JP2023022768A - Decision making support device, decision making support program, and decision making support method - Google Patents

Abstract

To provide a decision making support device, a decision making support program, and a decision making support method by which a problem can be solved even when the number of works that can be done per unit time by a subject that performs works included in the problem is great.SOLUTION: In a decision making support system 1, a decision making support device 10 includes: a data acquisition unit 11 that acquires size data indicating a size of a problem in which a plurality of objective functions can be defined; and a means determination unit 12 that determines means for searching for a solution of the problem on the basis of the size of the problem indicated by the size data.SELECTED DRAWING: Figure 1

Description

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

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

The problem of assigning multiple supervisors to multiple construction sites requires consideration of various factors. These factors are, for example, the distance a supervisor needs to travel to patrol multiple construction sites, the time required for a supervisor to patrol multiple construction sites, the knowledge the supervisor possesses, the supervisor's The experience of the supervisor and the consistency of at least one of the equipment possessed by the supervisor with the content of the work, and the burden borne by the supervisor when supervising the work. In addition, these factors may be related to each other or have a trade-off relationship. As a document disclosing a technique for solving such a problem, for example, Patent Document 1 can be cited.

Japanese Patent Application Laid-Open No. 2021-103390

However, the technology disclosed in Patent Literature 1 does not allow the amount of memory that can be used for calculations to solve problems when the number of construction works that a supervisor can supervise per unit time exceeds a predetermined number. It may not be possible to solve the above-mentioned problems due to constraints on the calculations, constraints on the time that can be spent on the calculations, and the like.

The present invention has been made in view of such circumstances, and is capable of solving the problem even when the number of tasks that the subject performing the task included in the problem can perform per unit time is large. It is an object of the present invention to provide a decision support device, a decision support program, and a decision support method.

One aspect of the present invention is a data acquisition unit that acquires size data indicating the size of a problem for which a plurality of objective functions can be defined, and searches for a solution to the problem based on the size of the problem indicated by the size data. and a means determination unit that determines means.

One aspect of the present invention is the above-described decision support device, wherein the data acquisition unit includes the size data indicating the number of tasks that can be performed per unit time by a subject who performs tasks included in the problem. and the means determiner progressively assigns constraints to the problem that need to be considered in solving the problem if the number of jobs indicated by the size data exceeds a predetermined number. a first means for searching for a solution to said problem by imposing a number on which to select said task to be assigned to said subject from among said tasks included in said problem and when said subject performs said assigned task; A second means for searching for a solution to the problem by a process that minimizes the cost in cases, and a dual problem of a relaxed problem that partially relaxes the problem by selecting a feasible solution from the solutions to the problem. A third means for searching for a solution to the problem by solving is determined as a means for searching for a solution to the problem.

One aspect of the present invention is the above-described decision support device, wherein the data acquisition unit includes the size indicating the product of the total number of jobs included in the problem and the total number of subjects included in the problem. Obtaining data, the means determiner determines the second means as the means for searching for a solution to the problem when the product indicated by the size data is less than a first threshold.

One aspect of the present invention is the above-described decision support device, wherein the data acquisition unit includes the size indicating the product of the total number of jobs included in the problem and the total number of subjects included in the problem. data, and the means determiner selects the first means from the problem if the product indicated by the size data is greater than or equal to a first threshold and less than a second threshold that is greater than the first threshold; is determined as a means of searching for the solution of

One aspect of the present invention is the above-described decision support device, wherein the data acquisition unit includes the size indicating the product of the total number of jobs included in the problem and the total number of subjects included in the problem. Data is acquired, and the means determination unit determines the third means as means for searching for a solution to the problem when the product indicated by the size data is equal to or greater than a second threshold.

One aspect of the present invention is the above-described decision support device, wherein the data acquisition unit includes size data indicating the number of tasks that a subject who performs work in the problem can perform per unit time, and the problem Acquiring first data indicating a solution obtained by solving and second data indicating a solution obtained by solving the problem under constraints related to at least two of the plurality of objective functions, When the number of jobs indicated by the size data is equal to or less than a predetermined number, the means determination unit determines the similarity of at least one of the solutions indicated by the second data to the solution indicated by the first data. A fourth means for searching for a solution to the problem based on the degree of similarity is determined as a means for searching for a solution to the problem.

One aspect of the present invention is a data acquisition function that acquires size data indicating the size of a problem for which multiple objective functions may be defined, and searching for a solution to the problem based on the size of the problem indicated by the size data. It is a decision-making support program that causes a computer to realize a means-determining function for determining means.

An aspect of the present invention obtains size data indicative of the size of a problem for which multiple objective functions may be defined, and determines means for searching for a solution to the problem based on the size of the problem indicated by the size data. .

According to the present invention, a decision-making support device and a decision-making support program are capable of solving a problem even when the number of tasks that a subject performing a task included in the problem can perform per unit time is large. and a decision support method can be provided.

