By Zbigniew Michalewicz
Adaptive company intelligence platforms mix prediction and optimization recommendations to aid selection makers in advanced, swiftly altering environments. those platforms handle basic questions: what's more likely to ensue sooner or later? what's the most sensible plan of action? Adaptive enterprise Intelligence explores components of knowledge mining, predictive modeling, forecasting, optimization, and suppleness. The publication explains the appliance of diverse prediction and optimization options, and indicates how those suggestions can be utilized to strengthen adaptive structures. insurance contains linear regression, time-series forecasting, choice timber and tables, synthetic neural networks, genetic programming, fuzzy platforms, genetic algorithms, simulated annealing, tabu seek, ant platforms, and agent-based modeling.
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Of course, the non-dominated solutions are of interest to us. Ideally, any system that deals with multiple-objective problems should return several diverse solutions (preferably all non-dominated). Each of these solutions might be of interest to us, but in most cases, we can only implement one solution. To decide between these various non-dominated solutions, either human expertise is used to find common denominators (e. , to express time and waste in dollars) or some higher-level knowledge. For example, we may assess the relative importance of each objective by assigning numeric weights (in the same manner they were applied to soft constraints in the previous section), or by imposing a ranking for all objectives and then selecting solutions that follow this ranking.
For example, in our simplified model above, note that the difference in transportation cost between 20 and 21 items is now the same as the difference between 19 and 20 items (which is not the case in the precise model). Consequently, the simplified model does not “see” the thresholds that play a major role in identifying the optimal solution. Thus, the “optimal” solution for an approximate model is usually more appropriate for the wastebasket than for implementation! 6 A Real-World Example Let us consider a real-world example to put these business problem characteristics into context.
Hence, the resulting pollution concentrations in each square were the sum of both the primary and background concentrations. Because weather played a major role in determining how pollution affected the environment in Poland (e. , gusty winds could easily spread high levels of pollution to remote areas of the country), it was necessary to use sophisticated weather forecasts for predicting the ecological damage that each 30 km × 30 km square would sustain during the next 48 hours. Thus, by using data on the amount and type of pollution emitted from each power station, along with a weather forecast for the speed and direction of winds, a prediction model was developed to predict how much pollution would be created, how it would be dispersed, and how much damage it would cause in the affected squares.