**4. Case Study**

In big cities with high population, healthcare centers are very important facilities which are designed to deal with health issues. Location selection for healthcare centers, specifically hospitals, is a very important and very complicated decision for authorities and decision-makers, since multiple decision factors are involved in the decision-making process for this problem. The significance of location selection process for a hospital is implied by many direct or indirect relationships with economic, environmental, and social factors. Moreover, the location selection problem for a hospital is more important in critical time periods such as a disease outbreak. Starting in December 2019, COVID-19 became one of the biggest disease outbreaks of the last century. Soon after its emergence in China, COVID-19 became the most important issue to deal with for most countries in the world. In most countries, daily reports showed a sharp increasing trend in the number of infected patients. The high number of infected patients brought forward many problems in terms of the capacity of hospitals, such as hardware resources or specialized human resources to treat these patients. On the other hand, the presence of COVID-19-infected patients in hospitals increases the risk of its outbreak inside those hospitals, which would have a great effect on patients in other wards. In this matter, governments decided to construct well-resourced temporary hospitals to exclusively treat COVID-19-infected patients. As the first country affected, China constructed a hospital, with 100 beds, for patients that were suspected to be infected by COVID-19. After construction of this special hospital by China, the necessity of constructing such hospitals was understood by other governments.

Istanbul is an international city with a population of more than 15 million people living on both Asian and European sides of the city. Istanbul is one of the biggest transportation and business hubs in the world, where more than hundreds of flights move people worldwide through Istanbul each day. The medical system in Turkey is divided into public and private sectors. Due to the population increase in Istanbul, many public and private hospitals were constructed in recent decades. Turkey remained one of the safest countries during the first days of the COVID-19 outbreak. However, a few months later, the COVID-19 outbreak happened very quickly with an increasing trend in most cities in Turkey, especially Istanbul, which has the highest number of infected patients. Istanbul can

play a key role in treating and preventing infections in a pandemic circumstance. The first case in Turkey was officially confirmed on 11 March 2020, and, as of 29 April, based upon a Johns Hopkins university report [36], 4,673,809 people were infected and 312,646 people died due to this disease around the world. Furthermore, according to the health ministry of Turkey [37], the total number of tests administered was 991,613, of which 117,589 returned positive, with 3081 passing away, whereas the total number of intensive care patients is 1574 and 44,040 patients are recovered. In order to treat COVID-19-infected patients, as well as prevent its outbreak in other hospitals, there is a necessity to construct a temporary hospital which serves only clients that are suspected to be infected by COVID-19. With respect to the geographical features of Istanbul, selecting a suitable location for the establishment of a new hospital is a very complicated process. *Symmetry* **2020**, *12*, x FOR PEER REVIEW 9 of 15 To show the feasibility and applicability of the proposed decision-making framework, we

To show the feasibility and applicability of the proposed decision-making framework, we applied the decision support framework to Istanbul in order to select a suitable location for a temporary hospital for COVID-19 patients. Istanbul is a very big city which is divided into Asian and European sides, where the population ratio is not the same on both sides. There are 25 districts on the European side and 14 districts on the Asian side, giving a total of 39 districts. The population of the European side is almost two times that of the Asian side, which is approximately 5.5 million. With this information in mind, five districts were selected as potential location alternatives for the hospital location selection problem. Due to the spread of the population and geographical features in Istanbul, three of these districts were located on the European side and the two other districts were located on the Asian side. As shown in Figure 1, the five districts of Beykoz (A1), Bakırköy (A2), Büyükçekmece (A3), Eyüp (A4), and Pendik (A5) were selected as potential locations for this case study. applied the decision support framework to Istanbul in order to select a suitable location for a temporary hospital for COVID-19 patients. Istanbul is a very big city which is divided into Asian and European sides, where the population ratio is not the same on both sides. There are 25 districts on the European side and 14 districts on the Asian side, giving a total of 39 districts. The population of the European side is almost two times that of the Asian side, which is approximately 5.5 million. With this information in mind, five districts were selected as potential location alternatives for the hospital location selection problem. Due to the spread of the population and geographical features in Istanbul, three of these districts were located on the European side and the two other districts were located on the Asian side. As shown in Figure 1, the five districts of Beykoz (A1), Bakırköy (A2), Büyükçekmece (A3), Eyüp (A4), and Pendik (A5) were selected as potential locations for this case study.

