CN110991704B - Emergency rescue station site selection and distribution method and system - Google Patents
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Abstract
The invention discloses a method and a system for site selection and distribution of emergency rescue stations, which are used for determining relevant information of the emergency rescue stations; according to the related information, an emergency rescue site location and distribution model is constructed, so that on the premise of considering both the waiting time of wounded persons at the disaster-affected points and the time for transporting vehicles of the emergency rescue sites to the disaster-affected points, proper emergency rescue sites are selected to distribute a corresponding number of vehicles to all the disaster-affected points; solving the model by adopting a simulated annealing algorithm nested genetic algorithm; repeatedly solving the model until the average waiting time of the wounded at each disaster-affected point reaches a preset time range; and outputting the site selection and distribution scheme of the emergency rescue stations to determine the distribution relation between the emergency rescue stations and the disaster-affected points and the quantity of vehicles distributed to the disaster-affected points by each emergency rescue station. The invention selects the address corresponding to the emergency rescue station, and comprehensively considers the total cost constraint, the station capacity constraint, the total quantity constraint of emergency rescue vehicles, the availability of the vehicles and the like of the emergency rescue station.
Description
Technical Field
The invention relates to the field of emergency risk decision-making method researches of emergency events, in particular to an emergency rescue site location and distribution method and system.
Background
China is located at the junction 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 Chinese of the 20 th century commonly occurs more than 6 grades of land administration 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 is 61 thousands, which accounts for about 36% of the total number of dead people in the world due to earthquake and accounts for the first place 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.
At present, the site selection and distribution optimization method of emergency rescue sites mainly deals with emergency medical rescue under daily conditions, the existing site selection and distribution method of emergency rescue vehicles does not aim at rapid increase of the number of wounded persons generated under large-scale earthquake disasters, facilities such as the existing emergency vehicles and hospitals possibly face insufficient resources, the site selection and distribution need to be carried out corresponding to the emergency rescue sites again, and meanwhile, a new medical center is established aiming at the problems of insufficient capacity of the existing hospitals and the like. In addition, existing research rarely comprehensively considers the total cost constraint of emergency rescue stations, the station capacity constraint, the emergency rescue vehicle number constraint, the availability of emergency vehicles and the like in practical problems. Therefore, the site selection and distribution method for the emergency vehicle sites can meet the requirements of site selection and distribution of the emergency rescue sites after the earthquake and realize rapid response of the emergency rescue, meanwhile, the position of the medical center is determined, various constraint conditions can be comprehensively considered, and rapid rescue response and efficient emergency rescue distribution can be realized.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to solve the technical problems that the existing site selection and distribution method of the emergency rescue vehicle does not have a solution for the situation that the number of wounded persons generated under a large-scale earthquake disaster is increased and the existing facilities such as the emergency vehicle and the hospital are likely to face insufficient resources.
In order to achieve the above object, in a first aspect, the present invention provides an emergency rescue site location and allocation method, including the following steps:
step S1, determining relevant information of the emergency rescue station; the related information includes: the position of a disaster-affected point, the positions of all emergency rescue stations, the position of a hospital, the position of a medical center and the vehicle capacity of all emergency rescue stations;
the emergency rescue station is used for providing vehicles for a disaster site, and the vehicles are used for transporting wounded persons at the disaster site to a hospital or a medical center;
step S2, according to the related information, an emergency rescue site location and distribution model is constructed, so that on the premise of considering both the waiting time of wounded persons at the disaster-affected site and the time for transporting vehicles at the emergency rescue site to the disaster-affected site, a proper emergency rescue site is selected to distribute a corresponding number of vehicles to each disaster-affected site; the addressing and allocation model comprises: the average waiting time sum of wounded persons at all disaster-affected points and the time sum of vehicles at the emergency rescue station for transporting to all disaster-affected points;
wherein the waiting time of the wounded is the time from the time of becoming the wounded at the disaster point to the time of being transported to the hospital or medical center;
step S3, solving the model in the step S2 by adopting a simulated annealing algorithm nested genetic algorithm;
step S4, repeating step S3 until the average waiting time of the wounded at each disaster-stricken point reaches the preset time range;
and step S5, outputting an emergency rescue station site selection and distribution scheme to determine the distribution relation between the emergency rescue station and the disaster-stricken points and the quantity of vehicles distributed to the disaster-stricken points by each emergency rescue station.
Optionally, the emergency rescue site location and distribution model meets the following preset conditions: the wounded is preferentially transported to the nearest hospital or medical center for treatment, and if the capacity of the nearest hospital or medical center is insufficient, the wounded is sent to the next nearest hospital or medical center.
Optionally, the emergency rescue site location and distribution model is as follows:
wherein the content of the first and second substances,
the average waiting time of the wounded at each disaster-affected point is the sum,
sum of time taken for vehicle transportation of emergency rescue station to disaster site(ii) a T represents the total cycle number, I represents the set of disaster-affected points, and J represents the set of emergency rescue stations; x is the number ofitThe number p of emergency rescue vehicles distributed to the disaster-affected point i at the moment t is representeditRepresenting the available probability of the emergency rescue vehicle at the disaster-affected point i at the moment t; lambda [ alpha ]it，μitRepresents the average generation rate and the service rate of the wounded from the disaster-affected point i at the moment t, xijtIndicating whether the disaster-affected point i is distributed to the emergency rescue station j, tau at the moment tijThe distance between the disaster-affected point i and the emergency rescue station j is shown.
