Hi every body.
I am currently working on a model where I need
to aggregate the demand of the customers in a minimum of
classes while the behaviour of these customers relative to the
price is similar. My activity : short term car rental.
The criterion of classification is simple: they should have the same
probability to buy at a specific level of price.
The customers have different characteristics.
Level of anticipation (the delay between the date of inquiry and the moment they will need the service in days.)
The fact of having already bought our service in the past.
The duration of the rent.
The kilometres by day
The position of the rent in the week, in the year or in holidays, to accommodate for the seasonality of the demand.
The type of customer, private, business or association.
The type of vehicle.
Eventually the location of the agency.
And maybe one to three others depending on the type of vehicle.
I have all the data that resume the past experiences about 100000 events every year that tells if a certain type of demands, has bought or not with a certain price.
I will have to model each type of vehicle one after the other for planning considerations, so the statistics will not go further then 10000 events by vehicle.
It is necessary for me to aggregate these demands so as to simulate different price politics and see the results in term of planning utilisation, availability, cash flow etc…
I have browsed on the SD mailing list, to see if there were any threads on classification, clustering, taxonomy, typology or aggregation discussing on the different ways to classify to better the aggregation process. I did not find anything.
I browsed on the net only on classification and found an universe of sites and software that use a lot of different techniques, based on rigorous methods but not equally adapted to all the subjects.
I decided to download some free software, easy to understand to make me familiar with the subject at first.
Does anybody have any experience on that subject?
The question has been posted too on the SD mailing list.
Thank you in advance and best regards.
J.J. Laublé. Allocar, rent a car
Strasbourg France.
better classification to better aggregation
complementary information about aggregation
Hi
I have recently received an individual message about my aggregation question which makes me think that i have not been clear enough.
Underneath is the answer from my correspondent and my own answer to his message.
Is this useful?
"To determine whether activities taking place serially can be aggregated, consider the average residence time of items in each stock (the average time between entering and exiting the stock). Stocks with short residence times relative to the time scale for the dynamics of interest generally do not need to be represented explicitly and can either be omitted or lumped into adjacent stocks." A short residence time in two stocks relative to the time scale makes it unnecessary to distinguish between whether a unit is in one stock or the other.
"Parallel activities can legitimately be aggregated together if the individual flows are governed by similar decision rules and if the time the different items spend in the individual stocks is similar." If the decision rules and times are the same, then for all practical purposes the flows are the same.
Hi
I thank you first for having made the effort to read my query.
I have browsed the Sterman’s book and read the chapter 6 paragraphs on aggregation.
There is another place where he refers to the Forrester’s market growth model it is in chapter 3, page 100 ‘defining the system boundary and the degree of aggregation are two of the most difficult steps in successful modelling.’
But all this does not solve my problem which I think needs further explanation.
I will try to explain it with a simplistic though unrealistic example:
Suppose that you have customers who want to rent cars and you want to determine the optimal pricing policy.
You know that there are mainly two characteristics in the demand:
the duration of each individual renting and the kilometres by day.
Suppose that you have only 6 experiences, and that you intend to consider
people that have short, middle and long durations, and short, middle and high
kilometres by day.
If you want to determine the behaviour of any demand based on the characteristics
you need to study the average behaviour of all the demands that share the same characteristics. There are 9 combinations of the different values of the characteristics,
And only 6 observations, which does not even make one observation for each classification
based on the characteristics. That does not make a valid statistic. I must then find another way to determine the behaviour of any demand. The only method is to construct another classification than the one based on each value of the characteristics, probably which a much smaller number of classes to make it possible to calculate valid statistics for them.
Example:
Experiences Duration of renting kilometres by day price proposition has bought or not
1. low middle 100 yes
2. high low 105 yes
3. high low 120 no
4. middle high 100 no
5. high high 90 no
6. high middle 70 yes
One can see that even with this simple case, it is not evident to find the classification that fits
better to the experiences
One can for example visually say that for a two classes partition, the first class fits better than the second one in the following two classes.
