**2. Literature Review**

Location selection is always an important issue in decision-making science. Business owners, managers, and decision-makers are always faced with this issue in different circumstances, with an effect on tangible profits or losses. As choosing an appropriate location is a part of decision-making science, multi-criteria decision-making (MCDM) was discussed in many previous studies. These studies can be classified using different themes as follows: construction, energy, and production, with some subsets. As there are many articles which discussed site selection with MCDM methodologies, this study presents the most significant studies in each theme.

Firstly, we present the prior studies that used MCDM techniques in order to select the best choice in a construction theme. Hashemkhani Zolfani et al. [7] implemented a hybrid multi-attribute decision-making (MADM) methodology called SWARA–WASPAS, in order to find a suitable location for a shopping mall with foresight perspective. This hybrid model was presented for the first time in this article. The SWARA technique was used to prioritize criteria and the weighted aggregated sum product assessment (WASPAS) method was applied to assess the alternatives. Moreover, Ijadi Maghsoodi et al. [8] propounded BWM–CODAS for site selection in terms of a mega-structure project involving a shopping mall in Iran.

Finding the best place for constructing a forest road was considered in [9]. The article presented the site selection of a forest road among three places in an expanded forested region in Iran. AHP was implemented to calculate the importance of criteria; then, COPRAS-G methodology was applied to evaluate places in order to find the best location. Rezaeiniya et al. and Haghnazar Kouchaksaraei et al. [10,11] presented hybrid models to find the best location for a greenhouse. Rezaeiniya et al. [10] studied the importance of finding an appropriate site for a greenhouse by using the ANP method to weight the criteria and applying COPRAS-G to rate the selected places. In addition, presented the SWARA–COPRAS hybrid model to find a suitable location for a glasshouse. With this description, choosing an optimum place for a glasshouse is a significant decision because it needs an incredibly large area and must have financial feasibility. SWARA was applied to weight and COPRAS was applied to rate the important places. Two new methodologies were applied to find a proper location for waste disposal systems in previous studies [12,13]. Kahraman et al. [12] proposed an intuitionistic fuzzy EDAS method to evaluate solid waste disposal site alternatives. Moreover, Krylovas et al. [13] applied the KEMIRA-M method find the best place for constructing a non-hazardous waste incineration plant in Lithuania. The KEMIRA-M methodology is based on searching solutions of an optimization problem.

The site selection of professional workplaces was also discussed in prior studies [14,15]. While considering environmental risk, Suder & Kahraman [15] proposed the fuzzy TOPSIS method to find the most suitable location for a faculty university in Turkey. Rock or soil structure, remoteness to health facilities, transportation availability, and transportation costs were prioritized as having high to low importance, respectively. Computer workstation selection was achieved with hesitant fuzzy linguistic term sets (HFLTS) by using AHP and TOPSIS in [14], where a new fuzzy quality function was deployed in order to solve a real industrial application.

This study focuses on Istanbul in Turkey as a case study in order to determine the optimum location for a temporary hospital for infected people. Some previous studies discussed the same topic such as [16], who proposed a mixed integer linear programming method to select the location of temporary shelter sites in terms of unpredictable earthquakes in Istanbul, Turkey. A cause-and-effect model for the location of temporary shelters in disaster operations management was proposed by [17]. Furthermore, an MCDM method called DEMATEL was presented in a fuzzy environment with 14 different criteria. Iqbal et al. [18] studied the effectiveness of natural disaster management using stochastic model and Mont Carlo simulation. Then, in order to check the sampled numbers from a random space, they proposed a statistical model to check for relief supply location and distribution related to the healthcare system in natural disaster management.

Another subject that was discussed in the MCDM framework was finding the best residential place, which was proposed [19,20]. Neighborhood selection was presented for a newcomer in Chile by (Hashemkhani Zolfani et al. 2020). BWM and revised MAIRCA were applied to investigate which neighborhoods were appropriate, and the approach was compared with three other MADM methods: MABAC, VIKOR, and CODAS. Karasan et al. [20] proposed the hesitant fuzzy CODAS method to find the optimum location for a construction site and implemented a sensitivity analysis to stabilize the ranking result. Hotel site selection was proposed by [21], where BWM–WASPAS methodologies were presented with a sustainable perspective.

The last part of the construction theme is related to healthcare system site selection. Lin & Tsai [22] developed an integrated model consisting of ANP and TOPSIS to determine the best ranking for hospital selection. A case study was performed in China for foreign investment. Furthermore, Senvar et al. [23] considered hesitant fuzzy sets and TOPSIS to locate the best site for constructing a hospital in Istanbul. Establishing a well-organized and distributed network of a hospital in order to deliver healthcare services to patients was considered and discussed in the study. In addition, Kutlu Gündogdu et al. [ ˇ 24] presented a new hesitant fuzzy EDAS with TOPSIS method to find the best place for an organ transplantation hospital. This novel model is an extension of classical EDAS which considers the hesitancies of decision-makers. The last study in the construction theme and healthcare subset applied the flexible and interactive tradeoff (FITradeoff) method in order to evaluate healthcare facility stakeholders in order to select an optimum site for a hospital in Milan, Italy.

Secondly, we introduce publications which discussed site selection using MCDM methodologies in the energy theme. Currently, renewable energies have a high priority in the energy industry; thus, site selection of new energy resources were investigated by different researchers from different countries all around the world. The number of energy site selection publications is too extensive to cover in this literature review; thus, we refer only to some of them. Vafaeipour et al. [25] prioritized regions for solar power plants in Iran by applying a hybrid model SWARA–WASPAS. Many criteria such as environmental, economic, technical, social, and risk factors were considered in 25 cities by using a GIS map of the country. Moreover, Marques-Perez et al. [26] studied photovoltaic power plant site selection by applying PROMETHEE and AHP methods with a GIS-based approach in Spain. Furthermore, Ekmekçioglu et al. [27] proposed a SWOT analysis model and then developed this model by applying fuzzy TOPSIS and fuzzy AHP methods to find an optimum location for nuclear power plants in Istanbul. Another renewable energy which is a hot issue in energy science is wind energy. Moradi et al. [28] implemented the AHP methodology to determine an appropriate location for a wind farm. Structural, topological, and ecological criteria were discussed based upon an ArcGIS map.

Finally, the last theme in our classification, i.e., production, introduces previous studies that discussed site selection by applying MCDM methods in the manufacturing industry. Athawale et al. [29] proposed the PROMETHEE II method to achieve facility location selection in the manufacturing industry. This application is faced with complex problems, and the best decision can result in better economic benefits by increasing productivity and qualified network distribution. Logistic center site selection was brought up as a complex decision problem in [30], which was solved with a multi hybrid model. As a first step, communities were compared using DEA to find beneficial alternatives. In the next step, a model was constructed to assess the performance of efficient communities with the R-FUCOM method and they were prioritized with R-COCOSO. The discussed literature is presented in Table 1, containing study subjects and methodologies.


**Table 1.** Application of multi-criteria decision-making (MCDM) in location selection.

After reviewing different studies on site selection with MCDM techniques, it was concluded that the MCDM framework can enable better decision-making, especially in disaster situations. However, there were not many studies which worked on hospital locations in a pandemic or disaster circumstance with multi-criteria decision-making. This issue motivated us to implement hybrid MADAM techniques to find an optimum location in Turkey during the coronavirus pandemic in 2020. This fact shows the novelty of this study which can be used as a guide for future studies.