Optionally, the average production rate of the wounded persons at the disaster-affected point is determined according to the disaster-affected situation of the disaster-affected point and the population distribution situation of the disaster-affected point;
the service rate of the disaster-stricken point is determined according to the time of the vehicle starting from the emergency rescue station to the disaster-stricken point, the residence time of the vehicle at the disaster-stricken point and the time of the vehicle transporting the wounded from the disaster-stricken point to the hospital or medical center.
Optionally, the step S3 specifically includes the following steps:
step S31, optimizing and selecting the position of the emergency rescue station by adopting an outer-layer simulated annealing algorithm so as to meet the requirement of budget of the emergency rescue station after the earthquake;
the sum of the opening cost of all emergency rescue sites is smaller than the total budget of the emergency rescue sites;
step S32, optimizing and selecting the number of vehicles distributed by the emergency rescue station, the distribution relation between the disaster-affected points and the emergency rescue station and the number of emergency vehicles of the disaster-affected points by adopting an inner-layer genetic algorithm so as to meet the requirements of the capacity of the emergency rescue station and the total number of all vehicles of the emergency rescue station; and substituting the distribution result determined in the step S32 into the outer-layer simulated annealing algorithm in the step S31, and calculating an objective function of the emergency rescue station site selection and distribution model.
Optionally, the step S31 specifically includes the following steps:
s311, initializing the outer layer simulated annealing algorithm, and setting basic parameters of the simulated annealing algorithm: initial temperature T0End temperature of cooling TkTemperature decay coefficient ε, number of internal cycles K, current temperature Tem equal to initial(ii) temperature;
s312, generating an initial feasible solution x, substituting the initial feasible solution x into an outer simulated annealing algorithm to obtain an initial utility function value f (x), and enabling the optimal solution to be equal to the initial feasible solution and the optimal value to be equal to the initial utility function value;
s313, generating a new solution x 'according to the iteration rule, and calculating a utility function value f (x') of the new solution;
s314, if the new solution f (x') is superior to the initial solution f (x), making the optimal solution equal to the new solution, and making the optimal value equal to the utility function value of the new solution, otherwise, determining whether to accept the new solution according to Metropolis criterion; probability of accepting new solution P:
wherein Tem is the current temperature;
s315, if the iteration times of the internal cycle times K are reached, the next step is carried out, otherwise, the step S314 is returned to;
and S316, if the current temperature Tem reaches the end temperature, returning to the optimal solution, otherwise, cooling, calculating epsilon Tem as a new current temperature, and returning to the step S314.
Optionally, the step S32 specifically includes the following steps:
s321, initializing an inner-layer genetic algorithm, and setting basic parameters of the genetic algorithm: population size K, maximum number of iterations N, crossover probability pcProbability of variation pm；
S322, initializing the population of the genetic algorithm: initializing a K genetic algorithm individual as an initial population P according to a solution of emergency rescue site location obtained by a simulated annealing algorithm;
s323, calculating the fitness value of each individual of the population to obtain an optimal individual and an optimal individual value;
s325, keeping the population dominant individuals to the next generation population;
s324, roulette through a genetic algorithm to select individuals in the population;
s325, generating next generation population individuals in a crossed manner;
s326, obtaining a new population through mutation, and updating the optimal individual and the optimal individual value;
and S327, if the maximum iteration number N is reached, returning to the optimal solution, otherwise, returning to the step S324.
In a second aspect, the present invention provides an emergency rescue site location and distribution system, including:
the information determining unit is used for determining relevant information of the emergency rescue station; the related information includes: the position of a disaster-affected point, the positions of all emergency rescue stations, the position of a hospital, the position of a medical center and the vehicle capacity of all emergency rescue stations; the emergency rescue station is used for providing vehicles for a disaster site, and the vehicles are used for transporting wounded persons at the disaster site to a hospital or a medical center;
the model construction unit is used for constructing an emergency rescue site location and distribution model according to the related information so as to select a proper emergency rescue site to distribute a corresponding number of vehicles to each disaster-stricken point on the premise of considering both the waiting time of wounded persons at the disaster-stricken point and the time for transporting the vehicles of the emergency rescue site to the disaster-stricken point; the addressing and allocation model comprises: the average waiting time sum of wounded persons at all disaster-affected points and the time sum of vehicles at the emergency rescue station for transporting to all disaster-affected points; wherein the waiting time of the wounded is the time from the time of becoming the wounded at the disaster point to the time of being transported to the hospital or medical center;
the model solving unit is used for solving the site selection and distribution model by adopting a simulated annealing algorithm nested genetic algorithm; repeatedly solving the site selection and distribution model until the average waiting time of the wounded at each disaster point reaches a preset time range; and outputting the site selection and distribution scheme of the emergency rescue stations to determine the distribution relation between the emergency rescue stations and the disaster-affected points and the quantity of vehicles distributed to the disaster-affected points by each emergency rescue station.
