Emergency response service location optimization system, service location optimization program, and service location optimization method

Abstract

To provide a service base location optimization system, a service base location optimization program, and a service base location optimization method for an emergency response service capable of realizing further optimization of service base location.SOLUTION: A service base location optimization system comprises an information input unit 101 for inputting information regarding demand and service bases, a derivation unit 102 for deriving, based on the information, an assignment ratio of service provision at each service base and location candidates for the service bases that can respond to the demand, and a service base location optimization unit 103 for calculating, as an optimal value, service base location information that minimizes the weighted average of travel time according to the assignment ratio, based on the assignment ratio and the location candidate. The information regarding the demand and the service bases includes information regarding a set of the location candidates for the service bases, information about a set of the demand, information on the travel time and distance from the location candidate to each demand point, information on a response rate, and information on the maximum number of the service bases to be located.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an emergency response service location optimization system, a service location location program, and a service location location method for optimizing the placement of stores, facilities, etc. that serve as service bases when providing emergency response services such as ambulance transport services and roadside assistance for vehicles. [Background technology]

[0002] For emergency response services that have been in operation for some time, such as ambulance transport services and roadside assistance for vehicles, it is desirable to arrive at the location of the person in need as quickly as possible.

[0003] In particular, in emergency response services such as ambulance-based emergency medical services, where service providers respond to requests from users, travel to the scene, and carry out life-saving activities, it is essential to arrive at the scene in the shortest possible time, as human lives may be at stake.

[0004] One effective strategy for providing emergency response services to users in the shortest possible time by utilizing a predetermined number of service locations is to optimize the placement of service locations.

[0005] Furthermore, the "p-median problem" has traditionally been used as one of the mathematical models for theoretically optimizing the placement of service locations.

[0006] Here, the "p-median problem" refers to the problem of arranging facilities such that the sum of the distances from the customers (demanders) to the facilities closest to them is minimized, given that a set of customers (demanders), a set of facilities (e.g., service locations or stores), and the number of facilities to be selected (p) are given as possible locations on points or edges in a graph composed of a set of points and a set of edges, or at any point in space.

[0007] In the "p-median problem", it is assumed that the nearest service base provides services in response to requests from service consumers. However, in reality, it is assumed that the nearest service base may not be able to respond to requests from consumers, and another base may be used to respond.

[0008] Also, when considering the probability (response rate) that a service base cannot respond to a request, cases where the response rate varies for each service base (for example, when the infrastructure such as personnel and service vehicles varies for each service base) are also considered.

[0009] Therefore, in order to further optimize the placement of service bases, it is necessary to consider expanding the mathematical model that takes into account the above-mentioned situations that can occur in reality.

[0010] Here, in the optimization of the placement of service bases for emergency response services, the following Non-Patent Documents 1 and 2 are cited as "extended models of the p-median problem" that consider cases where the nearest base cannot respond.

Prior Art Documents

Non-Patent Documents

[0011]

Non-Patent Document 1

Non-Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0012] However, different response rates for each service base are not considered even in the "extended model of the p-median problem" disclosed in Non-Patent Documents 1 and 2.

[0013] That is, at the time of filing this application, there is no "extended model of the p-median problem" that takes into account different response rates for each service base, and sufficient optimization of service base placement has not been achieved.

[0014] Therefore, the present invention has been made in view of the above problems, and an object thereof is to provide a service base placement optimization system for emergency response services, a service base placement optimization program, and a service base placement optimization method that can achieve further optimization of service base placement.

Means for Solving the Problems

[0015] A service base placement optimization system for emergency response services according to an aspect of the present invention is a service base placement optimization system that optimizes the placement of service bases that provide emergency response services to demands, and includes an information input unit that inputs information regarding the demands and the service bases, and a derivation unit that derives, based on the information, the sharing rate of service provision at each service base and the placement candidates of service bases that can respond to the demands. A service base placement optimization unit that calculates, as an optimum value, the placement information of service bases that minimizes the weighted average of travel times according to the sharing rate, based on the derived sharing rate and the placement candidates. The information regarding the demands and the service bases includes information regarding a set of placement candidates of the service bases, information regarding a set of the demands, information regarding travel times and travel distances from the placement candidates to each demand location, information regarding a response rate as an actual probability of response for each placement candidate, and information regarding the maximum number of the service bases to be placed. The gist is characterized by this.