It is a figure showing an example of a decision support system concerning an embodiment of the present invention. It is a figure for demonstrating an example of the 1st means 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. FIG. 4 is a diagram showing an example of a similarity matrix calculated from the viewpoint of the order of construction sites on each patrol route by the decision support device according to the embodiment of the present invention; FIG. 5 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 combinations of supervisors and patrol routes; FIG. 11 is a diagram showing an example of a similarity matrix obtained by calculating a weighted sum of the similarity matrix shown in FIG. 9 and the similarity matrix shown in FIG. 10 by the decision support device according to the embodiment of the present invention; is. 4 is a flow chart showing an example of processing executed by the decision support device according to the embodiment of the present invention; A diagram showing an example of the relationship between the number of construction works that can be supervised per day by the supervisor according to the embodiment of the present invention and the exponent of the memory capacity that can be used for the calculation executed to solve the problem by the fourth means. is. FIG. 4 is a diagram illustrating an example of the relationship between problem size and exponent of the number of feasible solutions according to an embodiment of the present invention; FIG. 4 is a diagram showing an example of the relationship between problem size and time required to search for an optimal solution according to an embodiment of the present invention; FIG. 4 is a diagram showing an example of the relationship between parameters related to the size of the problem, the total number of feasible solutions, and the time required to calculate the optimal solution by the second means according to the embodiment of the present invention; 1 shows an example of the relationship between a parameter related to the size of a problem according to an embodiment of the present invention and the time required to calculate the optimal solution by the first, second, third, or fourth means; It is a diagram.

FIG. 1 is a diagram showing an example of a decision support system according to an embodiment of the invention. The decision support device shown in FIG. 1 is a device for searching for solutions to problems such as multi-objective optimization problems. A multi-objective optimization problem is a problem for which multiple objective functions can be defined. An objective function is a function that represents the factors that need to be considered in a multi-objective optimization problem. For example, if the multi-objective optimization problem is that of assigning multiple supervisors to multiple construction sites, the objective function is the total distance each supervisor must travel to tour the multiple construction sites, The total amount of time required for a supervisor to patrol multiple construction sites, the consistency of at least one of the supervisor's knowledge, supervisor's experience, and supervisor's equipment with the content of the work. It is the degree of burden that the supervisor bears when supervising the construction work. The equipment referred to here is, for example, equipment such as stepladders used when supervising construction work. In addition, the solution of the multi-objective optimization problem preferably maximizes or minimizes as many objective functions as possible, and more preferably maximizes or minimizes as many objective functions as possible. .

As shown in FIG. 1 , the decision support system 1 includes a decision support device 10 , a terminal 20 , an arithmetic device 30 and an output device 40 . The decision support device 10, the terminal 20, the arithmetic 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 decision support device 10 includes a data acquisition unit 11, a means determination unit 12, a solution search unit 13, and a data output unit 14, as shown in FIG. In the following description, a case where the decision support device 10 searches for a solution to the problem of allocating a plurality of supervisors to a plurality of construction sites will be described as an example.

The data acquisition unit 11 acquires size data indicating the size of a problem such as a multi-objective optimization problem in which multiple objective functions can be defined. The problem size referred to here is, for example, the product of at least two of the total number of works considered in the problem, the total number of supervisors considered in the problem and the number of works an individual supervisor can supervise per day. is.

In addition, the size data indicates the number of tasks that can be performed per unit time by a subject who performs tasks included in the problem. Furthermore, size data indicates the product of the total number of jobs involved in the problem and the total number of subjects involved in the problem. A subject is an individual supervisor if the multi-objective optimization problem is that of assigning multiple supervisors to multiple construction sites. A task is the task of supervising individual construction works, where the multi-objective optimization problem is that of assigning multiple supervisors to multiple construction sites. The unit time is, for example, one day.

The means determination unit 12 determines means for searching for a solution to the problem based on the size of the problem indicated by the size data.

For example, when the number of jobs indicated by the size data exceeds a predetermined number, the means determination unit 12 determines one of the first, second, and third means as means for searching for a solution to the problem. do. The predetermined number referred to here is, for example, based on at least one of the amount of memory available for calculations performed by the decision support device 10 to solve the problem and the time required for the decision support device 10 to search for a solution. is a number determined by

The first means is to search for the solution of the problem by progressively imposing constraints on the problem that need to be considered when finding the solution of the problem. The means determination unit 12 determines that the product of the total number of jobs included in the problem indicated by the size data and the total number of subjects included in the problem is equal to or greater than the first threshold and is less than the second threshold, which is greater than the first threshold. , the first means is determined as the means for searching for the solution of the problem.

FIG. 2 is a diagram for explaining an example of the first means according to the embodiment of the present invention. FIG. 2 shows an example of a vector space representing a set of solutions to a problem. The two arrows shown in FIG. 2 indicate an example of axes that define the vector space. 2 also shows a closed curve C1, a closed curve C2, a closed curve C3, a closed curve C4, a region R and a point P. FIG.

