JP6971297B2 - Decision support device, decision support program and decision support method - Google Patents

Description

The present invention relates to a decision support device, a decision support program, and a decision support method.

In recent years, the aging of infrastructure such as roads has become a serious social problem. For this reason, the number of works for inspecting infrastructure and repairing infrastructure is increasing. These works may affect lifeline-related equipment and other equipment such as information and communication equipment, transmission lines, gas pipes, and water pipes located around the construction site. Therefore, when these works are carried out, it is necessary to have a supervisor who has knowledge of lifeline-related equipment and other equipment witness the work.

The task of assigning multiple supervisors to multiple construction sites needs to be carried out in consideration of various factors. These factors include, for example, the journey the supervisor needs to travel to patrol multiple construction sites, the time required for the supervisor to patrol multiple construction sites, the knowledge the supervisor has, and the supervisor. It is the burden that the supervisor bears when supervising the construction, the consistency that the supervisor has at least one of the equipment possessed by the supervisor and the contents of the construction. In addition, these elements may be related to each other or may be in a trade-off relationship. As a document that discloses a technique for assisting such an allocation work, for example, Non-Patent Document 1 can be mentioned.

"Field Assistant (registered trademark): NTT HOME", [Searched on December 12, 1st year of Reiwa], Internet <URL: https://www.ntt.co.jp/kankyo/protect/label/field_assistant.html ＞

However, the technique disclosed in Non-Patent Document 1 is a technique for assisting the work of the person in charge of carrying out the allocation work. Therefore, the result of the allocation work depends on the knowledge, experience, feeling, etc. of the person in charge of the allocation work. Further, since the allocation work is a work carried out in consideration of various factors, it is difficult to judge whether or not the result is suitable, and the judgment criteria are often unclear. Further, there are many tasks in the real world, such as such allocation work, which are performed in consideration of a plurality of factors and it is difficult to judge whether or not a suitable result is obtained according to the actual situation.

The present invention has been made in view of such circumstances, and is a decision support device, a decision support program, and a decision making that can support making a suitable decision in consideration of a plurality of factors. The challenge is to provide support methods.

One aspect of the present invention obtains first data indicating a solution obtained by solving a problem in which a plurality of objective functions can be defined, and the problem is subject to constraints on at least two of the plurality of objective functions. A data acquisition unit that acquires a plurality of second data indicating the solution obtained by solving, and a process of converting the solution indicated by the first data into a simple undirected graph and converting the simple undirected graph into an adjacent matrix. Is executed for each viewpoint considered when solving the problem, at least one of the solutions shown by the second data is converted into a simple undirected graph, and the simple undirected graph is converted into an adjacent matrix. The elements of the upper triangular matrix of the adjacent matrix of the solution shown by the first data and the elements of the upper triangular matrix of the adjacent matrix of the solution shown by the second data are executed for each viewpoint considered when solving the problem. A similarity matrix whose elements are numbers whose values are different from each other is calculated for each viewpoint considered when solving the problem, and each viewpoint is based on a predetermined viewpoint for each similarity matrix. With a similarity calculation unit that calculates a similarity matrix using a weighted sum equation that includes weights that represent importance, and calculates the elements of the similarity matrix as the similarity to the solution shown by the first data. , A decision support device including a data output unit for outputting the second data whose similarity satisfies a predetermined condition.

One aspect of the present invention is the above-mentioned decision support device, and the data output unit is a decision support device that further outputs similarity data indicating the similarity.

One aspect of the present invention is the above-mentioned decision support device, in which the data output unit indicates the degree of similarity for each viewpoint, which is the degree of similarity for each viewpoint considered when solving the problem. It is a decision support device that outputs more data.

