Material supply support device, material supply support method, and material supply support program

Abstract

An embodiment of the present invention is a material supply support device that supports the service of sequentially supplying materials to a plurality of points. This material supply support device is provided with: a first processing unit that acquires parameter information related to the supply of materials to a plurality of points and including the importance of each point; a second processing unit that, on the basis of the parameter information, calculates a weighted time to depletion for each point, which is the time that it takes the resources at the point to be depleted and reflects the importance of the point; a third processing unit that sets, as an objective function, a weighted maximum time to depletion, which is the largest of the values of the weighted times to depletion, solves an optimization problem that minimizes the objective function by a branch-and-bound method, and thereby determines an optimal route that minimizes the maximum time to depletion for each point; and a fourth processing unit that generates and outputs support information including information that represents the optimal route.

Description

[Technical Field]

[0001] One aspect of the present invention relates to a materials supply support device, a materials supply support method, and a materials supply support program used to support the task of supplying materials to a plurality of locations. [Background technology]

[0002] The delivery and supply of resources, goods, and other materials to multiple locations by vehicle is essential in modern society. In particular, in the event of a large-scale disaster, the transport and supply of necessary materials such as power supply equipment, water, and food to multiple demand points by vehicle is extremely important in preventing the depletion of supplies at each location. Therefore, it is necessary to search for routes that minimize the time it takes for resources to be depleted at demand points.

[0003] A well-known method for selecting such a route is to use the branch and bound algorithm, which is often used to find an exact solution to the vehicle routing problem (VRP).

[0004] For example, Non-Patent Document 1 proposes a technology that uses the "maximum time for resources to be depleted at each demand point" (hereinafter referred to as "maximum depletion time") as an objective function, and solves an optimization problem that minimizes this objective function using a branch-and-bound method to search for a route that minimizes the maximum depletion time at each demand point. [Prior art documents] [Non-patent literature]

[0005] [Non-Patent Document 1] M. Ogawa et al. "A route searching method using two-dimensional coordinates," IEICE, DOI:10.34385 / proc.63.E4-3, 2020. Summary of the Invention [Problem to be solved by the invention]

[0006] In some cases, multiple demand points are assigned a level of importance according to the size of the demand point. In this case, the more important the demand point, the shorter the time it takes for the supply to run out.

[0007] In the technique proposed in Non-Patent Document 1, in the relaxation problem used in the bounding operation of the branch and bound method, multiple demand points are treated equally, and the importance of each demand point is not taken into consideration.

[0008] This invention has been made in light of the above circumstances, and its purpose is to provide a technology for determining an optimal route that minimizes the maximum depletion time while taking into account the importance of each demand point. [Means for solving the problem]

[0009] One aspect of the present invention is a materials supply support device that supports the task of sequentially supplying materials to a plurality of locations. The materials supply support device includes a first processing unit that acquires parameter information related to the supply of materials to the plurality of locations and including the importance of each location, a second processing unit that calculates a weighted depletion time, which is the time it takes for resources to be depleted at each location, based on the parameter information and reflecting the importance of each location, a third processing unit that determines an optimal route that minimizes the maximum depletion time at each location by using a branch-and-bound method to solve an optimization problem that minimizes an objective function, where the weighted maximum depletion time, which is the maximum value of the weighted depletion times, is used as the objective function, and a fourth processing unit that generates and outputs support information including information representing the optimal route. The parameter information includes the remaining time and the expected arrival time at each point in addition to the importance. i Weighted exhaustion time T i (r) is calculated using the following formula.

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[0010] One aspect of the present invention is a materials supply support method executed by a materials supply support device that supports the task of sequentially supplying materials to a plurality of locations. The materials supply support method includes a first step of acquiring parameter information related to the supply of materials to the plurality of locations and including the importance of each location, a second step of calculating a weighted depletion time, which is the time it takes for resources to be depleted at each location, reflecting the importance of each location based on the parameter information, a third step of determining an optimal route that minimizes the maximum depletion time at each location by using a branch-and-bound method to solve an optimization problem that minimizes an objective function, where the weighted maximum depletion time is the maximum value of the weighted depletion times, and a fourth step of generating and outputting support information including information representing the optimal route. The parameter information includes the remaining time and expected arrival time at each point, in addition to the importance. i Weighted exhaustion time T i (r) is calculated using the following formula.

