Optimization device, optimization method, and program

Abstract

An optimization device according to the present disclosure comprises: an optimization display means for displaying an optimization result that has been obtained on the basis of an objective function used in the optimization of an event; a change reception means for receiving a change in a parameter that determines a calculated value regarding a feature amount constituting the objective function; an optimization execution means for optimizing the event on the basis of the changed parameter; and an optimization updating means for updating and displaying the optimization result.

Description

[Technical Field]

[0001] The present disclosure relates to an optimization device, an optimization method, and a recording medium. [Background technology]

[0002] There are various tools available to adjust and verify conditions to find the optimum outcome for an event.

[0003] For example, Patent Document 1 discloses a multi-objective optimization device that performs optimization by adjusting the weighting of multiple evaluation items (feature quantities that constitute an objective function) in multi-objective optimization. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2009-181195 Summary of the Invention [Problem to be solved by the invention]

[0005] In addition to the optimization device described in Patent Document 1, there is a need for a mechanism that can easily adjust the conditions for optimizing an event and verify the validity of the objective function used in optimization.

[0006] An example of an objective of the present disclosure is to provide an optimization device that can easily perform trial and error on objective functions and constraint conditions by changing conditions used in optimizing an event. [Means for solving the problem]

[0007] An optimization device according to one aspect of the present disclosure includes an optimization display means for displaying an optimization result obtained based on an objective function used to optimize an event, a change receiving means for receiving changes to parameters that determine the calculated values of feature quantities constituting the objective function, an optimization execution means for optimizing the event based on the changed parameters, and an optimization update means for updating and displaying the optimization result.

[0008] An optimization method according to one aspect of the present disclosure displays optimization results obtained based on an objective function used to optimize an event, accepts changes to parameters that determine calculated values of feature quantities constituting the objective function, optimizes the event based on the changed parameters, and updates and displays the optimization results.

[0009] A recording medium according to one aspect of the present disclosure records a program that causes a computer to execute the following steps: display an optimization result obtained based on an objective function used to optimize an event; accept changes to parameters that determine calculated values of feature quantities that constitute the objective function; optimize the event based on the changed parameters; and update and display the optimization result. [Effects of the Invention]

[0010] One example of the effect of the present disclosure is to provide an optimization device that can easily change the conditions used to optimize an event and perform trial and error to find an objective function. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram showing a configuration including an optimization device according to the first embodiment. [Figure 2] FIG. 2 is a diagram showing a hardware configuration in which the optimization device according to the first embodiment is realized by a computer device and its peripheral devices. [Figure 3] FIG. 3 is an example of a parameter change screen in the first embodiment. [Figure 4] FIG. 4 shows another example of the parameter change screen in the first embodiment. [Figure 5]FIG. 5 is a flowchart showing the optimization operation in the first embodiment. [Figure 6] FIG. 6 is a diagram showing a configuration including an optimization device according to a modification of the first embodiment. [Figure 7] FIG. 7 shows an example of a parameter change screen in a modified example of the first embodiment. [Figure 8] FIG. 8 is an example of a parameter change screen in the second embodiment. [Figure 9] FIG. 9 is a diagram showing a configuration including an optimization device according to a modification of the second embodiment. [Figure 10] FIG. 10 shows a portion of a parameter change screen in a modified example of the second embodiment. [Figure 11] FIG. 11 shows a portion of a parameter change screen in a modified example of the second embodiment. [Figure 12] FIG. 12 is a flowchart showing the optimization operation in the modified example of the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] Next, an embodiment will be described in detail with reference to the drawings.

[0013] [First embodiment] FIG. 1 is a diagram showing a configuration including an optimization device 100 in the first embodiment. Referring to FIG. 1, the optimization device 100 is communicatively connected to a terminal 200. The terminal 200 outputs information input by a user to the optimization device 100. The optimization device 100 accepts changes to optimization conditions input from the terminal 200 with respect to the optimization results. In this embodiment, an objective function used for optimization is stored in, for example, a storage device 505. Every time the objective function is updated, the updated objective function is stored in the storage device 505.