In one aspect of the present invention, a computer obtains first data indicating a solution obtained by solving a problem in which a plurality of objective functions can be defined, and is subject to constraints on at least two of the plurality of objective functions. A data acquisition function that acquires a plurality of second data indicating the solution obtained by solving the above problem, and a simple undirected graph that converts the solution indicated by the first data into an adjacent matrix. The conversion process is executed for each viewpoint considered when solving the problem , at least one of the solutions shown by the second data is converted into a simple undirected graph, and the simple undirected graph is converted into an adjacent matrix. The processing is executed for each viewpoint considered when solving the problem, and the elements of the upper triangular matrix of the adjacent matrix of the solution shown by the first data and the upper triangle of the adjacent matrix of the solution shown by the second data are executed. When a similarity matrix whose elements are numbers whose values are different from those of the matrix elements is calculated for each viewpoint considered when solving the problem, and each similarity matrix is based on a predetermined viewpoint. using the formula weighted sum to calculate a similarity matrix containing the weights representing the importance of each aspect, the similarity calculating an element of the similarity matrix as the similarity of the solution indicated by the first data It is a decision support program that realizes a calculation function and a data output function that outputs the second data whose similarity satisfies a predetermined condition.

In one aspect of the invention, a computer obtains first data indicating a solution obtained by solving a problem in which a plurality of objective functions can be defined, and is subject to constraints on at least two of the plurality of objective functions. In the data acquisition step of acquiring a plurality of second data indicating the solution obtained by solving the problem in, the computer converts the solution indicated by the first data into a simple undirected graph, and obtains a simple undirected graph. The process of converting to an adjacent matrix is executed for each viewpoint considered when solving the problem , at least one of the solutions shown by the second data is converted into a simple undirected graph, and the simple undirected graph is converted into an adjacent matrix. The process of converting to is executed for each viewpoint considered when solving the problem, and the elements of the upper triangular matrix of the adjacent matrix of the solution shown by the first data and the adjacent matrix of the solution shown by the second data are executed. A similarity matrix whose elements are different values from the elements of the upper triangular matrix is calculated for each viewpoint considered when solving the problem, and each similarity matrix is based on a predetermined viewpoint. using the formula weighted sum to calculate a similarity matrix containing the weights representing the importance of each aspect in the case where the calculated elements of the similarity matrix as the similarity of the solution indicated by the first data a similarity calculation step of, computer, wherein the similarity is a decision support method comprising: a data output step of outputting a predetermined condition is satisfied the second data.

According to the present invention, it is possible to provide a decision support device, a decision support program, and a decision support method that can support making a suitable decision in consideration of a plurality of factors.

It is a figure which shows an example of the decision support system which concerns on embodiment of this invention. It is a figure which shows an example of the image which displays the solution shown by the 1st data which concerns on embodiment of this invention. It is a figure which shows an example of the image which displays the solution shown by the 2nd data which concerns on embodiment of this invention. It is a figure which shows an example of the image which displays the solution shown by the 2nd data which concerns on embodiment of this invention. It is a figure which shows an example of the image which displays the solution shown by the 2nd data which concerns on embodiment of this invention. It is a figure which shows an example of the image which displays the solution shown by the 2nd data which concerns on embodiment of this invention. It is a figure which shows an example of the image which displays the solution shown by the 2nd data which concerns on embodiment of this invention. It is a figure which shows an example of the similarity matrix calculated from the viewpoint of the order of construction sites in each patrol route by the decision support apparatus which concerns on embodiment of this invention. It is a figure which shows an example of the similarity matrix calculated from the viewpoint of the combination of a supervisor and a patrol path by the decision support device which concerns on embodiment of this invention. The figure which shows an example of the similarity matrix obtained by the decision support apparatus which concerns on embodiment of this invention calculated the weighted sum of the similarity matrix shown in FIG. 8 and the similarity matrix shown in FIG. Is. It is a flowchart which shows an example of the process executed by the decision-making support apparatus which concerns on embodiment of this invention.

