Question

You have a revenue data set of a Metro Area, where a node represents an office, the value associated with every node is the annual revenue of this office. The data set includes 2000 offices in that Metro Area. Now, I would like to partition the Philadelphia Metro Area to 10 business sub-areas. Each of these 10 business sub-areas has one or more offices. The total anual revenue from each sub-area should equal or almost equals to others. How to partition these 10 business sub-areas?

-its like a Routing algorithm problem (for example- 8 queens problem). We have to distribute these nodes, such way,on average all sub-area get same revenue, and nearby nodes are in same sub area, but one sub-area can have, 100 nodes, other one can have 300 nodes, that does not matter.

Answer 1

Let me frame the Data flow for you here.

1. Start by dividing your total revenue by 10. You will need the revenues to be near this Amount. Call this ar (average revenue).

2. Now assign all of your offices a number in ascending or descending order via loop. Now total no. Of offices = n = 2,000

3. Start by adding 1+n and then check if it is within 10%(you can change this as per the materiality you need), if it is than it is your one group.

4. If not then add the revenue of 2nd office to it, if it is than it is your one group.

5. If not then add the revenue of (n-1)th office to it, if it is than it is your one group.

6. If not than repeat 4th & 5th step, make it into a loop.

Note as per the segregation of the data maybe at some point a data fail to enter in the category of 10% in that case you may need to add another loop in it for checking the absolute difference between your group revenue & ar. Use multiple loops.

Hope this meets your purpose.

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