SIMULATION AND OPTIMISATION - 2026/7

Module Overview

Simulation is one of the most applicable and widely used approaches in Prescriptive Analytics, and this module introduces students to its core methods and concepts, with an emphasis on where and how they are used in real business settings. The module also touches on optimisation as a complementary technique for supporting decision making.

The module covers two principal simulation paradigms. Discrete-Event Simulation (DES) models systems in which change occurs at distinct points in time, making it well suited to analysing operational processes such as queuing, throughput, and resource utilisation. System Dynamics (SD) represents systems as networks of stocks and flows governed by feedback loops, and is particularly valuable for understanding how decisions shape behaviour over time, including the unintended consequences that arise when policies interact with complex systems. The module also introduces Hybrid Modelling and Simulation, which combines these and other paradigms to tackle problems that no single approach can adequately address alone.

Throughout, the focus is practical. Students work through case studies, build their own models, and learn to communicate findings clearly, so that by the end of the module they are equipped to select, apply, and critically evaluate simulation and optimisation methods in professional contexts.

Module provider

Surrey Business School

Module Leader

TOLOO Mehdi (SBS)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 5

Module cap (Maximum number of students): N/A

Module Availability

Semester 2

Prerequisites / Co-requisites

N/A

Module content

• Fundamentals of Modelling and Simulation
• The Modelling and Simulation Life Cycle
• Discrete-Event Simulation
• System Dynamics and Systems Thinking
• Simulation Optimisation and Decision Making
• Hybrid Modelling and Simulation

Assessment pattern

Alternative Assessment

An alternative to the group project is available for students who are unable to participate in group work. This takes the form of an individually defined project based on a specified business scenario. The scope and expectations are equivalent to the group submission.

Assessment Strategy

The assessment strategy is designed to give students the opportunity to demonstrate that they:

• Understand the methods underpinning simulation and optimisation
• Are able to build a simulation model and use it to address a real business problem
• Can communicate technical findings clearly and professionally in writing

The summative assessment consists of two components.

Simulation Group Coursework (50%)

Students develop a simulation model of a business problem and write a report of no more than 1,000 words presenting their approach, findings, and recommendations. Submissions are due at the end of the semester. This component addresses learning outcomes 3, 4, and 5.

Individual Report (50%)

Students prepare a report of no more than 2,000 words on a topic related to simulation and modelling. The topic will be announced at the start of the semester. This component addresses learning outcomes 1, 2, and 4.

Formative Assessment and Feedback

Formative assessment is built into the module throughout the semester. Lab sessions provide regular opportunities for feedback on modelling work in progress, with staff available to comment and advise. Students are encouraged to use consultation hours and the module discussion forum for additional support.

Feedback

Written feedback on summative work is returned via SurreyLearn. It identifies what has been done well, where understanding could be stronger, and how performance could be improved. Students are encouraged to act on this feedback and to seek further discussion during consultation hours.

Module aims

• Introduce students to the principal concepts, methods, and life cycle of modelling and simulation
• Develop students' ability to build, run, and critically evaluate simulation models in response to real business problems
• Provide students with an understanding of both discrete-event and feedback-based simulation approaches and their respective strengths
• Introduce optimisation as a complementary approach to simulation for supporting business decision making
• Develop students' ability to communicate model-based findings clearly and professionally in written form

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to:

Encourage critical understanding of the role played by analytics for business decision making. Learning will be directed, supported, and reinforced through a combination of lectures, computer laboratory sessions, and online discussion groups, plus directed and self-directed study. The course may include research-led elements and offers a mix of theoretical insights and case study material that will be delivered both online and offline where appropriate.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: MAN2187

Other information

Employability

This module will equip students with a number of professional skills, including communication, information and digital skills that are required to participate in today¿s working world.

Digital Capabilities

Students engage with specialist modelling and simulation software throughout the module, building practical proficiency alongside conceptual understanding. The emphasis is on understanding what the software is doing and why, rather than on mechanical operation. Skills developed in this module are applicable to a range of industry tools used in data-driven and operational roles.

Sustainability

Through solving business problems (operational and strategic), this module provides students with the opportunity to explore decision making in modern businesses to ensure their sustainability and profitability.

Resourcefulness and Resilience

In this module, students will be given the opportunity to effectively use information and resources when working on their coursework. Students¿ success is developed through both formative and summative assessment, with feedback focused on development and improvement in problem solving and self-efficacy while also encouraging determination, flexibility, and adaptability in the face of change and disruption.

Programmes this module appears in

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2026/7 academic year.