The Optimization of Emergency Logistics Based on Quantitative Risk Measurement

. High-risk societies are uncertain, ambiguous and unpredictable. Risk ﬂuctuations are prone to the intertwining and overlapping of various accident hazards and safety risks. China is prone to the interweaving and over-lapping of various accident hazards and safety risks, which leads to an increase in the number of factors a ﬀ ecting public safety as well [1]. Therefore, this paper will quantify speciﬁc ranking indicators in terms of frequency of hazards, degree of hazards and vulnerability to di ﬀ erent hazards. The ranking is based on the quantiﬁed results in red, yellow and green and the hazard risk level is calculated according to the risk formula. This study will take the Chengdu-Chongqing region as an example and optimise the storage of materials in the emergency logistics base in order to maximise the precise and rapid supply of materials to the main demand points. The e ﬃ ciency of emergency material deployment will be further improved and emergency materials will be well secured.


Introduction
At present, the world is entering a high-risk society [2], concentrating on economic and social stagnation or even regression, social structural tensions and fractures, increased interactivity of social elements and risk overlap, leading to the unpredictability of systemic risk generation [3]. Humanity is vulnerable both to natural disasters and to sudden and extreme events in social systems [4]. The above description of the realistic background makes it important for mega-city regions to establish emergency logistics bases to cope with this uncertain risk in a high-risk society. Therefore, the risk of regional emergencies should be considered comprehensively. In this paper, we study the overall regional risk measure and the emergency material demand measure, quantify the abstract and uncertain risk values into clear rank indicators, and conduct rank evaluation and analysis.
When conducting risk measurement studies, there are many types of risks, all with different grades. First, major public emergencies are graded for the specific conditions of the Chengdu-Chongqing region [5]. At the same time, the frequency of occurrence, the degree of hazard, and the ability of the Chengdu-Chongqing region to withstand disasters are quantified and graded, and risk measurement values are calculated to produce relatively objective measurement indicators. Secondly, the demand for emergency supplies is quantified. Due to the diversity of materials and the variability in the demand for emergency materials when disasters occur [6], the differences in the demand for various types of materials under different emergencies in the Chengdu-Chongqing region were quantified and analysed, and the levels of material demand were correlated with the risk measurement values. Based on these results, the basic conditions for establishing an emergency logistics base in the Chengdu-Chongqing region are analysed.
In order to more effectively address the demand for emergency supply security materials, we have developed an emergency material storage plan based on the establishment of an emergency logistics base. In this paper, we give priority to factors such as material storage characteristics and storage costs, and construct a multi-objective optimisation model in the expectation of providing reasonable decision-making recommendations for material storage solutions in emergency situations. This will reduce the consumption of emergency resources and maximise the utility of supplies. This paper studies the material storage scheme in conjunction with the characteristics of the regional mechanism, which is conducive to promoting the economic development of the Chengdu-Chongqing region and helping synergistic development.
This is developed below. Firstly, we apply the risk quantification formula to visualise the abstract disaster risk concept and investigate the quantitative measurement of disaster risk in the Chengdu-Chongqing region. Then, based on the results of disaster risk measurement in the Chengdu-Chongqing region, we calculate the measure of material requirements under the corresponding disaster types. Finally, a dual-objective optimisation model is developed according to the location advantages and the current situation of materials. The model takes into account the storage cost and the degree of material demand satisfaction to obtain a reasonable range of various types of material reserves to cope with emergencies.

Risk Quantification Formula
From the perspective of the risk system, the frequency of events, the assessment of hazard degree and the vulnerability of the disaster-bearing areas are used to calculate their risk degrees, i.e.: Risk degree (R) = Frequency of events (F)× Hazard degree of events (H)× Vulnerability of disaster-bearing areas (V) [7]. Based on the results of grading the hazard degree, hazard frequency and vulnerability of the disaster-bearing area, the corresponding multiplication method is used to obtain the risk degree of each major emergency event in the Chengdu-Chongqing area. According to the results of the final risk assessment values, they are classified into three levels as shown in the figure below.
(1) Risk assessment value D ≥ 0.5 for level 1, indicated in red.
(3) Risk assessment value D < 0.2 is level 3 and is indicated in green.

Hazard Characteristics of the Emergency Event
According to the National Public Emergency Response Plan, disasters are classified as natural disasters, accidents, public health incidents and social security incidents.

Frequency of Emergency Events and Harm Degree
As a rule, the higher the frequency and the greater the frequency of event activity, the more intense the activity and the more serious the damage and losses caused [8]. By combining the above data, a general table of the number of different emergencies in the Chengdu-Chongqing region can be derived. Based on the classification and frequency of emergencies, the annual average frequency of each major event is calculated. The classification is based on the frequency of occurrence.
Level 1: events with a frequency greater than or equal to 0.08, indicated in red. Level 2: events with a frequency greater than or equal to 0.01 and less than 0.08, indicated in yellow.
Level 3: events with a frequency of less than 0.01, indicated in green. The hazard level of an emergency event is based on the hazard characteristics and probability of occurrence, and is derived from the event characteristics and frequency of occurrence, i.e. "hazard = event characteristics × Frequency" [9,10]. Based on the analysis of the major emergencies in the Chengdu-Chongqing region, three levels of emergencies are assigned to the characteristics of emergencies, and the hazard level of the corresponding emergencies is calculated by combining the frequency of the events.
According to the hazard characteristics of the event in Section 2.2 and the frequency of emergencies in Section 2.3, the hazard classification is obtained according to the calculated data according to the hazard formula, and the classification is as follows.  Level 1: events with a hazard value greater than or equal to 0.25, indicated in red. Level 2: events with a hazard value greater than or equal to 0.05 and less than 0.25, indicated in yellow.
Level 3: events with a hazard value of less than 0.05, indicated in green.

