Development, Distribution and Evaluation of Online Tourism Services in China
Electronic Commerce Research
A knowledge-based approach to merging information
Knowledge-Based Systems
Extension of the expected value method for multiple attribute decision making with fuzzy data
Knowledge-Based Systems
Cost-benefit factor analysis in e-services using bayesian networks
Expert Systems with Applications: An International Journal
A linguistic multi-criteria group decision support system for fabric hand evaluation
Fuzzy Optimization and Decision Making
Ranking and selection of unsupervised learning marketing segmentation
Knowledge-Based Systems
| Hi-index | 0.00 |
The solution to location optimization for an E-service network is a difficult, complex, tedious, and protracted process which requires evaluation to be made on a number of aspects according to certain criteria. This paper proposes an online shop location optimization method which combines a fuzzy location choice method based on neighborhood infrastructure with a fuzzy multi-criteria decision-making (FMCDM) method. The study process includes two stages. The first stage identifies the location choice characteristics for online shops selling digital products, and provides candidate sites for further location optimization consideration. The second stage incorporates the information provided by two experts leading to fulfillment of the selection of optimum locations. The research findings are as follows: (1) The location choices of online shops which sell valuable goods, such as digital products, are mainly aggregated and still rely on traditional neighborhood infrastructure. The selection of an optimum location is a process of transference from overlap locations to complete locations. (2) Online shop location optimization can be modeled and calculated by fuzzy numbers to handle uncertain data. The research findings have shown that the proposed fuzzy location choice method and the FMCDM method have a well-structured architecture and a great computational power. It helps to promote decision-making quality in decision-making processes under uncertainty.