Delivery plan generating device, delivery plan generating method, and delivery plan generating program

Abstract

A delivery plan generation device (10) is provided with an objective function generation unit (23), a constraint condition generation unit (24), and an optimization unit (25). The objective function generation unit generates an objective function including: a first primary coefficient associated with a first site; a second primary coefficient associated with a second site; and a secondary coefficient associated with a set of the first site and the second site. The constraint condition generation unit generates a constraint condition regarding a carrier for delivery of a resource to at least any of: the first site; the second site; and the first site and the second site. The optimization unit optimizes the objective function under the constraint condition.

Description

[Technical field]

[0001] The embodiments relate to a delivery plan generating device, a delivery plan generating method, and a delivery plan generating program. [Background technology]

[0002] A communication network is formed by a number of base stations. When some of the base stations become unable to communicate due to a disaster or the like, some of the functions of the communication network are lost. A base station that becomes unable to communicate can be restored to a state where communication is possible by delivering resources such as power. However, particularly during a disaster, there are often restrictions on the delivery of resources.

[0003] For this reason, an optimization problem has been studied that maximizes the degree to which the restoration of base stations contributes to the restoration of the communication network (relief scale) under constraints. As a method for quantitatively solving this optimization problem, a method for quantifying the relief scale has been proposed. [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] Kimihiko Fukami and 3 others, "A study on a method to grasp the service impact caused by network failures," IEICE Technical Report, February 28, 2019, vol. 118, no. 483, ICM2018-51, pp. 13-18 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the combinations of base stations to be restored exponentially increase according to the number of base stations. For this reason, it is not realistic to use the proposed method to calculate the quantified relief scale for all combinations within a limited time when a disaster occurs. Accordingly, it is also difficult to generate a resource delivery plan that can maximize the relief scale within a limited time. In other words, it is difficult to generate a delivery plan in a short time.

[0006] The present invention has been made in view of the above circumstances, and an object of the present invention is to provide a means for generating a delivery plan in a short period of time. [Means for solving the problem]

[0007] A delivery plan generating device according to one aspect includes an objective function generating unit, a constraint condition generating unit, and an optimization unit. The above for multiple locations including the first and second locations First base only To The second shows the degree of contribution by 1 First-order coefficient Item 1 based on , the above Second base only To The second shows the degree of contribution by 2 First-order coefficients Item 2 based on , and the pair of the first and second bases The contribution of Next coefficient 3 based on Objective function including and an objective function that indicates the total amount of relief by the resources delivered to the base by the carrier. The constraint condition generating unit generates Based on information on the carrier's location and resource supply capacity, and information on the locations of the plurality of bases and resource demands, the above Multiple locations The optimization unit optimizes the objective function under the constraints. Effect of the Invention

[0008] According to the embodiment, it is possible to provide a means for generating a delivery plan in a short time. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a schematic diagram illustrating an example of the configuration of a delivery system according to an embodiment. [Diagram 2]FIG. 2 is a diagram illustrating a schematic of features of a relief scale model according to an embodiment. [Diagram 3] FIG. 3 is a diagram illustrating a schematic diagram of a feature of a relief scale model according to an embodiment. [Figure 4] FIG. 4 is a diagram illustrating a schematic diagram of a feature of a relief scale model according to an embodiment. [Diagram 5] FIG. 5 is a block diagram showing an example of a hardware configuration of a delivery plan generating device according to an embodiment. [Figure 6] FIG. 6 is a block diagram illustrating an example of a functional configuration of a delivery plan generating device according to an embodiment. [Figure 7] FIG. 7 is a diagram illustrating an example of the configuration of delivery van information according to the embodiment. [Figure 8] FIG. 8 is a diagram illustrating an example of the configuration of the base station information according to the embodiment. [Figure 9] FIG. 9 is a flowchart showing an example of a delivery plan generating method in the delivery plan generating device according to the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, the embodiments will be described with reference to the drawings. In the following description, components having the same function and configuration are given the same reference symbol. In addition, when multiple components having the same reference symbol are to be distinguished from each other, they are distinguished from each other by an additional reference symbol (e.g., a hyphen and a number such as "-1") that is added following the common reference symbol.

[0011] 1. Embodiment 1.1 Configuration The configuration of the delivery system according to the embodiment will be described.

[0012] 1.1.1 Overall Configuration First, a configuration of a delivery system according to an embodiment will be described. Fig. 1 is a block diagram showing an example of the configuration of a delivery system according to an embodiment.