A closed curve C1 represents the set of solutions to the problem calculated using the first mathematical model. A closed curve C2 represents a set of solutions to the problem calculated using a second mathematical model with constraints added to the first mathematical model. A closed curve C3 represents the set of solutions to the problem calculated using the third mathematical model, which is the constraint of the second mathematical model. A closed curve C4 represents a set of solutions to the problem calculated using the fourth mathematical model, which is a constraint on the third mathematical model.

Region R represents a set of feasible solutions among the solutions to the problem. A feasible solution here is, for example, if the multi-objective optimization problem is the problem of assigning multiple supervisors to multiple construction sites, all supervisors involved in the problem are A solution that allows construction to be supervised and all construction involved in the problem is supervised by a supervisor.

Point P represents the optimal solution among the solutions to the problem. The optimum solution referred to here is one of feasible solutions, and is a solution in which at least one of the factors that need to be considered in solving the problem satisfies a predetermined condition. Also, such factors are, for example, if the multi-objective optimization problem is that of assigning multiple supervisors to multiple construction sites, the distance a supervisor needs to travel to tour multiple construction sites, The time required for the supervisor to patrol multiple construction sites, the consistency of at least one of the supervisor's knowledge, the supervisor's experience, and the supervisor's equipment with the content of the construction, the supervisor is the burden borne when supervising construction work.

For example, as shown in FIG. 1, the first means applies these four mathematical models to the problem in the order of a first mathematical model, a second mathematical model, a third mathematical model and a fourth mathematical model, and obtains a feasible solution. and means for searching for at least one of the optimal solutions.

The second means is a means of searching for a solution to the problem by minimizing the number of cases in which tasks to be assigned to the subject are selected from among the tasks included in the problem, and the cost when the subject performs the assigned tasks as much as possible. is. If the product of the total number of jobs included in the problem indicated by the size data and the total number of subjects included in the problem is less than the first threshold, the means determination unit 12 searches for the solution of the problem using the second means. Decide as a means.

A third means searches for a solution to the problem by selecting a feasible solution from among the solutions to the problem and solving the dual problem of the relaxed problem in which a part of the problem is relaxed. If the product of the total number of jobs included in the problem indicated by the size data and the total number of subjects included in the problem is equal to or greater than the second threshold, the means determining unit 12 searches for the solution of the problem using the third means. Decide as a means.

The above-described first threshold and second threshold are, for example, the amount of memory that can be used for calculations executed by the decision support device 10 to solve the problem and the time required for the decision support device 10 to search for a solution. is a threshold determined based on at least one of

The solution searcher 13 searches for a solution to the problem by means determined by the means determiner 12 . Specifically, when the number of jobs indicated by the size data exceeds a predetermined number, the solution search unit 13 determines the problem by the first means, the second means, or the third means determined by the means determination unit 12. search for a solution of

Further, for example, when the number of jobs indicated by the size data is equal to or less than the above-described predetermined number, the means determination unit 12 determines the fourth means as means for searching for a solution to the problem. In this case, the data acquisition unit 11 acquires the first data and the second data. The first data is, for example, data indicating a solution obtained by manually solving a problem in which multiple objective functions can be defined, and is generated by terminal 20 . The second data is, for example, data representing a solution obtained by mathematically solving a problem under constraints relating to at least two of a plurality of objective functions, and is generated by the computing device 30 . The fourth means is a means for calculating a degree of similarity between at least one solution indicated by the second data and the solution indicated by the first data, and searching for a solution to the problem based on the degree of similarity.

The terminal 20 is, for example, a computer or a tablet, and enables the use of applications that assist the operator in manually solving the multi-objective optimization problem.

This application displays the equipment possessed by the supervisor, such as an icon indicating the location of the construction site, an icon indicating the current position of the supervisor, numbers, letters, symbols indicating the level of knowledge and experience possessed by the supervisor. An image in which the icon shown is superimposed on the map is displayed on the display. The construction work displayed as an icon in this image may be only construction work notified by telephone, facsimile, e-mail, or the like that requires supervision by a supervisor in advance. The data indicating the supervisor's current position is generated, for example, by measuring using a GPS (Global Positioning System) installed in a mobile terminal carried by the supervisor. In addition, this application appropriately updates the image displayed on the display when the supervisor's current position or the like is updated. The above-described application enables a person in charge to immediately and intuitively assign a plurality of supervisors to a plurality of construction sites while viewing images displayed on the display.

After the multi-objective optimization problem is solved using the applications described above, the terminal 20 sends the first data indicating this solution to the decision support device 10 .

Arithmetic device 30 is, for example, a computer, and applies a mathematical model to solve the same multi-objective optimization problem as the multi-objective optimization problem solved using the application described above.

