This application claims priority to Japanese Patent Application No. 2019-196325 filed on Oct. 29, 2019, the entire contents of which are incorporated by reference herein.
The present invention relates to a plan generating device and a plan generation method, and more particularly to a plan generating device and a plan generation method that can generate a plan in which modification know-how extracted via modification by a planner has been reflected.
The number of events for which advance plans are important and that are the manufacture of products, the operation and management of a large system, and the like is large. In the generation of such a plan, it is necessary to generate the plan satisfying the plan's objective functions of maximizing production, maximizing facility utilization, and minimizing the number of workers, while complying with constraints for resources such as time, a space, a facility, and a person. Since manually generating the plan is time-consuming too much, a computer is used in many cases.
In an actual environment, a plurality of constraints and a plurality of objective functions are complex. Therefore, there is a case where it is difficult to accurately define all the constraints and all the objective functions and input all the constraints and all the objective functions to the computer. In this case, it is possible to improve the accuracy of the plan by defining the constraints and the objective functions in possible ranges and combining the constraints and the objective functions with empirical logic. However, it is very difficult to define, in the computer, the constraints and the objective functions that completely simulate the actual environment, and the empirical logic lacks versatility and extensibility. Therefore, a plan output by the computer hardly satisfies a planner.
To avoid this, there is a proposed technique for measuring ambiguous priorities of the plurality of constraints and the plurality of objective functions based on a result of modifying, by the planner, the plan output by the computer, and for reflecting the priorities in the generation of a next plan.
For example, a priority determining device that is disclosed in Japanese Unexamined Patent Application Publication No. Hei 06-333064 and configured to determine priorities of a plurality of devices determines the priorities using a weight coefficient for a requirement. When the determined priorities are not satisfied, a planner modifies the priorities so that the planner is satisfied with the priorities. Then, the planner treats, as a teacher signal, a weight coefficient given by evaluating a requirement based on the modified priorities, and causes the priority determining device to learn relationships between the teacher signal and input data.
In addition, Japanese Unexamined Patent Application Publication No. 2013-14387 discloses an evaluation parameter learning device that receives an automatic vehicle allocation plan generated by an automatic vehicle allocation plan generating device, a manual vehicle allocation plan modified by a planner, an evaluation value of the automatic vehicle allocation plan, and a target evaluation value, treats evaluation item values of the received manual vehicle allocation plan and the received automatic vehicle allocation plan as input data of teacher data, and learns the evaluation value and the target evaluation value as output values of the teacher data.
In Japanese Unexamined Patent Application Publication No. Hei 06-333064 and Japanese Unexamined Patent Application Publication No. 2013-14387, ambiguous priorities of a plurality of constraints and a plurality of objective functions can be reproduced based on preference of a planner. However, as described above, the actual environment is not completely reflected in the constraints defined in the computer and the objective functions defined in the computer. It is not possible to support the case where a constraint not defined in the computer exists or the case where a constraint and an objective function that are complex and are hardly defined latently exist.
The present invention enables plan generation with high accuracy by reflecting modification know-how extracted from a plan before modification and a plan after the modification by a planner.
A plan generating device according to an aspect of the present invention includes a storage device, an input device, and a plan generator. The storage device stores plan requirement data indicating a constraint and an objective function that are used to generate a plan, and modification know-how data indicating modification know-how for the plan. The input device receives new plan-related information data indicating predetermined plan-related information including an explanatory variable of the constraint and the objective function for the new plan. The plan generator uses the new plan-related information data received from the input device, the plan requirement data read from the storage device, and the modification know-how data read from the storage device to determine a decision variable for the constraint and the objective function. The modification know-how data includes a plurality of groups for which an anti-pattern indicating trends of job patterns of a plurality of plans before modification, a reference pattern indicating trends of job patterns of a plurality of plans after modification that have been obtained by modifying the plans before the modification by a planner, and a modification rate that is a statistical amount of the plan-related information and the decision variable of the plurality of plans before the modification, have been obtained. The plan generator determines the decision variable for the new plan so that the new plan satisfies the constraint and the objective function and is aligned with any of the groups included in the modification know-how data.
