WO2023170917A1 - 最適化装置、最適化方法、及び記録媒体 - Google Patents

最適化装置、最適化方法、及び記録媒体 Download PDF

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Publication number
WO2023170917A1
WO2023170917A1 PCT/JP2022/010899 JP2022010899W WO2023170917A1 WO 2023170917 A1 WO2023170917 A1 WO 2023170917A1 JP 2022010899 W JP2022010899 W JP 2022010899W WO 2023170917 A1 WO2023170917 A1 WO 2023170917A1
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Prior art keywords
optimization
parameter
objective function
feature amount
change
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English (en)
French (fr)
Japanese (ja)
Inventor
英恵 下村
力 江藤
大 窪田
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NEC Corp
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NEC Corp
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Priority to JP2024505806A priority Critical patent/JP7726370B2/ja
Priority to US18/708,353 priority patent/US20250005229A1/en
Priority to PCT/JP2022/010899 priority patent/WO2023170917A1/ja
Publication of WO2023170917A1 publication Critical patent/WO2023170917A1/ja
Anticipated expiration legal-status Critical
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass

Definitions

  • the present disclosure relates to an optimization device, an optimization method, and a recording medium.
  • Patent Document 1 discloses a multi-objective optimization device that performs optimization by adjusting the weighting of a plurality of evaluation items (features forming an objective function) in multi-objective optimization.
  • An example of the purpose of the present disclosure is to provide an optimization device that allows easy trial and error of objective functions and constraint conditions by changing the conditions used for optimizing an event.
  • An optimization device includes an optimization display unit that displays optimization results obtained based on an objective function used for optimizing an event, and a calculated value of a feature amount that constitutes the objective function.
  • the apparatus includes a change accepting means for accepting a change in a parameter to be determined, an optimization executing means for optimizing an event based on the changed parameters, and an optimization updating means for updating and displaying an optimization result.
  • An optimization method includes displaying optimization results obtained based on an objective function used for optimizing an event, and changing 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 results.
  • a recording medium displays optimization results obtained based on an objective function used for optimizing an event, and changes parameters that determine calculated values of feature quantities that constitute the objective function.
  • a program is recorded that causes a computer to accept, optimize events based on the changed parameters, and update and display the optimization results.
  • An example of the effects of the present disclosure is to provide an optimization device that allows easy trial and error of an objective function by changing the conditions used for optimizing an event.
  • FIG. 1 is a diagram showing a configuration including an optimization device in the first embodiment.
  • 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.
  • FIG. 3 is an example of a parameter change screen in the first embodiment.
  • FIG. 4 is another example showing the parameter change screen in the first embodiment.
  • FIG. 5 is a flowchart showing the optimization operation in the first embodiment.
  • FIG. 6 is a diagram showing a configuration including an optimization device in a modification of the first embodiment.
  • FIG. 7 is an example of a parameter change screen in a modification of the first embodiment.
  • FIG. 8 is an example of a parameter change screen in the second embodiment.
  • FIG. 9 is a diagram showing a configuration including an optimization device in a modification of the second embodiment.
  • FIG. 10 shows a portion of the parameter change screen in a modification of the second embodiment.
  • FIG. 11 shows a portion of the parameter change screen in a modification of the second embodiment.
  • FIG. 12 is a flowchart showing the optimization operation in a modification of the second embodiment.
  • FIG. 1 is a diagram showing a configuration including an optimization device 100 in the first embodiment.
  • an optimization device 100 is communicatively connected to a terminal 200.
  • the terminal 200 outputs information input by the user to the optimization device 100.
  • the optimization device 100 receives changes to optimization conditions input from the terminal 200 with respect to the optimization results.
  • the objective function used for optimization is stored in the storage device 505, for example. Each time the objective function is updated, the updated objective function is stored in the storage device 505.
  • the optimization device 100 includes an optimization display section 101, a change reception section 102, an optimization execution section 103, and an optimization update section 104. Next, the configuration of the optimization device 100 in the first embodiment will be described in detail.
  • FIG. 2 is a diagram showing 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.
  • the optimization device 100 includes a CPU (Central Processing Unit) 501, a ROM (Read Only Memory) 502, a RAM (Random Access Memory) 503, and other memories, and a hard disk that stores a program 504. It includes a device 505, a communication interface 508 for network connection, and an input/output interface 511 for inputting and outputting data.
  • the optimization device 100 receives parameter information input to the terminal 200 through the communication interface 508.