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[0011] One aspect of the present invention is a method for supplying a material supply support device, comprising: process The material supply support program causes a processor included in the material supply support device to execute the above. [Effects of the Invention]

[0012] According to one aspect of the present invention, a technique is provided for determining an optimal route that minimizes the maximum depletion time while taking into consideration the importance of each demand point. [Brief explanation of the drawings]

[0013] [Figure 1] FIG. 1 is a block diagram showing an example of the hardware configuration of a materials supply support device according to an embodiment of the present invention. [Figure 2] FIG. 2 is a block diagram showing an example of the software configuration of the materials supply support device according to an embodiment of the present invention. [Figure 3] FIG. 3 is a flowchart showing an example of the overall processing procedure and processing content of the materials supply support control executed by the control unit of the materials supply support apparatus shown in FIG. [Figure 4] FIG. 4 is a flowchart showing an example of the processing procedure and processing contents of the optimum route determination processing shown in FIG. [Figure 5] FIG. 5 is a flowchart showing an example of the processing procedure and processing contents of the lower limit value calculation processing shown in FIG. [Figure 6] FIG. 6 is a diagram schematically illustrating an example of a route from a start point to an end point that can be counted in the lower limit calculation process shown in FIG. 5 when the number of vertices (demand points) is sufficiently small. [Figure 7] FIG. 7 is a diagram schematically illustrating an example of routes near the end points that are listed when the number of vertices (demand points) is not sufficiently small in the lower limit calculation process shown in FIG. [Figure 8] FIG. 8 is a diagram illustrating an example of travel times between a plurality of vertices (demand points). [Figure 9] FIG. 9 is a diagram showing an example of the arrival time and weighted exhaustion time at each vertex and the lower limit of the weighted maximum exhaustion time in the optimal path when the number of vertices is sufficiently small. [Figure 10] FIG. 10 is a diagram schematically illustrating an example of an optimum route determined when the number of vertices (demand points) is sufficiently small. [Figure 11]FIG. 11 is a diagram showing an example of a part of an optimum route near the final point determined when the number of vertices (demand points) is large. DETAILED DESCRIPTION OF THE INVENTION

[0014] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0015] [One embodiment] (Configuration example) The materials supply support device SV according to one embodiment of the present invention is, for example, a server computer that is deployed on the web or in the cloud. The materials supply support device SV may also be a personal computer dedicated to the use of a local government or a delivery company.

[0016] 1 and 2 are block diagrams showing an example of the hardware and software configurations of a materials supply support device SV, respectively.

[0017] The materials supply support device SV includes a control unit 1 that uses a hardware processor such as a central processing unit (CPU). A storage unit having a program storage unit 2 and a data storage unit 3, and a communication interface (I / F) unit 4 are connected to the control unit 1 via a bus 5.

[0018] Under the control of the control unit 1, the communication I / F unit 4 uses a communication protocol defined by a network NW including the Internet to send and receive information data, for example, between a management terminal used by a local government or delivery company, or between a vehicle terminal installed in a vehicle used as a transportation medium.

[0019] The program storage unit 2 is configured, for example, by combining a non-volatile memory such as an HDD (Hard Disk Drive) or SSD (Solid State Drive) as a storage medium that can be written to and read at any time, with a non-volatile memory such as a ROM (Read Only Memory), and stores application programs necessary to execute various control processes according to one embodiment of the present invention, in addition to middleware such as an OS (Operating System).

[0020] The data storage unit 3 is, for example, a combination of a non-volatile memory such as an HDD or SSD as a storage medium that can be written to and read from at any time, and a volatile memory such as RAM (Random Access Memory), and is provided with a parameter information storage unit 31 and an optimal route information storage unit 32 as storage areas in one embodiment.

[0021] The parameter information storage unit 31 is used to store parameter information input from a management terminal of a local government or delivery company or a vehicle terminal installed in a vehicle. The parameter information includes multiple locations to which supplies are to be delivered and attribute information related to the delivery of supplies between those locations. The attribute information includes, for example, location information for each location, estimated travel time between each location, estimated arrival time at each location, estimated stay time at each location, importance of each location, remaining time at each location, etc.

[0022] The optimum route information storage unit 32 is used to store optimum route information that indicates the optimum delivery route determined by the control unit 1.

[0023] The control unit 1 includes, as processing functions according to an embodiment of the present invention, a parameter information acquisition processing unit 11, a weighted exhaustion time calculation processing unit 12, an optimal route determination processing unit 13, and a support information output processing unit 14. These processing units 11 to 14 are all realized by causing a hardware processor in the control unit 1 to execute an application program stored in the program storage unit 2.

