Comparing the Performance of Different Artificial Intelligence Based Clustering Algorithms in Healthcare Waste Disposal Location

Gergin, Zeynep
Esnaf, Şakir
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CRC Press/Balkema Taylor and Francis Group
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In this study four different clustering algorithms are compared with respect to their cost performance for a real world multi facility location problem. First two algorithms are Fuzzy C-Means and Self Organizing Maps. Then, these algorithms are integrated with Center of Gravity Method, named as FCM-COG and SOM-COG. All algorithms are applied to a multi facility location problem for clustering geographical coordinate data of Istanbul hospitals, to serve Istanbul Metropolitan Municipality Environmental Management Industrial and Trade Inc. (ISTAÇ) in its medical waste management activities. ISTAÇ will use the results to identify the alternative places of sterilization facilities, and reducing the distance that hazardous wastes are transferred. Clustering is performed for different number of clusters predefined by ISTAÇ, and results are compared with respect to CPU time performance, and total cost which is defined as the amount of waste produced by a hospital, multiplied by its distance to the sterilization facility.
Fuzzy C-Means, Self Organizing Maps, Center of Gravity, Healthcare waste disposal location