[0013] As shown in Fig. 1, the delivery system 1 is a system for supplying resources within a certain area using multiple delivery vehicles 4-1 and 4-2. The delivery system 1 includes a delivery support center 2, multiple delivery bases 3-1 and 3-2, and multiple base stations 5-1, 5-2, 5-3, 5-4, 5-5, and 5-6.

[0014] In the following, when there is no particular distinction between the multiple delivery bases 3-1 and 3-2, they may be referred to as "delivery bases 3." When there is no particular distinction between the multiple delivery vehicles 4-1 and 4-2, they may be referred to as "delivery vehicles 4." When there is no particular distinction between the multiple base stations 5-1 to 5-6, they may be referred to as "base stations 5."

[0015] The delivery support center 2 is a control center in the delivery system 1. The delivery support center 2 generates a delivery plan for comprehensively controlling the delivery of resources. The delivery support center 2 is configured to be wirelessly connected to the delivery base 3. As a result, the delivery support center 2 wirelessly transmits the generated delivery plan to the delivery base 3.

[0016] At least one delivery vehicle 4 is deployed at the delivery base 3. In the example of FIG. 1, delivery vehicles 4-1 and 4-2 are deployed at the delivery bases 3-1 and 3-2, respectively. In addition, resources to be delivered to the base station 5 are stored at the delivery base 3. Based on the delivery plan received from the delivery support center 2, the delivery base 3 instructs the delivery vehicle 4 to depart.

[0017] The delivery vehicle 4 is a carrier that delivers resources within the delivery base 3 where the delivery vehicle 4 is located to the base station 5 in accordance with various constraint conditions. The constraint conditions include, for example, the distance that the delivery vehicle 4 can travel, the time that the delivery vehicle 4 can travel, the amount of resources that can be carried, etc. The resources delivered by the delivery vehicle 4 include, for example, power for restoring a base station 5 that is in an uncommunicable state to a communication-enabled state.

[0018] The base station 5 is, for example, a base station of the communication network NW. The base station 5 provides communication services to users (not shown) located within a predetermined area centered on the base station itself. The base station 5 is also configured to communicate with other base stations 5 located within a predetermined area centered on the base station itself. In the example of FIG. 1, the base station 5-1 is placed in a position where it can communicate with the base stations 5-3 and 5-6. The base station 5-2 is placed in a position where it can communicate with the base stations 5-4 and 5-5. The base station 5-3 is placed in a position where it can communicate with the base stations 5-1 and 5-4. The base station 5-4 is placed in a position where it can communicate with the base stations 5-2 and 5-3. The base station 5-5 is placed in a position where it can communicate with the base stations 5-2 and 5-6. The base station 5-6 is placed in a position where it can communicate with the base stations 5-1 and 5-5. With the above configuration, a plurality of base stations 5 that can communicate with each other can form one huge communication network NW.

[0019] The base stations 5-1 to 5-6 include base stations that are not able to communicate and base stations that are able to communicate. In the example of FIG. 1, the base stations 5-1, 5-2, 5-3, and 5-4 are not able to communicate, and the base stations 5-5 and 5-6 are able to communicate. In this case, the communication network NW is limited to between the base stations 5-5 and 5-6 that are able to communicate. The base station 5 that is not able to communicate can be restored to a state where it is able to communicate by delivering resources. Therefore, the base station 5 that is not able to communicate is a candidate for a delivery destination to which resources are delivered by the delivery vehicle 4.

[0020] Next, a description will be given of the internal configuration of the delivery support center 2. The delivery support center 2 includes a relief scale model generating device 30 and a delivery plan generating device 10. The relief scale model generating device 30 and the delivery plan generating device 10 are, for example, computers.

[0021] The rescue scale model generating device 30 generates an approximate rescue scale model 30a. The approximate rescue scale model 30a is a rescue scale model for quantifying the rescue scale achieved by delivering resources to the base station 5. The rescue scale can be regarded as, for example, the contribution or influence of the restored base station 5 to the communication network NW. The rescue scale model generating device 30 transmits the generated approximate rescue scale model 30a to the delivery plan generating device 10.

[0022] The delivery plan generating device 10 generates a delivery plan that can maximize the relief scale under the constraint conditions based on the approximate relief scale model 30a received from the relief scale model generating device 30. The delivery plan generating device 10 transmits the generated delivery plan to the delivery base 3.