[0014] 1, the optimization device 100 includes an optimization display unit 101, a change receiving unit 102, an optimization execution unit 103, and an optimization update unit 104. Next, the configuration of the optimization device 100 in the first embodiment will be described in detail.

[0015] 2 is a diagram illustrating an example of a hardware configuration in which the optimization device 100 according to the first embodiment of the present disclosure is realized by a computer device 500 including a processor. As shown in Fig. 2, the optimization device 100 includes a CPU (Central Processing Unit) 501, memories such as a ROM (Read Only Memory) 502 and a RAM (Random Access Memory) 503, a storage device 505 such as a hard disk for storing a program 504, a communication interface 508 for network connection, and an input / output interface 511 for inputting and outputting data. In the first embodiment, the optimization device 100 receives parameter information input to a terminal 200 via the communication interface 508.

[0016] The CPU 501 runs an operating system to control the entire optimization device 100 according to the first embodiment of the present invention. The CPU 501 also reads programs and data into memory from a recording medium 506 attached to a drive device 507, for example. The CPU 501 also functions as the optimization display unit 101, change acceptance unit 102, optimization execution unit 103, optimization update unit 104, or parts thereof, according to the first embodiment, and executes processing or commands in the flowchart shown in FIG. 5, which will be described later, based on the program.

[0017] The recording medium 506 is, for example, an optical disk, a flexible disk, a magneto-optical disk, an external hard disk, or a semiconductor memory. Some of the recording media in the storage device are non-volatile storage devices, and the programs are recorded therein. The programs may also be downloaded from an external computer (not shown) connected to a communication network.

[0018] The input device 509 is realized by, for example, a mouse, a keyboard, built-in key buttons, etc., and is used for input operations. The input device 509 is not limited to a mouse, a keyboard, or built-in key buttons, and may be, for example, a touch panel. The output device 510 is realized by, for example, a display, and is used to check output.

[0019] As described above, the first embodiment shown in FIG. 1 is realized by the computer hardware shown in FIG. 2. However, the means for realizing each unit included in the optimization device 100 of FIG. 1 is not limited to the configuration described above. The optimization device 100 may be realized by a single physically coupled device, or by two or more physically separated devices connected by wire or wirelessly. For example, the input device 509 and the output device 510 may be connected to the computer device 500 via a network. The optimization device 100 in the first embodiment shown in FIG. 1 may also be configured using cloud computing or the like.

[0020] The optimization display unit 101 displays the optimization results obtained based on the objective function used to optimize the event. In this embodiment, for example, an objective function calculated based on the user's decision-making history is stored in the storage device 505. The optimization display unit 101 displays the optimization results obtained by inputting variable values into this objective function. In this embodiment, the optimization target is the product order quantity that an employee should order in a store, but is not limited to this. For example, an event that can reflect the results of decisions made by the user in the past can be the target of optimization.

[0021] The change receiving unit 102 receives changes to parameters that determine the calculated values of the feature quantities that constitute the objective function. The change receiving unit 102 receives parameters input to the terminal 200 and outputs them to the optimization execution unit 103.

[0022] Here, the objective function, feature quantities, and parameters will be described. The objective function in this embodiment is a criterion for optimizing the target, and is expressed as a weighted linear sum of feature quantities. The objective function is calculated using a known machine learning method. The parameters are variables that determine the calculated values of the feature quantities that make up the objective function, and examples of the parameters include variables of state data included in the decision-making history and weighting coefficients. In this embodiment, the value of the objective function is expressed as Z that maximizes f(x), as shown in the following equation (1).

[0023]

number

[0024] Next, an example of a parameter change operation by a user will be described. FIG. 3 is an example of a parameter change screen in the first embodiment. As shown in FIG. 3, the parameter change screen 10 has a first display area 11 that displays a plurality of parameters. The parameter change screen 10 also has a second display area 12 that displays the values of each parameter. The parameter change screen 10 also has a third display area 13 that displays an adjustment bar such as a seek bar for accepting parameter changes. The parameter change screen 10 also has a button image 14 that the user uses to instruct the optimization device 100 to change parameters, and a result display area 16 that displays the optimization results. In this embodiment, the optimization target displayed in the result display area 16 is the product order quantity.

