CN109102868B - Site selection method for post-earthquake medical emergency facility - Google Patents

Site selection method for post-earthquake medical emergency facility Download PDF

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CN109102868B
CN109102868B CN201810917226.6A CN201810917226A CN109102868B CN 109102868 B CN109102868 B CN 109102868B CN 201810917226 A CN201810917226 A CN 201810917226A CN 109102868 B CN109102868 B CN 109102868B
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李艳鸽
韩征
刘柯楠
黄健陵
王卫东
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Abstract

The invention discloses a site selection method for post-earthquake medical emergency facilities, which comprises the following steps: (1) determining candidate points of post-earthquake medical emergency facilities; (2) analyzing influence factors of optimized site selection of the post-earthquake medical emergency facilities according to the specific conditions of the areas; (3) calculating the weight of each influence factor by adopting TOPSIS; (4) establishing a basic level emergency medical facility site selection model by taking fairness, high efficiency, maximum coverage range and proper consideration of cost as targets; (5) establishing an address selection model of the post-earthquake advanced emergency medical command on the basis of the address selection model of the basic level emergency medical facility in the step (4); (6) and solving the model by adopting a nested genetic algorithm to determine a basic medical emergency facility point set and an advanced emergency medical command post. The site selection method can rapidly and scientifically determine medical emergency facility points and has great significance for reducing casualties after earthquakes.

Description

Site selection method for post-earthquake medical emergency facility
Technical Field
The invention belongs to the technical field of post-disaster emergency management, and particularly relates to a site selection method for post-earthquake medical emergency facilities.
Background
China is located at the intersection of the Pacific earthquake zone and the Asia-Europe earthquake zone, has the advantages of multiple earthquakes, high intensity, wide distribution and shallow earthquake sources, and is one of the most seriously affected countries in the world by earthquake disasters. According to statistics, the 20 th century China commonly generates more than 6-grade earthquakes 650 times: wherein, the Lei 7 grade earthquake is 100 times, and the earthquake with more than 8 grades is 10 times; the number of dead people caused by earthquake reaches 61 thousands, which accounts for about 36 percent of the total number of dead people in the world, and is the first in the world. In fact, according to the rescue experience after disaster, a gold 72 hours exists after the earthquake occurs, and if timely and scientific rescue can be carried out in the time, the survival rate of the wounded is greatly improved. However, after an earthquake happens, rescue mechanisms are often seriously damaged, the loss of material equipment is huge, and disaster areas have no capacity to immediately carry out emergency self rescue; and the change of the landform and the landform is huge, the traffic is blocked, and the external rescue force cannot enter a disaster area to carry out rescue work in time. Therefore, how to organize limited manpower and material resources and carry out planned coordinated medical emergency management work after an earthquake in the first time of the earthquake becomes the primary premise and work requirement of disaster prevention and reduction work.
The site selection of the post-earthquake medical emergency facilities is a very important ring in an emergency management system, and the key problem is to determine the number, the position and the capacity of the medical facilities. However, the existing site selection models for the post-earthquake medical emergency facilities are few, and the general service facility site selection model is often used for reference to solve the problems. The common service facility site selection model comprises a P median model, a P center model, a position set coverage model, a maximum coverage model and the like, but the single model has the following problems:
(1) the P median model-based site selection model mainly considers the service efficiency of service facilities, so that the target function of the model requires the minimum total weighted distance between each demand point and a service facility point, so that the model is suitable for general service facilities with low timeliness requirements on emergency response, and is not suitable for site selection of post-earthquake medical emergency facilities with the requirements of reducing the number of dead people and reducing property loss;
(2) the P center model-based site selection mainly considers the fairness of service facilities, so that the maximum distance from each demand point to a service facility point is required to be minimum by an objective function, and service can be provided for the farthest demand point at the fastest speed; however, the site selection method can cause resource waste for emergency disaster relief, and increase the financial burden of a disaster area;
(3) the objective function of the location set coverage model requires that the number of the set service facilities is minimum and all demand points can be covered, but the model does not consider the size of the demand, which can cause excessive consumption of system resources; in addition, the maximum coverage distance (or time) is difficult to be scientifically determined, often resulting in too large a number of facilities to be calculated beyond the actual bearing capacity;
(4) the objective function of the maximum coverage model requires that the total value of the covered demand points is maximum, and the limitation of capital budget is mainly considered, but the model assumes that the number of required facilities is determined and is not consistent with the site selection target of the post-earthquake medical emergency facilities. And the standard maximum coverage distance cannot be scientifically determined, so that the calculation result is not practical.