Disaster Vulnerability
Vulnerability of a hazard-bearing body is the degree of vulnerability of a region to injury or damage in a certain socio-political, economic, and cultural context which is shown in  On this basis, the vulnerability of disaster-bearing bodies is graded in relation to the disaster situation in the Chengdu-Chongqing region, as shown in figure 4.

Analysis of Quantitative Risk Results
The specific grading criteria are as follows.
Level 1: A risk assessment value of ≥ An outbreak with a value of 0.3 is a Level 1, indicated in red.
Level 2: 0.05 ≤ Risk assessment value < An outbreak of 0.25 is a Level 2, indicated in yellow.
Three: Risk assessment value < An outbreak of 0.05 is a Level 3, indicated in green. According to the classification results, only the new crown epidemic under the public health classification in the Chengdu-Chongqing area is a Grade 1 red level, and the outbreak of the COVID-19 has a strong impact on the social and economic development, which is the emergency event with the greatest impact; the risk level of natural disasters is not high, with flood and drought disasters, mudslides and earthquakes being Grade 3 green levels, which means that the risk level of these three types of disasters is low. The risk level of traffic accidents is higher than the other three types of accidents, but the overall impact of accidents and hazards is low; in addition, the risk level of social security is extremely low and is a green level 3.Among the social security incidents, the incidence of terrorist attacks, economic security incidents and foreign-related emergencies is extremely low with a small overall number, a narrow scope of influence and low disaster losses.

Emergency Material Requirements under Different Risk
This paper defines a measure of material requirements:δ i . δ i = Σ (event risk assessment value*material requirement level) When the event risk assessment value is greater, the higher the material requirement level, the greater the corresponding material requirement measure [12]. Combined with the results of the quantification of event risk in figure 5 above, the following demand measures were calculated for the seven categories of materiel.
The higher the demand measurement value of emergency supplies, the higher the priority of the supplies needed in emergency [13,14]. According to the results, the demand measurement of food, oil, vegetables, meat and eggs, and protective supplies is relatively high.

Emergency Material Storage Planning Model
To reduce storage costs while considering the level of demand satisfaction, a bi-objective optimization model is developed with the objectives of minimizing storage costs and maximizing the level of demand satisfaction of the material respectively. The mathematical notation required for the models in this chapter has shown in table 3.
Mathematical model: (3) (4) The objective function (1) represents minimizing the daily storage cost of the material and the objective function (2) (3) indicates that the quantity of material in storage is less than or equal to the upper bound of the quantity of material demanded during the storage cycle. Constraint (4) indicates that the quantity of material in storage is less than or equal to the maximum supply. Constraint (5) ensures that when the maximum supply of the material is less than or equal to the lower bound of the demand, the material is warehoused according to the maximum supply of the material.

Model Solving
To better reflect the trade-off between the cost of storage and the degree to which the demand for materials is met, the problem is solved using the constraint method, which is solved as follows. s.t. x Where s T is a non-negative integer between 0 and

Analysis of Results
By solving the model: when the lowest single-day storage cost of materials is $102,187.6, the demand satisfaction level of materials is only -3.45 (extreme case 1); when the highest satisfaction level of materials is 3.65, the single-day storage cost of materials is as high as $138,476.9 (extreme case 2). The final Pareto frontier curve is shown in figure 7. To simultaneously considering the two objectives and obtain a reasonable range of various types of material storage, the degree of material demand satisfaction is limited to the middle 1/2 regional range, i.e.: the degree of material demand satisfaction takes -1.675 and 1.875 as critical values. Table 4-table 5 shows the degree of satisfaction of material demand corresponding to two extreme values and two critical values of material storage solutions.
As can be seen from the table, because the daily supply of grain, oil and fruit is less than the lower limit of demand, the base can only store the maximum supply within the storage cycle of 180 days and 7 days; the daily supply of meat and eggs, vegetables and disinfection supplies is between the upper and lower boundaries of demand, and the warehouse storage gradually grows from the lower to the maximum supply of demand within the storage cycle as the degree of demand satisfaction increases; medicines and the daily supply of pharmaceuticals and disinfection supplies exceeds the demand and the supplies are abundant. And as the demand is met, the storage capacity gradually increases from the lower to the upper boundary of the demand in the storage cycle.
In the two extreme cases, the degree of satisfaction of material requirements and the optimization of storage costs are reversed. Suppose white is used to mark the objective function 6300-11700 cases Disinfection products 6032-66240 kg Protective equipment 10,080-1,125,000 pieces as optimal and black as the worst, as in Table. In extreme case 1, the cost is optimal, but the degree of satisfaction is lowest, and in efig:li08yanfeifig5xtreme case 2, the degree of satisfaction is optimal, but the cost is highest, with a gradual color shift as the two extreme cases converge towards the middle. In reality, extreme situations are extremely unlikely to occur, so we have chosen the range in the middle of the two thresholds as the range for material storage.
Weighing the two objectives together, it is recommended that the base use the quantity of each material stored corresponding to the 2 critical values between as a storage reference, as shown in table 5.

Conclusion
At present, mankind is in the stage of risk society, and various global risks pose a serious threat to human survival and development [15]. The Chengdu-Chongqing region is a typical high-risk urban agglomeration in China, and the issue of securing emergency supplies in its risk management is particularly important. Using the Chengdu-Chongqing region as a case study, this paper quantifies the frequency of major hazards, the magnitude of hazards and the vulnerability of different hazards to specific levels of risk, thus visualising the abstract concept of risk. On this basis, a dual-objective optimisation model that considers storage costs and the degree of material demand satisfaction is constructed to develop a material storage plan for the emergency logistics base, improve the efficiency of emergency material deployment, enhance the operability of material response measures and promote the integrated development of the regional economy.