A plan generation method according to another aspect of the present invention uses a plan generating device including a storage device, an input device, and a plan generator. The storage device stores plan requirement data indicating a constraint and an objective function that are used to generate a plan and modification know-how data indicating know-how for the plan. The input device receives plan-related information data indicating predetermined plan-related information including an explanatory variable of the constraint and the objective function for the plan. The plan generator determines a decision variable for the constraint and the objective function. The modification know-how data includes a plurality of groups for which an anti-pattern indicating trends of job patterns of a plurality of plans before modification, a reference pattern indicating trends of job patterns of a plurality of plans after modification that have been obtained by modifying the plans before the modification by a planner, and a modification rate that is a statistical amount of the plan-related information and the decision variable of the plurality of plans before the modification, have been obtained. The plan generation method includes the steps of causing the input device to receive new plan-related information data indicating the plan-related information of the new plan, and causing the plan generator to use the new plan-related information data received from the input device, the plan requirement data read from the storage device, and the modification know-how data read from the storage device to determine the decision variable for the new plan so that the new plan satisfies the constraint and the objective function and is aligned with any of the groups included in the modification know-how data.
It is possible to generate a plan with high accuracy by reflecting modification know-how extracted from a plan before modification and a plan after the modification by a planner.
Other challenges and new features will be clarified from the description of the present specification and the accompanying drawings.
Hereinafter, an embodiment of the present invention is described with reference to the drawings. A plan generating device according to the embodiment learns modification know-how from a plan before modification and a plan after the modification and uses the learned modification know-how to generate a plan with high accuracy. The plan to be generated by the plan generating device is not limited. The embodiment may be applied to various plans such as plans for production in a facility, maintenance of social infrastructure, and personnel allocation. The embodiment describes, as an example, the plan generating device that generates a production plan to work in a process determined in advance and manufacture a product.
The plan generating device 10 has the following hardware configuration. Specifically, the plan generating device 10 includes a storage device 120, a memory 150, a central processing unit (CPU) 110, an input device 130, and an output device 140. The storage device 120 is composed of a nonvolatile storage device such as a solid state drive (SSD), magnetic medium such as a hard disk drive, or the like. The memory 150 is a composed of a volatile storage device such as a random-access memory (RAM). The central processing unit 110 reads a program 115 held in the storage device 120 into the memory 150 and executes the program 115 to comprehensively control the plan generating device 10. The central processing unit 110 executes various types of determination, calculation, and control. The input device 130 receives key input and audio input from a user. The output device 140 is a display or the like that displays processing data. The hardware units 110 to 150 are connected to and able to communicate with each other via a bus.
The central processing unit 110 reads the program 115 stored in the storage device 120 into the memory 150 and executes the program 115, thereby implementing a function of a modification know-how learning section 111 for learning the modification know-how and a function of a plan generator 112 for generating a plan having the modification know-how reflected therein. In the storage device 120, data necessary to execute the functions is stored. Specifically, the data necessary to execute the functions is a constraint/objection function (plan requirement) 121, plan-related information 122, a plan result 123 before modification, a plan result 124 after the modification, a modification log 125, a modification rate table 126, an anti-pattern 127, a reference pattern 128, and modification know-how 129. Details of the data are described later. The program 115 is stored in the storage device 120, but may be introduced by the plan generating device 10 into the storage device 120 from another device via a predetermined medium when necessary, for example, at the time of the execution of the program 115. The medium is a storage medium attachable to and detachable from a predetermined interface of the plan generating device 10 or is a communication medium, for example.
In the example illustrated in
Data of the foregoing constraints, the objective function, the decision variables, and the explanatory variables is stored as the constraint/objective function (plan requirement) 121 of the storage device 120. Next, a data structure of data to be used by the plan generating device 10 to generate the plan is described.
The plan-related information 122 is collected for a past plan and is a set of data related to the plan. Details of the plan-related information 122 can be arbitrarily selected by a user as information that affects whether the plan is excellent or not. For example, it is desirable to determine data to be accumulated as the plan-related information 122 based on knowledge of which information is used to determine whether the modification is required or not, when the planner modifies the plan. As a specific example of the plan-related information 122, plan information 122a and product information 122b are used in this example.
In each of records of the plan information 122a exemplified in
In each of records of the product information 122b exemplified in
Next, the plan result 123 before the modification and the plan result 124 after the modification are described. The planner may modify a start time determined by the plan generating device 10 based on knowledge and know-how of the planner when necessary and carry out a modified plan. In this case, the plan before the modification and the plan after the modification are stored as the plan result 123 before the modification and the plan result 124 after the modification. Therefore, the plan (after the modification) stored in the corresponding plan result 124 after the modification exists corresponding to the plan (before the modification) stored in the plan result 123 before the modification.