A first example of this mathematical model is a mathematical model that minimizes the value calculated by the following equation (1). The first term of equation (1) represents the total distance _DS that each supervisor must travel to patrol multiple construction sites. The second term of formula (1) represents the sum of _csj , the degree of consistency between at least one of the supervisor's knowledge, supervisor's experience, and supervisor's equipment and the content of the construction work. there is Both the first term of formula (1) and the second term of formula (1) are examples of objective functions. Also, the second term of the equation (1) is multiplied by the weight α. S, J, and T included in equation (1) respectively represent a set of supervisors, a set of construction workers, and a set of time periods during which construction work is carried out. Also, s, j, and t included in formula (1) represent elements of S, J, and T, respectively.

The decision variable x _sjt included in the second term of equation (1) is "0" or "1" as expressed by the following equation (2). The decision variable x _sjt is "1" if supervisor s is assigned to construction j performed during time period t, and "0" otherwise.

Also, the decision variable x _sjt is subject to the constraint represented by the following equation (3) and the constraint represented by the following equation (4). Equation (3) expresses that it is impossible to have one supervisor supervise two or more construction works with overlapping time slots. Formula (4) expresses that the number of supervisors assigned to one construction project is one, and that the one construction project is carried out in one time slot.

Furthermore, the path D _S included in the first term of equation (1) includes the decision variables x _sj and the path d _kl between the site where construction k is performed and the site where construction l is performed. (5).

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

A second example of the mathematical model described above is a mathematical model that minimizes the value calculated by the equation (1) described above. However, in the second example of the mathematical model, the following _formula ( 6) Make the value calculated by the formula (1) as small as possible under the constraint condition expressed in 6). Equation (6) sets an upper limit on the total degree of burden each supervisor bears when supervising construction work.

The third example of the mathematical model, like the second example of the mathematical model described above, is the total distance traveled by each supervisor to patrol a plurality of construction sites, the knowledge possessed by the supervisor, the supervision The degree of consistency between the experience of the supervisor and at least one of the equipment possessed by the supervisor and the content of the work, and the total degree of burden each supervisor bears in supervising the work.

However, in the third example of the mathematical model, focusing on the fact that each supervisor actually supervises at most three construction works, the solution can be calculated in a shorter calculation time than in the second example of the mathematical model. making it possible. Specifically, the third example of the mathematical model is the combination of construction patrol routes that the supervisor can patrol, the time required to move on each patrol route, and the experience and possession of the supervisor. Calculate in advance the degree of consistency between at least one of the installed equipment and the details of the construction work. A third example of a mathematical model is that all construction sites are included in one of the patrol routes, the time required to move on each patrol route is as short as possible, and the experience of the supervisor is Also, a solution that maximizes the total degree of consistency between at least one piece of equipment possessed by the supervisor and the contents of the construction work is selected.

A third example of a mathematical model is a mathematical model that minimizes the value calculated by the following equation (7). The first term _cr in parentheses in equation (7) represents the time required to move on the cyclic route r. The second term q _rs in the parenthesis of formula (7) represents the degree of consistency between at least one of the experience of the supervisor and the equipment possessed by the supervisor and the content of the construction work, and the weight α is is hung. R included in equation (7) represents a set of cyclic routes. Also, r included in formula (7) represents an element of R.

The decision variable _{y_rs} included in equation (7) is "0" or "1" as expressed in equation (8) below. The decision variable y _rs is "1" if the supervisor s supervises the construction included on the patrol route r, otherwise it is "0".

Also, the decision variable _yrs is subject to the constraints expressed by the following equations (9) to (12). Equation (9) expresses that each construction work is not included in multiple patrol routes and that each construction work is not supervised by multiple supervisors. Equation (10) expresses that there is one patrol route that one supervisor patrols. Equation (11) expresses that the total number of supervisors is greater than or equal to the total number of patrol routes. Equation (12) sets an upper limit for the sum of the degree of burden _mr that each supervisor bears when supervising the construction on the patrol route r.

Arithmetic unit 30 applies any of the above-described first to third examples of mathematical models to obtain a solution to the multi-objective optimization problem. The computing device 30 then transmits the second data indicating this solution to the decision support device 10 .

The data acquisition unit 11 acquires first data from the terminal 20 and acquires second data from the arithmetic device 30 . In the following description, a case where the arithmetic unit 30 obtains a solution using the above-described third example of the mathematical model will be described as an example.

FIG. 3 is a diagram showing an example of an image displaying the solution indicated by the first data according to the embodiment of the present invention. The image PH shown in FIG. 3 depicts the construction sites W1 to W9 on the map, and the patrol routes of each supervisor are depicted by solid lines, dashed lines, or dashed lines.

The image PH shown in FIG. A solution H is shown in which the person C visits the construction site W7, the construction site W8, and the construction site W9 in this order.

4 to 8 are diagrams showing examples of images displaying solutions indicated by the second data according to the embodiment of the present invention. The images shown in FIGS. 4 to 8 depict construction sites W1 to W9 on a map, and the patrol routes of each supervisor are depicted by solid lines, dashed lines, or dashed lines.

