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Clustering Approach Using Artificial Bee Colony Algorithm for Healthcare Waste Disposal Facility Location Problem

Clustering Approach Using Artificial Bee Colony Algorithm for Healthcare Waste Disposal Facility Location Problem

Zeynep Gergin, Nükhet Tunçbilek, Şakir Esnaf
Copyright: © 2019 |Volume: 10 |Issue: 1 |Pages: 20
ISSN: 1947-9328|EISSN: 1947-9336|EISBN13: 9781522566144|DOI: 10.4018/IJORIS.2019010104
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MLA

Gergin, Zeynep, et al. "Clustering Approach Using Artificial Bee Colony Algorithm for Healthcare Waste Disposal Facility Location Problem." IJORIS vol.10, no.1 2019: pp.56-75. http://doi.org/10.4018/IJORIS.2019010104

APA

Gergin, Z., Tunçbilek, N., & Esnaf, Ş. (2019). Clustering Approach Using Artificial Bee Colony Algorithm for Healthcare Waste Disposal Facility Location Problem. International Journal of Operations Research and Information Systems (IJORIS), 10(1), 56-75. http://doi.org/10.4018/IJORIS.2019010104

Chicago

Gergin, Zeynep, Nükhet Tunçbilek, and Şakir Esnaf. "Clustering Approach Using Artificial Bee Colony Algorithm for Healthcare Waste Disposal Facility Location Problem," International Journal of Operations Research and Information Systems (IJORIS) 10, no.1: 56-75. http://doi.org/10.4018/IJORIS.2019010104

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Abstract

In this study, an Artificial Bee Colony (ABC) based clustering algorithm is proposed for solving continuous multiple facility location problems. Unlike the original version applied to multivariate data clustering, the ABC based clustering here solves the two-dimensional clustering. On the other hand, the multiple facility location problem the proposed clustering algorithm deals with is aimed to find site locations for healthcare wastes. After applying ABC based clustering algorithm on test data, a real-world facility location problem is solved for identifying healthcare waste disposal facility locations for Istanbul Municipality. Geographical coordinates and healthcare waste amounts of Istanbul hospitals are used to decide the locations of sterilization facilities to be established for reducing the medical waste generated. ABC based clustering is performed for different number of clusters predefined by Istanbul Metropolitan Municipality, and the total cost—the amount of healthcare waste produced by a hospital, multiplied by its distance to the sterilization facility—is calculated to decide the number of facilities to be opened. Benchmark results with four algorithms for test data and with two algorithms for real world problem reveal the superior performance of the proposed methodology.