[0025] If the user wishes to change the value of any parameter, the user can change the value by using an input device 509 such as a numeric keypad or mouse to drag and move the position of the indicator 15 in the third display area 13 of the parameter change screen 10.

[0026] In this embodiment, when the user moves the indicator 15 to the left, the value of each parameter decreases (Down), and when the user moves the indicator 15 to the right, the value of the parameter increases (Up).

[0027] 4 is another example of a parameter change screen in the first embodiment. The screen in FIG. 4 accepts changes to the weighting factors λ1 and λ2 for the reduction of waste loss of feature x1 and the reduction of stockout loss of feature x2, respectively, within a range of, for example, +0.1 to +1.0. However, the range in which the weighting factors can be changed is not limited to this.

[0028] The optimization execution unit 103 is a means for executing optimization of an event based on the received parameters. When the optimization execution unit 103 detects that the user has pressed the change start button image 14, the optimization execution unit 103 executes optimization by reflecting the changed parameter values. That is, in the example of FIG. 3, the optimization execution unit 103 calculates feature amounts from the parameter values and updates the objective function based on equation (1). In the example of FIG. 4, the optimization execution unit 103 inputs the changed weight coefficient values into equation (1) and updates the objective function.

[0029] Next, the optimization execution unit 103 obtains the product order quantity, which is the optimization target, based on the updated objective function. The optimization execution unit 103 outputs the obtained optimization result to the optimization update unit 104.

[0030] The optimization update unit 104 is a means for updating and displaying the results optimized by the optimization execution unit 103. The optimization update unit 104 updates and displays the optimization results in the result display area 16 on the parameter change screen 10. The optimization update unit 104 may highlight and display not only the updated optimization results but also the differences (changes) between the updated optimization results.

[0031] The operation of the optimization device 100 configured as above will be described with reference to the flowchart of FIG.

[0032] 5 is a flowchart showing an outline of the operation of the optimization device 100 in the first embodiment. Note that the processing according to this flowchart may be executed based on program control by the processor described above.

[0033] As shown in FIG. 5, first, the optimization display unit 101 displays the optimization result obtained based on the objective function used to optimize the event (step S101). Next, the change receiving unit 102 receives a change to the parameter that determines the value of the feature quantity (step S102). Next, the optimization execution unit 103 optimizes the event based on the received parameters (step S103). Finally, the optimization update unit 104 updates and displays the result optimized by the optimization execution unit 103 (step S104). The optimization device 100 repeats the flow of steps S102 to S104 every time it detects a change in the parameter (step S105). With this, the optimization device 100 ends the optimization operation.

[0034] In the optimization device 100, the optimization execution unit 103 executes the optimization of the event based on the parameters changed by the user. This allows the objective function to be easily optimized by trial and error by changing the conditions used for optimizing the event.

[0035] In the first embodiment, the change receiving unit 102 receives a change to a parameter that determines the value of a feature. In this case, the condition that determines the value of the feature can be changed, and the objective function can be easily refined through trial and error. In particular, the change receiving unit 102 receives a change to the weighting coefficient of the feature as a parameter. As a result, if there is a feature that the user wants to emphasize, it is possible to output an optimization result that reflects that emphasis.

[0036] [Modification of the first embodiment] Next, a modified example of the first embodiment will be described using the drawings. Below, to the extent that the description of this embodiment is not unclear, descriptions of content that overlaps with the above description will be omitted. FIG. 6 shows a configuration including an optimization device 110 in a modified example of the first embodiment. As shown in FIG. 6, the optimization device 110 in the modified example of the first embodiment differs from the configuration of the first embodiment in that it includes a learning unit 105 in addition to the configuration of the first embodiment. Also, in the first embodiment, the change receiving unit 102 receives changes to parameters, and the optimization execution unit 103 executes optimization based on the changed parameters. In contrast, in the modified example of the first embodiment, the change receiving unit 102 receives changes to the optimization result in addition to changes to parameters.