FIG. 1 is a diagram showing an example of a decision support system according to an embodiment of the present invention. As shown in FIG. 1, the decision support system 1 includes a terminal 10, an arithmetic unit 20, a decision support device 30, and an output device 40. The terminal 10, the arithmetic unit 20, the decision support device 30, and the output device 40 are all connected to the network NW. The network NW is, for example, the Internet, a WAN (Wide Area Network), or a LAN (Local Area Network).

The terminal 10 is, for example, a computer or a tablet, and enables a person in charge to use an application that assists in manually solving a multi-objective optimization problem.

A multi-objective optimization problem is a problem in which multiple objective functions can be defined. An objective function is a function that represents an element that needs to be considered in a multi-objective optimization problem. For example, if the multi-objective optimization problem is a problem of assigning multiple supervisors to multiple construction sites, the objective function is the sum of the paths each supervisor needs to travel to patrol multiple construction sites, each. The total time required for the supervisor to patrol multiple construction sites, the knowledge possessed by the supervisor, the experience possessed by the supervisor, and the consistency of at least one of the equipment possessed by the supervisor with the contents of the construction. It is the degree and the degree of burden that the supervisor bears when supervising the construction. The equipment referred to here is, for example, equipment such as a stepladder used when supervising construction work. Further, in the solution of the multi-objective optimization problem, it is preferable to make as many objective functions as large or as small as possible, and it is more preferable to maximize or minimize as many objective functions as possible. ..

The above-mentioned application is, for example, an icon indicating the location of a construction site, an icon indicating the current position of the supervisor, numbers, letters, symbols, etc. indicating the level of knowledge and experience possessed by the supervisor, and the equipment possessed by the supervisor. The image indicating that the icon indicating is superimposed on the map is displayed on the display. The work displayed as an icon in this image may be only the work notified in advance by telephone, facsimile, e-mail, etc., which is determined to require supervision by a supervisor. The data indicating the current position of the supervisor is generated by measuring using, for example, GPS (Global Positioning System) mounted on the mobile terminal carried by the supervisor. In addition, this application updates the image to be displayed on the display as appropriate when the current position of the supervisor is updated. The above-mentioned application enables a person in charge to perform the work of assigning a plurality of supervisors to a plurality of construction sites immediately and intuitively while looking at the image displayed on the display.

After the solution of the multi-objective optimization problem is obtained by using the above-mentioned application, the terminal 10 transmits the first data indicating the solution to the decision support device 30.

The arithmetic unit 20 is, for example, a computer, and applies a mathematical model to the same multi-objective optimization problem as the multi-objective optimization problem solved by using the above-mentioned application.

The first example of this mathematical model is a mathematical model in which the value calculated by the following equation (1) is made as small as possible. The first term of Equation (1) represents the total journey D _S of the supervisor needs to move in order to patrol the plurality of construction sites. The second term of equation (1), expressed knowledge possessed by the supervisor, the total integrity of the degree c _sj and at least one with the contents of the construction of equipment experience and supervisor supervisor has is in possession There is. The first term of the equation (1) and the second term of the equation (1) are both examples of the objective function. Further, the second term of the equation (1) is multiplied by the weight α. S, J, and T included in the formula (1) represent a set of supervisors, a set of works, and a set of time zones in which the works are carried out, respectively. Further, s, j and t included in the equation (1) represent elements of S, J and T, respectively.

_{The coefficient} of determination variable x sjt included in the second term of the equation (1) is "0" or "1" as represented by the following equation (2). The decision variable x _sjt is "1" when the supervisor s is assigned to the construction work j performed in the time zone t, and is "0" in other cases.

Further, the decision variable x _sjt is subject to the constraint expressed by the following equation (3) and the constraint expressed by the following equation (4). Equation (3) indicates that it is impossible for one supervisor to supervise two or more works with overlapping time zones. Equation (4) indicates that one supervisor is assigned to one construction work, and the time zone in which one construction work is carried out is fixed to one.

Additionally, the following journey D _S included in the first term of equation (1) is, the site and construction l of decision variables x _sj and construction k may be practiced includes a journey d _kl between sites to be performed It is expressed by the equation (5) of.