Optionally, the emergency rescue site location and distribution model meets the following preset conditions: the wounded is preferentially transported to the nearest hospital or medical center for treatment, and if the capacity of the nearest hospital or medical center is insufficient, the wounded is sent to the next nearest hospital or medical center.
Optionally, the emergency rescue site location and distribution model is as follows:
wherein the content of the first and second substances,
the average waiting time of the wounded at each disaster-affected point is the sum,
the total time for transporting the vehicle of the emergency rescue station to the disaster site is calculated; t represents the total cycle number, I represents the set of disaster-affected points, and J represents the set of emergency rescue stations; x is the number ofitThe number p of emergency rescue vehicles distributed to the disaster-affected point i at the moment t is representeditRepresenting the available probability of the emergency rescue vehicle at the disaster-affected point i at the moment t; lambda [ alpha ]it，μitRepresents the average generation rate and the service rate of the wounded from the disaster-affected point i at the moment t, xijtIndicating whether the disaster-affected point i is distributed to the emergency rescue station j, tau at the moment tijThe distance between the disaster-affected point i and the emergency rescue station j is shown.
Generally, compared with the prior art, the above technical solution conceived by the present invention has the following beneficial effects:
the invention provides an emergency rescue station site selection and distribution method and system. Meanwhile, aiming at the fact that the number of wounded persons is increased sharply under the large-scale earthquake disaster, the existing facilities such as emergency vehicles and hospitals are likely to face resource deficiency, the site selection of a medical center is also considered besides the site selection of emergency rescue stations. In addition, existing research rarely comprehensively considers the total cost constraint of emergency rescue stations, the station capacity constraint, the emergency rescue vehicle number constraint, the availability of emergency vehicles and the like in practical problems.
The invention provides a site selection and distribution method and system for emergency rescue stations, which are used for constructing a mathematical model aiming at the site selection and distribution problems of emergency rescue vehicles after an earthquake and designing a nesting algorithm based on the combination of a simulated annealing algorithm and a genetic algorithm, can realize site selection and distribution of emergency rescue stations after the earthquake and quick response of emergency rescue, and have great significance for reducing casualties after the earthquake.
Drawings
FIG. 1 is a schematic flow chart of a post-earthquake emergency rescue site location and allocation method based on a simulated annealing algorithm and a genetic algorithm according to the present invention;
FIG. 2 is a flowchart of an algorithm in the post-earthquake emergency rescue site location and allocation method based on a simulated annealing algorithm and a genetic algorithm provided by the invention;
FIG. 3 is a graph of the convergence of the fitness of the simulated annealing algorithm nested genetic algorithm provided by the present invention;
FIG. 4 is a schematic diagram illustrating the site selection and distribution results of emergency rescue stations in case analysis in the emergency shelter site selection and resource distribution method considering the grade difference provided by the present invention;
fig. 5 is an architecture diagram of an emergency rescue site location and distribution system provided by the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
In order to overcome the defects of site selection and distribution planning of emergency rescue sites in emergency management in the prior art, a method for site selection and distribution of emergency rescue sites after earthquake based on simulated annealing and genetic algorithm is provided. The method is used for improving the accuracy and the rationality of the emergency rescue site location and distribution method, realizing the rapid response of rescue to the wounded personnel at the disaster-affected site, and having great significance for reducing the casualties after the earthquake.
In order to achieve the purpose, the invention adopts the technical scheme that: a post-earthquake emergency rescue site location and distribution method based on simulated annealing and genetic algorithm is shown in figure 1 and comprises the following steps:
s1, acquiring information of the location of the disaster site after the earthquake, estimating the wounded generation rate of the disaster site, the location of the alternative emergency rescue sites, the location of the available hospital, the location of the potential medical center, the capacity of the emergency rescue sites, the budget for constructing the emergency rescue sites, the number of emergency rescue vehicles, the transportation time between each alternative emergency rescue site and the disaster site, the transportation time between the disaster site and the hospital and the like according to the disaster site and population distribution;
s2, constructing an emergency rescue station site selection and distribution model after the earthquake according to the acquired related information; when the emergency rescue site location and distribution problems are considered, importance of total cost constraint of emergency rescue sites, site capacity constraint, emergency rescue vehicle quantity constraint, availability of emergency vehicles and the like in practical problems are considered, and a post-earthquake emergency rescue site location and distribution model is constructed. The model takes into account emergency rescue vehicle availability using queuing theory.
Considering emergency rescue vehicle availability specifically as follows: clustering the disaster areas to I disaster-affected points, wherein each disaster-affected point is provided with a plurality of emergency rescue vehicles, and K emergency rescue vehicles are shared; emergency vehicles have only 2 states: busy or idle; according to the generation rate of the wounded at the disaster site, the number of emergency vehicles distributed by the disaster site and the service rate of the emergency vehicles at the disaster site, the average waiting time of each wounded at the disaster site can be calculated by using a queuing theory;
the model follows the following assumptions:
suppose 1. the location of the disaster site is known and clustered. Each demand point can only be covered by one emergency rescue station.
