Looking for a model to represent this problem, which I suspect may be NP-complete

(I've changed the details of this question to avoid NDA issues. I'm aware that if taken literally, there are better ways to run this theoretical company.)

There is a group of warehouses, each of which are capable of storing and distributing 200 different products, out of a possible 1000 total products that Company A manufactures. Each warehouse is stocked with 200 products, and assigned orders which they are then to fill from their stock on hand.

The challenge is that each warehouse needs to be self-sufficient. There will be an order for an arbitrary number of products (5-10 usually), which is assigned to a warehouse. The warehouse then packs the required products for the order, and ships them together. For any item which isn't available in the warehouse, the item must be delivered individually to the warehouse before the order can be shipped.

So, the problem lies in determining the best warehouse/product configurations so that the largest possible number of orders can be packed without having to order and wait for individual 开发者_JAVA技巧items.

For example (using products each represented by a letter, and warehouses capable of stocking 5 product lines):

Warehouse 1: [A, B, C, D, E]
Warehouse 2: [A, D, F, G, H]

Order: [A, C, D] -> Warehouse 1
Order: [A, D, H] -> Warehouse 2
Order: [A, B, E, F] -> Warehouse 1 (+1 separately ordered)
Order: [A, D, E, F] -> Warehouse 2 (+1 separately ordered)

The goal is to use historical data to minimize the number of individually ordered products in future. Once the warehouses had been set up a certain way, the software would just determine which warehouse could handle an order with minimal overhead.

This immediately strikes me as a machine learning style problem. It also seems like a combination of certain well known NP-Complete problems, though none of them seem to fit properly.

Is there a model which represents this type of problem?

If I understand correctly, you have to separate problems :

• Predict what should each warehouse pre-buy
• Get the best warehouse for an order

For the first problem, I point you to the netflix prize : this was almost the same problem, and great solutions have been proposed. (My datamining handbook is at home and I can't remember for precise keyword to google, sorry.Try "data mining time series" )

For the second one, this is a problem for Prolog.

• Set a cost for separately ordering an item
• Set a cost, for, idk, proximity to the customer
• Set the cost for already owning the product to 0
• Make the rule to get a product : buy it if you don't have it, get it if you do
• Make the rule to get all products : foreach product, rule above
• get the cost of this rule
• Gently ask Prolog to get a solution. If it's not good enough, ask more.

If you don't want to use Prolog, there are several constraints libraries out there. Just google "constraint library <insert your programming language here>"

The first part of the problem (which items are frequently ordered together) is sometimes known as the co-occurrence problem, and is a big part of the data mining literature. (My recollection is that the problem is in NP, but there exist quite good approximate algorithms).

Once you have co-occurrence data you are happy with, you are still left with the assignment of items to warehouses. It's a little like the set-covering problem, but not quite the same. This problem is NP-hard.