In each of records of the plan result 123 (exemplified in
In each of records of the plan result 124 (exemplified in
The modification log 125 is a modification log in which a modification action by the planner is recorded for each step. In each of records of the modification log 125 exemplified in
The foregoing data is primary data serving as basic data to be used by the plan generating device 10 to learn modification know-how of the planner. The modification rate table 126 is generated to identify, from the primary data, the condition that the planner frequently carries out a modification or hardly carious out a modification. As described later, the modification know-how is classified into groups, each of which has the same modification trend. In other words, groups of the modification know-how correspond to populations of the plan result 123 before the modification and the plan result 124 after the modification. The same modification trend is found from each of the populations. The modification rate table 126 indicates the primary data and statistical amounts of the decision variables of the plan before the modification for each of the groups. The types and the number of modification rate tables 126 held in the plan generating device 10 depend on details of the held primary data and the abundance of analysis viewpoints of the modification know-how. An example in which modification rate tables for four types of modification rates exist is described below.
The modification rate table What 126a exemplified in
In each of records of the modification rate table When 126b exemplified in
In each of records of the modification rate table Where 126c exemplified in
The modification rate table Which 126d exemplified in
The plan generating device 10 recognizes a detail of a modification of a plan as a change in a pattern of such a Gantt chart as illustrated in
A pattern XA1, a pattern XA2, and a pattern XA3 are unit job patterns related to intervals between start times of jobs J of a certain process Li. The pattern XA1 is a pattern in which the jobs are left-aligned. The pattern XA2 is a pattern in which the jobs are aligned at equal intervals. The pattern XA3 is a pattern in which the jobs are randomly aligned. A pattern XB1, a pattern XB2, and a pattern XB3 are unit job patterns related to the order of jobs J of a certain process Li. The pattern XB1 is a pattern in which the jobs are aligned in order from a job to be executed for the shortest time period to a job to be executed for the longest time period. The pattern XB2 is a pattern in which the jobs are randomly aligned in terms of time periods for executing the jobs. The pattern XB3 is a pattern in which the jobs are aligned in order from the job to be executed for the longest time period to the job to be executed for the shortest time period. A pattern XC1, a pattern XC2, and a pattern XC3 are unit task patterns related to the order of jobs J when a certain process Li transitions to a process Lii. The pattern XC1 is a pattern in which the order of the jobs J before the transition is the same as the order of the jobs after the transition. The pattern XC2 is a pattern in which the order of the jobs before the transition is opposite to the order of the jobs after the transition. The pattern XC3 is a unit job pattern in which a job is not executed. A pattern XD1 and a pattern XD2 are unit job patterns related to a process Li1 and a process Li2 that are able to be executed at the same time. The pattern XD1 is a pattern in which jobs are executed in parallel. The pattern XD2 is a unit job pattern in which the jobs are executed in one of the processes. The foregoing patterns are examples. Various unit job patterns can be defined.
By defining patterns that appear in a Gantt chart, modification by the planner can be treated as conversion from a pattern before the modification to a pattern after the modification. In this case, a unit job pattern that is the pattern before the modification in many cases is considered to be avoided in the plan generation, and a unit job pattern that is the pattern after the modification in many cases is considered to be desirable for the plan generation. An anti-pattern 127 indicates trends of job patterns considered to be avoided in the plan generation. A reference pattern 128 indicates trends of patterns considered to be desirable for the plan generation.
The modification know-how 129 indicates correspondence between the modification rate tables 126, the anti-pattern 127, and the reference pattern 128. In this example, records of the data are corresponded based on the identity of populations from which the data is obtained.
Next, functions that are achieved by the plan generating device 10 using the foregoing data are described. As described above, the functions of the plan generating device 10 are implemented by the execution of the program 115.
The first function is a function of learning the modification know-how by the modification know-how learning section 111. The modification know-how learning section 111 analyzes the plan-related information 122, the plan result 123 that is before the modification and has been generated by the plan generating device 10, the plan result 124 that is after the modification and has been modified by the planner, and the modification log 125 of the modification. The modification know-how learning section 111 generates the data of the modification rate tables 126, the anti-pattern 127, the reference pattern 128, and the modification know-how 129.
The second function is a function of receiving, by the plan generator 112, new plan-related information for generation of a new plan via the input device 130, generating, by the plan generator 112, the new plan in which the modification know-how learned by the modification know-how learning section 111 has been reflected, and outputting the new plan by the plan generator 112 to the output device 140.
Process operations that are executed to achieve the functions are described below. The process operations are achieved by the program 115. The program 115 is composed of codes for executing various operations described below.
First, a process of achieving the first function is described.