In the image PM1 shown in FIG. 4, the supervisor A tours the construction site W1, the construction site W2, and the construction site W3 in this order, and the supervisor B tours the construction site W4, the construction site W5, and the construction site W6 in this order. A solution M1 is shown in which the person C 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. 5, the supervisor A tours the construction site W1, the construction site W2, and the construction site W3 in this order, and the supervisor B tours the construction site W4, the construction site W5, and the construction site W6 in this order. A solution M2 is shown in which the supervisor C visits the construction site W8, the construction site W7, and the construction site W9 in this order. In the image PM3 shown in FIG. 6, the supervisor A tours the construction site W1, the construction site W2, and the construction site W4 in this order, and the supervisor B tours the construction site W3, the construction site W5, and the construction site W7 in this order. A solution M3 is shown in which the person C patrols the construction site W6, the construction site W8, and the construction site W9 in this order.

In the image PM4 shown in FIG. 7, the supervisor A tours the construction site W1, the construction site W2, and the construction site W3 in this order, and the supervisor B tours the construction site W4, the construction site W5, and the construction site W7 in this order. , the supervisor C patrols the construction site W6, the construction site W8, and the construction site W9 in this order. In the image PM5 shown in FIG. 8, the supervisor A tours the construction site W1, the construction site W6, and the construction site W8 in this order, and the supervisor B tours the construction site W3, the construction site W5, and the construction site W7 in this order. A solution M5 is shown in which the person C patrols the construction site W2, the construction site W4, and the construction site W9 in this order.

At least one of the images shown in FIGS. 3 to 8 is displayed on a display connected to the decision support device 10, for example.

The solution searcher 13 searches for a solution to the problem by means determined by the means determiner 12 . Specifically, when the number of jobs indicated by the size data is equal to or less than a predetermined number, the solution searching section 13 searches for the solution of the problem by the fourth means determined by the means determining section 12 . The solution searching unit 13 calculates the degree of similarity between at least one of the solutions indicated by the second data and the solution indicated by the first data. Similarity is defined between two solutions. For example, the solution searching unit 13 calculates the similarity according to the procedure described below.

First, the solution searching unit 13 calculates the similarity by viewpoint between two solutions among the solutions shown in FIGS. This aspect is an example of the aspects considered when solving a multi-objective optimization problem.

The solution searching unit 13 converts the cyclic paths indicated by solid lines, dashed lines, or double-dotted lines in FIGS. 3 to 8 into simple undirected graphs, and converts the simple undirected graphs into adjacency matrices. Each of these simple undirected graphs has the number of edges equal to the number of construction sites on each patrol route. Then, the solution searching unit 13 takes the number of different values among the elements of the upper triangular matrix of the adjacency matrix of each of the two solutions as the elements of the similarity matrix. This is because when all edges of two simple undirected graphs are different, the number of elements of upper triangular matrices that are different from each other is twice the number of constructions included in one cyclic path. .

FIG. 9 is a diagram showing an example of a similarity matrix calculated from the viewpoint of the order of construction sites on each patrol route by the decision support device according to the embodiment of the present invention. The elements of the similarity matrix H _A shown in FIG. 9 indicate the similarity by viewpoint between two solutions. A smaller value of the similarity by viewpoint indicates that the two solutions are more similar, and a larger value indicates that the two solutions are more distant from each other. For example, as shown in the second row of FIG. 9, solution H indicated by the first data is exactly the same as solution M1, solution M2 and solution M3 when examined from the viewpoint of the order of construction sites on each patrol route. Yes, different from solution M4, solution M5 and solution M6.

Next, the solution searching unit 13 calculates the viewpoint-based similarity between two of the solutions shown in FIGS. 3 to 8 based on the viewpoint of the combination of the supervisor and the patrol route. This aspect is an example of the aspects considered when solving a multi-objective optimization problem.

The solution searching unit 13 calculates adjacency matrices that link supervisors and patrol routes with respect to the solutions shown in FIGS. 3 to 8 . Then, the solution searching unit 13 takes the number of different values among the elements of the adjacency matrix of each of the two solutions as the elements of the similarity matrix.

FIG. 10 is a diagram showing an example of a similarity matrix calculated from the viewpoint of combinations of supervisors and patrol routes by the decision support device according to the embodiment of the present invention. Elements of the similarity matrix H _B shown in FIG. 10 indicate the similarity between two solutions by viewpoint. A smaller value of the similarity by viewpoint indicates that the two solutions are more similar, and a larger value indicates that the two solutions are more distant from each other. For example, as shown on the second line in FIG. It can be seen that it differs from M3, solution M4, solution M5 and solution M6.

Then, the solution searching unit 13 calculates the similarity matrix H _C by calculating the weighted sum of the similarity matrix H _A shown in FIG. 9 and the similarity matrix H _B shown in FIG. For example, the solution searching unit 13 calculates the similarity matrix H _C using the following equation (13) including the weight β. The weight β represents the degree of importance of the combination of the supervisor and the patrol route based on the order of construction sites on each patrol route.

