Superdog revenue = number of customers *$11. ⢠Topdog revenue = number of customers * $ 13. ⢠Daily revenue = Superdog revenue+ Topdog revenue.
REPORT on AUTOMATIC CAR WASH SIMULATION by Atrayee Bhadra Heba Asultaan Mustafa Alkhafaji Sindhuja Nandikonda
Introduction Simulation is the part of imitation of real world process or system over time. It is basically the representation of the behaviour or characteristics of one system through the use of another system, especially a computer program designed for the purpose. The automatic car wash station consists of tunnel like buildings into which customers or attendants drive. Then car moves on a conveyor belt, where the conveyor belt is programmed in such a way that it knows where to stop and the amount of time to stop. Structure of The Wash Station The car wash station considered has a single queue and a single bay, where the customers enter and choose desired washing method. The assumed station has two kinds of methods to wash namely, Top Dog wash and Super Dog wash. Properties of Superdog: • • • • • • • •
Underbody spray. High pressure and wheel cleaner. Scrub and rinse the car. Pro-touch foam cleaner. Pro-touch wash spray. Brilliant Gloss. Spot free rinse. Pro swing super power dry.
Properties of Topdog: • Pro touch foam cleanser. • Pro touch wash process.
• Spot free rinse. • Pro swing super power dry. Resources used for car wash: • Water • Electricity • Washing liquids.
Data Collected: The data collected is the real-time data collected from one of the stations. • • • • •
Electric car wash one queue and one station. Station is opened for 24 hours a day from Monday to Saturday. Average number of cars that visit the station each day is 55. Traffic hours is 10.00 am to 4 pm. $1000000 has been required to build the station.
Kind Of Wash
Cost
Time Slot
Customers Expected
Superdog
$11
5 min
25
Topdog
$13
7 min
30
Parameters Collected: • • • • • • • • •
Arrival time. Service time. Departure time. Maximum queue length. Average customer wait times. Daily and monthly revenue for each wash. Cost and profit. Return on investment. Revenue.
Ø Performed Monte Carlo Analysis on return on investment to get back the range of time period in which the owner gets back his investment. Formulae Used to Calculate Different Parameters: • Maximum queue length = Total number of cars in the queue at any given time slot. • Average wait times of the customer= total customers wait times/ number of cars. • Superdog revenue = number of customers *$11 • Topdog revenue = number of customers * $ 13 • Daily revenue = Superdog revenue+ Topdog revenue. • Monthly income = Daily revenue *30. • Monthly resource cost= For both type of liquids (electricity+ water+ washing liquids) • Profit for carwash= Monthly income – monthly resource cost. • Monthly profit = monthly income- monthly expense • Return on investment period (days)= (Initial- Investment)/daily profit. Ø Revenue is the income that was made on a business day / month. Simulation Executed We utilized the Racket Simulation environment. The main program calculates the number of days in which the owner gets back his investment. The program is simulated 100 times to collect the Monte Carlo Values (here, Return on investment period values). Monte Carlo Analysis: Monte Carlo analysis is the probability simulation is a technique used in forecasting models. It’s a broad class of algorithms which depends on random sampling to obtain the numerical results. A general pattern of monte Carlo analysis:
Ø Ø Ø Ø
Define a domain of possible inputs. Generate inputs randomly from probability distribution over the domain. Perform a deterministic computation on the inputs. Aggregate the results.
Results & Analysis Sample of our results:
Histogram Analysis:
Conclusions Drawn: • Histogram analysis has driven us to a conclusion that the data collected is normally distributed • It also gave us the most frequency and least frequency in terms of number of days in which the owner will get back his investment • Minimum frequency is at 1449 days i.e. there is less frequency or chances for the owner to get back his income within 1449 days • Maximum frequency is at 2161 days i.e. there is more frequency or chances for the owner to get back his income within 2161 days.
Normal Distribution Curve Analysis
Conclusions Drawn: • [ mean- 2*(standard deviation) , mean + 2*(standard deviation)], gives the end points which covers 95% area under the curve. Here, these end points are: (1365.204,2615.684) • 95% confidently, we can assure the owner that he is going to get back his investment without exceeding 2615 days. What We Learned Simulation is the imitation of real world process or system over time. It is done to analyse the working of a real world system and draw conclusions based on the obtained computer generated values. By simulating a system or process we can suggest the ways for process improvement, managing risks and predicting outcome.
Automatic Car Wash Simulation: Process Improvement: • We used single bay, which increased the values of parameters like maximum queue length and average waiting time of customers. Increasing the numbers of bays will be a good idea to increase the income • Increasing the cost for each kind of wash reduces the return on investment period • Limiting the usage of resources like electricity (water and washing liquid are fixed for a wash so we can’t limit them) during less traffic times by fixing a sensor at the queue entrance would be a good idea to reduce the resource cost for the owner Predicting Outcome: • Histogram analysis has driven us to a conclusion that the data collected is normally distributed • It also gave us the most frequency and least frequency in terms of number of days in which the owner will get back his investment • Normal distribution curve drawn using Monte Carlo analysis helped us to find out the 95% confidence interval for the return on investment period