From the above, the site selection models of the common service facilities all belong to the problem of single-target planning, that is, only single requirements can be met, and the site selection models are not applicable to the problem of site selection of post-earthquake medical emergency facilities with complicated expected targets.
Disclosure of Invention
Aiming at the defects of the single site selection model, the invention aims to provide the site selection method for the medical emergency facilities which can cover the disaster area to the maximum extent, cure the wounded at the highest speed and save the resources to the maximum extent after the earthquake.
The site selection method of the post-earthquake medical emergency facility provided by the invention comprises the following steps:
(1) determining candidate points of post-earthquake medical emergency facilities;
(2) analyzing influence factors of optimized site selection of the post-earthquake medical emergency facilities according to the specific conditions of the areas;
(3) calculating the weight of each influence factor by adopting TOPSIS according to the influence factors determined in the step (2);
(4) establishing a basic emergency medical facility site selection model according to the candidate points determined in the step (1) and the weights of the influence factors in the step (3) by taking fairness, high efficiency, maximum coverage range and proper consideration of cost as targets;
(5) establishing an address selection model of the post-earthquake advanced emergency medical command on the basis of the address selection model of the basic emergency medical facility in the step (4);
(6) and (5) solving the models in the step (4) and the step (5) by adopting a nested genetic algorithm, and determining the number and the positions of the basic medical emergency facility point sets and the positions of the advanced emergency medical commanders.
In the step (1), the specific method for determining the alternative points of the post-earthquake medical emergency facilities comprises the following steps: quantifying and grading each index factor and giving weight by taking landform, geological disaster, vegetation and a water system as macroscopic indexes; and then, performing superposition analysis on all the quantized layers by using an ArcGIS space analysis module to obtain a place with the highest suitability index as an emergency facility candidate point.
In the step (2), the influencing factors are various factors of population density, economic level, traffic convenience degree, disaster-suffering situation, population age structure and education level.
In the step (3), the method for calculating the weight of the influence factors includes the following steps:
a, constructing an original matrix
N evaluation objects and m evaluation indexes are provided, and the original data can be written as a matrix X ═ Xij)n×m(1) (ii) a Where { I ∈ I } represents the set of all demand points; { J ∈ J } represents a set of emergency facility alternate points;
b, performing syntropy and normalization processing on the high-quality index and the low-quality index respectively to obtain matrix Z syntropy processing:
Figure BDA0001763334990000031
normalization treatment:
Figure BDA0001763334990000032
c, constructing the optimal and worst vector
After the homodromous and normalization processing, a matrix Z ═ (Z) is obtainedij)n×mThen, the optimal and worst vectors formed by the maximum and minimum values of each column are respectively recorded as:
Z+＝(ZMax1 ZMax2 … ZMaxm) (4)
Z-＝(ZMin1 ZMin2 … ZMinm) (5)
d, calculating the distance D from each evaluation object to the optimal and worst vector
Distance of the ith evaluation object optimal scheme:
Figure BDA0001763334990000033
distance of the worst scheme of the ith evaluation object:
Figure BDA0001763334990000034
e, calculating the closeness degree C of each evaluation object and the optimal vectoriI.e. the weight of each influencing factor
Figure BDA0001763334990000041
In the step (4), the site selection model of the basic level emergency medical facility is as follows:
Figure BDA0001763334990000042
Figure BDA0001763334990000043
Figure BDA0001763334990000044
Figure BDA0001763334990000045
Ni＝{j|dij≤S} (13)
Figure BDA0001763334990000046
wherein, wi(I ═ 1, 2, … I) is the weight of demand point I; dijThe distance from the demand point i to the emergency facility alternative point j is calculated; w is aidijA weighted distance between the demand point i and the emergency facility candidate point j; z is the number of emergency utility points; n is a radical ofiA set of alternative points from the coverage demand point i to all emergency facilities; s is the maximum distance allowed to be transported in unit time; y isj1 represents that the emergency facility alternate point j is selected, and 0 represents that the emergency facility alternate point j is not selected; x is the number ofijA demand point i is served by an emergency facility alternate point j, denoted 1, and an alternate point 0, denoted otherwise.
