# Multi-Level Site Selection of Mobile Emergency Logistics Considering Safety Stocks
###### Abstract  
With the increasing frequency of emergencies in recent years, the emergency response capacity of the emergency management system needs to be improved. Based on safety stock strategy, this paper proposes a multilevel sitting model on the topic of mobile emergency response. We modeled the emergency response needs during emergencies by incorporating the population distribution of each region. The uncertainty of emergencies is modeled by aggregating the frequency of crises in each region over the past 20 years. The site selection model minimizes contingency logistics costs that include transshipment, deployment, inventory, and safety stock costs. In this paper, the IA (Immunue Algorithm) is optimized to solve the constructed emergency site selection model. The experiments on the model were carried out with data from the area of Chongqing, Sichuan Province. The number of logistics centers and distribution storage warehouses was tested. The influence of safety stock strategy on the total cost of emergency logistics was analyzed. The research results found that the cost of safety stock is negatively related to the cost of transshipment. In addition, the total cost of emergency logistics has a lower bound. Adding distribution and storage warehouses does not further reduce the total emergency logistics cost. emergencies; site selection of mobile emergency logistics; safety inventory; immune algorithm +
Footnote †: journal: Applied  
1
Footnote 1: email: {[[https://doi.org/10.3390/appl32011245](https://doi.org/10.3390/appl32011245)]([https://doi.org/10.3390/appl32011245](https://doi.org/10.3390/appl32011245))}  
transportation cost, cycle stock, safety stock, ordering and facility activation. According to the problem of preference uncertainty in the decision-making process, [PERSON] [4] presents a Group Decision-Making (GDM) method with Interval Linguistic Fuzzy Preference Relationship (ILFPR). To verify the applicability and credibility of the proposed method, the article is validated with case studies. In response to the problem of high uncertainty in the evolutionary direction of emergencies, [PERSON] [5] offered a hybrid decision-making approach that takes into account intuitionistic fuzzy environments, linguistic environments, and their hybrid environments. The article verifies the effectiveness of the proposed model in the case of flooding in China. [PERSON] [6] solved the rescue path selection problem in the uncertain environment of vehicular traffic accidents by minimizing the vehicle travel time of nodes and road sections. The risk coefficient of the rescue path is calculated based on the elastic time window, and decision support is provided for the path selection of emergency rescue. To solve the emergency demand uncertainty problem, [PERSON] [7] introduced a two-stage model. The model is used to calculate the problem of the siting and material distribution of emergency logistics centers in the region. The NSGA-II algorithmic site selection model is combined to solve the problem. In order to solve the emergency rescue problem for driver-passenger hijacking incidents, [PERSON] [8] proposed a multi-vehicle mutual rescue (MMR) model for cab platforms. The Pareto evolutionary algorithm (SPEA II), Frank-Wolfe algorithm, and two-stage coding method are applied to solve the NP-hard problem of the MMR model. The MMR model is examined using road information and O-D traffic, and the utility of the hybrid algorithm and the MMR model is analyzed. In response to the problem of uncertain production disruptions affecting the supply chain during emergencies, [PERSON] [9] investigated a new recovery strategy. This strategy utilizes investments to adjust the speed and duration of capacity recovery. A two-stage stochastic programming model (RSTPM) that simulates the recovery behavior in response to different disruptions to avoid risks is proposed. To justify the efficiency of the method, the article proposed a trust-region-based decomposition method to solve the RTSPM.