[0024] Note that part or all of the processing units 11 to 14 may be realized using hardware such as an LSI (Large Scale Integration) or an ASIC (Application Specific Integrated Circuit).

[0025] Prior to the delivery of goods, the parameter information acquisition processing unit 11 receives parameter information sent via the network NW from, for example, a management terminal of a local government or delivery company, using the communication I / F unit 4, and performs processing to store the received parameter information in the parameter information storage unit 31.

[0026] The weighted depletion time calculation processing unit 12 performs a process of calculating a maximum depletion time that reflects the importance of each point, based on the importance of each point included in the parameter information stored in the parameter information storage unit 31. In the following description, the maximum depletion time that reflects the importance is referred to as a weighted maximum depletion time. An example of the calculation process of this weighted maximum depletion time will be described in detail in an operation example.

[0027] The optimum route determination processing unit 13 determines an optimum delivery route for a set of multiple locations that minimizes the supply depletion time of the material based on the weighted maximum supply depletion time calculated by the weighted supply depletion time calculation processing unit 12, and stores information representing the determined optimum route in the optimum route information storage unit 32. An example of this optimum route determination processing will also be described in detail in the operation example.

[0028] The support information output processing unit 14 generates information to support the supply of goods, including optimal route information stored in the optimal route information storage unit 32, and performs processing to transmit the generated support information from the communication I / F unit 4 to, for example, a management terminal of a local government or delivery company, or a terminal installed in a delivery vehicle.

[0029] (Example of operation) Next, an example of the operation of the supplies supply support device SV configured as above will be described with reference to Fig. 3. Fig. 3 is a flow chart showing an example of the supplies supply support operation executed by the control unit 1 of the supplies supply support device SV.

[0030] (1) Obtaining parameter information When a disaster occurs, for example, a local government or a delivery company identifies all points where supplies need to be supplied, and inputs parameter information including the location information of each point, the estimated travel time between each point, the estimated stay time at each point, and the importance of each point, into, for example, a management terminal.The parameter information is then transmitted from the management terminal to the supplies supply support device SV via the network NW.

[0031] In response to this, in step S10, the control unit 1 of the material supply support device SV receives the parameter information sent from the management terminal via the communication I / F unit 4 under the control of the parameter information acquisition processing unit 11, and stores the received parameter information in the parameter information storage unit 31.

[0032] Now, consider a two-dimensional plane that shows the disaster area. For this two-dimensional plane, we define various parameter information as follows. There are (n+1) vertices on the two-dimensional plane, and these vertices are called v i ,i∈{0,1,…,n},n∈Z. Here, vertex v i represents a demand point. In other words, in the following explanation, "vertex" can be read as "demand point." Also, vertex v i and vertex v j Estimated travel time t(v i ,v j )∈R >0 , vertex v i The remaining time d at time t=0 i ∈R >0 , the expected vehicle stay time p required at each vertex for supplying goods i ∈R >0 , the importance of each vertex w i∈ R >0 is given.

[0033] These parameter information items are input to the management terminal, sent to the material supply support device SV via the network NW, and stored in the parameter information storage unit 31 .

[0034] (2) Calculation of exhaustion time When the parameter information is acquired, the control unit 1 of the supplies supply support device SV calculates the weighted depletion time under the control of the weighted depletion time calculation unit 12 in step S20.

[0035] When a vehicle travels between vertices along a route r, the weighted exhaustion time T i (r) is the number of vertices v i Remaining time d i and expected arrival time a i (r) and importance w i can be calculated from the following formula:

[0036]

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[0037] (3) Determining the optimal route Next, in step S30, the control unit 1 of the materials supply support device SV calculates the optimal material supply route from among routes that visit unvisited demand points within a given time under the control of the optimal route determination processing unit 13. The optimal route can be calculated as follows.

[0038]

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[0039] Next, the optimum route determination process executed by the optimum route determination processing unit 13 will be described with reference to Fig. 4. Fig. 4 is a flowchart showing an example of the processing procedure and processing contents of the optimum route determination process executed by the optimum route determination processing unit 13.

[0040] (3-1) Definition of a subset To use the branch and bound method, the optimal route determination processing unit 13 divides the set S consisting of all feasible solutions into an appropriate subset S in step S31. i ⊂S,∪ i S i =S,S i ∩Sj =φ(i≠j).

