###### Abstract  
This paper presents an extension of the covering location problem as a hybrid covering model that utilizes the set covering and maximal covering location problems. The developed model is a multi-period model that considers strategic and tactical planning decisions. Hybrid covering location problem (HCLP) determines the location of the capacitated facilities by using dynamic set covering location problem as strategic decisions and assigns the constructive units of facilities and allocates the demand points by using dynamic modular capacitated maximal covering location problem as tactical decisions. One of the applications of the proposed model is locating first aid centers in humanitarian logistic services that have been addressed by studying a threat case study in Japan. In addition to validating the developed model, it has been compared to other possible combined problems, and several randomly generated examples have been solved. The results of the case study and model validation tests approve that the main hybrid developed model (HCLP) is capable of providing better coverage percentage compared to conventional covering models and other hybrid variants. c 20202015 September 2020; 9 October 20  
services. During the five decades since the introduction of covering problems, many extensions of SCLP and MCLP have been developed, such as: tour and path covering SCLP, SCLP covering games, multi-coverage SCLP, generalized MCLP, gradual coverage MCLP, and hierarchical MCLP, in addition to back-up coverage, stochastic and probabilistic, and implicit and explicit models for both SCLP and MCLP. Interested readers can refer to [3; 4; 5] and [1] for reviews on covering facility location problems. Extending the covering location models to a multi-period or dynamic structure is more common for MCLP [6; 7; 8] rather than SCLP. The reason is that SCLP provides full allocation of demand points from fixed located facilities, and there is no need to alter these locations in different time periods' decisions unless the problem is studied in the presence of demand fluctuation [9] and expansion possibilities for capacitated facilities. In dynamic MCLP, decision makers are interested in finding the optimal way of locating a definite number of facilities in different periods. The application of dynamic MCLP can be in locating emergency service centers in populated regions that on-road accidents may happen, and the number of facilities to be located may fluctuate between different periods of time because of daily traffic, the weather situation, etc. Moreover, each opened facility in the beginning of a time period can be closed at the end of that time period in a dynamic MCLP. Hierarchical facility location problems use \"hierarchy\" to describe the problem as the coordination of location decisions for different type of facilities in multi-level systems [10]. For example, healthcare systems are one of the most studied systems in the literature [11]. The number of levels in healthcare facility systems varies such that, in a three-level example, there may exist demand points, local clinics, hospitals, and regional hospitals as different levels of facilities. Modularity is almost a newer concept that is utilized by the researchers to attain multi-level systems. In the field of location problems, the modularity concept is used to represent different types of facilities [12] or may be applied in arcs when there are different kinds of vehicles for transportation [13]. The main difference between modular and hierarchical facility location problems is that modular facility location problems can have more levels of facilities, because various types of modules, the number of them, and different sizes of modules may create a diversity of multi-level facilities. Both SCLP and MCLP have advantages and disadvantages regarding the coverage they provide and the cost issues. SCLP guarantees full coverage but yields to a high cost of facility location. On the other hand, the cost issues are controllable in MCLP, but all points cannot be covered.