[0037] In this embodiment, a change to the optimization result refers to a change to the product order quantity, which is the optimization result. When the change receiving unit 102 receives a change to the optimization result, the learning unit 105 re-learns the objective function by including the received optimization result as a decision-making history. The learning unit 105 stores the re-learned objective function in the storage device 505.

[0038] FIG. 7 is an example of a parameter change screen in a modified example of the first embodiment. As shown in FIG. 7, the parameter change screen 20 includes a result display area 261 that displays the optimization result and an objective function display area 262 that displays the objective function used to calculate the optimization result. The objective function display area 262 displays weighting coefficient values λ1 to λ6 of the objective function. In this case, the change receiving unit 102 can receive a change to the product order quantity displayed in the result display area 261 on the screen. The example in FIG. 7 shows an example in which the order quantity of product C is changed from 15 to 20. In this case, the value of the weighting coefficient λ6 of the objective function displayed in the objective function display area 262 is updated to emphasize minimizing stockout loss. The change receiving unit 102 may also receive a change to the weighting coefficients (λ1 to λ6) of the objective function displayed in the objective function display area 262 on the screen in a similar manner.

[0039] In a modification of the first embodiment, the change receiving unit 102 receives changes to the optimization results in addition to changes to the parameters, which allows the user to perform optimization while experimenting with the optimization results through trial and error.

[0040] [Second embodiment] Next, a second embodiment of the present disclosure will be described in detail with reference to the drawings. The second embodiment has the same configuration as the first embodiment, but the optimization target is different. Below, to the extent that the description of this embodiment is not unclear, explanations of content that overlaps with the above description will be omitted. As with the computer device shown in FIG. 2 in the first embodiment, the functions of this embodiment can be realized not only by hardware but also by a computer device or software based on program control. In this embodiment, shift scheduling will be used as the optimization target. The objective function of this embodiment is an objective function that maximizes the evaluation of the shift.

[0041] FIG. 8 is an example of a parameter change screen 30 in the second embodiment. As shown in FIG. 8, a first display area 31 displays the feature quantities of the objective function. A second display area 32 displays weighting coefficients for the feature quantities of the objective function. A third display area 33 displays an adjustment bar, such as a seek bar, for accepting changes to each parameter. The parameter change screen 30 also includes a button image 34 for the user to instruct the optimization device 120 to change the parameters and a result display area 36 showing the optimization results. The feature quantities of the objective function include labor costs, the degree of reflection of vacation requests, and the degree of deviation from the basic shift. Labor costs are expressed as a negative number, and the higher the labor costs, the smaller the objective function. The degree of reflection of vacation requests is an indicator of whether employees' vacation requests are reflected and is expressed as a positive number, and the higher the number, the larger the objective function. The degree of deviation from the basic shift is expressed as a negative number, and the larger the number, the smaller the objective function. The result display area 36 displays the shift schedule as the optimization result.

[0042] In this embodiment, the parameter to be changed is a weight coefficient of the feature amount of the objective function. As shown in Fig. 8, the change receiving unit 102 receives a change to the weight coefficient of the feature amount of the objective function within the range of -1.0 to +1.0, for example.

[0043] When the optimization execution unit 103 detects that the user has pressed the change start button image 34, it executes optimization by reflecting the weighting coefficients changed as the user moves the position of the indicator 35 on the parameter change screen 30. That is, the optimization execution unit 103 updates the objective function using equation (1).

[0044] Next, the optimization execution unit 103 obtains the results of scheduling for the optimization target based on the updated objective function. Then, the optimization update unit 104 updates and displays the optimization results in the result display area 36 on the parameter change screen 30.

