sjl inn shaoying kou juan m cubillos lansana w. kallon is 603 decision making support systems final...
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SJL Inn
Shaoying KouJuan M Cubillos
Lansana W. Kallon
IS 603 Decision Making Support Systems
Final Project
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Overview
For our project we intend to focus our domain within the Hotel industry. Within an hotel we will try to maximize room occupation rate using different decision rules in order to increase profit. The first decision rule will be based off of the combination of different groups of customers to a room. Customers will be randomly allocated according to their combination rule. The second will be based on discounts. If we offer discounts to eligible( customers who stay at a minimum duration) customers, how many more days will they be willing to stay? SJL hotel will consist of several room sizes along with bed sizes. The cost of maintaining these rooms are incorporated in our simulation. We will try to tackle this problem by simulating each rule and honoring the rule that yields the most profit.
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General Methodology
Start
Define the Problem
Develop the model
Flowchart the model
Program the model
Collect the data
Validate the model
Excercise the model
Implementation
End
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Problems definition
1. Faced with different types of customer demands and duration. How to allocate rooms for them in order to achieve profit maxima?
2. If a certain discount is given to our customers, how to measure the affect on future profit?
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Develop the Model
Controllable Variables
• Individuals• Couples• Group of 3• Group of 4• Group of 5• Group of 6
Total Demand
Uncontrollable Variables
Days of Promotion
Percentage of Discount
Duration
Room Combination
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Parameters
Text Parameter 1 ValueNumber of WEEKS 52
In order for our simulation to portray feasible information we extended our model to represent data for 52 weeks(1 year). By simulating our model for a year we can reach an area of stability.
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Room Composition
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Customer Probability
Individuals20%
Couples20%
Group of 3 people25%
Group of 4 people15%
Group of 5 people15%
Group of 6 people5%
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Normal DistributionNormal Distribution is used within our model when we compute random numbers for the customer demand and customer duration (length of stay in hotel).
Two Uncertainties:a) Customer Demand
• Is calculated using random number generator• Calculation is based off of average weekly demand of 147• Calculation is also based of off standard deviation of 30
b) Customer Duration • Is calculated using random number generator• Calculation is based off of average weekly duration of 3.5 days.• Calculation is also based off of standard deviation of 1.
MeanStandard Deviation Units
DEMAND 147 30 PeopleDURATION 3.5 1 Days
NORMINV(RAND(),MEAN,SD)
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Rule 1: Combinations
According to maximal people of different room types, allocate different room types to different customer types randomly.
The possibility for comminuting different room types with different customer types
Customer Type Room Combinations
INDIVIDUAL A
INDIVIDUAL B
COUPLE A
COUPLE B
GROUP of 3 B
GROUP of 3 C
GROUP of 3 D
GROUP of 4 B
GROUP of 4 C
GROUP of 4 A A
GROUP of 5 A B
GROUP of 5 B B
GROUP of 5 C
GROUP of 5 A D
GROUP of 6 C
GROUP of 6 B B
GROUP of 6 A B
GROUP of 6 A D
GROUP of 6 B D
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Rule 2: Promotions
Based on duration days, different customer type will receive different percentage of discounts on price.
For customer types who received discounts, they would like to stay an extra N number of days.
TYPE DAYS PERCENTAGE OF DISCOUNT
INDIVIDUALS 3 2.50%
COUPLE 5 2.75%
GROUP of 3 4 3.00%
GROUP of 4 4 3.00%
GROUP of 5 6 4.00%
GROUP of 6 6 5.00%
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DEMONSTRATION