Equations (2) to (5) are examples of constraints imposed on the objective function in order to obtain a realistic solution to the multi-objective optimization problem.

The second example of the above-mentioned mathematical model is a mathematical model in which the value calculated by the above-mentioned equation (1) is made as small as possible. However, the second example of the mathematical model, in addition to the constraint represented by the formula (5) from equation (2) described above, the following equation including the degree i _k burden bear when overseeing the construction k ( The value calculated by the equation (1) is made as small as possible under the constraint condition represented by 6). Formula (6) sets an upper limit on the total degree of burden that each supervisor bears when supervising construction.

The third example of the mathematical model, like the second example of the mathematical model described above, is the total number of steps each supervisor needs to travel to patrol multiple construction sites, the knowledge the supervisor has, and the supervision. Consider the total of the experience of the person, the degree of consistency between at least one of the equipment possessed by the supervisor and the contents of the construction, and the degree of burden that each supervisor bears when supervising the construction.

However, the third example of the mathematical model focuses on the fact that there are actually at most three works supervised by each supervisor, and the solution is calculated in a shorter calculation time than the second example of the mathematical model. Is possible. Specifically, the third example of the mathematical model is a combination of construction patrol routes that the supervisor can patrol, the time required to move on each patrol route, the experience that the supervisor has, and the possession of the supervisor. Calculate in advance the degree of consistency between at least one of the equipment you are wearing and the contents of the construction. And the third example of the mathematical model is that all construction sites are included in one of the patrol routes, the time required to travel on each patrol route is as short as possible, and the supervisor has experience. And select a solution that maximizes the total degree of consistency between at least one of the equipment possessed by the supervisor and the contents of the construction.

The third example of the mathematical model is a mathematical model in which the value calculated by the following equation (7) is made as small as possible. Paragraph c _r in brackets in equation (7) represents the time required to move on the patrol route r. _{The second term qrs in} parentheses in equation (7) represents the degree of consistency between the experience of the supervisor and at least one of the equipment possessed by the supervisor and the contents of the construction, and the weight α is It is hung. R included in the equation (7) represents a set of cyclic paths. Further, r included in the equation (7) represents an element of R.

_{The coefficient of determination variable y rs} included in the equation (7) is "0" or "1" as represented by the following equation (8). The coefficient of determination variable y _rs is "1" when the supervisor s supervises the work included in the patrol route r, and is "0" in other cases.

Further, the coefficient of determination y _rs is constrained by the following equations (9) to (12). Equation (9) indicates that each work is not included in a plurality of patrol routes, and each work is not supervised by a plurality of supervisors. Equation (10) represents that one supervisor patrols one patrol route. Equation (11) represents that the total number of supervisors is equal to or greater than the total number of patrol routes. Equation (12) is an upper limit to the sum of the degree m _r burden bear when the supervisor supervises the work of the patrol route r.

The arithmetic unit 20 applies any one of the first example of the mathematical model to the third example of the mathematical model described above to obtain a solution of the multi-objective optimization problem. Then, the arithmetic unit 20 transmits the second data indicating this solution to the decision support device 30.

As shown in FIG. 1, the decision support device 30 includes a data acquisition unit 31, a similarity calculation unit 32, and a data output unit 33.

The data acquisition unit 31 acquires the first data from the terminal 10 and the second data from the arithmetic unit 20. The first data is, for example, data showing a solution obtained by manually solving a problem in which a plurality of objective functions can be defined. The second data is, for example, data showing a solution obtained by mathematically solving a problem under constraints on at least two of a plurality of objective functions. In the following description, a case where the arithmetic unit 20 obtains a solution by using the third example of the above-mentioned mathematical model will be described as an example.

FIG. 2 is a diagram showing an example of an image displaying a solution shown by the first data according to the embodiment of the present invention. Image PH shown in FIG. 2, the construction site W9 from construction site W1 visualized on the map, it is rendered the patrol route of each supervisor solid, one-dot chain line or two-dot chain line.