  • the CPU 501 operates an operating system to control the entire optimization apparatus 100 according to the first embodiment of the present invention. Further, the CPU 501 reads programs and data from a recording medium 506 attached to, for example, a drive device 507 to a memory. Further, the CPU 501 functions as the optimization display unit 101, change reception unit 102, optimization execution unit 103, optimization update unit 104 and a part thereof in the first embodiment, and is based on the program shown in FIG. Execute the processing or instructions in the flowchart shown in .
  • 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 recording media of the storage device are non-volatile storage devices, and programs are recorded therein. Further, the program may be downloaded from an external computer (not shown) connected to a communication network.
  • the input device 509 is realized by, for example, a mouse, a keyboard, a built-in key button, etc., and is used for input operations.
  • the input device 509 is not limited to a mouse, a keyboard, or a built-in key button, but may be a touch panel, for example.
  • the output device 510 is implemented as a display, for example, and is used to confirm the output.
  • the first embodiment shown in FIG. 1 is realized by the computer hardware shown in FIG. 2.
  • the implementation means of each part included in the optimization apparatus 100 of FIG. 1 is not limited to the configuration described above.
  • the optimization device 100 may be realized by one physically coupled device, or may be realized by two or more physically separated devices connected by wire or wirelessly. good.
  • input device 509 and output device 510 may be connected to computer device 500 via a network.
  • the optimization device 100 in the first embodiment shown in FIG. 1 can also be configured using cloud computing or the like.
  • the optimization display section 101 displays optimization results obtained based on the objective function used for event optimization.
  • an objective function calculated based on a user's decision-making history is stored in the storage device 505.
  • the optimization display unit 101 displays the optimization results obtained by inputting the values of variables to this objective function.
  • the optimization target is the quantity of products ordered by an employee at a store, but is not limited thereto. For example, events that can reflect the results of decisions made by the user in the past are subject to optimization.
  • the change accepting unit 102 accepts changes to parameters that determine the calculated values of the feature quantities that constitute the objective function.
  • the change reception unit 102 receives parameters input to the terminal 200 and outputs them to the optimization execution unit 103.
  • the objective function in this embodiment is a standard for optimizing a target, and is expressed as a weighted linear sum of feature amounts. Further, the objective function is calculated using a known machine learning method.
  • the parameter is a variable that determines the calculated value of the feature quantity that constitutes the objective function, and examples of the parameter include variables of state data included in the decision-making history, weighting coefficients, and the like.
  • the value of the objective function is expressed as Z that maximizes f(x), as shown in equation (1) below.
  • ⁇ n is a weighting coefficient and x m is a feature amount.
  • the objective function is an objective function for minimizing waste loss and stockout loss.
  • the feature quantity x 1 is the smallness of waste loss
  • the feature quantity x 2 is the smallness of stockout loss.
  • the parameters of this embodiment are the minimum value of expected sales quantity, the maximum value of expected sales quantity, inventory amount, purchase price, and sales price. These parameters are used to determine the feature quantity x 1 and the feature quantity x 2 , respectively.
  • Disposal loss (feature quantity x 1 ) is (inventory quantity - minimum value of expected sales quantity) x purchasing price, and stockout loss (feature quantity x 2 ) is calculated as sales price x (maximum value of expected sales quantity - inventory quantity) Each is determined by
  • FIG. 3 is an example of a parameter change screen in the first embodiment.
  • the parameter change screen 10 includes a first display area 11 that displays a plurality of parameters.
  • the parameter change screen 10 also includes a second display area 12 that displays the values of each parameter.
  • the parameter change screen 10 also includes 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 includes a button image 14 for the user to instruct the optimization device 100 to change parameters, and a result display area 16 that shows the optimization results.
  • the optimization target displayed in the result display area 16 is the order quantity of the product.
  • the user wants to change the value of any parameter, he or she can change the value by dragging the position of the indicator 15 in the third display area 13 of the parameter change screen 10 using an input device 509 such as a numeric keypad or a mouse. do.
  • an input device 509 such as a numeric keypad or a mouse. do.
  • FIG. 4 is another example showing the parameter change screen in the first embodiment.
  • the weighting coefficients ⁇ 1 and ⁇ 2 for the small amount of waste loss for the feature quantity x 1 and the small amount of stockout loss for the feature quantity x 2 are set, for example, from +0.1 to +1.0. Accept changes within the scope.
  • the range in which the weighting coefficient can be changed is not limited to this.
  • the optimization execution unit 103 is a means for optimizing an event based on the accepted parameters.
  • the optimization execution unit 103 detects that the user presses 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 the feature amount from the parameter value and updates the objective function based on equation (1). In the example of FIG. 4, the optimization execution unit 103 inputs the changed weighting coefficient value to equation (1) and updates the objective function.