[0023] 1.1.2 Relief scale model Next, a relief scale model according to the embodiment will be described.

[0024] (Features) First, features of the relief scale model according to the embodiment will be described.

[0025] 2 is a diagram showing a typical feature of a rescue scale model according to an embodiment. In FIG. 2, as an example of a general feature of the rescue scale model, a manner in which the rescue scale changes with respect to the number of base stations 5 to be restored is shown.

[0026] As shown in FIG. 2, the rescue scale model is a concave function (or an upward convex function) with respect to the number of base stations 5 to be restored. Specifically, the rescue scale increases as the number of base stations 5 to be restored increases (i.e., as the number of base stations 5 in a communicable state increases). On the other hand, the increment in the rescue scale decreases as the number of base stations 5 to be restored increases. This characteristic indicates that when there are a large number of base stations 5 in a communicable state, the contribution of the base station 5 that has newly become communicable to the entire communication network NW is smaller than when there are a small number of base stations 5 in a communicable state.

[0027] In addition to the features shown in Fig. 2, the rescue scale model is required to be calculated according to the combination of base stations 5 to be restored. Figs. 3 and 4 are diagrams that typically show features of the rescue scale model according to the embodiment. Figs. 3 and 4 show the rescue scale when the base stations 5-1 to 5-6 are restored from a state in which only the base station 5-1 is capable of communication to a state in which two new base stations 5 are capable of communication.

[0028] As shown in FIG. 3, when the base stations 5-4 and 5-5 are restored, the number of base stations 5 in a communicable state increases to three, but the base stations 5-1, 5-4, and 5-5 cannot communicate with each other. On the other hand, as shown in FIG. 4, when the base stations 5-3 and 5-6 are restored, the number of base stations 5 in a communicable state is three, which is the same as in FIG. 3, but the base stations 5-1, 5-3, and 5-6 can communicate with each other. In this case, it is preferable that the rescue scale model is constructed so that the rescue scale corresponding to the case where the base stations 5-3 and 5-6 are restored is larger than the rescue scale corresponding to the case where the pair of base stations 5-4 and 5-5 is restored. In this way, it is preferable that the rescue scale model expresses the effect of the restoration of a certain base station 5 spreading to the surrounding base stations 5.

[0029] (General expression) Next, a general representation of the relief size model will be described.

[0030] Explanatory variables for the relief size model: X:=(x1,…,x n ) T , x i When the number of base stations 5 is n, ∈{0,1},∀i is expressed as the following formula (1).

[0031]

number

[0032] The relief scale model is expressed as the function f(X) using the explanatory variable X described above, as shown in the following equation (2).

[0033]

number

[0034] Here, n is an integer equal to or greater than 3. i, j, and k are integers equal to or greater than 1 and equal to or less than n. The first term on the right side of equation (2) indicates the sum of the contribution (relief scale) of each individual base station 5 to the communication network NW. The second term on the right side of equation (2) indicates the sum of the contribution of any combination of two of the base stations 5 to the communication network NW. The third term on the right side of equation (2) indicates the sum of the contribution of any combination of three of the base stations 5 to the communication network NW.

[0035] (Approximate representation) Next, an approximate expression format of the relief scale model will be described. The relief scale model expressed in an approximate format is less accurate than the relief scale model expressed in a general format, but can be calculated in a shorter time. The approximate relief scale model 30a generated by the relief scale model generating device 30 corresponds to the relief scale model expressed in this approximate format.

[0036] The remedy scale model generating device 30 generates an approximate remedy scale model 30a by considering up to the quadratic term of the function f(X). That is, the remedy scale model generating device 30 generates an approximate remedy scale model 30a by considering up to the quadratic term of the function f(X) shown in the following equation (3). - Consider (X) as the approximate relief size model 30a.

[0037]

number

[0038] In equation (3), the first coefficient α' in the first term on the right side and the second coefficient β' in the second term on the right side are parameters. That is, by determining the first coefficient α' and the second coefficient β', the approximate rescue scale model 30a becomes an explicit function. In other words, the rescue scale model generation device 30 determines the first coefficient α' and the second coefficient β' as the approximate rescue scale model 30a and transmits them to the delivery plan generation device 10.

[0039] The function g(|X|) in the third term on the right side of equation (3) is the function f - The function g(|X|) is a correction term to make g(X) a good approximation of the function f(X). - It also functions as a term added so that (X) is a concave function. Here, |X| is the L1 norm of explanatory variable X. The L1 norm of explanatory variable X is the sum of the variables x that have a value of 1 among explanatory variables X. i The function g(|X|) is expressed, for example, as the following equation (4) using a parameter γ.