[0041] The solution to the optimization problem in this embodiment is a path, and a subset S of paths i is defined as follows:

[0042]

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[0043] (3-2) Calculating the upper limit of the maximum weighted maximum depletion time First, in step S32, the optimum path determination processing unit 13 calculates the upper limit of the maximum value of the weighted maximum exhaustion time. i The weighted maximum exhaustion time T of the routes visited in ascending order max (r) is calculated and this is set as the upper limit.

[0044] (3-3) Calculating the lower bound of the maximum weighted maximum depletion time (relaxation problem) Next, in step S33, the optimum route determination processing unit 13 calculates a lower limit value of the maximum value of the weighted maximum exhaustion time. Hereinafter, the process of calculating the lower limit value of the maximum value of the weighted maximum exhaustion time will be described with reference to Fig. 5. Fig. 5 is a flowchart showing an example of the processing procedure and processing contents of the lower limit value calculation process shown in Fig. 4.

[0045] The optimal route determination processing unit 13 first defines a relaxed problem in step S331. A condition exists that a route that is a feasible solution to the original problem (P) must pass through all demand points at most once. The optimal route determination processing unit 13 defines the relaxed problem as follows. The relaxed problem allows passing through the same vertex more than once and allowing the existence of vertices that are not visited. The minimum value of the maximum depletion time in this relaxed problem is set as the lower limit value of the maximum value of the weighted maximum depletion time.

[0046] Hereinafter, a method for determining the lower limit of the weighted maximum exhaustion time when all points other than the starting point have not been visited will be described.

[0047] Next, in step S332, the optimal path determination processing unit 13 defines a directed acyclic graph. To define the directed acyclic graph, a directed acyclic graph G=(V, A) proposed in an exact algorithm for solving a scheduling problem that minimizes the total sum of delivery delays caused by a single machine is used. Details of this exact algorithm are explained in the following document.

[0048] Tanaka, Shunji and Araki, Mituhiko, "An exact algorithm for the single-machine total weighted tardiness problem with sequence-dependent setup times," Computers & Operations Research, Vol. 40, No. 1, pp. 344-352, 2013. The input information required to construct a graph G is i Stay time p i and remaining time d i , different vertices v i and vertex v j Travel time t(v i ,v j ) In addition, each p i and d i is an integer multiple of the time span h>0, and the upper limit of the time span of the feasible path is T max Let's say.

[0049] Define a directed acyclic graph G=(V,A) as follows:

[0050] First, the vertex set V consists of a source node (starting point) s with an in-degree of 0, a sink node (final point) z with an out-degree of 0, and other intermediate nodes v it (i∈S,t∈T), where S is a set of unvisited vertices, and T is a set of vertices that are greater than or equal to α (α: the time when the power supply vehicle leaves the location). max It is a set of integer multiples of h below.

[0051] The edge set A is defined as follows:

[0052]

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[0053] A feasible solution to the original problem (P) is a directed path from vertex s to vertex z in graph G such that for each v i For ∈S, v it It corresponds to a path that is visited only once in (t∈T). Hereafter, this path is called a feasible path. Here, the lower bound T min can be obtained by solving the following assignment problem:

[0054]

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[0055] By solving the problem of maximizing the same objective function under the same constraints, we can obtain the upper bound T max can be obtained similarly.

[0056] Next, in step S333, the optimum path determination processing unit 13 defines the weight γ(e) of each edge eεA in the directed acyclic graph as follows:

[0057]

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[0058] Solving the original problem (P) is equivalent to finding the feasible path with the smallest exhaustion time among all feasible paths.

[0059] However, it is difficult in terms of computation time to search all feasible paths from the graph G. Instead, it is possible to find the lower bound of the original problem from a relaxed problem.

[0060] As explained in relation to the definition of the relaxed problem in step S331, the constraint that the same vertex must be passed through only once is relaxed as the relaxed problem. Then, a feasible solution of the relaxed problem becomes a directed path on graph G that connects the starting point s to the final point z, and the optimal value of the relaxed problem is equal to the minimum value of the maximum exhaustion time among the edges of the directed path.

[0061] However, unless n is a sufficiently small value, it takes a huge amount of time to enumerate all the paths from the starting point s to the final point z. Therefore, the method for finding the lower bound is divided into cases depending on the value of n.

