## 2 Literature Review
In the literature, on covering facility location models, some concepts have been studied for both SCLP and MCLP. One of these concepts is the backup service that is addressed by [PERSON] et al. [44], who considered both air and ground ambulances to provide services for emergency request modeling both SCLP and MCLP. Three coverage options were possible: only air ambulance, only ground ambulance, or a teamwork in such a way that the ground ambulance carries the patient to a place that can be reached by the air ambulance. Their model took into account the uncertainty in demands and used a greedy heuristic to solve the problem of a case study in New Mexico State. The uncertainty in response time and demands raising from an emergency situation was applied to SCLP and MCLP as separate models by [PERSON] et al. [45]. They developed two models for MCLP and one model for SCLP based on the fuzzy set theory. The service time in SCLP and the first variant of MCLP are considered to be uncertain; in this way, that the membership of response time is less than the coverage radius was set to be more than a specified value. On the other hand, the second variant of MCLP maximizes the chance of an uncertain event, i.e., a chance of coverage to be larger than a predetermined coverage level. To solve two variants of MCLP, the first variant can be reformulated as the second one for which they have developed a method to solve it using a case study in Sichuan, China. To solve very large-scale problems of partial SCLP and MCLP up to millions of demand points and obtaining an optimal solution for this huge size of problems, [PERSON] et al. [46] presented a Benders decomposition method. The good performance of their method is due to the utilization of a Branch-and-Benders-cut algorithm. Although some modeling ideas have been studied for both SCLP and MCLP, all of these studies have considered SCLP and MCLP as separate models. To the best of our knowledge, there is no study that has modeled SCLP and MCLP in an integrated model as the model developed in this paper. Table 1 reviews important models in the areas related to our study. Although there is no study combining SCLP and MCLP in one model, they are related to the model of this paper from at least one perspective. The decision level (last column of the Table 1) of the models might be not specified directly in the papers, but as the facility location problems generally belong to strategic decision levels, they have been classified as strategic decision models. The last row of Table 1 illustrates the contribution of this research compared to the literature. Modularity is a strategy recognized by academia and industry and plays an important role in the development of sustainable systems [47]. The modular location problem is one of the important research streams of the current study, which is applied to facility location problems recently. For instance, [PERSON] et al. [48] tackled a particular location problem, where two sets of facilities have to be located in which different devices can be installed at each site, providing different capacities with different costs. Moreover, [PERSON] and [PERSON] [12] presented a multi-period facility location problem with modular capacity adjustments and flexible demand fulfillment. In their research, customers were divided with different sensitivity to delivery lead times. They also proposed two mixed integer linear programming formulations and did an extensive numerical study on randomly generated data with different demand patterns. [PERSON] and [PERSON] [49] assumed the ambulances as the modules of the emergency service facilities and proposed two variants of the capacitated MCLP for fixed and unfixed number of facilities to be located. They formulated their model as a static model and utilized the geographical information system. Furthermore, [PERSON] and [PERSON] [50] developed the modular MCLP to locate the emergency facilities by considering backup services for the request of demand points.