By Zbigniew Michalewicz
Adaptive enterprise intelligence structures mix prediction and optimization thoughts to aid choice makers in complicated, speedily altering environments. those structures handle basic questions: what's more likely to ensue sooner or later? what's the most sensible plan of action? Adaptive company Intelligence explores components of knowledge mining, predictive modeling, forecasting, optimization, and flexibility. The ebook explains the applying of various prediction and optimization suggestions, and exhibits how those options can be utilized to advance adaptive platforms. assurance comprises linear regression, time-series forecasting, determination bushes 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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Extra info for Adaptive Business Intelligence
X The problem exists in a time-changing environment, and therefore requires a set of solutions (rather than a single solution). x The problem is so heavily constrained that finding even one feasible solution is often difficult, let alone searching for the optimum solution (or set of solutions). x The problem includes many (possibly conflicting) objectives. In the previous chapter, we gave an example of a real-world business problem in the car leasing industry. There is no question that the number of possible solutions to the car distribution problem is too large for any systematic search (we indicated that the distribution of 3,000 cars to 50 auction sites gives us 503000 possible solutions).
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. To understand the volume effect, imagine an auction site that holds car auctions every second Wednesday at 10 am.
This is a difficult question to answer, especially when we consider that some leasing companies lease more than one million cars each year. That is more than 3,000 cars per day – a truly staggering figure! 3 The Problem Now, imagine that we are in charge of selling off-lease cars for a fictitious leasing company. Let us assume that the company receives 3,000 off-lease cars each day and does business with 50 designated auction sites. The following figure illustrates the case (for a particular day of operation): 10 We could easily obtain the characteristics of each car by merging it with the previous table.
Adaptive Business Intelligence by Zbigniew Michalewicz