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)