[0016] Furthermore, the derivation unit may also determine the minimum weighted average of the travel time based on the service sharing ratio when multiple service locations share the responsibility of providing services for a single demand.

[0017] Furthermore, the derivation unit can derive the optimal value of the placement information based on the result of a formula based on a mathematical model that minimizes the weighted average of the travel time according to the distribution rate, with the weighted average of the travel time according to the distribution rate as the objective function.

[0018] Furthermore, the aforementioned share ratio can be constrained to be less than or equal to the aforementioned response rate of each service base.

[0019] Furthermore, another embodiment of the service base placement optimization program for emergency response services is a service base placement optimization program that runs on a computer and optimizes the placement of service bases that provide emergency response services in response to demand, comprising: an information input step of inputting information about the demand and the service bases; a derivation step of deriving the service provision share rate at each service base and candidate service bases that can respond to the demand based on the information; and a service base placement optimization step of calculating the optimal value of service base placement information that minimizes the weighted average of travel time based on the share rate, based on the derived share rate and the candidate placements, wherein the information about the demand and the service bases includes information about a set of candidate service base placements, information about a set of demands, information about travel time and travel distance from the candidate placements to each demand location, information about the response rate as the probability of actually being able to respond for each candidate placement, and information about the maximum number of service bases to be placed.

[0020] Furthermore, the derivation step may also involve determining the minimum weighted average of the travel time based on the service sharing ratio when multiple service locations share the responsibility of providing services to a single demand.

[0021] Furthermore, a method for optimizing the placement of service bases for emergency response services, according to another embodiment, is a method for optimizing the placement of service bases that provide emergency response services in response to demand, operated by a machine such as a computer, and comprising: an information input process for inputting information about the demand and the service bases; a derivation process for deriving the service provision share rate at each service base and candidate service bases that can respond to the demand based on the information; and a service base placement optimization process for calculating the optimal value of service base placement information that minimizes the weighted average of travel time based on the share rate, based on the derived share rate and the candidate placements, wherein the information about the demand and the service bases includes information about a set of candidate service base placements, information about a set of demands, information about travel time and travel distance from the candidate placements to each demand location, information about the response rate as the probability of actually being able to respond for each candidate placement, and information about the maximum number of service bases to be placed.

[0022] Furthermore, the derivation process can be used to determine the minimum weighted average of travel times based on the service sharing ratio when multiple service locations share the responsibility of providing services to a single demand. [Effects of the Invention]

[0023] According to the present invention, it is possible to provide a service base location optimization system, a service base location optimization program, and a service base location optimization method for emergency response services that can achieve further optimization of service base location. [Brief explanation of the drawing]

[0024] [Figure 1] This is a functional block diagram showing the functional configuration of a service base location optimization system for emergency response services according to an embodiment. [Figure 2] This is a schematic diagram of the service base layout when the mathematical model related to the comparative example is applied. [Figure 3]This is a schematic diagram of the service base arrangement when the mathematical model according to the present invention is applied. [Figure 4] This flowchart shows an example of the processing procedure for the service base location optimization process performed by the service base location optimization system for emergency response services according to the embodiment. [Figure 5] This is a schematic diagram illustrating an example of a hypothetical problem to which the present invention is applied. [Modes for carrying out the invention]

[0025] Referring to Figures 1 to 5, an emergency response service location optimization system S1 and a comparative example of the present invention will be described.

[0026] In the following drawings, identical or similar parts are denoted by the same or similar reference numerals.

[0027] (Outline configuration of the service base location optimization system) Referring to the functional block diagram in Figure 1, the general configuration of the service base location optimization system S1 for emergency response services according to this embodiment will be described.

[0028] The S1 system for optimizing the placement of service bases for emergency response services is realized through the collaboration of hardware such as general-purpose computers and servers with predetermined software (programs).

[0029] The service base location optimization system S1 for emergency response services according to this embodiment (hereinafter referred to as the service base location optimization system) is a system that optimizes the placement of service bases that provide emergency response services such as life-saving operations and accident handling to demands (demanders) such as emergency patients and traffic accidents.

[0030] As shown in Figure 1, the service base location optimization system S1 includes an information input unit 101 which consists of predetermined input devices for inputting information about demand and service bases.

[0031] Information regarding demand and service locations includes information on a set of potential service location locations (D1), information on a set of demand (D2), information on travel time and distance from the potential locations to each demand location (D3), information on the response rate R, which represents the probability that each potential location can actually respond (D4), and information on the maximum number of service locations to be deployed (D5).