FIG. 11 shows a similarity matrix obtained by calculating the weighted sum of the similarity matrix shown in FIG. 9 and the similarity matrix shown in FIG. 10 by the decision support device according to the embodiment of the present invention. It is a figure which shows an example. The similarity matrix H _C shown in FIG. 11 is calculated with the value of the weight β set to 0.67. Elements of the similarity matrix H _C are similarities that are weighted sums of the above-mentioned two types of similarities by viewpoint.

For example, as shown in the second row of FIG. 11, the solution H indicated by the first data includes the order of construction sites on each patrol route, the combination of supervisors and patrol routes, and the It can be seen that it is exactly the same as solution M1 and different from solution M2, solution M3, solution M4, solution M5 and solution M6 when considering the difference in relative importance of viewpoints.

The data output unit 14 outputs solution data indicating the solution of the problem. Specifically, the data output unit 14 transmits the solution data indicating the solution of the problem found by the first means, second means, third means or fourth means to the output device 40 shown in FIG. The output device 40 is, for example, a display, and displays the solution indicated by the solution data received from the data output unit 14 in any form.

Further, the data output unit 14 may output second data whose degree of similarity satisfies a predetermined condition. For example, the data output unit 14 transmits to the output device 40 shown in FIG. 1 the second data indicating the solution whose degree of similarity with the solution H exceeds a predetermined threshold among the solutions M1 to M5 described above. You may The output device 40 displays the solution indicated by the second data received from the data output unit 14 in any form.

Further, the data output unit 14 may output at least one of the similarity data indicating the above-described similarity and the viewpoint-based similarity data indicating the above-described viewpoint-based similarity. When receiving these data from the data output unit 14, the output device 40 displays the degree of similarity indicated by the data and the degree of similarity by viewpoint in an arbitrary manner.

Next, an example of processing executed by the decision support device according to the embodiment will be described with reference to FIG. 12 . FIG. 12 is a flow chart showing an example of processing executed by the decision support device according to the embodiment of the present invention.

In step S101, the data acquisition unit 11 acquires size data.

In step S102, the means determining unit 12 determines whether or not the number of jobs indicated by the size data exceeds a predetermined number. If the means determination unit 12 determines that the number of jobs indicated by the size data exceeds the predetermined number (step S102: YES), the process proceeds to step S103. On the other hand, when the means determination unit 12 determines that the number of jobs indicated by the size data is equal to or less than the predetermined number (step S102: NO), the process proceeds to step S108.

In step S103, the means determination unit 12 determines whether or not the product indicated by the size data is less than the first threshold. The means determination unit 12 determines whether or not the product indicated by the size data is less than the first threshold. If the means determination unit 12 determines that the product indicated by the size data is less than the first threshold (step S103: YES), the process proceeds to step S104. On the other hand, when the means determining unit 12 determines that the product indicated by the size data is equal to or greater than the first threshold (step S103: NO), the process proceeds to step S105.

In step S104, the means determination unit 12 determines the second means as the means for searching for the solution of the problem.

In step S105, the means determining unit 12 determines whether or not the product indicated by the size data is less than the second threshold. If the means determination unit 12 determines that the product indicated by the size data is less than the second threshold (step S105: YES), the process proceeds to step S106. On the other hand, when the product indicated by the size data is equal to or greater than the second threshold (step S105: NO), the means determination unit 12 advances the process to step S107.

In step S106, the means determining unit 12 determines the first means as means for searching for a solution to the problem.

In step S107, the means determination unit 12 determines the third means as means for searching for the solution of the problem.

In step S108, the data acquisition unit 11 acquires first data and second data.

In step S109, the means determining unit 12 determines the fourth means as means for searching for the solution of the problem.

In step S110, the solution searching unit 13 searches for a solution by the determined means.

In step S111, the data output unit 14 outputs solution data indicating the solution.

In the above, the decision support system 1 according to the embodiment has been mainly described with reference to the decision support device 10 . The decision support system 10 determines how to search for a solution to the problem based on the size of the problem indicated by the size data. Specifically, when the number of jobs indicated by the size data exceeds a predetermined number, the decision support device 10 searches for a solution to the problem using the above-described first means, second means, or third means. Decide as a means. If the number of jobs indicated by the size data is equal to or less than the predetermined number, the decision support device 10 determines the fourth means as means for searching for a solution to the problem.

With these processes, the decision support device 10 can perform a large number of tasks that can be performed per unit time by a subject who performs tasks included in a problem, and when the size of the problem is large, the first task suitable for the size is determined. Advantageously, the means, the second means or the third means can be used to search for a solution to the problem. Therefore, the decision support device 10 can solve the problem even if there is a limit to the amount of memory that can be used for calculations to solve the problem. In addition, by the above-described processing, the decision support device 10, when the number of tasks that can be performed per unit time by the subject who performs the task included in the problem is small and the size of the problem is small, the decision support device 10 The effect is that the solution of the problem can be searched for using the fourth means.