[0062] Therefore, in step S334, the optimum route determination processor 13 determines whether n is a sufficiently small value. The criteria for the determination are set appropriately based on, for example, the processing capacity of the materials supply support device SV.

[0063] [When the value of n is small enough] If the result of the determination in step S334 is that the value of n is sufficiently small, the optimum route determination processing unit 13 counts up all routes of length (n+1) from the starting point s to the final point z in step S335.

[0064] In this case, the route P' of length (n+1) from the starting point s to the final point z can be defined as follows: Note that route P' includes paths that pass through the same vertex (demand point) multiple times and paths that do not pass through at least one vertex (demand point).

[0065]

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[0066] FIG. 6 shows a schematic example of a path of length (n+1) that can be counted from a starting point s to an end point z when the value of n is sufficiently small.

[0067] Next, in step S336, the optimum route determination processing unit 13 calculates the minimum value of the maximum exhaustion time of all the counted routes of length (n+1) from the starting point s to the final point z, and sets this as the lower limit of the maximum weighted maximum exhaustion time. Since the routes to be counted include routes that visit the same vertex more than once, the minimum value of the maximum exhaustion time can be used as the lower limit.

[0068] [When n is not small enough] If the result of the determination in step S334 is that the value of n is not sufficiently small, the optimal path determination processing unit 13 enumerates all paths of length l that arrive at the final point z after passing through each of the (l-1) different vertices once, where l is a positive integer parameter. This process focuses on the fact that the edge with the longest exhaustion time tends to be located near the final point z on the path from the starting point s to the final point z.

[0069] In this case, a path P of length l near the final point z l ' can be defined as follows: l ' consists of a path that passes through each of (l-1) different vertices (demand points) once just before arriving at the final point z. That is, the path P l ' does not include paths that pass through the same vertex multiple times.

[0070]

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[0071] Figure 7 shows that when the value of n is not small enough, the path P of length l just before reaching the final point z to be enumerated. l ' is shown schematically as an example.

[0072] Next, in step S338, the optimum route determination processing unit 13 finds the minimum value of the maximum resource time of all the listed routes of length 1 near the final point z, and sets this as the lower limit of the maximum value of the weighted maximum resource time.

[0073] The upper and lower limits of the minimum value of the weighted maximum exhaustion time are determined by steps S32 and S33 described above. (3-4) Pruning Next, in step S34, the optimal route determination processing unit 13 divides two subsets S into the set S consisting of all feasible solutions. i ,S j and divide these two subsets S i ,S j For one subset S i The upper limit of and the other subset S j Then, in step S35, the optimum route determination processing unit 13 compares the subset S i From the upper limit of j It is determined whether the lower limit value of is larger.

[0074] As a result of the comparison, the subset S i The upper limit of the subset S j If the lower limit value of is large, the optimum route determination processing unit 13 selects the subset S j In step S37, it is determined that the feasible solutions included in the subset S j Abandon the search for

[0075] Conversely, the subset S i The upper limit of is a subset S j If the value is greater than the lower limit of the subset S i The feasible solutions included in are saved and updated as candidates for the optimal solution.

[0076] In step S38, the optimum route determination processing unit 13 determines whether the repetition of the limiting operation has been completed. i ,S j That is, the optimum route determination processing unit 13 determines whether or not there is a difference between the two subsets S i ,S j The processing in steps S32 to S38 is repeated until there are no more.

[0077] (3-5) Determining the optimal route When the repetition of the limiting operation is completed, the optimum route determination processing unit 13 performs a process of determining an optimum route in step S39. The optimum route determination processing unit 13 determines the subset S that remains as candidates for the optimum solution. i Then, the optimal route determination processing unit 13 determines the route with the smallest weighted maximum exhaustion time as the optimal route, and stores information representing this optimal route in the optimal route information storage unit 32.

[0078] (4) Output of support information Finally, in step S40, the control unit 1 of the materials supply support device SV, under the control of the support information output processing unit 14, reads information representing the optimum route from the optimum route information storage unit 32 and generates support information including the information representing the read optimum route. In addition to the optimum route information, the support information includes, for example, information specifying the type and amount of materials, road congestion information, weather information, etc. The road congestion information, weather information, etc. can be obtained, for example, from a website on the Web.

[0079] (Example of operation) Next, a specific example of the above-described operation will be described with reference to Fig. 8. Fig. 8 is a diagram showing travel times between multiple vertices (number of demand points). Fig. 8 shows seven vertices v0 to v6 and the estimated travel times between any two vertices among these vertices v0 to v6. The numerical values ​​of the estimated travel times are expressed in units of [h] (hours).