In the image PH shown in FIG. 2, the supervisor A patrols the construction site W1, the construction site W2, and the construction site W3 in this order, and the supervisor B patrols the construction site W4, the construction site W5, and the construction site W6 in this order, and supervises the image PH. Person C shows the solution H that patrols the construction site W7, the construction site W8, and the construction site W9 in this order.

3 to 7 are diagrams showing an example of an image displaying a solution shown by the second data according to the embodiment of the present invention. Image shown in FIGS. 3-7 are all visualized on a map the construction site W9 from construction site W1, it is rendered the patrol route of each supervisor solid, one-dot chain line or two-dot chain line.

In the image PM1 shown in FIG. 3, the supervisor A patrols the construction site W1, the construction site W2, and the construction site W3 in this order, and the supervisor B patrols the construction site W4, the construction site W5, and the construction site W6 in this order, and supervises the image PM1. Person C shows a solution M1 that patrols the construction site W8, the construction site W7, and the construction site W9 in this order.

Similarly, in the image PM2 shown in FIG. 4, the supervisor A patrols the construction site W1, the construction site W2, and the construction site W3 in this order, and the supervisor B patrols the construction site W4, the construction site W5, and the construction site W6 in this order. However, the supervisor C shows the solution M2 that patrols the construction site W8, the construction site W7, and the construction site W9 in this order. In the image PM3 shown in FIG. 5, the supervisor A patrols the construction site W1, the construction site W2, and the construction site W4 in this order, and the supervisor B patrols the construction site W3, the construction site W5, and the construction site W7 in this order, and supervises the image PM3. Person C shows a solution M3 that patrols the construction site W6, the construction site W8, and the construction site W9 in this order.

Further, in the image PM4 shown in FIG. 6, the supervisor A patrols the construction site W1, the construction site W2, and the construction site W3 in this order, and the supervisor B patrols the construction site W4, the construction site W5, and the construction site W7 in this order. , The supervisor C shows the solution M4 that goes around the construction site W6, the construction site W8, and the construction site W9 in this order. In the image PM5 shown in FIG. 7, the supervisor A patrols the construction site W1, the construction site W6, and the construction site W8 in this order, and the supervisor B patrols the construction site W3, the construction site W5, and the construction site W7 in this order, and supervises the image PM5. Person C shows a solution M5 in which the construction site W2, the construction site W4, and the construction site W9 are patrolled in this order.

At least one of the images shown in FIGS. 2 to 7 is displayed on, for example, a display connected to the decision support device 30.

The similarity calculation unit 32 calculates the similarity with the solution shown by the first data with respect to at least one of the solutions shown by the second data. Similarity is defined between the two solutions. For example, the similarity calculation unit 32 calculates the similarity according to the procedure described below.

First, the similarity calculation unit 32 calculates the similarity between two solutions among the solutions shown in FIGS. 2 to 7 based on the viewpoint of the order of the construction sites in each patrol route. This viewpoint is an example of a viewpoint considered when solving a multi-objective optimization problem.

The similarity calculation unit 32 converts the cyclic path shown by the solid line, the alternate long and short dash line, or the alternate long and short dash line in FIGS. 2 to 7 into a simple undirected graph, and converts the simple undirected graph into an adjacency matrix. In each of these simple undirected graphs, the number of sides is equal to the number of construction sites on each patrol route. Then, the similarity calculation unit 32 uses the number of the elements of the upper triangular matrix of the adjacency matrix of each of the two solutions having different values as the elements of the similarity matrix. This is because if all sides of two simple undirected graphs are different, the number of elements in the upper triangular matrix of the adjacency matrix that are different from each other is twice the number of works included in one patrol path. ..

