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|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|5758||2012||5 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Procedia Technology, Volume 4, 2012, Pages 401–405
Decision support systems are computer based programs which assists decision makers in effective and efficient decision making. Outliers are the data sets which are highly irrelevant with respect to main data sets. Spatial outliers are the spatial objects with distinct features from their surrounding neighbours. In this paper we have proposed a decision support system for strategic decision makers to establish an industry. We have shown how spatial outlier detection techniques may be used to aid decision makers. Each site is evaluated based upon multiple decision variables. Here, we use two different outlier detection algorithms to detect a site which is most unsuitable to establish an industry. We also perform an experiment on a synthetic dataset of fifteen different sites to locate ambiguous sites. We found that decision making process for site selection may be improved by using spatial outlier detection techniques.
ndustrial location is an increasingly important factor facing both national and international firms. To establish any industry a manager has to analyse large amount of information regarding available list of sites. Some of the critical factors of industrial location are transportation, labour, raw material, markets, utilities, electricity, climate, tax structure, technology, government policies & etc. The sites which are not suitable are completely discarded from the decision making process and such sites are called as spatial outlier sites. Spatial outlier is an object whose non-spatial attribute value is significantly different from the values of its spatial neighbours. Spatial outlier detection algorithms may be applied to find such ambiguous sites.
نتیجه گیری انگلیسی
In this paper, we used two spatial outlier detection algorithms using Mahalanobis distance to find most irrelevant sites from a spatial dataset with multiple attributes: one algorithm based on the average of the attribute values from neighborhoods and the other based on median of the attribute values. We have seen how decision making process to establish an IT industry may be improved. It is easy to analyze large numbers of sites which are spatially distributed. Proposed system is suitable in the situations where large numbers of spatial items are to be distinguished based upon their non spatial attributes. Performance of our system may be further improved by choosing more efficient spatial outlier detection methods. Furthermore, such systems may be generalized to use in other real life applications like financial decisions, demographic survey decisions, agricultural decisions and etc.