[0045] [Modification of the second embodiment] Next, a modified example of the second embodiment will be described using the drawings. Below, explanations of content that overlaps with the above explanation will be omitted to the extent that the explanation of this embodiment is not unclear. FIG. 9 is a diagram showing a configuration including an optimization device 120 according to a modified example of the second embodiment of the present disclosure. With reference to FIG. 9, the optimization device 120 according to the modified example of the second embodiment will be described, focusing on the parts that differ from the optimization device 100 according to the first embodiment.

[0046] An optimization device 120 according to a modification of the second embodiment includes an optimization display unit 111, a change receiving unit 112, an optimization determination unit 113, an optimization execution unit 114, and an optimization update unit 115. The optimization display unit 111 and the change receiving unit 112 have the same configuration as the optimization display unit 101 and the change receiving unit 102 in the first embodiment, and therefore a description thereof will be omitted.

[0047] 10 and 11 show portions of a parameter change screen in a modified example of the second embodiment. As shown in FIG. 10, the first display area 31 displays parameters that determine the calculated values of the feature quantities of the objective function. The second display area 32 displays the parameter values. In this embodiment, the optimization display unit 111 displays the calculated values of the feature quantities in the optimization results in the fourth display area 37. The calculated values of the feature quantities are indicators for confirming the effectiveness of the optimization. Furthermore, as shown in FIG. 10, the optimization display unit 111 may display the relationship between the parameters and the feature quantities within the parameter change screen 30. In the example of FIG. 10, when the minimum required number of employees is reduced ("-"), the headcount availability index increases. On the other hand, when the number of available employees is increased ("+"), the headcount availability index increases. In this case, for example, if the minimum required number of employees is greater than the predetermined number or the number of available employees is less than the predetermined number, the calculated value of the headcount availability index will not be met.

[0048] The optimization determination unit 113 is a means for determining whether the received parameters can take the calculated value of the feature quantity. When the parameters are input from the change receiving unit 112, the optimization determination unit 113 determines whether the feature quantity displayed in the fourth display area 37 of the parameter change screen 30 can take the value, based on the relational expression between the parameters and the feature quantity.

[0049] If the calculated value of the feature quantity based on the received parameters is a possible value, the optimization determination unit 113 outputs a signal indicating this to the optimization execution unit 114. On the other hand, if the calculated value of the feature quantity is not a possible value, the optimization determination unit 113 controls, for example, the terminal 220 to highlight the calculated value of the feature quantity that is not a possible value, as shown in Fig. 10. Furthermore, if the calculated value of the feature quantity is not a possible value, the optimization determination unit 113 controls the terminal 220 to highlight the parameters that affect the feature quantity, as shown in Fig. 11.

[0050] When the optimization execution unit 114 receives a signal from the optimization determination unit 113 indicating that the calculated value of the feature is a possible value, the optimization execution unit 114 updates the objective function based on the changed feature and obtains an optimization result.

[0051] The optimization update unit 115 updates and displays the results optimized by the optimization execution unit 114. The optimization update unit 115 may display the results so that the relationship between the parameters for which changes have been accepted and the affected feature quantities can be understood, as shown in Fig. 11 .

[0052] The operation of the optimization device 120 configured as above will be described with reference to the flowchart of FIG.

[0053] 12 is a flowchart showing an outline of the operation of the optimization device 120 in the second embodiment. Note that the processing according to this flowchart may be executed based on program control by the processor described above.

[0054] As shown in FIG. 12, the optimization display unit 111 first displays the optimization result obtained based on the objective function used to optimize the event (step S201). Next, the change acceptance unit 112 accepts changes to parameters that determine feature quantities (step S202). Next, the optimization determination unit 113 determines whether the changed parameters can obtain the calculated value of the feature quantities (step S203). If the optimization determination unit 113 determines that the changed parameters can obtain the calculated value of the feature quantities (step S203; Yes), the optimization execution unit 114 optimizes the event based on the accepted parameters (step S204). Next, the optimization update unit 115 updates and displays the result optimized by the optimization execution unit 114 (step S205). On the other hand, if the optimization determination unit 113 determines that the changed parameters cannot obtain the calculated value of the feature quantities (step S203; No), the optimization determination unit 113 highlights the feature quantities that cannot be obtained (step S206). Furthermore, the optimization determination unit 113 highlights the parameters that affect the feature amount (step S207). With this, the optimization device 120 ends the optimization operation.

