A decision support system for selecting convenience store location through integration of fuzzy AHP and artificial neural network
Location selection plays a very prominent role in retailing due to its high and long-term investments. It is very difficult to make up once an inappropriate convenience store (CVS) location has been established, The conventional approaches to location selection can only provide a set of systematic steps for problem-solving without considering the relationships between the decision factors globally. Therefore, this study aims to develop a decision support system for locating a new CVS. The proposed system consists of four components: (1) hierarchical structure development for fuzzy analytic hierarchy process (fuzzy AHP), (2) weights determination, (3) data collection, and (4) decision making. In the first component, the hierarchical structure of fuzzy AHP is formulated by reviewing the related references and interviewing the retailing experts. Then, a questionnaire survey is conducted to determine the weight of each factor in the second component, while the corresponding data are collected through some government publications and actual investigation. Finally, a feedforward neural network with error back-propagation (EBP) learning algorithm is applied to study the relationship between the factors and the store performance. The results show that proposed system is able to provide more accurate result than regression model in accuracy. (C) 2002 Elsevier Science B.V. All rights reserved.
artificial neural network location selection convenience store (cvs) error back-propagation learning algorithm