[0040]

number

[0041] Here, |X^2| is the L1 norm of a vector whose components are the squares of the explanatory variable X.

[0042] By the above approximation, the rescue scale model generating device 30 can transmit the approximate rescue scale model 30a to the delivery plan generating device 10 through a generation process in a relatively short time. Note that any method can be applied to the method of generating the approximate rescue scale model 30a by the rescue scale model generating device 30 (i.e., the method of determining the primary coefficient α' and the secondary coefficient β'), so a description thereof will be omitted in this specification.

[0043] 1.1.3 Delivery plan generator Next, the configuration of the delivery plan generating device according to the embodiment will be described.

[0044] (Hardware configuration) 5 is a block diagram showing an example of a hardware configuration of a delivery plan generating device according to an embodiment. As shown in FIG. 5, the delivery plan generating device 10 includes a control circuit 11, a memory 12, a communication module 13, a user interface 14, and a drive 15.

[0045] The control circuit 11 is a circuit that overall controls each component of the delivery plan generating device 10. The control circuit 11 includes a CPU (Central Processing Unit), a RAM (Random Access Memory), and a ROM (Read Only Memory).

[0046] The memory 12 is an auxiliary storage device of the delivery plan generating device 10. The memory 12 includes, for example, a hard disk drive (HDD), a solid state drive (SSD), a memory card, etc. The memory 12 stores various information used in the delivery plan generating operation and a delivery plan generating program. The delivery plan generating program can be stored in the memory 12 by being transmitted from outside the delivery plan generating device 10 via a network (not shown).

[0047] The delivery plan generating operation is a series of operations executed to generate a delivery plan in the delivery plan generating device 10. The delivery plan generating program is a program for causing the delivery plan generating device 10 to execute the delivery plan generating operation. Details regarding the delivery plan generating operation will be described later.

[0048] The communication module 13 is a circuit used for transmitting and receiving data via a network. The communication module 13 is constructed in accordance with, for example, Ethernet (registered trademark).

[0049] The user interface 14 is a circuit for communicating information between a user and the control circuit 11. The user interface 14 includes input devices and output devices. The input devices include, for example, a touch panel and an operation button. The output devices include, for example, an LCD (Liquid Crystal Display) and an EL (Electroluminescence) display, and a printer. The user interface 14 converts input from the user (user input) into an electrical signal and then transmits it to the control circuit 11. The user interface 14 outputs the execution result of the delivery plan generation program received from the control circuit 11 to the user.

[0050] The drive 15 is a device for reading a program stored in a storage medium 16. The drive 15 includes, for example, a CD (Compact Disk) drive and a DVD (Digital Versatile Disk) drive.

[0051] The storage medium 16 is a medium that stores information such as a program by electrical, magnetic, optical, mechanical, or chemical action. The storage medium 16 may store a delivery plan generation program.

[0052] (Functional configuration) FIG. 6 is a block diagram illustrating an example of a functional configuration of a delivery plan generating device according to an embodiment.

[0053] The CPU of the control circuit 11 loads the delivery plan generation program stored in the memory 12 or the storage medium 16 into the RAM. The CPU of the control circuit 11 then interprets and executes the delivery plan generation program loaded into the RAM to control the memory 12, the communication module 13, the user interface 14, the drive 15, and the storage medium 16. As a result, as shown in Fig. 6, the delivery plan generation device 10 functions as a computer including an input unit 21, a storage unit 22, an objective function generation unit 23, a constraint condition generation unit 24, an optimization unit 25, a plan generation unit 26, and an output unit 27.

[0054] The input unit 21 stores various information in the storage unit 22 based on input from a user. The various information stored in the storage unit 22 includes, for example, delivery vehicle information 22a and base station information 22b. In addition, the input unit 21 stores an approximated remedy scale model 30a in the storage unit 22 based on input from the remedy scale model generating device 30.

[0055] 7 and 8 are diagrams showing examples of the configuration of the delivery vehicle information and base station information according to the embodiment, respectively.

[0056] 7, the delivery vehicle information 22a is information on the delivery vehicle 4 waiting at the delivery base 3. Specifically, the delivery vehicle information 22a includes information on the delivery vehicle ID, the departure position, the remaining fuel, and the remaining power.

[0057] The delivery vehicle ID uniquely identifies the delivery vehicle 4.