[0080] In the example of Figure 9, the expected travel time between two vertices is t(v i ,v j) are as follows: t(v0,v1)=9.8, t(v0,v2)=12.0, t(v0,v3)=10.7, t(v0,v4)=11.3, t(v0,v5)=11.3, t(v0,v6)=2.1, t(v1,v2)=5.1, t(v1,v3)=1.0, t(v1,v4)=4.4, t(v1,v5)=4.1, t(v1,v 6)=8.1, t(v2,v3)=5.3, t(v2,v4)=4.2, t(v2,v5)=1.0, t(v2,v6)=3.6, t(v3,v4)=5. 3, t(v3,v5)=4.4, t(v3,v6)=8.6, t(v4,v5)=3.6, t(v4,v6)=5.0, t(v5,v6)=4.2.

[0081] Let us assume that the remaining time until supplies run out at vertices v1, v2, v3, v4, v5, and v6 are d1=17, d2=29, d3=27, d4=18, d5=29, and d6=18, respectively. Also, let us assume that the expected stay times for supplying supplies at vertices v1, v2, v3, v4, v5, and v6 are p1=3, p2=5, p3=4, p4=5, p5=1, and p6=6, respectively. Furthermore, let us assume that the importance of vertices v1, v2, v3, v4, v5, and v6 are w1=1, w2=1, w3=1, w4=2, w5=2, and w6=3, respectively. Let us assume that all vertices v1 to v6 have not yet been visited.

[0082] The minimum value of the maximum depletion time obtained by total counting was 10.66. We will calculate the lower limit of the weighted maximum depletion time that does not exceed this value.

[0083] [When n=6 is a sufficiently small value] Figure 9 shows the optimal route r * In this case, the arrival time a' at each vertex v1 to v6 is i (r * ) and weighted exhaustion time T' i (r *9 shows an example of the weighted maximum exhaustion time lower limit and the optimum route v6 → v5 → v1 → v3 → v5 → v2. In the example of FIG. 9, the optimum route v6 → v5 → v1 → v3 → v5 → v2 is obtained. FIG. 10 shows a schematic representation of this optimum route. For this optimum route, the lower limit of the weighted maximum exhaustion time is 2.25. It can be seen that the lower limit of the weighted maximum exhaustion time is indeed smaller than the optimum value.

[0084] [When n=6 is a large value] As a path to the final point z, (v 1,16.25 ,v 3,20.75 ,v 2,30.0 ,z) is obtained. This indicates that the route is v1 → v3 → v2 → z, and the arrival times at vertices v1, v3, and v2 are 16.25 [h], 20.75 [h], and 30.0 [h], respectively, based on the starting point. The weighted exhaustion times are v1 = -0.75, v2 = 1.0, and v3 = -7.75, so we obtain a lower limit of 1.0. This is certainly a value smaller than the optimal value.

[0085] In any of the above cases, the lower limit of the optimum value can be obtained.

[0086] (Actions and Effects) As described above, in one embodiment, the branch-and-bound method is used to solve an optimization problem that minimizes the weighted maximum depletion time that reflects the importance of each demand point, and a route that minimizes the maximum depletion time of each demand point is searched for among routes that visit unvisited demand points within a given time. Furthermore, the relaxation problem used in the limiting operation allows for passing the same demand point more than once and for there to be demand points that are not visited.

[0087] These techniques allow us to determine the optimal route that minimizes the maximum depletion time while taking into account the importance of each demand point. Furthermore, by using the concept of a scheduling problem, it is possible to set the time spent at each demand point for each point. This makes it possible, for example, in the event of a disaster, to supply power facilities, water, food, and other necessary supplies to affected areas while minimizing depletion time.

[0088] [Other embodiments] In the embodiment, a case has been described in which the entire series of processes from parameter acquisition processing to support information output processing is executed on a single server device, but all or part of the series of processes from parameter acquisition processing to support information output processing may be distributed and processed by multiple server devices or information processing terminals such as personal computers.

[0089] Although the embodiment has been described with reference to an example in which supplies are supplied to multiple demand points when a disaster occurs, the present invention is not limited to this example and can also be applied as a support system for delivering goods to multiple stores during normal times, for example.

