Location selection of field hospital for future pandemics using fuzzy combined MCDM methods
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Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Adana Alparslan Türkeş Bilim ve Teknoloji Üniversitesi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
İnsanoğlu binlerce yıldır deprem, sel, salgın hastalıklar, volkanik patlamalar gibi pek çok doğal afetle mücadele etmiştir. Bu doğal afetler arasında salgın hastalıklar yüzlerce insanın ölümüne neden olmuştur. Kara Ölüm olarak da bilinen Veba salgınından domuz gribine ve günümüzde COVID-19 pandemisine kadar pek çok salgın hastalık çok geniş bir alanda etkisini göstererek toplumları derinden etkilemiştir. Aralık 2019'da Çin'in Wuhan kentinde ortaya çıkan COVID-19 pandemisinin hızla yayılmasından dolayı dünya çapında birçok hastane kapasitelerini aşarak yetersiz kalmıştır. Bazı ülke yetkilileri bu soruna çözüm üretmek için salgın sırasında sahra hastaneleri inşa etmiştir. Bu tez çalışmasında, küreselleşen dünyada gelecekte artması öngörülen pandemiler için sahra hastanesi yer seçimi problemi ele alınmış ve Adana'da gerçek bir vaka çalışması yapılmıştır. Yöneylem araştırmasının konularından biri olan yer seçimi problemi, birden fazla birbiriyle çelişen kriter ve sınırlı sayıda alternatif içerdiğinden, bu tür problemleri çözmek için Çok Kriterli Karar Verme (ÇKKV) bilinen en etkili klasik yöntemlerden biridir. Bu klasik yöntemler gerçek yaşamdaki belirsiz ve kesin olmama durumlarını ele almada yetersiz kaldığından, bu yöntemlerin insan yargılarını dikkate alan bulanık versiyonlarına göre karar almak daha gerçekçi olacaktır. Bu nedenle bu tez çalışmasında sahra hastanesi yer seçimi probleminin çözümü için bulanık FUCOM & bulanık MARCOS modeli önerilmiştir. Kriter ağırlıkları bulanık FUCOM yöntemi ile belirlenmiştir. Ardından alternatif lokasyonlar bulanık MARCOS yöntemi ile sıralanmıştır. Analiz sonuçlarına göre en iyi alternatif lokasyonun TÜYAP Adana Fuar ve Kongre Merkezi Çevresi olduğu belirlenmiştir. Çalışmanın sonunda önerilen modelin doğruluğunu ve geçerliliğini test etmek amacıyla duyarlılık analizi yapılmıştır.
For thousands of years, human beings have struggled with many natural disasters such as earthquakes, floods, epidemics, and volcanic eruptions. Among these natural disasters, epidemics have caused the death of hundreds of people. Many epidemics, from the plague epidemic, also known as the Black Death to the Swine flu and today to the COVID-19 pandemic, have affected societies deeply by affecting a wide range of areas. Due to the rapid spread of the COVID-19 pandemic, which arised from Wuhan, China in December 2019, many hospitals around the world have exceeded their capacity and insufficient. Authorities of some countries have built field hospitals during the pandemic to find a solution to this problem. In this thesis study, field hospital location selection problem for pandemics that are predicted to increase in the future in the globalizing world was discussed and a real case study was conducted in Adana. Since the location selection problem, which is one of the topics of operations research, involves multiple conflicting criteria and a limited number of alternatives, Multi-Criteria Decision Making (MCDM) is one of the most effective classical methods known to solve such problems. Since these classical methods are insufficient to handle uncertain and imprecise situations in real life, it may be more realistic to make decisions based on fuzzy versions of these methods that take into account human judgments. Thus, in this study, a fuzzy FUCOM & fuzzy MARCOS model was suggested to solve the field hospital location selection problem. The weights of the criteria were calculated with the fuzzy FUCOM method. The alternative locations were then ranked with the fuzzy MARCOS method. According to the analysis results, it was determined that the best alternative location was TUYAP Adana Fair and Congress Center Surrounding. Lastly, a sensitivity analysis was conducted to test the accuracy and validity of the proposed model.
For thousands of years, human beings have struggled with many natural disasters such as earthquakes, floods, epidemics, and volcanic eruptions. Among these natural disasters, epidemics have caused the death of hundreds of people. Many epidemics, from the plague epidemic, also known as the Black Death to the Swine flu and today to the COVID-19 pandemic, have affected societies deeply by affecting a wide range of areas. Due to the rapid spread of the COVID-19 pandemic, which arised from Wuhan, China in December 2019, many hospitals around the world have exceeded their capacity and insufficient. Authorities of some countries have built field hospitals during the pandemic to find a solution to this problem. In this thesis study, field hospital location selection problem for pandemics that are predicted to increase in the future in the globalizing world was discussed and a real case study was conducted in Adana. Since the location selection problem, which is one of the topics of operations research, involves multiple conflicting criteria and a limited number of alternatives, Multi-Criteria Decision Making (MCDM) is one of the most effective classical methods known to solve such problems. Since these classical methods are insufficient to handle uncertain and imprecise situations in real life, it may be more realistic to make decisions based on fuzzy versions of these methods that take into account human judgments. Thus, in this study, a fuzzy FUCOM & fuzzy MARCOS model was suggested to solve the field hospital location selection problem. The weights of the criteria were calculated with the fuzzy FUCOM method. The alternative locations were then ranked with the fuzzy MARCOS method. According to the analysis results, it was determined that the best alternative location was TUYAP Adana Fair and Congress Center Surrounding. Lastly, a sensitivity analysis was conducted to test the accuracy and validity of the proposed model.
Açıklama
Lisansüstü Eğitim Enstitüsü, Endüstri Mühendisliği Ana Bilim Dalı
Anahtar Kelimeler
Endüstri ve Endüstri Mühendisliği, Industrial and Industrial Engineering
