{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:26:21Z","timestamp":1770747981423,"version":"3.49.0"},"reference-count":24,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,6,25]],"date-time":"2021-06-25T00:00:00Z","timestamp":1624579200000},"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>This article presents the possibilities in solving the Weighted Multi-Facility Location Problem and its related optimization tasks using a widely available office software\u2014MS Excel with the Solver add-in. To verify the proposed technique, a set of benchmark instances with various point topologies (regular, combination of regular and random, and random) was designed. The optimization results are compared with results achieved by a metaheuristic algorithm based on simulated annealing principles. The influence of the hardware configuration on the performance achieved by MS Excel Solver is also examined and discussed from both the execution time and accuracy perspectives. The experiments showed that this widely available office software is practical for solving even relatively complex optimization tasks (Weighted Multi-Facility Location Problem with 100 points and 20 centers, which consists of 40 continuous optimization variables in two-dimensional space) with sufficient quality for many real-world applications. The method used is described in detail and step-by-step using an example.<\/jats:p>","DOI":"10.3390\/a14070191","type":"journal-article","created":{"date-parts":[[2021,6,25]],"date-time":"2021-06-25T04:46:57Z","timestamp":1624596417000},"page":"191","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Optimization of the Weighted Multi-Facility Location Problem Using MS Excel"],"prefix":"10.3390","volume":"14","author":[{"given":"Petr","family":"N\u011bmec","sequence":"first","affiliation":[{"name":"Department of Logistics, University of Defence, Kounicova 65, 662 10 Brno, Czech Republic"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2251-8711","authenticated-orcid":false,"given":"Petr","family":"Stodola","sequence":"additional","affiliation":[{"name":"Department of Intelligence Support, University of Defence, Kounicova 65, 662 10 Brno, Czech Republic"}]},{"given":"Miroslav","family":"Pecina","sequence":"additional","affiliation":[{"name":"Department of Logistics, University of Defence, Kounicova 65, 662 10 Brno, Czech Republic"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2497-2739","authenticated-orcid":false,"given":"Ji\u0159\u00ed","family":"Neubauer","sequence":"additional","affiliation":[{"name":"Department of Quantitative Methods, University of Defence, Kounicova 65, 662 10 Brno, Czech Republic"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2964-1229","authenticated-orcid":false,"given":"Martin","family":"Blaha","sequence":"additional","affiliation":[{"name":"Department of Fire Support, University of Defence, Kounicova 65, 662 10 Brno, Czech Republic"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,25]]},"reference":[{"key":"ref_1","unstructured":"Vazsonyi, A. 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