In the step (5), the method for establishing the address selection model of the post-earthquake advanced emergency medical command comprises the following steps:
the method comprises the following steps that a, the total weighted distance of emergency medical facilities of all base levels is the minimum in the target of an address model selected by the advanced emergency medical command;
b, assuming that the set of the basic-level medical emergency facility points obtained in the step (4) is { a belongs to A }, and selecting a high-level command department from the selected basic-level facility points; obtaining an address selection model of the post-earthquake advanced emergency medical command as follows:
let widijIs the weighted distance between nodes i and j;
Figure BDA0001763334990000047
Figure BDA0001763334990000048
Figure BDA0001763334990000049
Figure BDA0001763334990000051
Figure BDA0001763334990000052
Figure BDA0001763334990000053
Figure BDA0001763334990000054
the target function formula (17) represents that the total weighted distance between each basic medical emergency facility point and the advanced emergency medical command post is minimum; the constraint (18) indicates that the primary medical emergency facility site is covered at least once; constraint (19) indicates that only one senior emergency medical conductor is assigned a primary medical emergency facility point; the constraint formula (20) represents that the number of the advanced emergency medical command posts is 1; constraint (21) represents decision variable xjIs an integer variable from 0 to 1.
In the step (6), the concrete steps of solving the models in the steps (4) and (5) by the nested genetic algorithm are as follows:
a) the inner-layer genetic algorithm is used for optimizing the number and the positions of primary medical emergency facility points so as to meet the requirements of medical emergency rescue after earthquake on fairness, high efficiency, full coverage and cost saving as much as possible;
b) the outer layer genetic algorithm is used for selecting an advanced emergency medical command post from basic medical emergency facility points, and meets the requirements of unified management, unified command and coordinated dispatching.
The inner layer genetic algorithm comprises the following steps:
a-1, inputting a space distance matrix X of a demand point and facility alternative points, the number K of the alternative facility points, a coverage radius D of the facility points and a weight vector Vn of the demand point;
a-2, selecting a facility point number P which is 1: K;
a-3, when the GA tool box is operated to calculate the number of the selected facility points, using the position decision with the minimum weighted total distance;
a-4, if the distance between the facility point and the service demand point is larger than the service radius D of the facility point, abandoning the site selection scheme;
a-5, if the distance between the facility point and the service demand point is greater than the service radius D of the facility point, making P equal to P +1, and if P is greater than K, determining the addressing scheme; if P is less than K, repeating the steps a-3-a-5;
a-6, selecting an addressing scheme which meets the conditions and has the minimum number of equipment points; obtaining the number, the position, the corresponding demand points and the capacity of the service of the basic emergency medical facility points;
the outer genetic algorithm comprises the following steps: in the basic medical emergency facility points, the GA tool box is operated, and a facility point which enables the total weighted distance to be minimum is selected as an advanced emergency medical command post.
The invention has the beneficial effects that: the site selection method converts the single-target planning problem of the traditional site selection model into a double-layer planning problem solved by using a nested genetic algorithm, can simultaneously calculate the quantity, the position and the capacity of output facility points under the constraint conditions of minimum weighted transportation distance, minimum facility number (namely minimum cost) and full coverage of demand points, and can simultaneously output an advanced command center and basic medical emergency facility points, thereby providing decision reference for emergency response after earthquake. The site selection method can rapidly and scientifically determine medical emergency facility points and has great significance for reducing casualties after earthquakes.