FIG. 8 is a diagram showing an example of a similarity matrix calculated by the decision support device according to the embodiment of the present invention from the viewpoint of the order of construction sites in each patrol route. _{The elements of the similarity matrix HA} shown in FIG. 8 indicate the viewpoint-specific similarity between the two solutions. The smaller the value, the more similar the two solutions are, and the larger the value, the more the two solutions are separated. For example, as shown in the second line of FIG. 8, the solution H shown by the first data is exactly the same as the solution M1, the solution M2, and the solution M3 when examined from the viewpoint of the order of the construction sites in each patrol route. It can be seen that there is a difference from the solution M4, the solution M5, and the solution M6.

Next, the similarity calculation unit 32 calculates the similarity between two solutions among the solutions shown in FIGS. 2 to 7 based on the viewpoint of the combination of the supervisor and the patrol route. This viewpoint is an example of a viewpoint considered when solving a multi-objective optimization problem.

The similarity calculation unit 32 calculates an adjacency matrix associated with the supervisor and the patrol route for the solutions shown in FIGS. 2 to 7. Then, the similarity calculation unit 32 uses the number of elements of the adjacency matrix of each of the two solutions having different values as the elements of the similarity matrix.

FIG. 9 is a diagram showing an example of a similarity matrix calculated by the decision support device according to the embodiment of the present invention from the viewpoint of the combination of the supervisor and the patrol route. Elements of the similarity matrix H _B shown in FIG. 9 shows a viewpoint different similarity between two solutions. The smaller the value, the more similar the two solutions are, and the larger the value, the more the two solutions are separated. For example, as shown in the second line of FIG. 9, the solution H shown by the first data is exactly the same as the solution M1 and the solution M2 when examined from the viewpoint of the combination of the supervisor and the patrol route. It can be seen that it is different from M3, solution M4, solution M5 and solution M6.

Then, the similarity calculation unit 32 _{calculates the similarity matrix H C} by calculating the weighted sum of _{the similarity matrix H A} shown in FIG. 8 and the similarity matrix H _B shown in FIG. For example, the similarity calculating unit 32 uses the following equation (13) calculating a similarity matrix H _C that includes a weight beta. The weight β represents the importance of the viewpoint of the combination of the supervisor and the patrol route when the viewpoint of the order of the construction sites in each patrol route is used as a reference.

FIG. 10 shows the similarity matrix obtained by the decision-making support device according to the embodiment of the present invention calculating the weighted sum of the similarity matrix shown in FIG. 8 and the similarity matrix shown in FIG. It is a figure which shows an example. Similarity matrix H _C shown in FIG. 10 is calculated the value of the weight β as 0.67. Elements of the similarity matrix H _C is the similarity is weighted sum of two types of aspect-specific similarities described above.

For example, as shown in the second line of FIG. 10, the solution H shown by the first data is the viewpoint of the order of the construction site in each patrol route and the viewpoint of the combination of the supervisor and the patrol route. Considering the difference in the relative importance of the viewpoints, it can be seen that the solution M1 is exactly the same as the solution M2, the solution M3, the solution M4, the solution M5, and the solution M6.

The data output unit 33 outputs the second data whose similarity satisfies a predetermined condition. For example, the data output unit 33 transmits the second data indicating the solution in which the similarity between the solution M1 to the solution M5 and the solution H exceeds a predetermined threshold value to the output device 40 shown in FIG. do. The output device 40 is, for example, a display, and displays the solution indicated by the second data received from the data output unit 33 in any embodiment.

Further, the data output unit 33 may output at least one of the above-mentioned similarity data indicating the similarity and the above-mentioned viewpoint-specific similarity data indicating the viewpoint-specific similarity. When the output device 40 receives these data from the data output unit 33, the output device 40 displays the similarity degree and the viewpoint-specific similarity degree indicated by the data in an arbitrary manner.

Next, an example of the process executed by the decision-making support device according to the embodiment will be described with reference to FIG. FIG. 11 is a flowchart showing an example of a process executed by the decision-making support device according to the embodiment of the present invention.

