PRINCIPAL TOPICS OF STUDY
• Linear programming.
• Open-source convex optimization software.
• Matrix sparsity.
• Foundations of convexity.
• Interior point methods and semi-definite programming.
• Applications of convexity theory.

ASSESSMENT
Multiple-choice test

TEACHING TEAM
List of lecturers:
• Miguel Anjos (University of Edinburgh, UK).
• Julian Hall (HIGHS/University of Edinburgh, UK).
• Tamás Terlaky (Lehigh University, USA).
• Margherita Porcelli (University of Florence, Italy).
• Alper Yildirim (University of Edinburgh, UK).
• Filippo Zanetti (HIGHS/University of Edinburgh, UK).

Course organiser: Sergio García Quiles (University of Edinburgh, UK)

Predictive analytics as a general term uniting methods of forecasting and data mining is at the heart of almost all OR projects. Recent years have seen rapid developments in new methods, stimulated by increasing computing power and the growth of ‘big data’. The course’s objectives are to introduce students to these two key areas, important both for research and applications: (1) data mining where the dramatic growth in data availability has meant new methods have been developed with many applications in OR, statistics and operations, and (2) forecasting, which has a major role in many if not most OR modelling.

In this course, we will start with the discussion of basic forecasting principles then move to the time series analysis and introduce conventional statistical models for demand forecasting, such as ETS, ARIMA and regression. After that we will introduce some of machine learning techniques that include supervised and unsupervised learning, discuss principles behind neural networks and also provide working examples for a variety of purposes. We will also discuss the ideas behind the Artificial Intelligence and how it can be used in predictive analytics.

The course will consist of interactive lectures and workshops. The latter will be done in Python, the course participants will be provided all the necessary materials to make sure that everyone is on the same level and can learn without any delays.

Course Leader: Huijing Chen

Pre-requisites
The main pre-requisite is having a basic understanding of the main statistical concepts. Undergraduate level knowledge of statistics will suffice.

Aims
1. To develop knowledge of basic forecasting and data mining principles;
2. To develop an ability to analyse data using conventional statistical and machine learning tools implemented in Python;
3. To develop an ability to select the appropriate techniques for forecasting and data mining and to learn how to apply them in practice;
4. To be able to understand the basic principles behind artificial intelligence and machine learning.

Assessment
Assessment will consist of two parts:
1. A multiple-choice test based on the lecture material;
2. A hackathon-style group project with a real-world challenge, relying on the approaches introduced in the course.

Timetable
The online course will last for 10 days, starting at 9am each day and ending at 12pm. The course will be delivered by staff members at the University of Portsmouth and invited speakers, experts in areas of forecasting and data mining.

The exact timetable will be confirmed closer to the beginning of the course.

A presentation by Filotas Theodosiou will also be included on the course:

Bio: Filotas Theodosiou is a senior applied researcher and lecturer at the Research Group of Predictive AI and Digital Shift at VIVES University of Applied Sciences. His research focuses on designing, modeling, and effectively communicating machine learning-based forecasting solutions for complex business problems. With strong technical and engineering expertise evidenced by his contributions to multiple Python packages, Filotas brings practical implementation knowledge to theoretical concepts. His multi-dimensional ML-based forecasting experience spans diverse business challenges, including hierarchical forecasting systems, demand prediction models that perform well with limited historical data, interpretable and actionable prediction frameworks, and specialized forecasting solutions for highly perishable products requiring unique optimization approaches. Furthermore, he actively evaluates the rapidly evolving landscape of AI, with a keen focus on critically assessing the potential and limitations of LLMs and their applicability on forecasting tasks.

Title: Applied Machine Learning in Practice: Insights from Real-World Predictive Analytics Case Studies

Abstract: In today’s data-rich environment, the core objective for businesses is to translate vast amounts of information into a competitive advantage through smarter decisions. A fundamental step in that process is generating accurate and reliable predictions of future conditions. This is achieved through Predictive Analytics, a discipline where Machine Learning (ML) models serve as the engine, systematically learning from historical data to produce the predictions that enable efficient downstream optimization. This talk provides a practical introduction to applied Machine Learning, framing it as an algorithmic process for uncovering meaningful patterns within historical data to predict future outcomes. Beyond the fundamental theoretical concepts, we will explore how these tools are used in the real world to solve concrete business challenges. We will look at the entire toolbox of predictive models—from simple, transparent methods that are easy to explain, to powerful but complex “black box” systems. We will focus on the crucial trade-offs involved and highlight how choosing the wrong tool for the job can lead to costly business mistakes. Additionally, we will touch upon the current wave of AI, critically evaluating where powerful tools like Large Language Models (LLMs) offer groundbreaking solutions versus where they might be an inefficient or inappropriate choice. Drawing on real-world case studies, this talk provides a practical introduction for developing predictive machine learning models to solve complex business tasks.

Simulation is one of the most widely used operational research techniques. It involves the development of an imitation on a computer of the system under study, followed by experimentation to understand and investigate improvements to the system. This course provides an understanding of simulation, with a focus on the mathematical and statistical principles of stochastic simulation modelling. The main technique of interest is discrete-event simulation, although other simulation techniques will be introduced.

PRE-REQUISITES
An understanding of the basic principles of statistics including confidence intervals and hypothesis testing.
It would be useful to read some parts of the following books.