Drawings
FIG. 1 is a flow chart of the method for locating a post-earthquake medical emergency facility according to the present invention;
FIG. 2 is a flow chart of the initial site selection of the medical emergency facility of the post-earthquake hierarchy based on ArcGIS in the embodiment 1;
fig. 3 is a flowchart of the optimized site selection of the post-earthquake medical emergency facility based on the nested genetic algorithm in embodiment 1.
Detailed Description
Example 1
The technical scheme adopted by the embodiment is a post-earthquake level medical emergency facility site selection model, which complies with the following assumptions:
a) the demand points of the post-earthquake medical emergency facilities are known, namely, the demand points are arranged at the administrative central point (generally representing the most concentrated population) of the research area, so that the structural positions and the characteristics of the demand points are defined.
b) The distance from the alternative medical emergency facility point to the demand point is the straight line distance from the facility point to the center of the demand point;
c) in consideration of the serious damage of the road after the strong earthquake and the requirement of emergency rescue, the medical emergency rescue after the earthquake is transported by a helicopter;
d) the construction costs of all medical emergency facilities are the same.
The flow of this embodiment is shown in fig. 1, and the specific steps are as follows:
step one, determining backup points of medical emergency facilities after earthquake
The flow of determining the alternative point in this step is shown in fig. 2, and specifically includes the following steps:
after-earthquake medical emergency facilities are required to be established in places with flat terrain, far distance from fault, far distance from geological disaster points, stable lithology and close distance from a water system. Therefore, six indexes of Elevation, Slope, distance from a water system to Stream, distance from a Fault to a geological disaster point to Landslide and lithology Strata are extracted as influence factors influencing initial site selection of the post-earthquake medical emergency facility; then quantifying and grading each factor (as shown in table 1), reclassifying the evaluation factors by using a data analysis module of the ArcGIS platform, and performing buffer analysis; and finally, performing superposition analysis on all layers by using a spatial analysis module of the ArcGIS platform, calculating the suitability index of the quantized value of the grid corresponding to each layer by a weighted average method, and dividing the weighted average result into four types, namely an unsuitable region, a low-suitable region, a medium-suitable region and a high-suitable region, wherein the high-suitable region is used as a candidate point for site selection of the post-earthquake medical emergency facility.
Table 1 evaluation index layer partition quantization table
Figure BDA0001763334990000071
Secondly, analyzing influence factors of optimized site selection of the post-earthquake medical emergency facilities;
in the embodiment, 4 factors including population density, economic level, traffic convenience degree and disaster situation are adopted to further optimize the site selection of the post-earthquake medical emergency facility. Wherein:
suppose the population density of each demand point is Pn；
Economic level local one year public financial budget income CnRepresents;
convenience of transportation
Figure BDA0001763334990000072
(Lhighway: local highway mileage; l israilway: local railway mileage, S: total area of the local area);
casualty number for disaster InTo indicate.
Step three, calculating the weight of each influence factor by adopting TOPSIS
The method for calculating the weight of the influence factors comprises the following steps:
1 constructing an original matrix
N evaluation objects and m evaluation indexes are provided, and the original data can be written as a matrix X ═ Xij)n×m (2)；
Where { I ∈ I } represents the set of all demand points; { J ∈ J } represents a set of emergency facility alternate points;
b, performing syntropy and normalization processing on the high-quality index and the low-quality index respectively to obtain a matrix Z
Carrying out homodromous treatment:
Figure BDA0001763334990000081
normalization treatment:
Figure BDA0001763334990000082
c, constructing the optimal and worst vector
After the homodromous and normalization processing, a matrix Z ═ (Z) is obtainedij)n×mThen, the optimal and worst vectors formed by the maximum and minimum values of each column are respectively recorded as:
Z+＝(ZMax1 ZMax2 … ZMaxm) (5)
Z-＝(ZMin1 ZMin2 … ZMinm) (6)
d, calculating the distance D from each evaluation object to the optimal and worst vector
Distance of the ith evaluation object optimal scheme:
Figure BDA0001763334990000083
distance of the worst scheme of the ith evaluation object:
Figure BDA0001763334990000084
e, calculating the closeness degree C of each evaluation object and the optimal vectoriI.e. the weight of each influencing factor
Figure BDA0001763334990000085
Fourthly, establishing a site selection model of the basic emergency medical facility after the earthquake;
the objectives of the site selection model for the primary level emergency medical facility include: fairness, high efficiency, maximum coverage, and proper cost considerations, are expressed as a mathematical formula:
Figure BDA0001763334990000086
Figure BDA0001763334990000087
Figure BDA0001763334990000091
Figure BDA0001763334990000092
Ni＝{j|dij≤S} (14)
Figure BDA0001763334990000093
wherein, wi(I ═ 1, 2, … I) is the weight of demand point I; dijDistance from demand point i to candidate service facility point j; w is aidijA weighted distance between the demand dielectric i and the candidate service point j; n is a radical ofiA set of coverage demand points i to all candidate service facility points; s is the maximum distance allowed to be transported in unit time; y isj1 represents candidate service facility point j, and 0 represents else; x is the number ofijA value of 1 represents that the demand point i is serviced by the candidate service facility point j, and a value of 0 represents otherwise.