**Figure 1.** Location alternatives in Istanbul. **Figure 1.** Location alternatives in Istanbul.

Based on the characteristics of stakeholders, several criteria were identified and defined using the location selection studies in the literature. The identified criteria were categorized into three main groups of technological, economic, and social criteria. Firstly, we defined the technological criteria involved in the decision-making process. Traffic congestion (C1) was defined as a qualitative criterion to measure the level of traffic congestion in each district. Ease of transportation is very important when promptly delivering patients to a hospital. Accessibility via roads (C2) denotes how many different roadways are available in the district in order to deliver patients. Accessibility via airports (C3) measures the distance of the hospital to its closest international or local airport, which can be used to deliver patients in a critical situation. Health centers in the district (C4) measure how Based on the characteristics of stakeholders, several criteria were identified and defined using the location selection studies in the literature. The identified criteria were categorized into three main groups of technological, economic, and social criteria. Firstly, we defined the technological criteria involved in the decision-making process. Traffic congestion (C1) was defined as a qualitative criterion to measure the level of traffic congestion in each district. Ease of transportation is very important when promptly delivering patients to a hospital. Accessibility via roads (C2) denotes how many different roadways are available in the district in order to deliver patients. Accessibility via airports (C3) measures the distance of the hospital to its closest international or local airport, which can be used to deliver patients in a critical situation. Health centers in the district (C4) measure how many

Land price (C6) measures the range of land cost for an infrastructure like a hospital. Transportation cost (C7) measures transportation cost in terms of transportation means available to deliver patients in different districts. Future expansion potential (8) denotes the possibility of expanding the temporary hospital to a permanent one with respect to financial indexes. We considered two criteria for environmental and social aspects. The distance from industrial areas (C9) measures how far the hospital is from industrial areas with respect to the fact that the emissions of factories in an industrial area can have a negative effect on the air quality level. Local regulation (10) represents a

many hospitals are in the district that the hospital would be built in. The distance from populated

hospitals are in the district that the hospital would be built in. The distance from populated residential areas (C5) measures how far the hospital is from areas with a high population density. Land price, transportation cost, and future expansion potential are considered as economic criteria. Land price (C6) measures the range of land cost for an infrastructure like a hospital. Transportation cost (C7) measures transportation cost in terms of transportation means available to deliver patients in different districts. Future expansion potential (8) denotes the possibility of expanding the temporary hospital to a permanent one with respect to financial indexes. We considered two criteria for environmental and social aspects. The distance from industrial areas (C9) measures how far the hospital is from industrial areas with respect to the fact that the emissions of factories in an industrial area can have a negative effect on the air quality level. Local regulation (10) represents a series of official and unofficial regulations related to each district with respect to its demography characteristics.

Table 2 shows a comparison among alternatives with respect to the defined criteria for Istanbul. In the first step, an initial decision matrix was constructed using gray values (Table 3). For each criterion, a gray value was indicated for each alternative with respect to expert experience, knowledge, etc. In Table 4, the initial decision matrix values were normalized using Equations (2) and (3) based on the benefit and cost criteria in step 2 of the CRITIC method. In the next step, the matrix of correlation was generated, as presented in Table 5, where the correlations of each pair of criteria are shown. Finally, we calculated the weights of criteria using Equations (4) and (5), as shown in the last row of Table 6. The distance from industrial areas (C9), future expansion potential (8), and land price (C6) were determined as the most important criteria.

After determining the weights of the criteria, we normalized the initial gray-based decision matrix using Equations (18) and (19), as presented in Table 7. In the next step of the gray-based CoCoSo method, we constructed the weighted decision matrix by multiplying the normalized decision matrix and weight vector using Equation (20), as presented in Table 7. Gray *S<sup>i</sup>* values were calculated for each alternative in Table 8. In Table 9, we calculated the power-weighted normalized decision matrix using Equation (21). Gray *P<sup>i</sup>* values were calculated for each alternative in Table 9. In order to prioritize the alternatives, three appraisal score strategies were used with respect to Equations (22)–(24). As shown in Table 10, we calculated the gray-based relative weights of alternatives, and the results were finally combined using Equation (25) as *K<sup>i</sup>* values. Using *K<sup>i</sup>* values, the ranking of alternatives was determined using gray lengths. As represented in Table 10, the Beykoz (A1) and Bakırköy (A2) districts were ranked as the first and second priorities to build a temporary hospital. Pendik (A5) and Eyüp (A4) were ranked as the third and fourth preferred locations. Büyükçekmece (A3) was the least preferred location to build a temporary hospital.


**Table 2.** Profile of experts.


0.29 0.50 0.68 0.47 0.00 0.13 0.29 0.12 0.38 0.25 0.00 0.56 0.77 0.85 0.40 0.20 0.00 1.00 0.46 0.38



**Table 6.** The weights of decision-making criteria.

344

#### *Symmetry* **2020**, *12*, 886