LECTURERS
Dr Steffen Bayer, University of Southampton
Prof Christine Currie, University of Southampton
Prof Kathy Kotiadis, University of Kent
Dr Thomas Monks, University of Exeter
Dr Lucy Morgan, Analytics Manager, The Strategy Unit; Visiting Researcher, Lancaster University Management School
Prof Stephan Onggo, University of Southampton
Dr Luke Rhodes-Leader, University of Lancaster
Please not this may be subject to change.

Statistical Aspects
Krzanowski, W.J. 2010. An Introduction to Statistical Modelling. Wiley, Chichester, UK.
Makridakis, S. Wheelwright, S.C. and Hyndman, R.J. 1998. Forecasting: Methods and Applications. 3rd ed., New York, Wiley.

Simulation Concepts
Pidd, M. (2005). Computer Simulation in Management Science, 5th ed. Wiley, Chichester, UK.Robinson, S. (2014). Simulation: The Practice of Model Development and Use. 2nd ed., Palgrave, UK.
Banks et al. (2010) Discrete Event System Simulation by (5th edition), Prentice Hall.
Law, A.M. (2015) Simulation Modeling and Analysis (5th edition).
Nelson, B.L. (2013) Foundations and Methods of Stochastic Simulation: A First Course.

Anylogic
Arash Mahdavi, The Art of Process-Centric Modeling with AnyLogic, http://www.anylogic.com/resources/books/the-art-of-process-centric-modeling-with-anylogic/

AIMS
The aim of the course is to provide an understanding of the mathematical and statistical principles of stochastic simulation modelling. The specific objectives are as follows:
• To understand the alternative simulation methods and the requirements for simulation studies
• To develop skills in simulation modelling and simulation computer packages
• To understand the nature of, and approaches to, input data modelling
• To be able to analyse the output from a simulation model using appropriate methods

LEARNING OUTCOMES
Subject Knowledge and Understanding
On completion of the course students will be expected to:
• Understand the basic principles of simulation and performing simulation studies.
• Understand the basic theory of simulation analysis including input data analysis, experimentation and output analysis.
• Understand the key stages in developing and using simulation models
• To be aware of the strengths and limitations of the approaches covered.

Intellectual Skills
On completion of the course students will be expected to:
• Be able to develop and use a simulation model for a given problem situation.
• Be able to evaluate the quality of a simulation analysis

Practical Skills
On completion of the course students will be expected to:
• Be familiar with the use of a simulation software package (Anylogic) as well code (Python) for DES modelling.
• Be able to follow an appropriate life-cycle for the development and use of simulation models

PRINCIPAL TOPICS OF STUDY

• basics of DES modelling
• modelling process
• input modelling
• model validation
• experimentation
• output analysis
• modelling in Python and Anylogic
• simulation optimisation
• simulation applications in industry and healthcare

COVERAGE OF WEB-BASED MATERIAL AVAILABLE IN ADVANCE
Material intended for preliminary reading discussing basics of the statistical aspects of simulation

The preliminary timetable is as follows:
7th -11th July 2025

Monday
12.15 – 13.00 Lunch provided & Registration
13.00 – 13.30 Welcome
13.30 – 14.30 Overview Modelling Approaches (Kathy Kotiadis)
14.30 – 15.30 Modelling Process (Kathy Kotiadis)
15.30 – 16.30 Intro DES (Kathy Kotiadis)

16.45 – 17.30 check-in Glen Eyre student residence
18.00 – 20.00 Dinner & Social Event (Glen Eyre student residence)

Tuesday
9.00 – 10.00 AnyLogic 1 (Steffen Bayer)
10.00 – 11.00 Anylogic 2
11.00 – 11.30 Break (Refreshments)
11.30 – 12.30 AnyLogic 3
12.30 – 13.30 Lunch break
13.30 – 14.30 Input Modelling 1 (Lucy Morgan)
14.30 – 15.30 Input Modelling 2
15.30 – 16.00 Break (Refreshments)
16.00 – 17.00 Input Modelling 3

Wednesday
9.00 – 10.00 SymPy (Tom Monks)
10.00 – 11.00 SymPy
11.00 – 11.30 Break (Refreshments)
11.30 – 12.30 SymPy
12.30 – 13.30 Lunch break
13.30 – 14.30 Output Analysis 1 (Christine Currie)
14.30 – 15.30 Output analysis 2
15.30 – 16.00 Break (Refreshments)
16.00 – 17.00 Validation (Stephan Onggo)

19.00 Dinner (Brewhouse)

Thursday
9.00 – 10.00 Group work
10.00 – 11.00 Group work
11.00 – 11.30 Break (Refreshments)
11.30 – 12.30 Group work
12.30 – 13.30 Lunch break
13.30 – 14.30 Simulation Optimisation 1 (Luke Rhodes-Leader)
14.30 – 15.30 Simulation Optimisation 2
15.30 – 16.00 Break (Refreshments)
16.00 – 17.00 Simulation Optimisation 3

Friday
9.00 – 10.00 Guest lecture (TBC)
10.00 – 11.00 Group work
11.00 – 11.30 Break (Refreshments) + Assessment
11.30 – 12.30 Groupwork presentation
12.30 – 13.00 Reflections and Closure