In step S10, the data acquisition unit 31 acquires first data indicating a solution obtained by solving a problem in which a plurality of objective functions can be defined, and is subject to constraints on at least two of the plurality of objective functions. Acquire a plurality of second data indicating the solution obtained by solving the problem.

In step S20, the similarity calculation unit 32 calculates the similarity with the solution shown by the first data with respect to at least one of the solutions shown by the second data.

In step S30, the data output unit 33 outputs the second data whose similarity satisfies a predetermined condition. In step S30, the data output unit 33 may further output at least one of the similarity data and the viewpoint-specific similarity data.

The decision support system 1 according to the embodiment has been described above, focusing on the decision support device 30. The decision support device 30 acquires first data indicating a solution obtained by solving a problem in which a plurality of objective functions can be defined, and solves the problem under constraints on at least two of the plurality of objective functions. A plurality of second data indicating the obtained solution are acquired. Then, the decision support device 30 calculates the similarity with the solution indicated by the first data with respect to at least one of the solutions indicated by the second data, and outputs the second data in which the similarity satisfies a predetermined condition. ..

As a result, the decision support device 30 is close to a solution that seems to be in line with the actual situation when it is necessary to obtain a solution to a problem that requires appropriate decision making in consideration of a plurality of factors. A solution can be provided. Further, by providing such a solution, the decision support device 30 streamlines the work performed by the person who solves the problem requesting the decision, and sufficiently acquires the knowledge and experience to solve the problem. It is possible to support the work done by those who do not.

Further, the decision support device 30 outputs similarity data indicating the similarity. As a result, the decision support device 30 presents to the user the degree to which the solution obtained by mathematically solving the multi-objective optimization problem is similar to the solution obtained by manually solving the multi-objective optimization problem. And can help the user choose the appropriate solution.

Further, the decision support device 30 outputs the viewpoint-specific similarity data indicating the viewpoint-specific similarity. As a result, the decision support device 30 determines the degree to which the solution obtained by mathematically solving the multi-objective optimization problem is similar to the solution obtained by manually solving the multi-objective optimization problem according to the viewpoint. Can be presented to help the user select the appropriate solution.

In the above-described embodiment, as a multi-purpose optimization problem, a problem of allocating a plurality of supervisors to a plurality of works has been given as an example, but the problem is not limited to this.

For example, as the above-mentioned multi-objective optimization problem, there is a problem of integrating a plurality of hardware by virtualization and allocating them to a plurality of software. In this case, the objective function is, for example, the operating rate of each hardware, the load applied to each hardware, and the processing speed of each hardware.

Alternatively, as the above-mentioned multi-objective optimization problem, there is a problem of assigning a plurality of engineers to a plurality of themes when carrying out a technology development having a plurality of themes. In this case, the objective function is, for example, the knowledge and experience of each engineer, the importance of each theme, the delivery date of each theme, and the delivery date of the entire technology development.

Further, at least a part of the functions of the decision support device 30 is realized by, for example, a hardware processor such as a CPU (Central Processing Unit) executing a program or software. In addition, some or all of these include circuit units (circuitry) such as LSI (Large Scale Integration), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), GPU (Graphics Processing Unit), etc. It may be realized by hardware, or it may be realized by collaboration between software and hardware. The program may be stored in advance in a storage device provided with a non-transient storage medium such as an HDD (Hard Disk Drive) or a flash memory, or a removable non-transitory storage device such as a DVD or a CD-ROM. It is stored in a storage medium, and may be installed by mounting the storage medium on a drive device.

The embodiments of the present invention have been described above with reference to the drawings. However, at least one of the decision support system 1 and the decision support device 30 is not limited to the above-described embodiment, and various modifications, substitutions, combinations, or design changes can be made without departing from the gist of the present invention. May be added.

The contents of this specification will be submitted to peer-reviewed journals in the future.