Suppose 2. the potential site selection points for emergency rescue sites are known and each emergency rescue site has capacity limitations.
Suppose 3. the potential site of the medical center is known.
Suppose 4. each emergency rescue vehicle can only carry one injured person at a time. The present invention is directed only to patients with severe and moderate injuries.
Suppose 5, at the beginning of each cycle, the emergency rescue stations select the address points, the number of emergency rescue vehicles allocated to each emergency rescue station, the number of emergency rescue vehicles allocated to each disaster-stricken point, and the allocation relationship between the emergency rescue stations and the disaster-stricken points.
Assume 6 that at each time period, an emergency vehicle departs from an emergency rescue station to a disaster site and delivers the injured person to a hospital or medical center. If the emergency rescue vehicle is not available, the injured person enters a queue to wait for the emergency rescue vehicle.
Suppose 7. the injured person is preferentially sent to the nearest hospital or medical center for treatment, and if the capacity of the nearest hospital or medical center is insufficient, the injured person is sent to the next nearest hospital or medical center.
Suppose 8, the disaster site demands generate rates that are independent of each other and poisson distribution, and the service rates are independent of each other and meet exponential distribution.
The objective function of the model is as follows:
constraint conditions are as follows:
the objective function (1) consists of two parts, wherein one part is the average waiting time sum of wounded persons at each disaster-affected point, and the other part is the response time sum from the emergency rescue station to the disaster-affected point; x is the number ofitThe number p of emergency rescue vehicles distributed to the disaster-affected point i at the moment t is representeditRepresenting the probability of availability of emergency rescue vehicles at a disaster-affected point i at the moment t; lambda [ alpha ]it，μitRepresents the average generation rate and the service rate of the wounded from the disaster-affected point i at the moment t, xijtIndicating whether the disaster-affected point i is distributed to the emergency rescue station j, tau at the moment tijThe distance between the disaster-affected point i and the emergency rescue station j is shown. The average production rate of the wounded persons at the disaster-affected points is determined according to the specific disaster situation, the disaster-affected situation of the disaster-affected points observed by the satellite pictures and the population density situation of the disaster-affected points; the service rate of the disaster-stricken point is determined according to the time of the vehicle from the emergency rescue station to the disaster-stricken point, the residence time of the vehicle at the disaster-stricken point and the time of the vehicle for transporting the wounded from the disaster-stricken point to the hospital or medical center.
Wherein, I represents the set of disaster-affected points, J represents the set of emergency rescue stations, and T represents the set of all periods.
Constraint (2) represents the probability of availability of an emergency rescue vehicle at time t at disaster-affected point i, where λitRepresents the average generation rate of the wounded at the disaster-affected point i at the moment t, muitDenotes the average service rate, x, of the wounded at the disaster-affected point i at time titThe quantity of emergency rescue vehicles distributed at the disaster-affected point i at the moment t is represented; constraint (3) ensures that the cost of opening an emergency rescue site does not exceed a budget, where yjtIndicating whether an emergency rescue site is opened at a potential emergency rescue site j at time t, fjRepresents the cost of setting up an emergency rescue site at a potential emergency rescue site j, MtRepresenting the total budget for opening the emergency rescue station at the moment t; constraints (4) and (5) represent ambulance total volume limits, where xitThe number z of emergency rescue vehicles distributed to the disaster-affected point i at the moment tjtThe quantity of emergency rescue vehicles distributed by the emergency rescue station j at the moment t is represented, and K' represents the total quantity of the emergency rescue vehicles.
Constraint (6) indicates that the number of emergency rescue vehicles allocated to the disaster site by the emergency rescue station does not exceed the total number of emergency rescue vehicles at the emergency rescue station, wherein zjtNumber of emergency rescue vehicles, x, distributed to emergency rescue station j at time titThe quantity x of emergency rescue vehicles distributed to the disaster-affected point i at the moment tijtIndicating whether the disaster-affected point i is covered by the emergency rescue station at the moment t; constraining (7) each disaster point onlyIs assigned to an emergency rescue station, where xijtIndicating whether the disaster-affected point i is covered by the emergency rescue station at the moment t; constraint (8) represents that the number of emergency rescue vehicles at the emergency rescue site does not exceed the capacity limit of the emergency rescue site, wherein zjtNumber of emergency rescue vehicles, C, assigned to emergency rescue station j at time tjRepresents the maximum capacity of the emergency rescue station j; constraint (9) indicates that emergency rescue vehicles can only be allocated when an emergency rescue station is opened, where yjtIndicating whether an emergency rescue site is opened at a potential emergency rescue site j at the moment t or not, zjtThe quantity of emergency rescue vehicles distributed by the emergency rescue station j at the moment t is represented, and M represents a sufficiently large integer; constraints (10) and (11) are 0-1 and integer constraints of decision variables.
S3, solving the model in the S2 by adopting a simulated annealing algorithm nested genetic algorithm; FIG. 2 is a flow chart of a nested genetic algorithm that simulates an annealing algorithm. FIG. 3 is a simulated annealing algorithm nested genetic algorithm fitness convergence curve, wherein the horizontal axis is iteration times and the vertical axis is fitness values.