[0032] Furthermore, the service base location optimization system S1 includes a derivation unit 102 that derives the service provision share W at each service base and candidate service base locations C that can respond to demand, based on the input information D1 to D5.

[0033] As shown in Figure 1, the derivation unit 102 consists of a distribution rate derivation unit 102a that derives the distribution rate W, and a location candidate derivation unit 102b that derives the location candidate C for service bases. Through the cooperation of these two units, the distribution rate W and the location candidate C for service bases are derived simultaneously and in parallel.

[0034] Furthermore, the service base placement optimization system S1 includes a service base placement optimization unit 103 that calculates optimal service base placement information that minimizes the weighted average of travel time based on the distribution rate W, based on the derived distribution rate W and the placement candidate C.

[0035] Furthermore, the service base location optimization system S1 includes a display unit 104, which consists of a display that shows the calculated service base location information as, for example, a 2D map or a 3D map.

[0036] Furthermore, the service base location optimization system S1 includes a communication unit 105 that transmits the calculated service base location information C to an external device 200 consisting of a server and external terminal devices via a wireless network N or the like.

[0037] This configuration allows for further optimization of service location placement, taking into account factors such as the response rate R.

[0038] In addition, Service site location optimization unit 103 In cases where multiple service locations share the responsibility of providing a service for a single demand, the minimum weighted average of travel times can be determined based on the sharing rate W.

[0039] This allows for further optimization of service base locations based on the service share ratio W.

[0040] Also, Service site location optimization unit 103 This method uses the weighted average of travel times based on the distribution rate W as the objective function, and is formulated based on a mathematical model (described later as Mathematics II; further details will be provided later) that minimizes the weighted average, thereby deriving the optimal value of the placement information.

[0041] (Regarding comparative examples of mathematical models) Before describing the mathematical model (Equation 2) applied to the present invention, we will first describe the mathematical model (Equation 1) as a comparative example that served as the basis for the inventor's creation of mathematical model (Equation 2).

[0042]

number

[0043] The mathematical model (Equation 1) relating to the comparative example is a theoretically valid method that can be generally considered when it is necessary to take into account the response rate for each facility.

[0044] The mathematical model related to Mathematics 1 is composed of equations (a) to (f).

[0045] Equation (a) of the mathematical model related to Mathematics 1 minimizes the expected value of travel time, with the Bernoulli probability of whether a certain facility s can meet a given demand being the rate of response for each facility.

[0046] In equations (a) to (f), the symbol s is an index indicating a service base (facility), and the symbol S represents a set of candidate facilities (a set of candidate locations for service bases).

[0047] Furthermore, the symbol v is an index representing demand, and the symbol V represents a set of demands.

[0048] Also, the symbol T sv Let v be the travel distance from facility s to demand v, which is given (pre-determined).

[0049] Also, the symbol a sv The probability (or rate) of matching facility s is given (pre-determined).

[0050] Also, symbol b v Let v be a value representing the quantity of demand, and assume it is given (pre-determined).

[0051] Furthermore, the symbol P represents the maximum number of facilities (service bases) to be deployed, which is given (pre-determined).

[0052] Also, the symbol W p sv This variable indicates the p-th facility allocated to demand v. It takes the value "1" if facility s is allocated the p-th facility to demand v, and "0" otherwise.

[0053] Also, the symbol Z s This variable indicates whether or not facility s is placed; it takes the value "1" if facility s is placed, and "0" otherwise.

[0054] Here, equation (a) is the objective function, which minimizes the expected value of travel time.

[0055] Furthermore, equation (b) guarantees that the number of facilities to be deployed is less than or equal to P.

[0056] Furthermore, equation (c) guarantees that there is only one facility allocated to the p-th position for a given demand v.

[0057] Moreover, Equation (d) ensures that the facilities assigned to demand v are the facilities to be deployed and are different for all p.

[0058] Note that the above S, V, T sv , a sv , and P are essential input elements.

[0059] Referring to the schematic diagram shown in FIG. 2, an example of service base placement when applying the mathematical model according to the comparative example will be described.

[0060] In this example, it is assumed that it is applied to three service bases (facilities) A to C.

[0061] In this example, the response rate of facility A is a A , the response rate of facility B is (1 - a A )a B , and the response rate of facility C is (1 - a A )(1 - a B )a C as shown.

[0062] Also, the travel distance to demand v is T for facility A sAv , T for facility B sBv , and T for facility C sCv as shown.