1 ... decision support system, 10 ... terminal, 20 ... arithmetic unit, 30 ... decision support device, 31 ... data acquisition unit, 32 ... similarity calculation unit, 33 ... data output unit, 40 ... output device, NW ... network

Claims (5)

Obtained by acquiring first data indicating a solution obtained by solving a problem in which a plurality of objective functions can be defined, and solving the problem under constraints on at least two of the plurality of objective functions. A data acquisition unit that acquires multiple second data indicating a solution,
The process of converting the solution shown by the first data into a simple undirected graph and converting the simple undirected graph into an adjacent matrix is executed for each viewpoint considered when solving the problem, and is shown by the second data. At least one of the solutions to be solved is converted into a simple undirected graph, and the process of converting the simple undirected graph into an adjacent matrix is executed for each viewpoint considered when solving the problem, and the solution shown by the first data is executed. The problem is solved by using a similarity matrix whose elements are different values from the elements of the upper triangular matrix of the adjacent matrix of the above and the elements of the upper triangular matrix of the adjacent matrix of the solution shown by the second data. Calculate for each viewpoint considered in the case, and calculate the similarity matrix using a weighted sum formula that includes weights that represent the importance of each viewpoint when each similarity matrix is based on a predetermined viewpoint. , A similarity calculation unit that calculates the elements of the similarity matrix as the similarity with the solution shown by the first data.
A data output unit that outputs the second data whose similarity satisfies a predetermined condition, and
A decision support device equipped with. The data output unit further outputs similarity data indicating the similarity.
The decision support device according to claim 1. The data output unit further outputs viewpoint-specific similarity data indicating the viewpoint-specific similarity, which is the viewpoint-specific similarity considered when solving the problem.
The decision-making support device according to claim 1 or 2. On the computer
Obtained by acquiring first data indicating a solution obtained by solving a problem in which a plurality of objective functions can be defined, and solving the problem under constraints on at least two of the plurality of objective functions. A data acquisition function that acquires multiple second data indicating a solution,
The process of converting the solution shown by the first data into a simple undirected graph and converting the simple undirected graph into an adjacent matrix is executed for each viewpoint considered when solving the problem, and is shown by the second data. At least one of the solutions to be solved is converted into a simple undirected graph, and the process of converting the simple undirected graph into an adjacent matrix is executed for each viewpoint considered when solving the problem, and the solution shown by the first data is executed. The problem is solved by using a similarity matrix whose elements are different values from the elements of the upper triangular matrix of the adjacent matrix of the above and the elements of the upper triangular matrix of the adjacent matrix of the solution shown by the second data. Calculate for each viewpoint considered in the case, and calculate the similarity matrix using a weighted sum formula that includes weights that represent the importance of each viewpoint when each similarity matrix is based on a predetermined viewpoint. , A similarity calculation function that calculates the elements of the similarity matrix as the similarity with the solution shown by the first data.
A data output function that outputs the second data whose similarity satisfies a predetermined condition, and
Decision support program to realize. A computer obtains first data indicating a solution obtained by solving a problem in which a plurality of objective functions can be defined, and solves the problem under constraints on at least two of the said objective functions. A data acquisition step to acquire multiple second data indicating the obtained solution, and
The computer executes the process of converting the solution shown by the first data into a simple undirected graph and converting the simple undirected graph into an adjacent matrix for each viewpoint considered when solving the problem, and the second At least one of the solutions shown by the data is converted into a simple undirected graph, and the process of converting the simple undirected graph into an adjacent matrix is executed for each viewpoint considered when solving the problem, and the first data is used. The similarity matrix whose elements are different values from the elements of the upper triangular matrix of the adjacent matrix of the solution shown and the elements of the upper triangular matrix of the adjacent matrix of the solution shown by the second data is described above. A similarity matrix calculated for each viewpoint considered when solving a problem, using a weighted sum equation that includes weights that represent the importance of each viewpoint when each similarity matrix is based on a given viewpoint. And the similarity calculation step of calculating the elements of the similarity matrix as the similarity with the solution shown by the first data.
A data output step in which the computer outputs the second data whose similarity satisfies a predetermined condition.
Decision support methods including.

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