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A novel framework for facility location evaluation

A novel framework for facility location evaluation Is a given location appropriate to set a facility, and if it is, how will this facility perform? This is denoted as the facility location problem, which has attracted interests of academic researchers and industrial practitioners. This problem is traditionally considered in spatial analysis and forecasting literature. However, existing methods can hardly handle the problem well because of the fragmented information and insufficient training data. In this paper, we present a novel framework which combines spatial analysis and forecasting analysis for facility location. Firstly, a classifier is built on spatial information to evaluate locations' environmental patterns. For each pattern, a predictor is then constructed to predict facility performance. Besides, we also propose a data aggregation method to pre-process raw spatial data, and a semi-supervised learning method to expand insufficient training data. Experimental results of a case study demonstrate the effectiveness of the framework on supporting real-world facility location decisions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Services Operations and Informatics Inderscience Publishers

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1741-539X
eISSN
1741-5403
DOI
10.1504/IJSOI.2010.03105
Publisher site
See Article on Publisher Site

Abstract

Is a given location appropriate to set a facility, and if it is, how will this facility perform? This is denoted as the facility location problem, which has attracted interests of academic researchers and industrial practitioners. This problem is traditionally considered in spatial analysis and forecasting literature. However, existing methods can hardly handle the problem well because of the fragmented information and insufficient training data. In this paper, we present a novel framework which combines spatial analysis and forecasting analysis for facility location. Firstly, a classifier is built on spatial information to evaluate locations' environmental patterns. For each pattern, a predictor is then constructed to predict facility performance. Besides, we also propose a data aggregation method to pre-process raw spatial data, and a semi-supervised learning method to expand insufficient training data. Experimental results of a case study demonstrate the effectiveness of the framework on supporting real-world facility location decisions.

Journal

International Journal of Services Operations and InformaticsInderscience Publishers

Published: Jan 1, 2010

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