The specific steps of solving the nested genetic algorithm of the simulated annealing algorithm are as follows:
the parameter settings for the simulated annealing algorithm are shown in table 1.
TABLE 1 simulated annealing parameters
S31, the outer layer simulated annealing algorithm is used for optimizing the position of the emergency rescue station so as to meet the requirement of budget of the emergency rescue station after earthquake; the specific steps of solving the nested genetic algorithm of the simulated annealing algorithm are as follows:
s311, initializing the algorithm, setting basic parameters of the simulated annealing algorithm: initial temperature T0End temperature of cooling TkTemperature decay coefficient epsilon, internal cycle times K, and current temperature Tem equal to the initial temperature.
S312, generating an initial feasible solution x, substituting the initial feasible solution x into an outer simulated annealing algorithm to obtain an initial utility function value f (x), and enabling the optimal solution to be equal to the initial feasible solution and the optimal value to be equal to the initial utility function value; the initial solution yields: and J belongs to J, wherein the J is {1,2,3, …, J, …, n }, wherein n is the total number of the potential emergency rescue stations, and the initial solution represents a feasible solution for address selection of the emergency rescue stations.
The initial solution is a series of 0-1 variables representing whether the emergency rescue station is open. The feasible solution is represented by a vector: y ═ Y1,y2,…,yj,…,ynIn which y isjIs a binary variable (0,1) and n is the total number of potential emergency rescue sites. For example: considering a site selection with five potential emergency rescue sites, one feasible solution is: {1,0,0,1,0}, where the open sites are site 1 and site 4.
S313, generating a new solution x 'according to the iteration rule, and calculating a utility function value f (x') of the new solution;
s314, if the new solution f (x') is superior to the initial solution f (x), making the optimal solution equal to the new solution, and making the optimal value equal to the utility function value of the new solution, otherwise, determining whether to accept the new solution according to the Metropolis criterion. Probability of accepting new solution:
wherein Tem is the current temperature;
s315, if the iteration times of the internal cycle times K are reached, the next step is carried out, otherwise, the step returns to S314;
and S316, if the current temperature Tem reaches the end temperature, returning to the optimal solution, otherwise, cooling, calculating epsilon Tem as a new current temperature, and returning to S314.
Generation of a new solution: and generating a new domain solution, randomly selecting a station from the potential emergency rescue stations and changing the state of the station. For example: considering a site selection with five potential emergency rescue sites, one feasible solution is {1,0,0,1,0}, and the domain feasible solution may be {0,1,0,1,0}, where the open sites are site 2 and site 4.
S32, the inner layer genetic algorithm is used for optimizing the number of vehicles distributed by the emergency rescue stations, the distribution relation between the disaster-affected points and the emergency stations, and the number of emergency vehicles of the disaster-affected points so as to meet the requirements of the capacity of the emergency rescue stations, the total number of emergency rescue vehicles and the like; and substituting the distribution result into an outer algorithm to calculate an integral objective function.
The parameter settings of the genetic algorithm are shown in table 2:
TABLE 2 genetic algorithm parameters
S321, initializing an algorithm, setting basic parameters of a genetic algorithm: population size K, maximum iteration number N, cross probability pcProbability of variation pm；
S322, initializing the population of the genetic algorithm: initializing K genetic algorithm individuals as an initial population G according to a solution of emergency rescue site location obtained by a simulated annealing algorithm. Chromosome design: for each demand point I e and each emergency rescue station j e N selected to be opened, wherein I is {1,2,3, …, I, …, m } andN is {1,2,3, …, j, …, N }, m is the total number of disaster-stricken points, and N is the number of opened emergency vehicle stations. The chromosome in the genetic algorithm represents the number of emergency rescue vehicles distributed to a disaster-stricken point and the coverage condition of the disaster-stricken point by an emergency rescue station, and is designed as follows: x ═ X1,x2,x3,…,xm}{x'1,x'2,x'3,…,x'mAnd the first m genes represent the number of emergency rescue vehicles distributed to the disaster-stricken point, and the first m genes are all integers from 0 to K, wherein K is the total number of emergency rescue vehicles. For example: x is the number of1The number of the emergency rescue vehicles distributed by the disaster site 1 is 5. The latter m genes represent the coverage of disaster-affected sites by emergency rescue sites, and each gene ranges from 1 to N, where N is the number of open emergency vehicle sites. For example: x'12 means that the disaster site 1 is covered by the emergency rescue station 2.
S323, calculating the fitness value of each individual of the population to obtain an optimal individual and an optimal individual value;
s325, keeping the population dominant individuals to the next generation population;
s324, roulette through a genetic algorithm to select individuals in the population;
s325, generating next generation population individuals in a crossed manner;
s326, obtaining a new population through mutation, and updating the optimal individual and the optimal individual value;
and S327, if the maximum iteration number N is reached, returning to the optimal solution, otherwise, returning to S324.