  • the optimization execution unit 103 obtains the order quantity of the product to be optimized based on the updated objective function.
  • the optimization execution unit 103 outputs the obtained optimization result to the optimization update unit 104.
  • 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.
  • FIG. 5 is a flowchart showing an overview 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.
  • the optimization display unit 101 first displays the optimization results obtained based on the objective function used for event optimization (step S101).
  • the change accepting unit 102 accepts a change in a parameter that determines the value of a feature amount (step S102).
  • the optimization execution unit 103 optimizes the event based on the accepted parameters (step S103).
  • the optimization update unit 104 updates and displays the results optimized by the optimization execution unit 103 (step S104).
  • the optimization device 100 repeats the flow of steps S102 to S104 every time a parameter change is detected (step S105). With this, the optimization device 100 ends the optimization operation.
  • the optimization execution unit 103 executes optimization of the event based on the parameters changed by the user. Thereby, the objective function can be easily determined by trial and error by changing the conditions used for optimizing the event.
  • the change accepting unit 102 accepts changes to parameters that determine the values of feature amounts.
  • the objective function can be easily determined by trial and error by changing the conditions for determining the value of the feature amount.
  • the change accepting unit 102 accepts changes in weighting coefficients of feature amounts as parameters. As a result, if there is a feature amount that the user wants to emphasize, it is possible to output an optimization result that reflects that feature amount.
  • FIG. 6 shows a configuration including an optimization device 110 in a modification of the first embodiment.
  • an optimization device 110 according to a modification of the first embodiment differs in that it includes a learning section 105 in addition to the configuration of the first embodiment.
  • the change reception unit 102 received a parameter change, and the optimization execution unit 103 executed optimization based on the changed parameter.
  • the change accepting unit 102 accepts changes in optimization results in addition to changes in parameters.
  • a change in the optimization result is a change in the product order quantity, which is the optimization result.
  • the learning unit 105 relearns the objective function by including the received optimization result as a decision history.
  • the learning unit 105 stores the relearned objective function in the storage device 505.
  • FIG. 7 is an example of a parameter change screen in a modification of the first embodiment.
  • the parameter change screen 20 includes a result display area 261 that shows optimization results and an objective function display area 262 that displays the objective function used to calculate the optimization results.
  • Numerical values of weighting coefficients are shown in ⁇ 1 to ⁇ 6 of the objective function shown in the objective function display area 262.
  • the change accepting unit 102 can accept changes to the product order quantity shown 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.
  • the value of the weighting coefficient ⁇ 6 of the objective function shown in the objective function display area 262 is updated so that less stockout loss is emphasized.
  • the change accepting unit 102 may similarly accept changes to the weighting coefficients ( ⁇ 1 to ⁇ 6 ) of the objective function shown in the objective function display area 262 on the screen.
  • the change accepting unit 102 accepts changes in optimization results in addition to changes in parameters. This allows the user to perform optimization while checking the optimization results through trial and error.
  • FIG. 8 is an example of the parameter change screen 30 in the second embodiment.
  • the first display area 31 displays the feature amount of the objective function.
  • the second display area 32 displays weighting coefficients of the feature amounts of the objective function.
  • the third display area 33 displays adjustment bars 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 parameters, and a result display area 36 that shows the optimization results.
  • the characteristic quantities of the objective function are personnel costs, the degree of reflection of vacation requests, and the degree of deviation from the basic shift. Personnel costs are expressed as negative numbers, and the larger the personnel costs, the smaller the objective function.
  • the degree of reflection of vacation requests is an index of whether the employee's 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 fundamental shift is expressed as a negative number, and the larger the number, the smaller the objective function.
  • the shift schedule is displayed in the result display area 36 as the optimization result.
  • the parameter to be changed is the weighting coefficient of the feature amount of the objective function.
  • the change accepting unit 102 accepts a change in the weighting coefficient of the feature amount of the objective function, for example, in the range of -1.0 to +1.0.
  • the optimization execution unit 103 When the optimization execution unit 103 detects that the user has pressed the change start button image 34, the optimization execution unit 103 reflects the weighting coefficient that has been changed as the user moves the position of the indicator 35 on the parameter change screen 30. Perform optimization. That is, the optimization execution unit 103 updates the objective function using equation (1).
