{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T19:33:06Z","timestamp":1775849586429,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T00:00:00Z","timestamp":1711324800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The optimal placement of healthcare facilities, including the placement of diagnostic test centers, plays a pivotal role in ensuring efficient and equitable access to healthcare services. However, the emergence of unique complexities in the context of a pandemic, exemplified by the COVID-19 crisis, has necessitated the development of customized solutions. This paper introduces a bi-objective integer linear programming model designed to achieve two key objectives: minimizing average travel time for individuals visiting testing centers and maximizing an equitable workload distribution among testing centers. This problem is NP-hard and we propose a customized local search algorithm based on the Voronoi diagram. Additionally, we employ an \u03f5-constraint approach, which leverages the Gurobi solver. We rigorously examine the effectiveness of the model and the algorithms through numerical experiments and demonstrate their capability to identify Pareto-optimal solutions. We show that while the Gurobi performs efficiently in small-size instances, our proposed algorithm outperforms it in large-size instances of the problem.<\/jats:p>","DOI":"10.3390\/a17040135","type":"journal-article","created":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T12:28:06Z","timestamp":1711369686000},"page":"135","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Test Center Location Problem: A Bi-Objective Model and Algorithms"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1010-4121","authenticated-orcid":false,"given":"Mansoor","family":"Davoodi","sequence":"first","affiliation":[{"name":"Center for Advanced Systems Understanding, Untermarkt 20, 02826 Goerlitz, Germany"},{"name":"Helmholtz-Zentrum Dresden-Rossendorf, Bautzner Landstra\u00dfe 400, 01328 Dresden, Germany"},{"name":"Faculty of Electrical Engineering and Information Technology, Ruhr-University Bochum, 44801 Bochum, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0575-6408","authenticated-orcid":false,"given":"Justin M.","family":"Calabrese","sequence":"additional","affiliation":[{"name":"Center for Advanced Systems Understanding, Untermarkt 20, 02826 Goerlitz, Germany"},{"name":"Helmholtz-Zentrum Dresden-Rossendorf, Bautzner Landstra\u00dfe 400, 01328 Dresden, Germany"},{"name":"Department of Ecological Modelling, Helmholtz Centre for Environmental Research\u2014UFZ, 04318 Leipzig, Germany"},{"name":"Department of Biology, University of Maryland, College Park, MD 20742, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Daskin, M.S. 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