And S4, analyzing the average waiting time of the wounded at each disaster-affected point according to the result of the step S3. Determining a medical center site selection; repeating the step S3 until the average waiting time of the wounded at each disaster-affected point reaches a reasonable range; determining the medical center position: an iterative framework is used to determine the location of the medical center. Firstly, the problem of site selection of rescue vehicle stations is solved, and the average waiting time of wounded persons at each disaster-affected point is obtained. And a medical center is arranged at a potential medical center point which is closest to a region with longer waiting time of the wounded at the disaster point. After the rescue vehicle station is added into the medical center, the problem of site selection of the rescue vehicle station is solved again, and a new temporary hospital is set up until the average waiting time of all wounded persons at the disaster-affected point reaches a reasonable range.
And S5, outputting the position of the emergency rescue station and the distribution scheme thereof, and determining the position of the emergency rescue station, the position of the medical center, the number of vehicles distributed to the emergency rescue station and the distribution relation between the emergency rescue station and the disaster-stricken point. Fig. 4 shows an example distribution result, where the abscissa is longitude and the ordinate is latitude, a circular point represents a location of a disaster point, a square represents a location of an emergency rescue station, and a connection line represents a distribution relationship between the emergency rescue station and the disaster point.
Fig. 5 is an architecture diagram of an emergency rescue site addressing and distributing system provided in the present invention, as shown in fig. 5, including: an information determination unit 510, a model construction unit 520, and a model solution unit 530.
An information determining unit 510, configured to determine relevant information of an emergency rescue station; the related information includes: the position of a disaster-affected point, the positions of all emergency rescue stations, the position of a hospital, the position of a medical center and the vehicle capacity of all emergency rescue stations; the emergency rescue station is used for providing vehicles for a disaster site, and the vehicles are used for transporting wounded persons at the disaster site to a hospital or a medical center;
a model construction unit 520, configured to construct an emergency rescue site location and allocation model according to the relevant information, so as to select a suitable emergency rescue site to allocate a corresponding number of vehicles to each disaster-stricken point on the premise of considering both the waiting time of the wounded at the disaster-stricken point and the time for transporting the vehicles to the disaster-stricken point; the addressing and allocation model comprises: the average waiting time sum of wounded persons at all disaster-affected points and the time sum of vehicles at the emergency rescue station for transporting to all disaster-affected points; wherein the waiting time of the wounded is the time from the time of becoming the wounded at the disaster point to the time of being transported to the hospital or medical center;
the model solving unit 530 is used for solving the site selection and distribution model by adopting a simulated annealing algorithm nested genetic algorithm; repeatedly solving the site selection and distribution model until the average waiting time of the wounded at each disaster point reaches a preset time range; and outputting the site selection and distribution scheme of the emergency rescue stations to determine the distribution relation between the emergency rescue stations and the disaster-affected points and the quantity of vehicles distributed to the disaster-affected points by each emergency rescue station.
Optionally, the emergency rescue site location and distribution model meets the following preset conditions: the wounded is preferentially transported to the nearest hospital or medical center for treatment, and if the capacity of the nearest hospital or medical center is insufficient, the wounded is sent to the next nearest hospital or medical center.
Optionally, the emergency rescue site location and distribution model is as follows:
wherein the content of the first and second substances,
the average waiting time of the wounded at each disaster-affected point is the sum,
the total time for transporting the vehicle of the emergency rescue station to the disaster site is calculated; x is the number ofitThe number p of emergency rescue vehicles distributed to the disaster-affected point i at the moment t is representeditRepresenting the available probability of the emergency rescue vehicle at the disaster-affected point i at the moment t; lambda [ alpha ]it，μitRepresents the average generation rate and the service rate of the wounded from the disaster-affected point i at the moment t, xijtIndicating whether the disaster-affected point i is distributed to the emergency rescue station j, tau at the moment tijThe distance between the disaster-affected point i and the emergency rescue station j is shown.