  • the optimization execution unit 103 obtains the scheduling result of 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.
  • FIG. 9 is a diagram showing a configuration including an optimization device 120 according to a modification of the second embodiment of the present disclosure.
  • an optimization device 120 according to a modification of the second embodiment will be described, focusing on the different parts from the optimization device 100 according to the first embodiment.
  • An optimization device 120 includes an optimization display section 111, a change reception section 112, an optimization determination section 113, an optimization execution section 114, and an optimization update section 115.
  • the optimization display section 111 and the change reception section 112 have the same configuration as the optimization display section 101 and the change reception section 102 in the first embodiment, so a description thereof will be omitted.
  • 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 values of the parameters.
  • the optimization display unit 111 displays the calculated value of the feature amount in the optimization result in the fourth display area 37 .
  • the calculated value of the feature amount is an index for confirming the effectiveness of optimization.
  • the optimization display unit 111 may display the relationship between the parameters and the feature amounts within the parameter change screen 30. In the example of FIG. 10, the relationship is such that when the minimum required number of employees is lowered (“-”), the number of employees securing index increases.
  • the number of available employees increases (“+”), the number of employees secured index increases.
  • the minimum required number of employees is greater than a predetermined number, or if the number of available employees is less than a predetermined number, the calculated value of the number of employees securing index will not be satisfied.
  • the optimization determination unit 113 is a means for determining whether the received parameter can take the calculated value of the feature amount.
  • the optimization determination unit 113 determines the possible values of the feature amount displayed in the fourth display area 37 of the parameter change screen 30 based on the relational expression between the parameter and the feature amount. Determine whether or not.
  • the optimization determination unit 113 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, when the calculated value of the feature amount is not a possible value, the optimization determination unit 113 controls, for example, the terminal 220 to highlight the calculated value of the feature amount that is not a possible value, as shown in FIG. . Furthermore, when the calculated value of the feature amount is not a possible value, the optimization determination unit 113 controls the terminal 220 to highlight the parameter that affects the feature amount, as shown in FIG.
  • the optimization execution unit 114 When the optimization execution unit 114 receives a signal from the optimization determination unit 113 indicating that the calculated value of the feature amount is a possible value, it updates the objective function based on the changed feature amount, and calculates the optimal value. Get results.
  • the optimization update unit 115 updates and displays the results optimized by the optimization execution unit 114. As shown in FIG. 11, the optimization update unit 115 may display the relationship between the parameter for which change has been accepted and the feature quantity that has an influence so that the relationship can be understood.
  • FIG. 12 is a flowchart showing an overview 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.
  • the optimization display unit 111 first displays the optimization results obtained based on the objective function used for event optimization (step S201).
  • the change accepting unit 112 accepts changes to parameters that determine feature amounts (step S202).
  • the optimization determination unit 113 determines whether the changed parameter can take the calculated value of the feature amount (step S203). If the optimization determination unit 113 determines that the changed parameter can take the calculated value of the feature amount (step S203; Yes), the optimization execution unit 114 optimizes the event based on the received parameter ( Step S204).
  • the optimization update unit 115 updates and displays the results optimized by the optimization execution unit 114 (step S205).
  • the optimization determination unit 113 determines that the calculated value of the feature cannot be obtained with the changed parameters (step S203; No)
  • the optimization determination unit 113 highlights the feature that cannot be obtained (step S206).
  • 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.
  • the optimization determination unit 113 when the calculated value of the feature cannot be obtained with the changed parameters, the optimization determination unit 113 highlights the feature and the parameter. This allows the user to redo the optimization by changing unrealizable parameters or feature amounts that affect the parameters.
  • the order of the description does not limit the order in which the plurality of operations are executed. Therefore, when implementing each embodiment, the order of the plurality of operations can be changed within a range that does not interfere with the content.
  • the optimization determination unit 113 controls to highlight an impossible feature amount (step S206)
  • the control is performed (step S207)
  • this order is not limited to this.
  • the optimization determination unit 113 may be controlled to highlight the parameters and then highlight the feature amounts. Further, the optimization determination unit 113 may be controlled to highlight either the feature amount or the parameter.

