System Modelling and Simulation
College of Engineering, Design and Physical Sciences
Module:
Module Code: MN5617
Module Title: System Modelling and Simulation
Module Leader: Dimitris Folinas
Assessment Method: Project and Report
Submission Deadline: 11:59 am UK time, Thursday, 29th August 2024
Required Software: Arena Software
Learning Outcomes
Upon completing this assignment, you should be able to:
- Critically assess and implement systems analysis principles.
- Effectively apply manufacturing concepts to real-world industrial systems, addressing the challenges encountered in daily management.
- Develop skills for modelling, simulating, and analyzing the performance of both deterministic and stochastic systems.
- Recognize key elements of supply chains, logistics, and reverse logistics, and apply this knowledge to improve management and engineering processes.
- Utilize tools and techniques for the planning and evaluation of enterprise system designs.
- Model interactions and negotiations within enterprise systems.
- Demonstrate integrated modelling of key manufacturing processes.
- Use simulation and optimization techniques to enhance enterprise integration.
- Prepare comprehensive written reports.
- Evaluate complex scenarios critically and make informed decisions.
- Exhibit initiative and personal responsibility in your work.
Assignment Theme
Each year, the Systems Modelling and Simulation module explores a different theme. In previous years, topics such as healthcare, banking services, airports, and logistics were covered. This year's focus is on a manufacturing company, offering a chance to see how this fundamental subject can be applied across various domains with similar performance indicators. Enjoy tackling this year's challenge!
Scenario Overview
XYZ Company produces three distinct products: Product A, Product B, and Product C. Each product requires a specific set of components (C1, C2... C5) for assembly. The requirements for each product are illustrated in Figure 1.
Components C1 to C5 are introduced into the production line according to exponential distributions with parameters of 50, 45, 52, 80, and 49 minutes, respectively. The assembly times for the products are defined by triangular distributions: TRIA (10, 12, 15) for Product A, TRIA (37, 48, 55) for Product B, and TRIA (18, 20, 22) for Product C.
The production facility is organized into five stations. Stations 1 to 4 are dedicated to machining, and each component follows a specific processing sequence through these stations. The operation times at each station are provided in Table 1. After machining, all components are directed to the fifth station for assembly according to their respective product types.
At each machining station, priority is given to the components that have been in the system the longest.
Station 5 has two assembly robots that can be selected randomly; however, one of these robots experiences periodic breakdowns. Historical data indicates that the mean uptime follows an exponential distribution with a λ value of 120 minutes, while the mean repair time is 4 minutes. The system operates on an 8-hour shift per day. Transfer times between stations follow a TRIA distribution (7, 8, 10) minutes.
Due to increasing demand and the need to reduce production costs, the company aims to minimize work-in-process and reduce throughput times while maintaining efficient resource utilization. To achieve this, the management seeks to identify bottleneck processes and potentially increase their capacities.
Task Requirements
As a systems analyst for XYZ Company, you are required to:
- Develop a Simulation Model: Create a simulation model of the production system with appropriate animations. You may use the system layout provided in Figure 2 as a guide for your animation.
- Identify and Improve Bottlenecks: Determine the bottleneck processes within the system and propose alternative improvement strategies to enhance performance. Model your proposed system and compare metrics such as resource utilization, work-in-process, throughput times, and the output of each product type against the original system.
- Prepare a Report: Draft a report for the company's management, discussing the improvements you propose. The report should strongly advocate for your recommendations, clearly presenting the performance metrics before and after the improvements (consider using Arena’s output analyzer for comparative analysis). Validate the results statistically using the lecture notes and associated references.
- Simulate a Conveyor System: The company is considering installing a conveyor system to transfer components between stations. Simulate a non-accumulating conveyor system and determine the best settings (e.g., velocity) that maintain resource utilization while reducing the time components spend in the system. Distances between stations are provided below:
- From Station No.To Station No.Distance (meters)121523734745155Out3In 133
- Run Conditions:
- Warm-up Period: 30 minutes
- Replication Length: 5 days
- Number of Replications: 10
- Decide whether to initialize statistics at each run replication and provide a rationale for your decision.
Submission Guidelines
Submit your work via the Wiseflow platform, ensuring you include the following files:
- Assignment Report: A comprehensive report based on your analysis and recommendations.
- Arena Models: All relevant Arena model files with the extension
.doe. - Output Summary Report: The
.outfile generated after a successful run of your models.
Marks will be awarded for intuitive modelling approaches, design, animation, and the presentation of simulation models and reports. The results must be clearly interpreted and explained.
Submission Deadline: 11:59 am UK time, Thursday, 29th August 2024
Submission Method: Via the WISEflow platform
Late Submission: Subject to the University's policy
Use of Artificial Intelligence (AI) in Assignments
When utilizing generative AI tools (e.g., ChatGPT, Bing, DALL-E) to assist with your assignment, you must include a statement titled "Use of Artificial Intelligence in this assignment" within your submission. This statement should transparently acknowledge the use of these tools. For example:
"Use of Artificial Intelligence in this assignment - ChatGPT was used to provide a draft outline and structure of this research proposal. I then fully extended this work with further reading, critical analysis, and referencing."
Refer to the latest university policy on using AI in your studies here.
Academic Integrity Notice:
All submissions will be electronically checked for academic misconduct. Ensure you are familiar with the university's policies on good academic practice and plagiarism. More information can be found in the Senate Regulations: Senate-Regulation-6-2020-07-01.pdf.
Presenting AI-generated text or images as your own work constitutes academic misconduct.
Annex I: Brunel University Generic Masters-Level Grade Descriptors
These descriptors serve as a guide throughout the assessment process at Masters Level. They are non-disciplinary and should be read in conjunction with the specific learning outcomes associated with this assessment.
- Grade A++: Exceptional work, suitable for publication in a highly esteemed peer-reviewed journal. Demonstrates sophisticated, critical understanding, originality, and systematic argumentation. Uses literature, theory, methodologies, and tools with exceptional clarity, focus, and communication.
- Grade Band A (A+, A, A-): High-quality work showing critical understanding, originality, and systematic argumentation. Demonstrates excellent use of relevant literature, theory, methodologies, and tools with clear, focused communication.
- Grade Band B (B+, B, B-): Well-developed work with comprehensive understanding and systematic argumentation. Shows high competence in using relevant literature, theory, methodologies, and tools, with clear communication.
- Grade Band C (C+, C, C-): Substantial understanding with systematic argumentation. Demonstrates competence in using relevant resources with evidence of clear communication.
- Grade Band D (D+, D, D-): Some critical understanding and structured argumentation. Acceptable competence in relevant resources but not at Masters Level. Shows effective communication.
- Grade Band E (E+, E, E-): Work with significant weaknesses but strong evidence that a Grade D is attainable.
- Grade F: Unacceptable work.
[Available from intranet: https://intra.brunel.ac.uk/s/QSO/_layouts/15/WopiFrame.aspx?sourcedoc=/s/QSO/Handbook%2 0Text/Postgraduate%20grade%20descriptors.doc&action=default&DefaultItemOpen=1]
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