[0055] In a modification of the second embodiment of the present disclosure, when a calculated value of a feature cannot be obtained with a changed parameter, the optimization determination unit 113 highlights the feature and the parameter, allowing the user to change an infeasible parameter or a feature that affects the parameter and redo the optimization.

[0056] Although the present invention has been described above with reference to the embodiments, the present invention is not limited to the above-described embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention.

[0057] For example, although multiple operations are described in a sequential order in the form of a flowchart, the order of description does not limit the order in which the multiple operations are performed. Therefore, when implementing each embodiment, the order of the multiple operations can be changed as long as it does not interfere with the content. For example, in the modified example of the second embodiment, the optimization determination unit 113 controls the highlighting of impossible feature quantities (step S206) and then controls the highlighting of parameters that affect the feature quantities (step S207). However, this order is not limited to this. The optimization determination unit 113 may control the highlighting of parameters and then control the highlighting of feature quantities. Furthermore, the optimization determination unit 113 may control the highlighting of either feature quantities or parameters. [Explanation of symbols]

[0058] 100, 110, 120 Optimizer 101, 111 Optimization display section 102, 112 Change Reception Department 103, 114 Optimization execution unit 104, 115 Optimization update section 105 Learning Department 113 Optimization decision unit

Claims

1. an optimization display means for displaying an optimization result obtained based on the objective function used to optimize the event; a change receiving means for receiving a change of a parameter that defines a weight coefficient of a feature amount that constitutes the objective function; an optimization execution means for optimizing the event based on the changed parameters; an optimization update means for updating and displaying the optimization result; Optimizer.

2. An optimization display means for displaying an optimization result obtained based on an objective function used in optimizing an event; a change receiving means for receiving a change to a parameter that determines a calculated value of a feature quantity that constitutes the objective function; an optimization execution means for optimizing the event based on the changed parameters; an optimization update means for updating and displaying the optimization result and further displaying the relationship between the received parameter and the calculated value of the feature amount. Optimizer.

3. An optimization display means for displaying an optimization result obtained based on an objective function used in optimizing an event; a change receiving means for receiving a change to a parameter that determines a calculated value of a feature quantity that constitutes the objective function; an optimization determination means for determining whether the received parameters are values that can be used as calculated values of feature quantities; an optimization execution means for optimizing the event based on the changed parameters; an optimization update means for updating and displaying the optimization result; Optimizer.

4. 4. The optimization device according to claim 3, wherein said optimization determination means highlights the calculated value of said feature amount when the calculated value is an impossible value.

5. 4. The optimization device according to claim 3, wherein said optimization determination means, when the calculated value of said feature is an impossible value, highlights parameters that affect said feature.

6. 6. The optimization device according to claim 2, wherein the parameter is a parameter that defines a weighting coefficient of the feature amount.

7. 7. The optimization device according to claim 1, wherein the change accepting means accepts the change to the parameter by changing the position of an indicator of an adjustment bar displayed on the screen.

8. the change acceptance means accepts a change to the optimization result of the event; 8. The optimization device according to claim 1, further comprising: a learning unit that re-learns the objective function by including the received optimization result as a decision-making history.

9. A computer comprising: displaying the optimization results obtained based on the objective function used to optimize the phenomenon; Accepting a change in a parameter that defines a weighting coefficient of a feature amount that constitutes the objective function; optimizing the event based on the modified parameters; and updating and displaying the optimization results.

10. displaying the optimization results obtained based on the objective function used to optimize the phenomenon; Accepting a change in a parameter that defines a weighting coefficient of a feature amount that constitutes the objective function; optimizing the event based on the modified parameters; A program that causes a computer to update and display the optimization results.

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