Next, a specific example of the effect produced by the decision support device 10 will be described with reference to FIG. 13 . FIG. 13 shows the relationship between the number of construction works that can be supervised per day by the supervisor according to the embodiment of the present invention and the exponent of the memory capacity that can be used for the calculations performed to solve the problem by the fourth means. It is a figure which shows an example. The horizontal axis of FIG. 13 indicates the number of works that the supervisor can supervise per day. The vertical axis of FIG. 13 indicates the exponent of the amount of memory available for the calculations performed to solve the problem. As shown in FIG. 13, the exponent of the memory capacity available for the calculations to be executed to solve the problem is the number of works that the supervisor can supervise per day or the number of works that the supervisor can supervise per day. Increases as the number increases. In addition, for example, in a region where the exponent of the memory capacity that can be used for the calculations executed to solve the problem exceeds 11, it becomes difficult for the decision support device 10 to search for the solution of the problem by the fourth means. Sometimes.

However, the decision-making support device 10 is capable of performing the task included in the problem even if the available memory capacity is insufficient due to the large number of tasks that can be performed per unit time. A solution to the problem can be sought by one means, second means or third means.

If the product of the total number of jobs included in the problem indicated by the size data and the total number of subjects included in the problem is less than the first threshold, the decision support device 10 searches for a solution to the problem using the second means. Decide as a means to In addition, the decision support device 10 determines that the product of the total number of jobs included in the problem indicated by the size data and the total number of subjects included in the problem is equal to or greater than the first threshold, and the second threshold is greater than the first threshold. If it is less than, determine the first means as the means for searching for a solution to the problem. Furthermore, the decision support device 10 selects the third means to solve the problem when the product of the total number of jobs included in the problem indicated by the size data and the total number of subjects included in the problem is equal to or greater than the second threshold. is determined as a means of searching for

As a result, the decision support device 10 performs the first means, the second means, and the third means according to the product of the total number of jobs included in the problem indicated by the size data and the total number of subjects included in the problem. It is possible to search for the solution of the problem by using suitable means for searching for the solution of the problem from among.

Next, a specific example of the effect produced by the decision support device 10 will be described with reference to FIGS. 14 and 15. FIG. FIG. 14 is a diagram showing an example of the relationship between problem size and exponent of the number of feasible solutions according to an embodiment of the present invention. The horizontal axis of FIG. 14 indicates the size of the problem. The problem size is the product of the total number of constructions involved in the problem and the total number of supervisors involved in the problem. The vertical axis in FIG. 14 indicates the total number of feasible solutions among the solutions to the problem. As shown in FIG. 14, the total number of feasible solutions increases as the size of the problem or the number of works the supervisor can supervise per day increases, with problem sizes ranging from about 160 to about 504 increase more rapidly.

FIG. 15 is a diagram showing an example of the relationship between the problem size and the time required to search for the optimal solution according to an embodiment of the present invention. The horizontal axis of FIG. 15 indicates the size of the problem. The problem size is the product of the total number of constructions involved in the problem and the total number of supervisors involved in the problem. The vertical axis in FIG. 15 indicates the time required to search for the optimum solution. As shown in FIG. 15, the time required to search for the optimal solution increases as the size of the problem increases or as the number of constructions a supervisor can supervise per day increases.

However, the decision-making support device 10 does not allow the amount of time that can be spent on calculations to solve the problem because the number of tasks that can be performed per unit time by the subject who performs the task included in the problem is large. Even if it is insufficient, it is possible to search for a solution to the problem by the first means, second means or third means.

Next, FIG. 16 and FIG. 17 are used to show data on which FIGS. 13 to 15 are based. FIG. 16 shows an example of the relationship between the parameters related to the problem size, the total number of feasible solutions, and the time required to calculate the optimal solution by the second means according to an embodiment of the present invention. It is a diagram. 16, the second to fourth lines from the top, the fifth to seventh lines from the top, the eighth to tenth lines from the top, and the eleventh to thirteenth lines from the top , if the total number of constructions involved in the problem and the total number of supervisors involved in the problem are constant, the total number of feasible solutions increases with the number of constructions a supervisor can supervise per day. It can be seen that it increases according to In FIG. 16, the second to fourth lines from the top, the fifth to seventh lines from the top, the eighth to tenth lines from the top, and the eleventh to thirteenth lines , if the total number of works involved in the problem and the total number of supervisors involved in the problem are constant, the optimal solution is found by the second means as the number of works a supervisor can supervise per day increases. This indicates that the time required to calculate

FIG. 17 shows the relationship between the parameters related to the size of the problem according to the embodiment of the present invention and the time required to calculate the optimal solution by the first, second, third or fourth means. It is a figure which shows an example. 17, the second to fourth lines from the top, the fifth to seventh lines from the top, the eighth to tenth lines from the top, and the eleventh to thirteenth lines from the top , if the total number of construction works involved in the problem and the total number of supervisors involved in the problem are constant, the time required to calculate the optimal solution by the fourth means is the number of supervisors per day. It can be seen that it increases as the number of supervisable works increases. Also, referring to the first column from the right in FIG. 17, the time required to calculate the optimum solution by the fourth means can be used for calculation when the number of constructions that the supervisor can supervise in a day is large. This indicates that it may become impossible to search for solutions due to insufficient memory capacity.

