Posted: 18 Oct 2016 18:35 EDT Last activity: 4 Oct 2018 13:54 EDT
Looking through the material, I am interested in a situation where I have a set of propositions where they themselves are enriched. Can propositions be more dynamic? Can I used declared expressions to get propositions to have changes within their own characteristics so that I can model strategies against those characteristics? For example quantity remaining?
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However, in the decision data rule, usually you specify the 'static' attributes only. The strategy, using the Set - Property component allows you to 'extend' this proposition record and add dynamic info to it; like for e.g. remaining quantity. You can not use declare expressions directly, but you can use the expression editor inside strategies and build as complicated expression as you wish. Please check out the Decision Strategy lesson, and learn how a Margin, calculated in the strategy, is added to a proposition.
I am also looking at a similar problem statement. We are developing an application where the propositions are 'Cars' which can be hired. Once a car is hired it's Availability characteristic should be marked as False so that it is not displayed as proposition when someone else makes a search.
Margin calculation mentioned above is little different. Its applicable to only the current case in context. What I want to achieve is to update the proposition characteristics so next time in some other case they get filtered out or can be set to different priority.
If proposition can not be updated frequently can we use a combination of data set and proposition to achieve the same?
There are always multiple approaches to solve a problem. This is the "Product Holding" approach. Create a proposition or each car and associate this proposition with individual cars (Car ID). When a car proposition is accepted (the car is rented) by a customer, the product holding table (car rental table) is updated. When the next customer comes in, the Next-Best-Action strategy will import all available propositions (cars) and all product holdings (rented cars) and join the data by Car ID. As the next step we filter out all car propositions that are currently rented out. When a car is returned the product holding table is updated accordingly and hence the proposition becomes avaiable again to determine the Next-Best-Car.