CMA6134 Computational Methods Group Assignment Term 2025 | MMU

Published: 05 Jun, 2025
Category Assignment Subject Computer Science
University Multimedia University Module Title CMA6134 Computational Methods

Instructions:

  • This is a group assignment to be completed in teams of four students.
  • The submission deadline is Friday of Week 13. The assignment must be completed using FreeMat or Octave. A demo session will be held during Week 14, and attendance is compulsory for all group members. Demos will be scheduled by the respective tutors.
  • For the report, prepare a cover page, elaborate on the details of your simulation, construct a diagram such as a flowchart, provide an explanation for the implementation of some important source codes and print-screen the table( service time, inter-arrival time and simulation) and results (evaluation results).
  • Creativity and extra effort in the report or simulation will be rewarded with higher marks.
  • Plagiarism is not accepted under any circumstances. Any form of copying, including from peers, will result in a zero mark for the entire group.

Queue simulator

Create a simulator to simulate the arrival of vehicles at a petrol station for refuelling. Choose a petrol station brand such as Petronas, Shell, or any other of your choice. Assume there are four fueling islands and two queuing lanes. Vehicles in Lane 1 can access only Pump 1 and Pump 2, while vehicles in Lane 2 can access only Pump 3 and Pump 4.

Queueing Lane 1 Fueling island 1 Fueling island 2 Queueing Lane 2 Fueling island 3 Fueling island 4

CMA6134 Computational Methods Group Assignment Term 2025

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Firstly, auto-generate/predefine the table for the time to fill up the petrol, type of petrol and inter-arrival time at the beginning of the simulation. Please ensure that you generate/predefine the appropriate range of the filling time and inter-arrival time to generate the appropriate results of the simulation.

To generate random values for petrol type, quantity, refuelling time, and inter-arrival time, you may use linear congruential generators or other suitable random number generators. Use the rand function to generate the seed (initial value) for each generator. Ensure that the generated numbers are adjusted to fall within appropriate ranges. For more details, please refer to Chapter 4

a) The user should be able to select the type of random number generator to be used and input the number of vehicles for the simulation.

b) Display messages such as customer arrivals, departures, and other relevant events in the command window as the simulation progresses.

Vehicle 1 arrived at minute 0 and began refuelling with Primax95 at Pump Island 1.
Vehicle 2 arrived at minute 2 and began refuelling with Primax97 at Pump Island 3.
Vehicle 1 finished refuelling and departed at minute 4.
Vehicle 3 arrived at minute 5 and began refuelling with Dynamic Diesel at Pump Island 1

c) Display the table of the refuelling time, type of petrol and thetable of the inter-arrival time

    Inter-arrival time Probability CDF Random number range
    ....... ....... ....... .......
    
    Type of petrol Probability CDF Random number range Price/litre
    ....... ....... ....... .......
    
    Refuelling time Probability CDF Random number range 0
    ....... ....... ....... .......

Set the conditions to determine which lane the vehicles will queue in or proceed to for petrol refuelling. Simulate the results for both non-peak and peak hours (expecting longer waiting times during peak hours). You may consider generating different series of random numbers for inter-arrival times during non-peak and peak hours.

You may consider splitting the results of the simulation into 2 tables if you have a problem displaying them in one table.

Vehicle number Type of petrol Quantity (litre) Total price (RM) Random number for interarrival time Interarrival time Arrival time Line number Random number for refuelling time Pump 1 Refuelling time Time begins Time ends
    1 Primax95 1 5 0 5
    2 Primax97 2 
    3 Dynamic Diesel 1 

Finally, evaluate the results of the simulation—for example, the average waiting time, average time spent in the system, the probability that a customer has to wait, and the average service time at each counter (refer to the example in the notes).

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