EMS704: Simulation and Model-Based Systems Engineering

Coursework 1: Group Report and Presentation on Simulation Approaches

1    Outline

Coursework 1 weighting:                               30% of total grade

Coursework 1 release date:                           Monday, 27th  January (week 1)

Coursework 1 submission format:                 Group report and presentations (read briefing at QM+)

Coursework 1 report due date:                       Tuesday, 11th  March 23:59 (week 6)

Coursework 1 presentation date:                   Friday, 14th  March (week 8)

Coursework 1 group allocation:                     You  will  be  allocated  a  random  group  with  3  to  5 students on Monday 27th  January

EMS704 Coursework 1 focuses on the application of simulation approaches taught in Weeks 1–6 to design, build, and validate a simulation model of a real-world system. Students will demonstrate their understanding of various simulation paradigms (discrete, continuous, stochastic, agent-based) and apply relevant tools (e.g., Python, MATLAB, Simulink, NetLogo). The objective is to engage in a full simulation modelling process, including:

•    Problem definition and requirements specification

•    Selection and justification of the simulation approach

•    Simulation model building and analysis

•    Presentation of outcomes and critical insights

2    Coursework briefing

The coursework involves creating a simulation model for a system selected (not limited) from a provided list in Section 3. Each group will perform. the following tasks:

Problem Definition and Objectives

Students must clearly define the problem their simulation model will address. This involves:

•    Identifying the system of interest and providing an overview of its context, importance, and purpose;

•    Outlining the key functionalities and challenges associated with the system;

•    Defining specific objectives for the simulation, including the goals the model is expected to achieve (e.g., performance evaluation, optimisation, decision support);

•    Including  a  visual  representation  of  the  system  (e.g.,  diagram,  flowchart)  to  enhance understanding.  This  could  highlight  the  system's  boundaries,  major  components,  or processes;

•    Explicitly stating any assumptions made during problem formulation.

Simulation Approach

Students need to select and justify the simulation approaches(s) used in building their model. This process should demonstrate a clear understanding of how the chosen approaches align with the system’s objectives and characteristics. Mixed approaches could be considred when appropriate, as many real-world systems benefit from a combination of simulation approaches to capture their complexities. Key elements to address include:

•     Choice of Approache(s): Clearly identify the simulation paradigms selected for the model. These  could  include,  but  are   not  limited  to:  discrete-event  simulation,   Monte-Carlo simulation, agent-based modelling, bayesian networks. Consider mixed approaches when necessary.  For example, combining agent-based modelling with  Monte Carlo simulation allows for capturing both individual agent behaviours and system-wide uncertainties.

•    Justification: Justify the selection of paradigm(s) and tools based on system characteristics. Explain how the approach fits the system’s complexity, dynamics, data availability, and modelling objectives.

•    Assumptions and Limitations: Discuss assumptions made during the selection process and potential limitations of the approach. Highlight how these may affect model accuracy or scope.

•    Trade-offs:  Identify trade-offs  between  model fidelity, computational efficiency, scalability, and data requirements. Justify how the chosen approach balances these considerations.

Model Design and Implementation

Students must develop a conceptual model of the system and implement it using simulation tools. This involves:

•     Conceptual Model Development: Create diagrams such as flowcharts, block diagrams, or pseudo-code   representations   to   communicate   the   design   process;   define   the   key components, parameters, and processes in the model; describe the relationships between components and how they interact within the system.

•     Implementation:  Implement the conceptual model using at least one simulation tool (e.g., Python,  Simulink,  NetLogo);  provide  details  on  the  steps  taken  during  implementation, including setting up input parameters, defining outputs, and coding workflows if applicable.

•     Integration:   Highlight   how   various   components   were   integrated   into   the   simulation environment;  if  applicable,  explain  the  handling  of  multi-domain  aspects  or  interfaces between different paradigms in mixed approaches.

Verification, Validation, and Analysis

Students must ensure the accuracy and reliability of their model and derive meaningful insights from simulation results. This involves:

•    Verification:  Demonstrate that the  model functions as intended and adheres to its design specifications;  include  methods such as debugging,  reviewing the  logic of implemented code, and testing individual components.

•    Validation:  Confirm that the  model represents the real-world system accurately; compare simulation results with empirical data, theoretical predictions, or expert knowledge; conduct sensitivity analyses to evaluate the model’s robustness against variations in inputs.

•    Analysis of Results:  Present  results using appropriate visuals, such as graphs, tables, or charts; interpret findings, identify  trends or patterns, and explain their implications for system behaviour or decision-making.

•     Insights and Recommendations: Provide insights drawn from the analysis and suggest possible improvements or optimisations for the system; discuss any limitations  in  the experimental process and how they may affect conclusions.

Report and Presentation

Students must document their work in a professional report and deliver a concise presentation. This includes:

•     Report:  Prepare a detailed report that summarises the entire process, including problem definition,  approach,  design,  results,  and  insights;  ensure  the  report  is  well-structured, clear, and visually appealing, with appropriate use of headings, diagrams, and references;

Submit a compressed document of the simulation and modelling source code via QM+ with the report. The  report should be limited  to a maximum of 20 pages, excluding references and appendices.  It  is  recommended  to  organise  the  report  as follows:

o  Executive Summary: The report starts with an executive summary on the cover

page, which includes the names of group members and provides an overview of the problem, the approaches taken, key findings, and recommendations.

o  Problem Definition and Objectives: This section defines the problem, outlines the system’s purpose, and specifies the simulation objectives with assumptions and  visuals.

o  Simulation Approach: This section describes the chosen simulation approaches, justifies their selection, and discusses assumptions, limitations, and trade-offs.

o  Model Design and Implementation: The model design and implementation section explains the conceptual model, its components, and the simulation tool used.

o  Verification, Validation, and Analysis: This section covers the methods used to verify and validate the model and presents key findings from the analysis.

o  Conclusions and Recommendations: The conclusions and recommendations summarise the findings and suggest improvements for the system.

o  References and Appendix

•     Presentation: In Week 8, each group will deliver a 15-minute presentation highlighting the key aspects of their project, including findings and recommendations, and should be prepared to answer questions from peers and instructors. Additionally, each group will give a 10-minute mock presentation in either Week 5 or Week 6 to outline their progress on the coursework. Note the mock presentations are formative, aimed at providing feedback, and will not be graded.

3    Suggested systems for coursework

•   Infrastructure systems (e.g., Hyperloop system, HS2 project)

•   Automotive systems (e.g., electric cars, Formula 1 cars, hybrid cars)

•   Space systems (Columbia space shuttle, Europa Clipper Mission, James Webb Telescope)

•   Robotic systems (e.g., an articulated robot)

•   Healthcare systems (e.g., medical equipment, pharmaceutical systems)

•   Smart cities (e.g., transportation systems, IoT)