Adaptive Business Intelligence by Zbigniew Michalewicz

By Zbigniew Michalewicz

Adaptive company intelligence structures mix prediction and optimization options to help choice makers in advanced, speedily altering environments. those structures tackle basic questions: what's more likely to occur sooner or later? what's the top plan of action? Adaptive enterprise Intelligence explores components of knowledge mining, predictive modeling, forecasting, optimization, and flexibility. The publication explains the appliance of diverse prediction and optimization ideas, and exhibits how those recommendations can be utilized to strengthen adaptive structures. assurance comprises linear regression, time-series forecasting, choice timber and tables, synthetic neural networks, genetic programming, fuzzy structures, genetic algorithms, simulated annealing, tabu seek, ant platforms, and agent-based modeling.

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Note that the “best answer” is based on the prediction module’s output. For example, to evaluate the merit of a particular distribution of cars, we need to predict their sale prices. The relationship between the prediction and optimization modules can be displayed as follows: In the above diagram, the optimization module generates a distribution solution that serves as input data for the prediction module. This input data provides a destination assignment (i. , auction site) for each car, which the prediction module uses to generate the predicted sale prices.

Using historical data for weather forecasts and emission levels, several experiments were conducted using this new system. The optimization process was applied to this historical data, and then the optimized results were compared with the actual, non-optimized results. From these experiments, the following conclusions were drawn: The amount of energy produced was equal in both cases, the aggregate operating costs were also equal, but the ecological damage in Poland was 15% to 18% less when the optimization process was applied.

The volume effect for a particular car at a particular auction site might look something like: This graph illustrates the volume effect phenomenon: We get more money per car by selling fewer similar cars. For example, the current average sale price for a particular car at a particular auction site might be $10,400. We can get the same price if we ship up to seven cars to this location. However, if we ship 30 similar cars, then the average sale price per car will drop to $9,450. Note that the term “similar” can mean more than just the same make/model or color.

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