[0063] That is, the expected value of the travel distance to demand v is T sAv a A + T sBv (1 - a A )a B + T sCv (1 - a A )(1 - a B )a C as expressed.

[0064] To explain a more specific hypothetical problem, for example, we envisioned a hypothetical problem where, using past rescue request reception data for emergency road services for large vehicles, we had to select a limited number of facilities from a group of candidate facilities (a group of service bases) such that the average travel time from the time a rescue request is received until the service vehicle arrives at the scene (demand) is minimized.

[0065] Further details of the above hypothetical problem will be explained in the embodiments of the present invention described later.

[0066] Then, a program (software) was created that applied a mathematical model related to Mathematics 1, and 10 trials (simulations) were performed by randomly selecting input data (200 trouble data points and 12 facility data points) from pre-prepared trouble data and facility data.

[0067] As a result, the same combination of three facilities was selected in 8 out of 10 trials (for example, as shown in the schematic diagram in Figure 2), but the average calculation time for this comparative example was found to be a relatively long 771.7 seconds.

[0068] Thus, when applying the mathematical model related to the comparative example, if the number of facilities selected for optimization is p, the objective function is expressed as a nonlinear equation of order p, as shown in Equation 1. This presents the challenge of requiring relatively large computational resources and a relatively long computation time for solving the problem.

[0069] Therefore, in order to resolve the problems of the above comparative example, the inventors continued their research and, as a result, devised a new mathematical model (Equation 2) applicable to the present invention.

[0070] (Regarding the examples) Here, we will explain in detail the mathematical model (Equation 2) used in the embodiment of the present invention.

[0071]

number

[0072] The mathematical model related to Mathematics II consists of equations (1) to (6).

[0073] The mathematical model related to Mathematics 2 assumes that multiple locations share the responsibility of providing a service for a single demand, and aims to minimize the weighted average of travel times based on the sharing ratio.

[0074] Here, the mathematical model related to Mathematics II is a model that can reduce the computational cost of solving because the objective function (equation (1)) is linear.

[0075] In equations (1) to (6), the symbol s is a subscript indicating a service base (facility), and the symbol S represents a set of candidate facilities (a set of candidate locations for service bases).

[0076] Furthermore, the symbol v is an index representing demand, and the symbol V represents a set of demands.

[0077] Also, the symbol T sv Let v be the travel distance from facility s to demand v, which is given (pre-determined).

[0078] Also, the symbol a sv The probability (or rate) of matching facility s is given (pre-determined).

[0079] Also, symbol b v Let v be a value representing the quantity of demand, and assume it is given (pre-determined).

[0080] Furthermore, the symbol P represents the maximum number of facilities (service bases) to be deployed, which is given (pre-determined).

[0081] Also, the symbol W sv This variable indicates the facilities allocated to the demand v, as well as the allocation share, where 0 ≤ W sv It takes continuous values ​​≤ 1.

[0082] Also, the symbol Z sThis variable indicates whether or not facility s is placed; it takes the value "1" if facility s is placed, and "0" otherwise.

[0083] Note that the above S, V, T sv a sv P is an essential input element.

[0084] Here, equation (1) is the objective function, which minimizes the weighted average based on the proportion of travel time shared.

[0085] Furthermore, equation (2) guarantees that the number of facilities to be deployed is less than or equal to P.

[0086] Furthermore, equation (3) guarantees that the sum of the facility allocation rates for demand v equals "1".

[0087] Furthermore, equation (4) guarantees that the facilities allocated to demand v are the facilities that will be deployed.

[0088] Furthermore, equation (5) is the share of the facilities s allocated to the demand a sv This guarantees that the following applies:

[0089] Furthermore, by imposing a constraint (Equation 5) that the allocation rate must be less than or equal to the response rate for each facility, the weighted average of travel times becomes very close to the expected value of travel time in the mathematical model in the comparative example, thus yielding the same or very similar results as the comparative example.

[0090] Here, with reference to the schematic diagram shown in Figure 3, an example of service base arrangement when applying the mathematical model (Equation 2) in the present invention will be explained.

[0091] This example assumes the application to three service locations (facilities) A ​​through C.

[0092] In this example, facility A's share is Ws A v(≦a A), Facility B's share is Ws B v(≦a B ), the share of facility C is Ws C v(≦a C This is shown by ).

[0093] Furthermore, the distance traveled to demand v is T for facility A. sAv Regarding facility B, T sBv Regarding facility C, T sCv This is shown.

