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Download e-book for iPad: Adaptive Business Intelligence by Zbigniew Michalewicz

By Zbigniew Michalewicz

ISBN-10: 3540329285

ISBN-13: 9783540329282

Within the glossy info period, managers needs to realize the aggressive possibilities represented by means of decision-support instruments. Adaptive enterprise intelligence structures mix prediction and optimization innovations to aid determination makers in advanced, swiftly altering environments. those structures tackle the basic questions: what's prone to ensue sooner or later? and what's the simplest determination straight away? Adaptive enterprise Intelligence comprises parts of knowledge mining, predictive modeling, forecasting, optimization, and adaptability.The authors have massive educational study backgrounds in man made intelligence and similar fields, mixed with years of functional consulting adventure in companies and industries all over the world. during this publication they clarify the technology and alertness of diverse prediction and optimization ideas, in addition to how those innovations can be utilized to advance adaptive platforms. The concepts coated comprise linear regression, time-series forecasting, determination timber and tables, man made neural networks, genetic programming, fuzzy structures, genetic algorithms, simulated annealing, tabu seek, ant platforms, and agent-based modeling.This e-book is appropriate for enterprise and IT managers who make judgements in advanced commercial and repair environments, nonspecialists who are looking to comprehend the technological know-how in the back of higher predictions and judgements, and scholars and researchers who want a fast creation to this box.

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Note that the increments from one car to the next for the first 10 cars need not be linear. For example, the truck might have two levels and loading a car onto the upper level might be slightly more expensive than loading a car onto the lower level. For this reason, a straight line need not represent the “1 to 10 cars” part of the graph. Furthermore, if we want to transport 11 cars, then we will experience a “jump” in cost: We will pay $700 for 10 cars on the first truck, plus $250 for a single car on the second truck (for a total of $950).

Furthermore, if our database contains approximately three million cases (data collection spanning 30 months), then each prediction model will have an average of 300 cases. The distribution of these cases will be non-uniform. For example, we might have 2,800 cases per location for some makes/models (e. , Toyota Camry), but only one or two cases per location for some other makes/models (e. , Porsche 911)! Consequently, it would be impossible to create a prediction model for some makes/models. Regardless of these complications, let us assume for the moment that our basic prediction module makes a prediction of $11,384 (for the Honda Accord being sent to an auction site in Northern California).

710 cars: $95 per car. 1114 cars: $85 per car. ), $120 per car for the remaining 16 cars, $95 per car for the remaining 710 cars, and $120 per car for the remaining 1013 cars. Transporting 20 cars, for example, would cost $85 per car for the first 14 cars and then $120 per car for the remaining 6 cars, for a total of $1,910 (14 × $85 + 6 × $120). ). We will discuss some of these later in this chapter. 5 Volume Effect Although our goal is to ship each car to the auction site that offers the best price, we might inadvertently trigger the “volume effect” by sending too many cars of same color, make, and mileage to the same auction site.

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Adaptive Business Intelligence by Zbigniew Michalewicz


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