Different Multi-Criteria Strategies in Hospital Location Ranking using Dempster–Shafer Decision-Level Fusion and Quantifier-guided OWA, A Case Study

Document Type : Original Article


1 School of Surveying and Geospatial Eng., College of Eng., University of Tehran, Tehran, Iran

2 Shahrood University of Technology, Shahrood, Iran



This study focused on ranking hospital locations. Accordingly, the Objective weighting methods, Dempster-Shafer Theory (DST), and Quantifier-guided Order Weighting Average (QOWA) were proposed to consider different decision strategies and model the uncertainty in an integrated GIS-based multi-criteria decision making (MCDM) process. The objective weighting methods determine the weights of criteria by solving mathematical models without considering the preferences of experts. The DST fuses information received from two or more sources and this fusion improves reliability and reduses uncertainty in decision making. In this study the QOWA was used because the decision makers will be able to determine the decision risk level according to the decision situation and provide high-low risk solutions to solve the hospital location problem. The results showed that the criteria of seismic vulnerability, population density, and distance from the major roads were the most important criteria for selecting an optimal location for a hospital, respectively. Moreover, the results were compared with the results of four well-known MCDM methods as well as the rankings performed by the experts. The results showed that the rankings performed with different decision strategies of the proposed methodology were closer to experts’ opinions compared to the other MCDM methods. According to experts, the Neutral decision strategy (chosen by 46% of the experts) and Pessimistic decision strategy (chosen by 18% of the experts) have shown to be the most suitable decision strategies for selecting an optimal location for a hospital. In the process of spatial planning and urban development, designing different decision-making scenarios and measuring the performance of the proposed scenario is very important and enriches and improves knowledge about results of different decision-making scenarios.


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