[0058] The departure position is the position information of the delivery base 3 where the delivery vehicle 4 is waiting. The departure position is expressed, for example, by two-dimensional coordinates (X, Y). The departure position may also be expressed by latitude and longitude.

[0059] The remaining time indicates the time available for delivery by the delivery vehicle 4. The delivery vehicle 4 can deliver resources to the base station 5 within the remaining time.

[0060] The remaining power indicates the amount of power that the delivery vehicle 4 can supply to the base station 5. The delivery vehicle 4 can supply power to the base station 5 within the range of the remaining power.

[0061] In the example of FIG. 7, the delivery plan generating device 10 can grasp from the delivery vehicle information 22a that the delivery vehicle 4-1 is waiting at the departure position (X1, Y1) with a remaining time T1 and remaining power C1, and that the delivery vehicle 4-2 is waiting at the departure position (X2, Y2) with a remaining time T2 and remaining power C2.

[0062] 8, the base station information 22b is information about the base station 5. Specifically, the base station information 22b includes information about a base station ID, a target position, and required power.

[0063] The base station ID uniquely identifies the base station 5 .

[0064] The target position is the position information of the base station 5. The target position is expressed, for example, by two-dimensional coordinates (x, y). The target position may be expressed by latitude and longitude, similar to the starting position.

[0065] The required power indicates the total amount of power required by the base station 5. The base station 5 can recover from a state in which communication is not possible to a state in which communication is possible by being supplied with an amount of power equivalent to the required power from the delivery vehicle 4.

[0066] 8, the delivery plan generating device 10 can determine from the base station information 22b that the base stations 5-1 to 5-6 are located at target positions (x1, y1) to (x6, y6), respectively. The delivery plan generating device 10 can also determine that the required power of the base stations 5-1 to 5-6 is W1 to W6, respectively.

[0067] Returning to FIG. 6 again, the functional configuration of the delivery plan generating device 10 will be described.

[0068] The objective function generator 23 generates an objective function based on the approximate repair scale model 30a. The objective function generator 23 transmits the generated objective function to the optimization unit 25.

[0069] Specifically, when the number of delivery vehicles 4 is m and the number of base stations 5 is n, the objective function generating unit 23 calculates the explanatory variable Y:=(y 11 ,…,y 1n ,y 21 ,…,y mn ) T , y qi Define ∈{0,1},∀i,q as in equations (5) and (6) below.

[0070]

number

[0071] Here, m is an integer equal to or greater than 2. q is an integer equal to or greater than 1 and equal to or less than m.

[0072] The objective function generator 23 uses the following equations (8) to (10) to generate a function f - (X) as function f - Read as (Y).

[0073]

number

[0074] That is, the objective function generating unit 23 generates the vector p and the matrices Q1 and Q2 in the above-mentioned equation (7). The objective function generating unit 23 transmits the generated vector p and the matrices Q1 and Q2 to the optimization unit 25.

[0075] The constraint condition generating unit 24 generates constraint conditions based on the delivery vehicle information 22a and the base station information 22b. The constraint condition generating unit 24 transmits the generated constraint conditions to the optimization unit 25.

[0076] Specifically, the constraint condition generating unit 24 generates a constraint condition as shown in the following formula (11) as a constraint condition regarding the remaining power.

[0077]

number

[0078] Furthermore, the constraint generating unit 24 generates a constraint condition as shown in the following formula (12) as a constraint condition regarding the remaining time.

[0079]

number

[0080] Here, the parameter R q_i0 represents the travel time from the departure point of the q-th delivery vehicle 4 to the i-th base station 5. The first term on the left side of equation (12) is doubled to express the time required for one delivery vehicle 4 to travel back and forth between the departure point and the base station 5. In addition, parameter d is a coefficient indicating the ratio of the time required for the delivery vehicle 4 to supply the amount of power it supplies to the base station 5.

[0081] That is, the constraint condition generating unit 24 calculates the parameters Wi and Cq in the above formula (11) and the parameter Rq_i0 , d, and T q The constraint generating unit 24 transmits various parameters to the optimizing unit 25.

[0082] The optimization unit 25 solves an optimization problem based on the objective function received from the objective function generation unit 23 and the constraint condition received from the constraint condition generation unit 24. Specifically, the optimization unit 25 solves the optimization problem shown in the following equation (13).