The target function formula (10) represents that the total weighted distance between each demand point and p service facility points is minimum; (11) minimum number of service points(ii) a The constraint formula (12) represents at least one facility point providing emergency service for the emergency demand point; constraint (13) indicates that demand points are assigned to only one emergency facility; constraint (14) represents the set of demand points i to candidate service point j distances less than the maximum distance allowed for transport per unit time; constraint (15) represents decision variable xjIs an integer variable from 0 to 1.
Step five, the method for establishing the address selection model of the advanced emergency medical command after the earthquake comprises the following steps:
1. the goal of the address model selected by the advanced emergency medical command mainly considers that the total weighted distance of the emergency medical facilities of each base level is minimum;
2. assuming that the set of the basic-level medical emergency facility points obtained in the step (4) is { a ∈ A }, and selecting a high-level command department from the selected basic-level facility points; obtaining an address selection model of the post-earthquake advanced emergency medical command as follows:
let widijIs the weighted distance between nodes i and j;
Figure BDA0001763334990000094
Figure BDA0001763334990000095
Figure BDA0001763334990000096
Figure BDA0001763334990000097
Figure BDA0001763334990000101
Figure BDA0001763334990000102
Figure BDA0001763334990000103
the objective function (18) represents that the total weighted distance between each base-level medical emergency facility point and the high-level emergency medical command post is minimum; constraint (19) indicates that the primary medical emergency facility site is covered at least once; the constraint (20) represents the assignment of a primary medical emergency facility point to only one advanced emergency medical conductor; the constraint formula (21) represents that the number of the advanced emergency medical command posts is 1; the constraint (22) represents the decision variable xjIs an integer variable from 0 to 1.
Step six, solving the model by a nested genetic algorithm
The process of solving the post-earthquake medical emergency facility site selection by using the nested genetic algorithm in the step is shown in fig. 3, and the method comprises the following specific steps:
1. inputting a space distance matrix X of the demand points and facility alternative points, the number K of the alternative facility points, the coverage radius D of the facility points and a weight vector Vn of the demand points;
2. selecting the number P of the facility points as 1: K;
3. when the GA tool box is operated to calculate the number of the selected facility points, the position decision with the minimum weighted total distance is used;
4. if the distance between the facility point and the service demand point is larger than the service radius D of the facility point, abandoning the site selection scheme;
5. if the distance between the facility point and the service demand point is greater than the service radius D of the facility point, making P equal to P +1, and if P is greater than K, determining the addressing scheme; if P < K, repeating the step 3-5;
6. selecting an addressing scheme which meets the conditions and has the minimum number of equipment points; obtaining the number, the position, the corresponding demand points and the capacity of the service of the basic emergency medical facility points;
7. in the basic medical emergency facility points, the GA tool box is operated, and a facility point which enables the total weighted distance to be minimum is selected as an advanced emergency medical command post.