As a premise, the plan generating device 10 is aimed to output a plan that is similar to such a plan that is considered to satisfy the planner as a plan modified by the planner or a plan not modified by the planner, and is not similar to such a plan that is considered not to satisfy the planner as a plan before modification by the planner. Therefore, the plan generating device 10 firstly determines whether the candidate teacher data received is aligned with an existing population of modification know-how. When the candidate teacher data received is aligned with the existing population of the modification know-how, the plan generating device 10 adds the candidate teacher data to the population with which the candidate teacher data has been determined to be aligned as teacher data, and updates learning. In an initial state in which teacher data does not exist, 0.5 indicating an information amount of zero is stored as all values of the modification rate tables, the anti-pattern, and the reference pattern.
First, the modification know-how learning section 111 receives new candidate teacher data (S1010). The candidate teacher data is a data set including a plan before modification, a plan after the modification, corresponding plan-related information, and a corresponding modification log. When the candidate teacher data indicates a plan number “1113-1600”, the reception of the candidate teacher data corresponds to the reception of values of a record of the plan number “1113-1600” of the plan information 122a (refer to
Subsequently, the modification know-how learning section 111 refers the modification rate tables 126 and acquires values of modification rates for the candidate teacher data (S1020). When the candidate teacher data indicates a plan of the plan number “1113-1600”, the modification know-how learning section 111 refers the record Ra1 (refer to
Subsequently, the modification know-how learning section 111 determines whether or not the received candidate teacher data is aligned with any of groups registered as modification know-how. When one or more of the values of the modification rates acquired in step S1020 exceeds a predetermined threshold (YES in S1030), the process proceeds to step S1040. When all the values of the modification rates acquired in step S1020 do not exceed the predetermined threshold (NO in S1030), the process proceeds to step S1910. If a modification rate is close to 100% or close to 0%, the foregoing requirement established means that a plan is significantly modified or is not significantly modified. In addition, if the modification rate is close to 50%, whether the plan is to be modified or not cannot be predicted from the establishment of the foregoing requirement. Therefore, in the process of step S1030, thresholds are set to a value close to 100% and a value close to 0%. For example, when the predetermined threshold is set to 80%, the modification rate of 84% that is indicated for the modified process and is associated with the plan number “1113-1600” in the record Rc1 of the modification rate table Where 126c exceeds the predetermined threshold. Therefore, since there is a possibility that the plan of the plan number “1113-1600” is indicated in teacher data aligned with the “modification know-how 1”, the process proceeds to step S1040.
In step S1040, the modification know-how learning section 111 refers the modification know-how 129 and extracts an anti-pattern and a reference pattern of a modification know-how group (hereinafter referred to as “candidate modification know-how group”) with which the candidate teacher data may be aligned in step S1030. For example, according to the modification know-how 129 (refer to
Subsequently, the modification know-how learning section 111 compares the anti-pattern extracted in step S1040 and the reference pattern extracted in step S1040 with the plans that are before and after the modification and are indicated in the candidate teacher data. In step S1050, the modification know-how learning section 111 determines that the candidate teacher data does not include a pattern having a trend opposite to the candidate modification know-how group. In other words, when the plan before the modification includes many patterns considered to be desirable for the plan generation according to the plan of the candidate modification know-how group, and the plan after the modification includes many patterns considered to be avoided in the plan generation according to the plan of the candidate modification know-how group, there is a possibility that the plan may have been modified due to an unknown cause and that a feature of the plan of the candidate modification know-how group may be reduced by adding the candidate teacher data to the candidate modification know-how group, and thus the candidate teacher data should not be added to the candidate modification know-how group. When the candidate teacher data does not include the pattern having the opposite trend, the process proceeds to step S1060. When the candidate teacher data includes the pattern having the opposite trend, the process proceeds to step S1910. However, when sufficient information is collected as the plan-related information 122, the candidate teacher data is not considered to include the pattern having the opposite trend, except for a special case.
In step S1060, the modification know-how learning section 111 compares the plans that are before and after the modification and are indicated in the candidate teacher data with the anti-pattern and the reference pattern that are included in the candidate modification know-how group. When the plan before the modification is similar to the anti-pattern or the plan after the modification is similar to the reference pattern, the process proceeds to step S1070. When the plan before the modification is not similar to the anti-pattern and the plan after the modification is not similar to the reference pattern, the process proceeds to step S1910. To make the similarity determination in steps S1050 and S1060, the modification know-how learning section 111 may use an anti-pattern similarity Ra described later and a reference pattern similarity Rr (refer to
In step S1070, the modification know-how learning section 111 causes the plan before the modification, the plan after the modification, the corresponding plan-related information, and the corresponding modification log of the candidate teacher data to be added to and accumulated in the plan result 123 before the modification, the plan result 124 after the modification, the plan-related information 122, and the modification log 125 in the storage device 120. Teacher data to be used in subsequent steps of step S1080 is the candidate teacher data used in steps S1010 to S1070.