In addition, in FIG. , if the total number of works involved in the problem and the total number of supervisors involved in the problem are constant, the optimal solution by means of the first method increases as the number of works a supervisor can supervise per day increases. This indicates that the time required to calculate Furthermore, this also applies to the second and third means.

Moreover, the decision support apparatus 10 can provide a solution that is close to a solution considered to be in line with the actual situation by determining the means for searching for the solution by the method described above. Therefore, the decision support device 10 can improve the efficiency of the work performed by a person who solves a problem requiring decision making, or assist the work performed by a person who has not acquired sufficient knowledge and experience to solve the problem. can do.

In the above-described embodiment, the problem of allocating multiple supervisors to multiple construction works was exemplified as a multi-objective optimization problem, but the present invention is not limited to this.

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

Alternatively, as the multi-objective optimization problem described above, there is a problem of allocating a plurality of engineers to a plurality of themes when carrying out 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 technical development.

Moreover, at least part of the functions of the decision support device 10 are implemented by a hardware processor such as a CPU (Central Processing Unit) executing a program or software. In addition, some or all of these include circuits such as LSI (Large Scale Integration), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), and GPU (Graphics Processing Unit). It may be realized by hardware, or may be realized by cooperation of software and hardware. The program may be stored in advance in a storage device having a non-transitory storage medium such as a HDD (Hard Disk Drive) or flash memory, or may be stored in a removable non-transitory storage medium such as a DVD or CD-ROM. It may be stored in a storage medium and installed by loading the storage medium into 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 10 is not limited to the above-described embodiments, and various modifications, replacements, combinations, or design changes can be made without departing from the gist of the present invention. may be added.

DESCRIPTION OF SYMBOLS 1... Decision support system, 10... Decision support apparatus, 11... Data acquisition part, 12... Means determination part, 13... Solution search part, 14... Data output part, 20... Terminal, 30... Arithmetic device, 40... Output Device, NW...Network

Claims (8)

a data acquisition unit for acquiring size data indicating the size of a problem for which multiple objective functions may be defined;
a means determination unit that determines means for searching for a solution to the problem based on the size of the problem indicated by the size data;
A decision support device comprising: The data acquisition unit acquires the size data indicating the number of jobs that can be performed per unit time by a subject who performs the tasks included in the problem,
The means determining unit, when the number of jobs indicated by the size data exceeds a predetermined number, by progressively imposing constraints on the problem that need to be considered when finding a solution of the problem. a first means for searching for a solution to the problem, a number of cases of selecting the tasks to be assigned to the subject from the tasks included in the problem, and a cost when the subject performs the assigned tasks; a second means for searching for a solution to the problem by minimizing it as much as possible; and selecting a feasible solution from among the solutions to the problem and solving a dual problem of the relaxed problem that partially relaxes the problem. determining one of a third means for searching for a solution to the problem as a means for searching for a solution to the problem;
The decision support device according to claim 1. The data acquisition unit acquires the size data indicating the product of the total number of jobs included in the question and the total number of subjects included in the question,
The means determination unit determines the second means as a means for searching for a solution to the problem when the product indicated by the size data is less than a first threshold.
The decision support device according to claim 2. The data acquisition unit acquires the size data indicating the product of the total number of jobs included in the question and the total number of subjects included in the question,
The means determining unit causes the first means to search for a solution to the problem when the product indicated by the size data is greater than or equal to a first threshold and less than a second threshold that is greater than the first threshold. determine as a means of
The decision support device according to claim 2. The data acquisition unit acquires the size data indicating the product of the total number of jobs included in the question and the total number of subjects included in the question,
The means determination unit determines the third means as a means for searching for a solution to the problem when the product indicated by the size data is equal to or greater than a second threshold.
The decision support device according to claim 2. The data acquisition unit includes size data indicating the number of jobs that can be performed per unit time by a subject who performs work in the problem, first data indicating a solution obtained by solving the problem, and a plurality of obtaining second data indicative of a solution obtained by solving the problem subject to constraints on at least two of the objective functions;
When the number of jobs indicated by the size data is equal to or less than a predetermined number, the means determination unit determines the degree of similarity of at least one of the solutions indicated by the second data to the solution indicated by the first data. and determining a fourth means for searching for a solution to the problem based on the similarity as a means for searching for a solution to the problem;
The decision support device according to claim 1. a data acquisition function that acquires size data indicative of the size of a problem for which multiple objective functions may be defined;
a means determination function for determining means for searching for a solution to the problem based on the size of the problem indicated by the size data;
A decision-making support program that implements the obtaining size data indicating the size of the problem for which multiple objective functions may be defined;
determining means for searching for a solution to the problem based on the size of the problem indicated by the size data;
Decision support method.

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