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PCT/JP2022/010899 2022-03-11 2022-03-11 最適化装置、最適化方法、及び記録媒体 Ceased WO2023170917A1 (ja)

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JP2024505806A JP7726370B2 (ja) 2022-03-11 2022-03-11 最適化装置、最適化方法、及びプログラム
US18/708,353 US20250005229A1 (en) 2022-03-11 2022-03-11 Optimization device, optimization method, and recording medium
PCT/JP2022/010899 WO2023170917A1 (ja) 2022-03-11 2022-03-11 最適化装置、最適化方法、及び記録媒体

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Citations (3)

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Publication number Priority date Publication date Assignee Title
JP2005339402A (ja) * 2004-05-28 2005-12-08 Advanced Telecommunication Research Institute International シミュレーションプログラム、シミュレーション方法及びシミュレーション装置
US20070106550A1 (en) * 2005-11-04 2007-05-10 Andris Umblijs Modeling marketing data
JP2009181195A (ja) * 2008-01-29 2009-08-13 Internatl Business Mach Corp <Ibm> 多目的最適化装置、重み付けの調整をするための方法及び重み付け調整プログラム

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US8498954B2 (en) 2011-03-28 2013-07-30 Sap Ag Managing operations of a system using non-linear modeling techniques
JP7334796B2 (ja) 2019-11-18 2023-08-29 日本電気株式会社 最適化装置、最適化方法、プログラム
WO2021106060A1 (ja) 2019-11-26 2021-06-03 日本電気株式会社 最適化装置、最適化方法、記録媒体

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005339402A (ja) * 2004-05-28 2005-12-08 Advanced Telecommunication Research Institute International シミュレーションプログラム、シミュレーション方法及びシミュレーション装置
US20070106550A1 (en) * 2005-11-04 2007-05-10 Andris Umblijs Modeling marketing data
JP2009181195A (ja) * 2008-01-29 2009-08-13 Internatl Business Mach Corp <Ibm> 多目的最適化装置、重み付けの調整をするための方法及び重み付け調整プログラム

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