[0094] In other words, the weighted average of the distance traveled relative to demand v is T sAv W sAv +T sBv W sBv +T sCv W sCv It is represented as follows.

[0095] Then, we created a program (software) that applies the mathematical model related to Equation 1, enabling the execution of the service base location optimization process shown in Figure 4.

[0096] More specifically, the model calculations were performed using the pyomo package (a package for solving optimization problems) of Python®, a type of programming language.

[0097] Furthermore, GLPK was used as the MIP solver, and IPOPT was used as the NLP solver (nonlinear solver).

[0098] (Regarding the optimization process for service location placement) Referring to the flowchart in Figure 4, an example of the processing procedure for service site location optimization by the service site location optimization system S1, which can execute a program (software) that applies the mathematical model related to Equation 2, will be explained.

[0099] When this process begins, the first step, S10, is to input information regarding demand and service locations (facilities).

[0100] Specifically, information regarding a set of potential service location locations (D1), information regarding a set of demands (D2), information regarding travel time and travel distance from the location candidates to each demand location (D3), information regarding the response rate (D4), and information regarding the maximum number of service locations (D5) are input via a predetermined input device, and the process proceeds to step S11.

[0101] In step S11, the mathematical optimization model shown in equation 2 above is applied to derive the service provision share W at each service site and the candidate C for the placement of service sites that can meet the demand.

[0102] The service provision

[0103] Next, the process moves to step S12, where the optimal value for service base placement is calculated based on the allocation rate W and placement candidate C, and then the process moves to step S13.

[0104] In step S13, the optimized placement information D10 is displayed on a display unit 104 such as a liquid crystal display, or output to an external device 200 such as a server via a communication unit 105, and the process ends.

[0105] This allows for the optimization of service location deployment in a shorter time compared to the comparative example.

[0106] (Regarding hypothetical problems) Now, referring to Figure 5, we will describe a hypothetical problem set up as an example to which the service site placement optimization process according to this embodiment should be applied.

[0107] As shown in Figure 5, consider a case where an emergency road service is deployed for a specific type of tire (for example, a tire for a special vehicle) on the major expressways R1-R5 between Tokyo and Osaka. In the example shown in Figure 5, there are 12 potential facilities (stores) that can provide road service, designated as SC1 to SC12, and each is assumed to have historical response rate data.

[0108] Then, referring to data extracted from 200 locations T where past troubles occurred, the hypothetical problem was to select a combination of three facilities from the 12 facilities SC1 to SC12 that would minimize the travel time from the rescue request to the arrival of the service vehicle at the trouble site.

[0109] Please note that Figure 5 is merely a schematic diagram, and not all 200 trouble locations T are plotted.

[0110] Furthermore, it is assumed that travel time between all candidate facilities (stores) and all trouble locations T is provided.

[0111] Then, for this hypothetical problem, a service site placement optimization processing program (software) that applies a mathematical model related to Mathematics II was executed, and 10 trials (simulations) were performed by randomly selecting input data (trouble data: 200, facility data: 12) from the available trouble data and facility data.

[0112] As a result, the same combination of three facilities (stores) was selected in 8 out of 10 trials (for example, the result shown in the schematic diagram in Figure 3), and the average calculation time was 9.5 seconds.

[0113] As mentioned above, the average calculation time for a similar hypothetical problem in the comparative example using the mathematical model of Equation 1 was 771.7 seconds. Therefore, the service site location optimization process using the mathematical model of Equation 2 in this embodiment is approximately 81 times faster, demonstrating a dramatic reduction in processing time.

[0114] Thus, the objective function of the mathematical model related to Mathematics 2 can be expressed as a linear equation regardless of the number of facilities selected for optimization, and it can be seen that the computation time for solving is significantly smaller compared to the mathematical model of the comparative example (Mathematics 1).

[0115] This will enable the provision of more rapid emergency response services, such as life-saving and accident cleanup.

[0116] Furthermore, travel times in the model can also be expressed using various distances (route distances based on roads, Euclidean distances on maps, and other distances defined by various methods).

[0117] Furthermore, in the mathematical model (Equation 2) of this invention, there may be cases where no feasible solution exists when the correspondence rate for most facilities is low. In such cases, a feasible solution can be obtained by adjusting the right-hand side of equation (3) in Equation 2 from "1" to an appropriate value between "0 and 1".

