# Multi-Level Site Selection of Mobile Emergency Logistics Considering Safety Stocks
###### Abstract
To respond to the emergency relief problem of uncertainty in the last-mile delivery time, [PERSON] [10] proposed an opportunity constraint model. This model considers emergency fair siting as emergency logistics centers (ELCs). For the contingency decision-making problem with uncertain information, [PERSON] [11] proposed a novel consensus model to manage the non-cooperative behavior of experts in large-scale group decision-making problems. This model introduces a group consistency index that considers both the fuzzy preference value and the degree of cooperation to detect the non-cooperative behavior of experts. Among the studies related to safety stock, [PERSON] [12] proposed a typology of multilevel inventory management. Extensive research on multilevel inventory management under uncertain demand is categorized and reviewed. Maximizing the total cost of network population coverage and minimizing the risk of network management, [PERSON] [13] proposed a humanitarian-based approach to the stockpile grouping problem. This model optimizes the number of inventory groups and service levels, the distribution of relief supplies, the location of relief facilities, and the distribution of relief services. Based on blood expiration, shortage and capacity, [PERSON] [14] developed a dynamic emergency blood collection model. It was found that the safety stock, target stock level and emergency blood demand estimation could be moderately increased. This allows for emergency blood stock control parameters. To minimize the expected total cost per unit of time, [PERSON] [15] proposed an inventory strategy. By considering energy costs, the article calculated the optimal size of order quantity, safety stock and inventory cycle length. In order to design dynamic multi-commodity SSC networks to compute contingency safety stocks and shared safety stocks, [PERSON] [16] proposed a mixed integer nonlinear programming (MINLP) model and mixed integer linear programming (MILP) model. To demonstrate the stochastic humanitarian inventory control model, [PERSON] [17] validated the minimum safety stock level for contingency inventory through a case study. [PERSON] [18] presented two methods of centralizing inventory. Passive transshipment transfers inventory elsewhere in the network to cope with shortages at one location. Active inventory redistribution is carried out to minimize the possibility of future shortages. Aiming at the uncertainty of the actual demand at the demand point and the vehicle transportation time, [PERSON] [19] investigated a robust optimization model for the location path of emergency facilities. The article minimizes the sum of rescue time for material delivery to the demand point. To summarize, existing research on emergency logistics sitting problems mainly focuses on emergency uncertainty, safety stock strategy, and carrying out sitting problems in different contexts. This paper addresses the multilevel siting problem of mobile emergency logistics, considering safety stock under contingency. To solve the problems of frequent demand for emergencies and high emergency demand uncertainty, we calculate the emergency demand through statistical population distribution and propose a multilevel siting model for mobile emergency logistics, considering safety stock. The mobile emergency logistics system consists of logistics centers, distribution storage warehouses, and emergency distribution facilities at three levels. The logistics center can process national-level logistics, demand information and ensure that emergency supplies reach the disaster area as soon as possible based on diversified transportation modes. As the relay station of the mobile emergency logistics system, distribution storage warehouses are responsible for the temporary storage and secondary distribution of emergency supplies. The emergency distribution facility is the final link of the mobile emergency logistics and is responsible for delivering emergency supplies to the affected population.