First classification
Class 1 : (low,middle) + (high,low)
Classe 2 : (middle,high) + (high,high) + (high middle)
Second classification :
Class 1: (low,middle) + (high,high) + (middle,high)
Class 2: (high,low) + (middle,high)
The first classification has a more homogenous behaviour relative to the price than the second one. It will then be easier to determine an average price behaviour for both classes of the
first classification.
Imagine now having 8 characteristics and 5000 observations.
There are rigorous methods that define precisely an order relation between different classifications that permits to find the classification that is better then the other.
There are many methods of classifications using different algorithms: trees, neural networks
etc…
I hope that I may have better explained my case.
Feel free to ask more details if something is not clear in my explanation.
Regards.
J.J. Laublé. Allocar rent a car.
Strasbourg France.
I have recently received an individual message about my aggregation question which makes me think that i have not been clear enough.
Underneath is the answer from my correspondent and my own answer to his message.
Is this useful?
"To determine whether activities taking place serially can be aggregated, consider the average residence time of items in each stock (the average time between entering and exiting the stock). Stocks with short residence times relative to the time scale for the dynamics of interest generally do not need to be represented explicitly and can either be omitted or lumped into adjacent stocks." A short residence time in two stocks relative to the time scale makes it unnecessary to distinguish between whether a unit is in one stock or the other.
"Parallel activities can legitimately be aggregated together if the individual flows are governed by similar decision rules and if the time the different items spend in the individual stocks is similar." If the decision rules and times are the same, then for all practical purposes the flows are the same.
Hi
I thank you first for having made the effort to read my query.
I have browsed the Sterman’s book and read the chapter 6 paragraphs on aggregation.
There is another place where he refers to the Forrester’s market growth model it is in chapter 3, page 100 ‘defining the system boundary and the degree of aggregation are two of the most difficult steps in successful modelling.’
But all this does not solve my problem which I think needs further explanation.
I will try to explain it with a simplistic though unrealistic example:
Suppose that you have customers who want to rent cars and you want to determine the optimal pricing policy.
You know that there are mainly two characteristics in the demand:
the duration of each individual renting and the kilometres by day.
Suppose that you have only 6 experiences, and that you intend to consider
people that have short, middle and long durations, and short, middle and high
kilometres by day.
If you want to determine the behaviour of any demand based on the characteristics
you need to study the average behaviour of all the demands that share the same characteristics. There are 9 combinations of the different values of the characteristics,
And only 6 observations, which does not even make one observation for each classification
based on the characteristics. That does not make a valid statistic. I must then find another way to determine the behaviour of any demand. The only method is to construct another classification than the one based on each value of the characteristics, probably which a much smaller number of classes to make it possible to calculate valid statistics for them.
Example:
Experiences Duration of renting kilometres by day price proposition has bought or not
1. low middle 100 yes
2. high low 105 yes
3. high low 120 no
4. middle high 100 no
5. high high 90 no
6. high middle 70 yes
One can see that even with this simple case, it is not evident to find the classification that fits
better to the experiences
One can for example visually say that for a two classes partition, the first class fits better than the second one in the following two classes.
First classification
Class 1 : (low,middle) + (high,low)
Classe 2 : (middle,high) + (high,high) + (high middle)
Second classification :
Class 1: (low,middle) + (high,high) + (middle,high)
Class 2: (high,low) + (middle,high)
The first classification has a more homogenous behaviour relative to the price than the second one. It will then be easier to determine an average price behaviour for both classes of the
first classification.
Imagine now having 8 characteristics and 5000 observations.
There are rigorous methods that define precisely an order relation between different classifications that permits to find the classification that is better then the other.
There are many methods of classifications using different algorithms: trees, neural networks
etc…
I hope that I may have better explained my case.
Feel free to ask more details if something is not clear in my explanation.
Regards.
J.J. Laublé. Allocar rent a car.
Strasbourg France.