Specifically, the functions of the above units can be referred to the detailed description in the foregoing method embodiments, and are not described herein again.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (7)
1. An emergency rescue site location and distribution method is characterized by comprising the following steps:
step S1, determining relevant information of the emergency rescue station; the related information includes: the method comprises the following steps of acquiring information of a position of a disaster site after an earthquake, generating speed of wounded persons at the disaster site, positions of alternative emergency rescue sites, positions of available hospitals, positions of potential medical centers, capacity of the emergency rescue sites, budget for constructing the emergency rescue sites, the number of emergency rescue vehicles, transportation time between each alternative emergency rescue site and the disaster site, and transportation time between the disaster site and the hospitals;
the emergency rescue station is used for providing vehicles for a disaster site, and the vehicles are used for transporting wounded persons at the disaster site to a hospital or a medical center;
step S2, according to the related information, an emergency rescue site location and distribution model is constructed, so that on the premise of considering both the waiting time of wounded persons at the disaster-affected site and the time for transporting vehicles at the emergency rescue site to the disaster-affected site, a proper emergency rescue site is selected to distribute a corresponding number of vehicles to each disaster-affected site; the addressing and allocation model comprises: the average waiting time sum of wounded persons at all disaster-affected points and the time sum of vehicles at the emergency rescue station for transporting to all disaster-affected points;
wherein the waiting time of the wounded is the time from the time of becoming the wounded at the disaster point to the time of being transported to the hospital or medical center;
the emergency rescue station site selection and distribution model comprises the following steps:
constraint conditions are as follows:
xit，zjtare all integers
Wherein the content of the first and second substances,
the average waiting time of the wounded at each disaster-affected point is the sum,
the total time for transporting the vehicle of the emergency rescue station to the disaster site is calculated; t represents the total cycle number, I represents the set of disaster-affected points, and J represents the set of emergency rescue stations; x is the number ofitThe number p of emergency rescue vehicles distributed to the disaster-affected point i at the moment t is representeditRepresenting the available probability of the emergency rescue vehicle at the disaster-affected point i at the moment t; lambda [ alpha ]it，μitRepresents the average generation rate and the average service rate of the wounded from the disaster-affected point i at the moment t, xijtIndicating whether the disaster-affected point i is distributed to the emergency rescue station j, tau at the moment tijThe distance between the disaster-affected point i and the emergency rescue station j is obtained; y isjtIndicating whether the t moment is opened at a potential emergency rescue site j or notEmergency rescue station, fjRepresents the cost of setting up an emergency rescue site at a potential emergency rescue site j, MtRepresenting the total budget for opening the emergency rescue station at the moment t; k' represents the total number of emergency rescue vehicles; z is a radical ofjtRepresenting the quantity of emergency rescue vehicles distributed by the emergency rescue station j at the moment t; cjRepresents the maximum capacity of the emergency rescue station j; m is an integer;
step S3, solving the model in the step S2 by adopting a simulated annealing algorithm nested genetic algorithm; the method specifically comprises the following steps:
step S31, optimizing and selecting the position of the emergency rescue station by adopting an outer-layer simulated annealing algorithm so as to meet the requirement of budget of the emergency rescue station after the earthquake;
the sum of the opening cost of all emergency rescue sites is smaller than the total budget of the emergency rescue sites;
step S32, optimizing and selecting the number of vehicles distributed by the emergency rescue station, the distribution relation between the disaster-affected points and the emergency rescue station and the number of emergency vehicles of the disaster-affected points by adopting an inner-layer genetic algorithm so as to meet the requirements of the capacity of the emergency rescue station and the total number of all vehicles of the emergency rescue station; substituting the distribution result determined in the step S32 into the outer-layer simulated annealing algorithm in the step S31, and calculating a target function of the emergency rescue station site selection and distribution model;
step S4, repeating step S3 until the average waiting time of the wounded at each disaster-stricken point reaches the preset time range;
and step S5, outputting an emergency rescue station site selection and distribution scheme to determine the distribution relation between the emergency rescue station and the disaster-stricken points and the quantity of vehicles distributed to the disaster-stricken points by each emergency rescue station.
2. The emergency rescue station site selection and distribution method according to claim 1, wherein the emergency rescue station site selection and distribution model satisfies the following preset conditions: the wounded is preferentially transported to the nearest hospital or medical center for treatment, and if the capacity of the nearest hospital or medical center is insufficient, the wounded is sent to the next nearest hospital or medical center.
3. The emergency rescue site addressing and distributing method according to claim 1, wherein the average production rate of the wounded persons at the disaster-affected site is determined according to the disaster-affected situation of the disaster-affected site and the population distribution situation of the disaster-affected site;
the service rate of the disaster-stricken point is determined according to the time of the vehicle starting from the emergency rescue station to the disaster-stricken point, the residence time of the vehicle at the disaster-stricken point and the time of the vehicle transporting the wounded from the disaster-stricken point to the hospital or medical center.
4. An emergency rescue site addressing and allocating method according to any one of claims 1 to 3, wherein the step S31 specifically comprises the steps of:
s311, initializing the outer layer simulated annealing algorithm, and setting basic parameters of the simulated annealing algorithm: initial temperature T0End temperature of cooling TkTemperature attenuation coefficient epsilon, internal cycle times K, and current temperature Tem is equal to the initial temperature;
s312, generating an initial feasible solution x, substituting the initial feasible solution x into an outer simulated annealing algorithm to obtain an initial utility function value f (x), and enabling the optimal solution to be equal to the initial feasible solution and the optimal value to be equal to the initial utility function value;
s313, generating a new solution x 'according to the iteration rule, and calculating a utility function value f (x') of the new solution;
s314, if the new solution f (x') is superior to the initial solution f (x), making the optimal solution equal to the new solution, and making the optimal value equal to the utility function value of the new solution, otherwise, determining whether to accept the new solution according to Metropolis criterion; probability of accepting new solution P:
s315, if the iteration times of the internal cycle times K are reached, the next step is carried out, otherwise, the step S314 is returned to;
s316, if the current temperature Tem reaches the end temperature, returning to the optimal solution; otherwise, the temperature is decreased, and ε Tem is calculated as the new current temperature, and the process returns to step S314.
