Enhancing Facility Locating via a Novel Hybrid Model
Given its importance,facility location has long attracted many research efforts from both the academic and industrial areas.However,due to the cost and privacy issues,researchers usually suffer from the lack of business information which is crucial for the decision makings.Recent years have witnessed the explosion of geographical information systems (GISs),and the spatial information provided by GISs becomes a valuable supplement to the limited business information for facility location decision.Along this line,in this paper,we present a hybrid model which combines spatial analysis and forecasting analysis to solve this problem.That is,a classi.er is built.rst on the spatial data to evaluate environmental conditions of the location.Then based on the classi.cation results,a predictor is built on both the spatial and business data to predict the facility performance.To deal with the problem of missing much business information while building the classi.er,we also propose a semi-supervised learning method to expand the training data set.Finally,experimental results on a case study demonstrate that the hybrid model indeed shows merits on supporting realworld facility location decisions.
facility location Geographical Information System (GIS) data mining
Ming Xie Wenjun Yin Bin Zhang Jin Dong Lili Zhao
IBM China Research Lab Beijing 100093,China
国际会议
北京
英文
2008-10-12（万方平台首次上网日期，不代表论文的发表时间）