By Zbigniew Michalewicz
Adaptive enterprise intelligence platforms mix prediction and optimization ideas to help choice makers in advanced, speedily altering environments. those structures handle primary questions: what's more likely to take place sooner or later? what's the top plan of action? Adaptive enterprise Intelligence explores parts of knowledge mining, predictive modeling, forecasting, optimization, and suppleness. The booklet explains the applying of various prediction and optimization recommendations, and exhibits how those strategies can be utilized to boost adaptive structures. insurance comprises linear regression, time-series forecasting, choice bushes and tables, man made neural networks, genetic programming, fuzzy structures, genetic algorithms, simulated annealing, tabu seek, ant structures, and agent-based modeling.
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Adaptive company intelligence platforms mix prediction and optimization suggestions to help determination makers in complicated, swiftly altering environments. those platforms handle primary questions: what's prone to take place sooner or later? what's the top plan of action? Adaptive enterprise Intelligence explores components of knowledge mining, predictive modeling, forecasting, optimization, and suppleness.
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5 Modeling the Problem 17 Because of this two-step process, we must realize that we are only finding a solution to the model of a problem. If the model is accurate, then the solution will be meaningful. But if the model has too many vague assumptions and approximations, the solution may be meaningless, or worse. Consider the following example: Suppose a company has 80 warehouses and 5 distribution centers, and every possible route between each warehouse and distribution center has a measurable transportation cost.
The first step in solving this problem lay in identifying the relevant problem variables and all the relationships between them. This was accomplished by building a computational “grid model” of Poland’s land area and corresponding power grid. Each square in the grid corresponded to 30 km × 30 km, which collectively covered Poland’s approximate 900 km × 750 km land area. 6 A Real-World Example 21 square were computed. It was also necessary to take into account the pollution caused by private enterprises in Poland, as well as many foreign sources (mainly from Germany and the Czech Republic).
Similar comments are also applicable to regression problems. The general purpose of (multiple) regression is to discover the relationship between several independent (“predictor”) variables and a dependent (“criterion”) variable, with the output being a concrete number. For example, we may want to predict salary levels as a function of position, number of years at the position, number of supervised employees, etc. ”2 Again, the issue of time is either non-existent or included as a variable of the case.