Simulation project on Burger King
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Transcript of Simulation project on Burger King
Modeling and Simulation Lab
Queuing Theory for
Presentation by :-
Rajat Goyal(2014UME1504)Pawan Yadav(2014UME1488)
Sagar Kartik Dharmendra(2014UME1456)
Presented to:
Dr. Gunjan Soni
WHY BURGER KING ????
Huge foot fall in the Peak Hours Proper Utilization of Restaurant’s resources
Location : GT CENTRAL
Rush hours : 6:30 – 8:30 pm
The evening hours experiences almost 1000 customers at the shop
In the rush hours the average waiting time of the customer reaches up to 15 minutes at the cash counters itself..………!!!!!!!!!!!!
With competition being nearby itself, efficiency must be worked upon
• There are no breaks for the workers during the time when the model is running. Every counter has a single server.
• Only one customer from each group place an order and even that is only once. No repetition in order is observed.
• Group of 4 customers or exceeding are averaged as group of five based on data.
• There is no intermixing in queue of different cash counters and service counters as observed in minimum cases.
• The time is assumed to be same for both cash counters and also at service counters.
• The ice cream ordered by a person dining is taken at the ice cream counter separately.
Data Collection
Input Analyzer Modelling Simulation Result
Interpretation
Finding the Distribution• Data Entry• Curve fitting
Finding the Distribution• Data Entry• Curve fitting
Multiple iteration on the basis of entered Data
Interpret the data (output) to put in form of Physical Model
ARENA –STUDENT VERSION
Process Followed
DATA COLLECTION
Time of data collection: 6:30-8:30 pmNo. of visits made: 5
DATA
CO
LLEC
TIO
NRelative arrival time of customers with
group size for both take away counter and dine in counters.
Service time at cash counters for a set of customers.
Service time at food counters for same set of customers.
Sitting time also of same group.
Service time at ice cream counter.
DATA SAMPLESGroup size Stop watch time(mins) Time in seconds
2 0:00 0
3 0:13 131 1:25 853 2:08 1281 5:15 3181 5:35 3353 7:17 4372 7:25 4452 8:32 512
ORDER START ORDER END ORDER TIMEMIN SEC TIME IN SEC MIN SEC TIME IN SEC SEC
3 40 220 6 16 376 156
18 38 1118 27 51 1671 553
25 40 1540 26 26 1586 46
32 6 1926 39 4 2344 418
33 51 2031 36 32 2192 161
35 58 2158 37 1 2221 63
41 27 2487 44 50 2690 203
Data of Arrival
Sample of Data of Cash Counter (also similar for food counter service and the Take away service)
DATA FITTING TO DISTRIBUTIONS
Raw data collected was modified as shown below in tabular form:FREQUENCY TABLE FORMATION FOR ARRIVAL OF PEOPLE IN GROUP OF 2
TIME INTERVAL (sec) MEAN VALUE FREQUENCY
0-100 50 23
100-200 150 8
200-300 250 5
300-400 350 0
400-500 450 1
500-600 550 0
600-700 650 1
700-800 750 1
DATA FITTING TO DISTRIBUTIONS
Frequencies of different interval were arranged in text file and distribution for each data file was plotted to find the required expression.
DATA FITTING TO DISTRIBUTIONS
• The expression obtained with a least square error is: 20 + EXPO(85.4).
ARRIVAL OF ONE PERSON 50 + EXPO(158).
ARRIVAL OF THREE PERSON 50 + EXPO (200)
ARRIVAL OF MORE THAN FOUR PERSON
TRIA( 50, 95.8, 1.15e+003).
CASH COUNTER SERVICE RATE TRIA(62,117,238)
FOOD COUNTER SERVICE RATE NORM(264,189).
IDLE TIME IN SITTING TRIA( 50, 200, 1.15e+003).
SITTING TIME FOR MEAL TRIA (450, 582, 2.25e+003).
ICE CREAM COUNTER/ TAKE AWAY ARRIVAL RATE
25 + EXPO(116).
TIME IN TAKE AWAY/ ICE CREAM COUNTER
NORM (71.2, 36.6)
DATA FITTING TO DISTRIBUTIONS
RUN SETUP
• Simulation run for 2 hours
• Total no. of
replication were 20 for most appropriate result
RESULTS
Total average number of seats occupied in a system is 40 while number of person prefers ice cream or take away the order is 53.
Increase the customer productivity by decreasing
time at cash counters.
Increase the labor productivity by increasing the
resource utilization factor.
FINALLY….REDUCTION IN QUEUE LENGTH
INTERPRETED SOLUTION
Automated Soda Fountain Machine
TOKEN SYSTEM
USED…..!!!!!
INTERPRETED SOLUTION
Digital Display Queue Management System for order status
• Decrease the crowd and for instantaneous acknowledgement of order delivery to the customer.
• Decrease the overall service time and queue length at the service counters.
INTERPRETED SOLUTION
INCREMENT IN BURGER MACHINE
REDUCTION IN SERVICE TIME
Approximate cost : Rs 7,00,000
INTERPRETED SOLUTION
Get your order here app
No standing
in queue
Direct food
service with
generated token no.
• Will increase the potential of customer• Lead to mass expansion of Burger King
CHANGES PROPOSED
Expression 20 + EXPO(31).
Resource Self Serviced
Action Seize Delay Release
Customer Preference 70%
Improved Cash Counter Service Expression
NORM(120, 40.5)
Improved Food Counter Service Expression
NORM(180,160)
Self service soda fountain Machine
Digital Display Queue Management System
PROCESS ANALYZER OUTPUTS
• The number out is increased to 233.• Average customers in system is decreased to 60
BY ENTITY
STATISTICAL COMPARISION
Cash Counter1
Cash Counter2
Food Counter1
Food Counter
Actual Model 544 438 1466 1378
Proposed Model
259 183 1115 787
Time decrement
4 Minutes 45 Seconds
4 Minutes 15 Seconds
5 Minutes 52 Seconds
9 Minutes 52 Seconds
Cash Counter1
Cash Counter2
Food Counter1
Food Counter
Actual Model 4.6 3.4 10.7 9.4
Proposed Model
2.1 1.4 8.2 5.4
•Waiting Time( in seconds)
•Waiting Queue( in numbers)
STATISTICAL COMPARISION
RESOURCE UTILIZATION
Resource 1 Resource 2 Resource 3 Resource 4 Resource 50
0.2
0.4
0.6
0.8
1
1.2
Actual ModelProposed Model
BY USER SPECIFIED
Number of customer occupying a sit is increased by 5 persons.
STATISTICAL COMPARISION