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A new method to improve the Group to Group rule in Price Optimization Disclosure Number: IPCOM000234849D
Publication Date: 2014-Feb-11
Document File: 8 page(s) / 306K

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The Prior Art Database


This disclosure discloses a new solution for Group to Group rule in Price Optimization, it introduces an intermediate price for the Group to Group rule and gain a big performance improvement in both Application and Science

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A new method to improve the Group to Group rule in Price Optimization

Price Optimization is designed to enable retailers to optimize base prices to achieve their sales, volume, profit and price image objectives for regular, everyday items. In the Price Optimization there are 4 types of rules: 1) Unary product rule, e.g. price of product should be no more than a certain level; 2)Unary group rule, e.g. the average of all products in a group has to be at a certain level; 3) Binary product to product rule, e.g. price of first product should be equal to price of the second product; 4) Binary group to group rule, e.g. the price of each product in first group should be no more than the price of each product in the second group.

In most cases, the group to group rule is handled as the product to product rule, it really takes time and even break down when the groups have thousands of products. By the feedback from customers, they have to wait for a long time when they use the group to group rule. From this point this disclosure proposes a new solution for the group to group rule which can meet the customers' need and have a good performance.

This disclosure introduces an intermediate price for the group to group rule, it is no need to loop two groups at the same time in this new method. In the interface between Application and Science this new method will gain performance in import/export, as in science input it doesn't need so many binary rules any more. In science this new method save memory as the new method reduce greatly in constraints and gain better performance while solving the optimization model, and in the rule relaxation the new method can relax the group to group rule very quickly as it relax the group bound rather than product to product bounds. So with this invention the group to group rule will perform in an efficient way.

In Price optimization, there are business rules with priority, the business rules indicates what customer wants and these rules are constraint conditions in Price optimization model, the priority indicates which rule is more important and which is less important. Suppose there are two groups, group 1 has 4 products(p1,p2,p3,p4) and group 2 has 3 products(p5,p6,p7), and the rule says the retail price of the product in group 1 must be no more than 10% above the corresponding products in group 2. In the old method it will be handled in
the product to product way, every product in group 1 have to related with every product in group 2, show it directly in diagram:


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there are 3*4 = 12 constraints. If there is m products in group 1 and n products in group 2, it will get m*n constraints. But in the new method, An intermediate price is introduced to build the relationship between group 1 and group 2, so in the new method it will behavior like this:

Science will loop the products in the group 1 and build a relationship with intermediate price, then do it again for the group 2, now there are 3+4 = 7 con...