[0083]

number

[0084] The optimization unit 25 solves the above optimization problem by using, for example, the explanatory variable y qi By continuously relaxing y between 0 and 1, it can be solved as a convex optimization problem by applying existing solvers. qi By continuously relaxing between 0 and 1, the explanatory variable y qi The value of β' can be made to correspond to the ratio of the amount of resources that the qth delivery vehicle 4 delivers to the i-th base station to the amount of resources requested by the i-th base station. Here, β' ij =β' ji Since (i ≠ j), the matrix (Q1-γ / nQ2) is a symmetric matrix. Therefore, by defining the parameter γ so as to satisfy γ / n>0, the matrix -(2Q1-2γ / nQ2) is a positive semidefinite symmetric matrix. Therefore, the above formula (13) can be reduced to a convex quadratic optimization problem (or a convex quadratic programming problem) shown in the following formula (14). A solver using various solving methods such as the interior point method and the conjugate gradient method can be applied to a convex quadratic optimization problem.

[0085]

number

[0086] The optimization unit 25 transmits the optimized explanatory variable Y to the plan generation unit 26.

[0087] The plan generating unit 26 generates a delivery plan based on the explanatory variable Y received from the optimizing unit 25. The delivery plan includes information such as how much power is to be supplied to which base station 5, in what order, and by which delivery vehicle 4. The plan generating unit 26 transmits the generated delivery plan to the output unit 27.

[0088] The output unit 27 outputs the delivery plan received from the plan generating unit 26 to the delivery base 3 and the user.

[0089] With the above configuration, the delivery plan generating device 10 can generate a delivery plan based on the approximate repair scale model 30a. Then, the delivery base 3 can restore the communication network NW on the maximum scale based on the generated delivery plan.

[0090] 1.2. Operation Next, the operation of the delivery plan generating device according to the embodiment will be described.

[0091] 1.2.1 Dispatch plan generation operation 9 is a flowchart showing an example of a delivery plan generating operation in the delivery plan generating device according to the embodiment. In the example of FIG. 9, it is assumed that the delivery vehicle information 22a and the base station information 22b are stored in the memory 12 in advance by user input.

[0092] 9, when the approximate repair scale model 30a from the repair scale model generating device 30 is stored in the memory 12 (start), the objective function generating unit 23 generates an objective function based on the approximate repair scale model 30a (S10). That is, the objective function generating unit 23 generates a vector p and matrices Q1 and Q2 based on the first-order coefficient α' and the second-order coefficient β'.

[0093] The constraint condition generating unit 24 generates constraint conditions based on the delivery vehicle information 22a and the base station information 22b (S20). That is, the constraint condition generating unit 24 generates constraint conditions based on the starting position (X, Y), remaining time T, and remaining power C of each delivery vehicle 4, and the target position (x, y) and required power W of each base station 5, and calculates the parameters R q_i0, d, and T q It should be noted that the constraint condition generating unit 24 does not need to take into consideration the delivery vehicle 4 whose remaining power C is 0 and the base station 5 whose required power W is 0 in the constraint conditions.

[0094] The optimization unit 25 optimizes the explanatory variable Y based on the objective function generated in the process of S10 and the constraint condition generated in the process of S20 (S30).

[0095] The plan generating unit 26 generates a delivery plan based on the explanatory variable Y optimized in the process of S30 (S40).

[0096] The output unit 27 outputs the delivery plan generated in the process of S40 to the delivery base 3 and the user (S50).

[0097] When the process of S50 ends, the operation of generating a delivery plan ends (END).

[0098] 9, the case where the process of S20 is executed after the process of S10 has been described, but this is not limiting. For example, the process of S20 may be executed before the process of S10. Furthermore, the process of S20 may be executed in parallel with the process of S10.

[0099] 1.2.2 Specific examples Next, a specific example of the delivery plan generation operation shown in Fig. 9 will be described. In the specific example, the following conditions are assumed.

[0100] A delivery vehicle 4-1 located at the delivery base 3-1 and a delivery vehicle 4-2 located at the delivery base 3-2 are used (m=2). Here, the remaining time and remaining power of the delivery vehicles 4-1 and 4-2 are (T1, C1) = (50, 6) and (T2, C2) = (50, 8), respectively.

[0101] In the communication network NW, it is assumed that base stations 5-1 to 5-4 are in a state where communication is not possible (n=4). Here, the required power of base stations 5-1 to 5-4 is assumed to be W1=4, W2=6, W3=3, and W4=4, respectively. Note that base stations 5-5 and 5-6 are in a state where communication is possible, so the required power is assumed to be 0. In other words, base stations 5-5 and 5-6 are not taken into consideration in the optimization calculation.