Wherein 1-6 are inner genetic algorithms used for optimizing the number and positions of primary medical emergency facility points so as to meet the requirements of medical emergency rescue after earthquake on fairness, high efficiency, full coverage and cost saving as much as possible; and 7, an outer layer genetic algorithm used for selecting an advanced emergency medical command from the basic medical emergency facility points to meet the requirements of unified management, unified command and coordinated dispatching.
Some parameters of the genetic algorithm used in this step are described as follows:
fitness function: the objective function is designed based on the efficiency of use, i.e. the total weighted distance is minimal, with its constraints based on equations (12-14).
Parameter coding: in both the inner and outer layer genetic algorithms, P facilities need to be selected from the positions of N facility point candidates, and the spatial distances of the N facility point candidates form an N × N matrix.
Population quantity: in the inner and outer layer genetic algorithms, the initial population is defined as 1000, the maximum propagation generation number is 2000, and the number of elite individuals is 50.
The coding form is as follows: this embodiment employs integer coding because the unknown variables in the problem structure are in integer form.
Selecting a function: this example uses an elite selection strategy that will keep the best individual from participating in the next generation hybridization and will avoid the best individual from being destroyed by the hybridization operation.

Claims (8)

1. A site selection method for post-earthquake medical emergency facilities comprises the following steps:
(1) determining candidate points of post-earthquake medical emergency facilities;
(2) analyzing influence factors of optimized site selection of the post-earthquake medical emergency facilities according to the specific conditions of the areas;
(3) calculating the weight of each influence factor by adopting TOPSIS according to the influence factors determined in the step (2);
(4) establishing a basic emergency medical facility site selection model according to the candidate points determined in the step (1) and the weights of the influence factors in the step (3) by taking fairness, high efficiency, maximum coverage range and proper consideration of cost as targets;
(5) establishing an address selection model of the post-earthquake advanced emergency medical command on the basis of the address selection model of the basic emergency medical facility in the step (4);
(6) solving the models in the step (4) and the step (5) by adopting a nested genetic algorithm, and determining the number and the positions of the basic-level medical emergency facility point sets and the positions of the advanced emergency medical commanders;
in the step (5), the method for establishing the address selection model of the post-earthquake advanced emergency medical command comprises the following steps:
a, the total weighted distance of the emergency medical facilities of each base level is considered to be the minimum by the target of the address model selected by the advanced emergency medical command;
b, assuming that the set of the basic-level medical emergency facility points obtained in the step (4) is { a belongs to A }, and selecting a high-level command department from the selected basic-level facility points; obtaining an address selection model of the post-earthquake advanced emergency medical command as follows:
let widijIs the weighted distance between nodes i and j;
Figure FDA0003313794340000011
Figure FDA0003313794340000012
Figure FDA0003313794340000013
Figure FDA0003313794340000014
Figure FDA0003313794340000015
Figure FDA0003313794340000016
Figure FDA0003313794340000017
the target function formula (17) represents that the total weighted distance between each basic medical emergency facility point and the advanced emergency medical command post is minimum; the constraint (18) indicates that the primary medical emergency facility site is covered at least once; constraint (19) indicates that only one senior emergency medical conductor is assigned a primary medical emergency facility point; the constraint formula (20) represents that the number of the advanced emergency medical command posts is 1; constraint (21) represents decision variable xijIs an integer variable from 0 to 1.
2. The method for locating the post-earthquake medical emergency facility according to claim 1, wherein in the step (1), the specific method for determining the alternative point of the post-earthquake medical emergency facility comprises the following steps: quantifying and grading each index factor and giving weight by taking landform, geological disaster, vegetation and a water system as macroscopic indexes; and then, performing superposition analysis on all the quantized layers by using an ArcGIS space analysis module to obtain a place with the highest suitability index as an emergency facility candidate point.
3. The method as claimed in claim 1, wherein in the step (2), the influencing factors are selected from a group consisting of population density, economic level, traffic convenience, disaster, population age structure and education level.