[0118] The service base location optimization system, service base location optimization program, and service base location optimization method for emergency response services of the present invention have been described above based on the illustrated embodiments. However, the present invention is not limited thereto, and the configuration of each part can be replaced with any configuration having a similar function.

[0119] For example, in the present invention, the processing device (computer, etc.) that executes the service site location optimization processing program to which the mathematical model related to Equation 2 is applied is not limited to a von Neumann type computer, but may be a non-von Neumann type computer such as a quantum computer. [Explanation of Symbols]

[0120] S1 Service Site Location Optimization System S1 101 Information Input Section 102 Derivation part 102a Sharing rate derivation part 102b Placement candidate derivation unit 103 Service Site Placement Optimization Department 104 Display section 105 Communications Department 200 External device

Claims

1. A service base location optimization system that optimizes the placement of service bases to provide emergency response services in response to demand, An information input unit for inputting information regarding the aforementioned demand and the aforementioned service bases, Based on the aforementioned information, the derivation unit derives the service provision share at each service base and candidate service base locations that can respond to the aforementioned demand. A service base placement optimization unit calculates optimal service base placement information that minimizes the weighted average of travel time based on the derived distribution rate and the candidate placements, Equipped with, An emergency response service location optimization system characterized in that the information relating to the demand and the service locations includes information relating to a set of candidate locations for the service locations, information relating to a set of demands, information relating to travel time and travel distance from the candidate locations to each demand location, information relating to the response rate as the probability of actually being able to respond for each candidate location, and information relating to the maximum number of service locations to be deployed.

2. The service base placement optimization unit is characterized in that, when multiple service bases share the responsibility of providing a service for a single demand, it determines the minimum value of the weighted average of the travel time based on the responsibility ratio, as described in claim 1 for the service base placement optimization system for emergency response services.

3. The service base placement optimization system for emergency response services according to claim 2, characterized in that the service base placement optimization unit derives the optimal value of the placement information based on the following result formulated based on a mathematical model that minimizes the weighted average of travel time according to the distribution rate, with the weighted average of travel time according to the distribution rate as the objective function.

4. The service base placement optimization system for emergency response services according to claim 3, characterized in that the aforementioned share ratio is constrained to be less than or equal to the aforementioned response rate of each service base.

5. A service location optimization program, which is run on a computer and optimizes the placement of service locations to provide emergency response services in response to demand, An information input step in which information regarding the aforementioned demand and the aforementioned service base is entered, Based on the aforementioned information, a derivation step is taken to derive the service provision share at each service base and candidate service base locations that can respond to the aforementioned demand. A service base placement optimization step that calculates optimal service base placement information that minimizes the weighted average of travel time based on the derived distribution rate and the candidate placement, It has, An emergency response service location optimization program characterized in that the information relating to the demand and the service locations includes information relating to a set of candidate locations for the service locations, information relating to a set of demands, information relating to travel time and travel distance from the candidate locations to each demand location, information relating to the response rate as the probability of actually being able to respond for each candidate location, and information relating to the maximum number of service locations to be deployed.

6. The service base location optimization step is characterized in that, when multiple service bases share the responsibility of providing a service for a single demand, the minimum value of the weighted average of the travel time is determined based on the responsibility ratio, as described in claim 5, for the service base location optimization step.

7. A method for optimizing the placement of service bases, which are operated by machines such as computers and provide emergency response services in response to demand, The aforementioned computer or other machine performs an information input process in which it inputs information regarding the demand and the service bases, The aforementioned computer or other machine derives, based on the aforementioned information, the service provision sharing ratio at each service base and candidate service base locations capable of responding to the aforementioned demand, in a derivation process, A service base placement optimization process in which the aforementioned computer or other machine calculates optimal service base placement information that minimizes the weighted average of travel time based on the derived distribution rate and the placement candidates, It has, A method for optimizing the placement of service bases for emergency response services, characterized in that the information relating to the demand and the service bases includes information relating to a set of candidate locations for the service bases, information relating to a set of demands, information relating to travel time and travel distance from the candidate locations to each demand location, information relating to the response rate as the probability of actually being able to respond for each candidate location, and information relating to the maximum number of service bases to be placed.

8. The method for optimizing the location of service bases for emergency response services according to claim 7, characterized in that, in the service base location optimization process, when multiple service bases share the responsibility of providing services for a single demand, the machine such as a computer determines the minimum value of the weighted average of the travel time based on the responsibility sharing rate.

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