## 2 Literature Review  
[PERSON] et al. [18] are the pioneers that developed the SCLP and solved the model by a linear relaxation technique using cuts to generate integer solutions. They also analyzed their model related to the p-median problem. Due to the difficulty in solving medium to large sizes of the SCLP, [PERSON] and [PERSON] [19] presented a computational approach to remove errors in the solution of SCLP, especially for  
Figure 1: Strategic and tactical decision differentiation in the proposed model. the cases that utilize geographical information systems in applications like emergency warning sirens. The probabilistic SCLP was addressed by [PERSON] and [PERSON] [20], who investigated four possible models for SCLP by considering the assumptions of dependency and independency. Moreover, a mixed integer reformulation for probabilistic SCLP was developed by [PERSON] et al. [21], for which they presented some approaches to reduce the constraints in the developed model and to have stronger formulations. They evaluated their theoretical findings by conducting extensive instances. A significant part of the literature of SCLP focuses on the application of SCLP in real-world problems. [PERSON] [22] considered the application of the SCLP in the optimization of gas detectors in process plants as a 0-1 integer programming model in order to calculate the best location and the minimum number of gas detectors in a facility site. Another application of SCLP is to locate the traffic counting stations that are used to monitor the traffic flow of transportation vehicles in highways. This application was studied by [PERSON] et al. [23], with the objective of minimizing the total number of stations to cover all origin and destination nodes of the network, for which they proposed a hybrid algorithm based on exact, heuristic, and hybrid approaches that could solve an acceptable fraction of instances to find the optimal solution. Furthermore, [PERSON] et al. [24] presented the flight plans for unmanned aerial vehicles that can be very useful for rescue operations in disaster situations using quadratic constraints. As the SCLP in the presence of quadratic constraints is a complex problem to solve even for small size problems, the authors developed an approximation model that was solved by a branch and price algorithm. The optimal number and locations of pharmaceutical warehouses is another application of SCLP that was studied by [PERSON] et al. [25]. They also conducted a sensitivity analysis to show how different coverage distances can affect the number of warehouses and the network configuration. MCLP was presented by [PERSON] and [PERSON] [26], and following their research, many researchers tried to improve MCLP models and developed many extensions of it. Probabilistic MCLP was studied by [PERSON] et al. [27] and [PERSON] et al. [28]. A stochastic optimization model for a multi-service MCLP was presented by [PERSON] et al. [29] with the objective of maximizing the total expected weighted coverage of the network. The authors integrated their model with a risk-averse approach in order to reduce the losses that can be resulted from lack of coverage and then validated their model using a real multi-disease case study in Africa. In addition, a robust formulation for MCLP with application in disaster relief that minimizes the maximal regret over all possible combinations of values for the columns benefit with the associated algorithms to solve the problem was presented by [PERSON] et al. [30]. [PERSON] et al. [31] studied an MCLP in a real case study in Australia by having double-coverage. In their model, all demands are ensured to be covered inside a coverage radius and a proportion of demands to be covered inside a smaller radius. The maximum set k-covering problem is to choose k columns out of total columns in such a way that the number of rows covered by the selected columns is maximized. This problem was addressed by [PERSON] et al. [32], focusing mainly on solving methods of this complex extension of MCLP.