In step S1080, the modification know-how learning section 111 causes the teacher data to be included in a population for the modification know-how group with which the teacher data has been determined to be aligned, and updates values of a corresponding record of the anti-pattern 127 (refer to
In step S1090, the modification know-how learning section 111 causes the teacher data to be included in the population for the modification know-how group with which the teacher data has been determined to be aligned, and updates values of corresponding records of the modification rate tables 126 (refer to
On the other hand, when the process proceeds to step S1910, the modification know-how learning section 111 can determine that the candidate teacher data is not aligned with any of existing modification know-how groups registered in the modification know-how 129. In step S1910, the possibility of a new modification know-how group with which the candidate teacher data is aligned is considered. For example, the modification know-how learning section 111 compares the plan that is before the modification and is indicated in the candidate teacher data with the plan result 123 that is before the modification and is stored in the storage device 120. The modification know-how learning section 111 compares the plan that is after the modification and is indicated in the candidate teacher data with the plan result 124 that is after the modification and is stored in the storage device 120. When the plan before the modification is similar to a plan indicated in the plan result 123 before the modification, the modification know-how learning section 111 extracts the similar plan before the modification from the plan result 123 before the modification. When the plan after the modification is similar to a plan indicated in the plan result 124 after the modification, the modification know-how learning section 111 extracts the similar plan after the modification from the plan result 124 after the modification. The modification know-how learning section 111 treats plans that are extracted as the similar plans both before and after the modification as a population of a preliminary modification know-how group and calculates the modification rate tables. When a significant value exists in an item included in the calculated modification rate tables, the modification know-how learning section 111 sets the preliminary modification know-how group as a new modification know-how group (S1970). When the significant value does not exist, the process proceeds to step S1990.
The modification know-how learning section 111 can treat plans to be compared as such Gantt charts as illustrated in
In step S1970, the modification know-how learning section 111 causes the plan before the modification, the plan after the modification, the corresponding plan-related information, and the corresponding modification log of the candidate teacher data to be added to and accumulated in the plan result 123 before the modification, the plan result 124 after the modification, the plan-related information 122, and the modification log 125 in the storage device 120, newly additionally registers the modification rate tables calculated in step S1910, and registers a new modification know-how group in the modification know-how 129.
The foregoing process procedure is executed by the modification know-how learning section 111 to accumulate the candidate teacher data as teacher data and update the modification know-how. On the other hand, when the modification know-how group aligned with the candidate teacher data cannot be set in step S1910, the modification know-how learning section 111 does not accumulate the candidate teacher data as the teacher data. In this case, it is desirable that the modification know-how learning section 111 outputs the candidate teacher data and the modification rate tables calculated for the preliminary modification know-how group extracted in step S1910 (S1990). Therefore, the planner can individually analyze the candidate teacher data.
Next, a process procedure for generating a new plan using modification know-how in accordance with new plan-related information is described.
First, the plan generator 112 receives new plan-related information and uses existing logic to generate the plan (S2010). The existing logic is a general method of solving an optimization problem from the constraint/objective function 121 (refer to
Next, the plan generator 112 treats the preliminary plan as an initial solution and generates the plan using the modification know-how (S2030). Details of step S2030 are described later. Subsequently, the plan generator 112 evaluates the generated plan (S2040). Details of step S2040 are described later.
In step S2050, the plan generator 112 compares an evaluation value calculated in step S2040 with an evaluation value of the preliminary plan. When the evaluation value calculated in step S2040 is equal to or larger than the evaluation value of the preliminary plan, the plan generator 112 updates the plan generated in step S2030 as the preliminary plan. When the evaluation value calculated in step S2040 is smaller than the evaluation value of the preliminary plan, the plan generator 112 maintains the preliminary plan.