[0102] The travel time between the delivery vehicles 4-1 and 4-2 and the base stations 5-1 to 5-4 is R 1_10 =16, R 2_10 =8, R 1_20 =12, R 2_20 =16, R 1_30 =14, R 2_30 =8, R 1_40 =18, R 2_40 =12.

[0103] The parameters γ and d are set to γ=4 and d=0.5, respectively.

[0104] The vector p and matrix Q1 are expressed by the following equations (15) and (16), respectively.

[0105]

number

[0106] Based on the above conditions, when solving the optimization problem for the explanatory variable Y that is continuously relaxed between 0 and 1, Y opt =(y 11 ,y 12 ,y 13 ,y 14 ,y 21 ,y 22 ,y 23 ,y 24 ) T =(0.44, 0.27, 0.38, 0.37, 0.56, 0.26, 0.62, 0.59) TThe above optimal solution means that two delivery vehicles 4-1 and 4-2 each travel to four base stations 5-1 to 5-4, and each supplies power according to a predetermined allocation.

[0107] As a result, the delivery plan generating device 10 can instruct a delivery plan that can restore the communication network NW to the maximum extent possible within the scope of limited resources.

[0108] 1.3 Effects of the embodiment According to the embodiment, the objective function generating unit 23 generates an objective function including a vector p and matrices Q1 and Q2 based on the quadratic approximated approximate rescue scale model 30a. The constraint condition generating unit 24 generates constraint conditions for the delivery vehicle 4 and the base station 5 based on the delivery vehicle information 22a and the base station information 22b. The optimization unit 25 optimizes the objective function under the constraint conditions. Here, the explanatory variable Y of the objective function and the constraint conditions means the ratio of the amount of resources delivered by the q-th delivery vehicle 4 to the i-th base station to the amount of resources required by the i-th base station. This makes it possible to obtain an optimal solution to the problem of which delivery vehicle 4 should deliver how much resources to which base station 5 with priority.

[0109] In addition, the objective function generation unit 23 includes the first and second order terms in formula (2) in the objective function, but does not include the third or higher order terms in formula (2). Therefore, the objective function generation unit 23 can generate an objective function with a smaller calculation load than when a third or higher order term is included. This allows the optimization unit 25 to perform optimization calculations with a smaller calculation load than when a third or higher order term is included. Therefore, a delivery plan can be generated in a short time.

[0110] In addition, the objective function generating unit 23 includes a term g(|X|)) related to a norm based on the explanatory variable Y in the objective function. This allows the objective function to reflect the nature of a concave function. Therefore, the optimizing unit 25 can execute an optimization calculation with a more accurate approximate repair scale model 30a as the objective function. Therefore, the delivery plan can be brought closer to the true optimal solution.

[0111] In addition, the constraint condition generating unit 24 sets, as a constraint condition, that the sum of the power delivered by the delivery vans 4 to the base station 5 is equal to or less than the remaining power C of each delivery van 4. This enables the optimizing unit 25 to perform optimization calculations taking into account the delivery capacity of the delivery vans 4.

[0112] In addition, the constraint condition generating unit 24 sets, as a constraint condition, that the sum of the time taken by the delivery vans 4 to deliver to the base station 5 is equal to or less than the remaining time T for each delivery van 4. This enables the optimizing unit 25 to perform optimization calculations taking into account the time that the delivery vans 4 can use for delivery.

[0113] Moreover, the objective function and the constraint conditions are a convex quadratic programming problem with respect to the explanatory variable Y. This allows the optimization unit 25 to apply an existing solver to the optimization calculation. This makes it possible to suppress the calculation load of the optimization calculation and reliably obtain an optimal solution. This makes it possible to generate a highly accurate delivery plan in a short time.

[0114] 2. Other It should be noted that various modifications can be applied to the above-described embodiment.

[0115] For example, in the above embodiment, the relief scale model generating device 30 and the delivery plan generating device 10 are configured by two computers, but this is not limited to the above. For example, the relief scale model generating device 30 and the delivery plan generating device 10 may be configured by one computer.

[0116] In addition, for example, in the above-described embodiment, the delivery plan generation program is executed by the delivery plan generation device 10 in the delivery support center 2, but the present invention is not limited to this. For example, the delivery plan generation program may be executed by a computing resource on the cloud.