[0090] Furthermore, in addition to road vehicles such as trucks, it is possible to selectively use railroads, ships, and flying objects such as drones as transportation means. In addition, the functions of the material supply support device, the processing procedures and processing contents, the types of materials, the contents of the support information, etc. can be modified and implemented in various ways without departing from the spirit of this invention.

[0091] It should be noted that this invention is not limited to the above-described embodiments, and various modifications can be made in the implementation stage without departing from the spirit of the invention. Furthermore, the embodiments may be implemented in appropriate combinations, in which case the combined effects can be obtained. Furthermore, the above-described embodiments include various inventions, and various inventions can be extracted by combining selected elements from the disclosed elements. For example, if the problem can be solved and the desired effect can be obtained even if some elements are deleted from all elements shown in the embodiments, the configuration from which these elements are deleted can be extracted as an invention. [Explanation of symbols]

[0092] SV…Materials supply support device 1...Control unit 2...Program memory section 3...Data storage unit 4...Communication I / F section 5...Bus 11...Parameter information acquisition processing unit 12...weighted exhaustion time calculation processing unit 13...Optimal route determination processing unit 14...Support information output processing unit 31...Parameter information storage unit 32...Optimal route information storage unit

Claims

1. A supplies supply support device that supports a task of sequentially supplying supplies to a plurality of locations, a first processing unit that acquires parameter information related to the supply of the material to the plurality of locations and including the importance of each location; a second processing unit that calculates a weighted depletion time, which is the time it takes for the resource to be depleted at each point, based on the parameter information and reflecting the importance of each point; a third processing unit that determines an optimal route that minimizes the maximum depletion time of each point by solving an optimization problem that minimizes a weighted maximum depletion time, which is a maximum value of the weighted depletion times, using a branch and bound method; a fourth processing unit that generates and outputs support information including information representing the optimal route; Equipped with The parameter information includes, in addition to the importance, the remaining time at each point and the estimated arrival time, The second processing unit calculates a weighted exhaustion time T i (r) at a point v i by the following formula: [Equation 1] where r is a route, a i (r) is the expected arrival time at point v i on route r, d i is the remaining time at point v i , and w i is the importance of point v i . the third processing unit solves the optimization problem by assuming that a relaxation problem used in a bounding operation of the branch and bound method allows passing through the same point two or more times and allowing the existence of points that are not visited; The third processing unit includes: If the number of points is sufficiently small, count all the routes from the starting point to the final point, find the minimum value of the maximum exhaustion time of all the counted routes, and set this as the lower limit of the maximum value of the weighted maximum exhaustion time; If the number of points is not sufficiently small, the material supply support device lists routes that pass through each different point just before the final point once, finds the minimum value of the maximum depletion time of all the listed routes, and sets this as the lower limit value of the maximum weighted maximum depletion time.

2. A materials supply support method executed by a materials supply support device that supports a task of sequentially supplying materials to a plurality of locations, comprising: a first step of acquiring parameter information relating to the supply of the material to the plurality of locations, the parameter information including the importance of each location; a second step of calculating a weighted depletion time, which is the time it takes for the resource to be depleted at each location, based on the parameter information and reflecting the importance of each location; a third step of determining an optimal route that minimizes the maximum depletion time at each point by solving an optimization problem that minimizes a weighted maximum depletion time, which is the maximum value of the weighted depletion times, using a branch-and-bound method; a fourth step of generating and outputting assistance information including information representing the optimal route; Equipped with The parameter information includes, in addition to the importance, the remaining time at each point and the estimated arrival time, The second step calculates a weighted exhaustion time T i (r) at a point v i using the following formula: [Equation 2] where r is a route, a i (r) is the expected arrival time at point v i on route r, d i is the remaining time at point v i , and w i is the importance of point v i . The third step solves the optimization problem by assuming that a relaxation problem used in the bounding operation of the branch and bound method allows passing through the same point more than once and allowing the existence of points that are not visited; The third step includes: If the number of points is sufficiently small, count all the routes from the starting point to the final point, find the minimum value of the maximum exhaustion time of all the counted routes, and set this as the lower limit of the maximum value of the weighted maximum exhaustion time; If the number of points is not sufficiently small, a material supply support method is provided in which routes that pass through each different point just before the final point once are listed, the minimum value of the maximum depletion time of all the listed routes is found, and this is set as the lower limit value of the maximum weighted maximum depletion time.

3. A material supply support program that causes a processor provided in the material supply support device to execute the processing performed by the first to fourth processing units provided in the material supply support device described in claim 1.

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