5. An emergency rescue site addressing and allocating method according to any one of claims 1 to 3, wherein the step S32 specifically comprises the steps of:
s321, initializing an inner-layer genetic algorithm, and setting basic parameters of the genetic algorithm: population size K, maximum number of iterations N, crossover probability pcProbability of variation pm；
S322, initializing the population of the genetic algorithm: initializing a K genetic algorithm individual as an initial population P according to a solution of emergency rescue site location obtained by a simulated annealing algorithm;
s323, calculating the fitness value of each individual of the population to obtain an optimal individual and an optimal individual value;
s325, keeping the population dominant individuals to the next generation population;
s324, roulette through a genetic algorithm to select individuals in the population;
s325, generating next generation population individuals in a crossed manner;
s326, obtaining a new population through mutation, and updating the optimal individual and the optimal individual value;
and S327, if the maximum iteration number N is reached, returning to the optimal solution, otherwise, returning to the step S324.
6. An emergency rescue site location and distribution system, comprising:
the information determining unit is used for determining relevant information of the emergency rescue station; the related information includes: the method comprises the following steps of acquiring information of a position of a disaster site after an earthquake, generating speed of wounded persons at the disaster site, positions of alternative emergency rescue sites, positions of available hospitals, positions of potential medical centers, capacity of the emergency rescue sites, budget for constructing the emergency rescue sites, the number of emergency rescue vehicles, transportation time between each alternative emergency rescue site and the disaster site, and transportation time between the disaster site and the hospitals; the emergency rescue station is used for providing vehicles for a disaster site, and the vehicles are used for transporting wounded persons at the disaster site to a hospital or a medical center;
the model construction unit is used for constructing an emergency rescue site location and distribution model according to the related information so as to select a proper emergency rescue site to distribute a corresponding number of vehicles to each disaster-stricken point on the premise of considering both the waiting time of wounded persons at the disaster-stricken point and the time for transporting the vehicles of the emergency rescue site to the disaster-stricken point; the addressing and allocation model comprises: the average waiting time sum of wounded persons at all disaster-affected points and the time sum of vehicles at the emergency rescue station for transporting to all disaster-affected points; wherein the waiting time of the wounded is the time from the time of becoming the wounded at the disaster point to the time of being transported to the hospital or medical center;
the emergency rescue station site selection and distribution model comprises the following steps:
constraint conditions are as follows:
xit，zjtare all integers
Wherein the content of the first and second substances,
the average waiting time of the wounded at each disaster-affected point is the sum,
the total time for transporting the vehicle of the emergency rescue station to the disaster site is calculated; t represents the total cycle number, I represents the set of disaster-affected points, and J represents the set of emergency rescue stations; x is the number ofitThe number p of emergency rescue vehicles distributed to the disaster-affected point i at the moment t is representeditRepresenting the available probability of the emergency rescue vehicle at the disaster-affected point i at the moment t; lambda [ alpha ]it，μitRepresents the average generation rate and the average service rate of the wounded from the disaster-affected point i at the moment t, xijtIndicating whether the disaster-affected point i is distributed to the emergency rescue station j, tau at the moment tijThe distance between the disaster-affected point i and the emergency rescue station j is obtained; y isjtIndicating whether an emergency rescue site is opened at a potential emergency rescue site j at time t, fjRepresents the cost of setting up an emergency rescue site at a potential emergency rescue site j, MtRepresenting the total budget for opening the emergency rescue station at the moment t; k' represents the total number of emergency rescue vehicles; z is a radical ofjtRepresenting the quantity of emergency rescue vehicles distributed by the emergency rescue station j at the moment t; cjRepresents the maximum capacity of the emergency rescue station j; m is an integer;
the model solving unit is used for solving the site selection and distribution model by adopting a simulated annealing algorithm nested genetic algorithm; repeatedly solving the site selection and distribution model until the average waiting time of the wounded at each disaster point reaches a preset time range; outputting an emergency rescue site location and distribution scheme to determine the distribution relation between the emergency rescue sites and the disaster-affected points and the quantity of vehicles distributed to the disaster-affected points by each emergency rescue station; specifically, the address selection and distribution model is solved by the following steps:
step S31, optimizing and selecting the position of the emergency rescue station by adopting an outer-layer simulated annealing algorithm so as to meet the requirement of budget of the emergency rescue station after the earthquake;
the sum of the opening cost of all emergency rescue sites is smaller than the total budget of the emergency rescue sites;
step S32, optimizing and selecting the number of vehicles distributed by the emergency rescue station, the distribution relation between the disaster-affected points and the emergency rescue station and the number of emergency vehicles of the disaster-affected points by adopting an inner-layer genetic algorithm so as to meet the requirements of the capacity of the emergency rescue station and the total number of all vehicles of the emergency rescue station; and substituting the distribution result determined in the step S32 into the outer-layer simulated annealing algorithm in the step S31, and calculating an objective function of the emergency rescue station site selection and distribution model.
7. The emergency rescue site locating and distributing system of claim 6, wherein the emergency rescue site locating and distributing model satisfies the following preset conditions: the wounded is preferentially transported to the nearest hospital or medical center for treatment, and if the capacity of the nearest hospital or medical center is insufficient, the wounded is sent to the next nearest hospital or medical center.
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