In step S2060, the plan generator 112 checks whether the preliminary plan satisfies a termination requirement. When the preliminary plan does not satisfy the termination requirement, the plan generator 112 repeatedly executes the processes of steps S2030 to S2050. When the preliminary plan satisfies the termination requirement, the plan generator 112 outputs the preliminary plan as an optimal plan via the output device 140 and terminates the process illustrated in
The plan generator 112 lists a predetermined number of candidate values for each of decision variables (S2310). Specifically, the plan generator 112 selects one of the decision variables determined for the preliminary plan and calculates a predetermined number of candidate values in accordance with a predetermined algorithm. As the predetermined algorithm, a local search algorithm or the like may be used. The predetermined algorithm, however, is not limited. In this case, a weight w is set for each of the candidate values. At this stage, the weights w for the candidate values are equal to each other. For example, when 10 candidate values exist, each of the weights w for the candidate values is 0.1.
Next, the plan generator 112 acquires values of modification rates for each of the candidate values (S2320). Specifically, the plan generator 112 acquires the modification rates (hereinafter referred to as “preliminary decision variable”) while maintaining a value of a decision variable not to be subjected to the process of step S2310 as a value of the preliminary plan and treating a value of a decision variable to be subjected to the process of step S2310 as the candidate value. Therefore, when 10 candidate values exist, 10 sets of modification rates are acquired.
Subsequently, in step S2330, the plan generator 112 selects one set of modification rates, compares the modification rates acquired in step S2320 with modification rates of modification know-how groups (refer to
The plan generator 112 refers the modification know-how 129 (refer to
On the other hand, when a modification know-how group whose modification rates are similar to the acquired modification rates does not exist in step S2330, the plan generator 112 maintains the weights w. The plan generator 112 repeatedly executes the foregoing processes of steps S2330 and later on all the candidate values (S2370).
When the plan generator 112 completely adjusts the weights w for all the candidate values, the plan generator 112 selects a candidate value by stochastic selection (roulette selection) (S2380). In this case, by reducing the weights w for the candidate value whose selection in step S2380 leads to a plan including many anti-patterns or by increasing the weights w for the candidate value whose selection in step S2380 leads to a plan including many reference patterns, the probability that the candidate value that leads to a plan including many reference patterns is selected in step S2380 is increased.
The plan generator 112 executes the foregoing processes on all the decision variables (S2390) and determines values for all the decision variables. Then, the plan generator 112 terminates the plan generation (of step S2030) executed using the modification know-how.
An evaluation value E=the objective function f+α×1/the anti-pattern similarity Ra+β×the reference pattern similarity Rr (Equation 1)
In Equation (1), α and β are positive constants.
The plan generator 112 firstly identifies a modification know-how group aligned with the plan (S2410). In this case, the plan generator 112 identifies the modification know-how group by extracting the modification know-how group whose modification rates are similar to the acquired modification rates in the same manner as step S2330 illustrated in
Next, the plan generator 112 calculates the objective function f (S2420). The objective function f is given in advance (refer to
Next, the plan generator 112 calculates the anti-pattern similarity Ra of the identified modification know-how group (S2430). The anti-pattern similarity Ra can be defined as the sum of values obtained by multiplying similarities between an image of a Gantt chart of the plan to be evaluated and images of Gantt charts of the patterns illustrated in
Next, the plan generator 112 calculates the reference pattern similarity Rr of the identified modification know-how group (S2440). The reference pattern similarity Rr can be defined as the sum of values obtained by multiplying the similarities between the image of the Gantt chart of the plan to be evaluated and the images of the Gantt charts of the patterns illustrated in
In step S2450, the plan generator 112 uses the value of the objective function f calculated in step S2420, the value of the anti-pattern similarity Ra calculated in step S2430, and the value of the reference pattern similarity Rr calculated in step S2440 to calculate the evaluation value E according to Equation (1). The evaluation value E is larger as the objective function f and the reference pattern similarity Rr are larger and the anti-pattern similarity Ra is smaller. The plan is more highly evaluated as the objective function f is larger, the number of patterns desirable for the plan generation is larger, and the number of patterns to be avoided in the plan generation is smaller.
According to the embodiment described above, it is possible to formalize modification know-how of the planner from the plan result before the modification and the plan result after the modification, reflect the modification know-how in the plan generation, and output the plan satisfying the planner.
Although the embodiment of the present invention is described above in detail, the present invention is not limited to the embodiment and can be variously modified without departing from the gist of the present invention. Although the plan generating device 10 includes the modification know-how learning section 111 and the plan generator 112, a computer may include the modification know-how learning section 111 and another computer may include the plan generator 112, for example. In this case, the computer that includes the plan generator 112 may generate a plan using modification know-how of the computer including the modification know-how learning section 111.
Number | Date | Country | Kind |
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2019-196325 | Oct 2019 | JP | national |