4. The method for locating a post-earthquake medical emergency facility according to claim 1, wherein in the step (3), the method for calculating the weight of the influencing factor comprises the following steps:
a, constructing an original matrix
N evaluation objects and m evaluation indexes are provided, and the original data can be written as a matrix X ═ Xij)n×m(1) (ii) a Where { I ∈ I } represents the set of all demand points; { J ∈ J } represents a set of emergency facility alternate points;
b, carrying out syntropy treatment on the high-quality indexes and carrying out normalization treatment on the low-quality indexes to respectively obtain matrixes Zij；
Carrying out homodromous treatment:
Figure FDA0003313794340000021
normalization treatment:
Figure FDA0003313794340000022
c, constructing the optimal and worst vector
The matrix obtained after the homotropic processing is shown as formula (2), and the maximum value of each column forms the optimal vector and is marked as Z+(ii) a The matrix obtained after normalization is shown in formula (3), and the minimum value of each column forms the worst vector and is recorded as Z-：
Z+＝(ZMax1ZMax2…ZMaxm) (4)
Z-＝(ZMin1ZMin2…ZMinm) (5)
D, calculating the distance D from each evaluation object to the optimal and worst vector
Distance of the ith evaluation object optimal scheme:
Figure FDA0003313794340000031
distance of the worst scheme of the ith evaluation object:
Figure FDA0003313794340000032
e, calculating the closeness degree C of each evaluation object and the optimal vectoriI.e. the weight of each influencing factor
Figure FDA0003313794340000033
5. The method for locating medical emergency facilities after earthquake as claimed in claim 1, wherein in the step (4), the base level emergency medical facility locating model is as follows:
Figure FDA0003313794340000034
Figure FDA0003313794340000035
Figure FDA0003313794340000036
Figure FDA0003313794340000037
Ni＝{j|dij≤S} (13)
Figure FDA0003313794340000038
wherein, wiIs the weight of demand point I, I =1, 2, …, I; dijThe distance from the demand point i to the emergency facility alternative point j is calculated; w is aidijAs a demand point iAnd emergency facility alternate point j; z is the number of emergency utility points; n is a radical ofiA set of alternative points from the coverage demand point i to all emergency facilities; s is the maximum distance allowed to be transported in unit time; y isj1 represents that the emergency facility alternate point j is selected, and 0 represents that the emergency facility alternate point j is not selected; x is the number ofijA demand point i is served by an emergency facility alternate point j, denoted 1, and an alternate point 0, denoted otherwise.
6. The method for locating medical emergency facilities after earthquake according to any one of claims 1 or 5, wherein in the step (6), the nested genetic algorithm for solving the models in the steps (4) and (5) comprises the following specific steps:
a) the inner-layer genetic algorithm is used for optimizing the number and the positions of primary medical emergency facility points so as to meet the requirements of medical emergency rescue after earthquake on fairness, high efficiency, full coverage and cost saving as much as possible;
b) the outer layer genetic algorithm is used for selecting an advanced emergency medical command post from basic medical emergency facility points, and meets the requirements of unified management, unified command and coordinated dispatching.
7. The method of locating a post-earthquake medical emergency facility as claimed in claim 6, wherein said inner genetic algorithm comprises the steps of:
a-1, inputting a space distance matrix X of a demand point and facility alternative points, the number K of the alternative facility points, a coverage radius D of the facility points and a weight vector Vn of the demand point;
a-2, selecting a facility point number P which is 1: K;
a-3, when the GA tool box is operated to calculate the number of the selected facility points, using the position decision with the minimum weighted total distance;
a-4, if the distance between the facility point and the service demand point is larger than the service radius D of the facility point, abandoning the site selection scheme;
a-5, if the distance between the facility point and the service demand point is greater than the service radius D of the facility point, making P equal to P +1, and if P is greater than K, determining the addressing scheme; if P is less than K, repeating the steps a-3-a-5;
a-6, selecting an addressing scheme which meets the conditions and has the minimum number of equipment points; and obtaining the number, the position, the corresponding demand point and the corresponding capacity of the service of the basic emergency medical facility points.
8. The method of locating a post-earthquake medical emergency facility as claimed in claim 6, wherein said outer genetic algorithm comprises the steps of: in the basic medical emergency facility points, the GA tool box is operated, and a facility point which enables the total weighted distance to be minimum is selected as an advanced emergency medical command post.
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