[0117] Also, for example, in the above-mentioned embodiment, the case where n is an integer of 3 or more has been described, but this is not limited thereto. For example, n may be 2. In this case, the third term on the right side of the formula (2) is omitted.

[0118] In addition, for example, in the above-described embodiment, the delivery vehicle 4 is used as the carrier, but this is not limited to this. For example, any carrier such as a person, an airplane, a ship, etc. may be used as the carrier.

[0119] Also, for example, in the above embodiment, the case where the base station 5 in the communication network NW is restored has been described, but the present invention is not limited to this. For example, the target of restoration may be a base in any network other than a communication network that is configured by multiple bases that mutually affect each other. Also, the base may be a building.

[0120] The present invention is not limited to the above-mentioned embodiment, and can be modified in various ways without departing from the gist of the present invention. The embodiments may be combined as appropriate, and in that case, the combined effect can be obtained. Furthermore, the above-mentioned embodiment includes various inventions, and various inventions can be extracted by combinations selected from the multiple components disclosed. For example, if the problem can be solved and the effect can be obtained even if some components are deleted from all the components shown in the embodiment, the configuration from which the components are deleted can be extracted as an invention. [Explanation of symbols]

[0121] 1. Delivery system 2. Delivery Support Center 3-1, 3-2…Distribution base 4-1,4-2…Delivery vehicle 5-1,5-2,5-3,5-4,5-5,5-6…Base station 10...Distribution plan generator 11...Control circuit 12…Memory 13...Communication module 14...User Interface 15…Drive 16...Storage medium 21...Input section 22...Storage section 22a…Delivery vehicle information 22b…Base station information 23...Objective function generator 24…Constraint condition generator 25…Optimization section 26…Plan Generation Section 27...Output section 30...Relief scale model generator 30a…Approximate relief size model

Claims

1. An objective function generating unit that generates an objective function that indicates the sum of the relief scale of resources delivered to the bases by a carrier, the objective function including a first term based on a first primary coefficient indicating the contribution of only the first base, a second term based on a second primary coefficient indicating the contribution of only the second base, and a third term based on a secondary coefficient indicating the contribution of a set of the first base and the second base, for a plurality of bases including a first base and a second base; a constraint condition generating unit that generates constraint conditions for carriers for delivering resources to the plurality of bases based on information on the carrier's location and resource supply capacity and information on the locations of the plurality of bases and the requested amount of resources; an optimization unit that optimizes the objective function under the constraints; Equipped with Dispatch plan generator.

2. The explanatory variables of the objective function and the constraint condition include a first variable indicating a ratio of an amount of the resource delivered by the carrier to an amount required by the first base, and a second variable indicating a ratio of an amount of the resource delivered by the carrier to an amount required by the second base, The delivery plan generating device according to claim 1 .

3. the first term includes a product of the first linear coefficient and the first variable, the second term includes the product of the second linear coefficient and the second variable; the third term includes a product of the quadratic coefficient, the first variable, and the second variable. The delivery plan generating device according to claim 2.

4. The objective function further includes a fourth term: The fourth term includes a term related to a norm based on the explanatory variables. The delivery plan generating device according to claim 3.

5. the constraint condition includes at least one of a first condition and a second condition; the first condition includes a sum of a first amount of the resource delivered by the carrier to the first base and a second amount of the resource delivered by the carrier to the second base being equal to or less than a first threshold; the second condition includes that a sum of a first time required for the carrier to deliver the resource to the first base and a second time required for the carrier to deliver the resource to the second base is equal to or less than a second threshold; The delivery plan generating device according to claim 1 .

6. The objective function and the constraints are a convex quadratic programming problem with respect to the explanatory variables. The delivery plan generating device according to claim 2.

7. A delivery plan generating method in a delivery plan generating device, comprising: generating an objective function including a first term based on a first primary coefficient indicating a contribution degree only from the first base, a second term based on a second primary coefficient indicating a contribution degree only from the second base, and a third term based on a secondary coefficient indicating a contribution degree by a set of the first base and the second base, the objective function indicating a total sum of relief scales by resources delivered to the bases by a carrier; generating constraints for carriers delivering resources to the plurality of bases based on information on the carrier's location and resource supply capacity and information on the locations of the plurality of bases and resource demands; optimizing the objective function subject to the constraints; Equipped with How to generate a delivery plan.

8. A delivery plan generating program for causing a computer to function as each unit of the delivery plan generating device according to any one of claims 1 to 6.

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