TWI818176B - Planning aids and planning aids - Google Patents
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Abstract
本發明提供能夠制定根據輸入資訊來調整評價指標和限制條件的放寬量的計畫的計畫制定輔助裝置。計畫制定輔助裝置包括:生成部,其係從輸入歷史、以及計畫歷史,來生成知識模型,其中,該輸入歷史包含各計畫中的限制條件和評價指標,該計畫歷史包含有關放寬量的資訊,該放寬量表示違反了各計畫的限制條件的量,該知識模型表示限制條件的放寬量與評價指標的關係;以及輸出部,其係輸出知識模型來讓計畫制定部能夠使用,該計畫制定部使用知識模型來制定計畫。 The present invention provides a plan-making auxiliary device capable of formulating a plan that adjusts an evaluation index and a relaxing amount of a restriction condition based on input information. The plan formulation auxiliary device includes: a generation unit that generates a knowledge model from input history and plan history, where the input history includes constraints and evaluation indicators in each plan, and the plan history includes relevant relaxation information. Amount of information, the relaxation amount represents the amount of violation of the constraints of each plan, the knowledge model represents the relationship between the relaxation amount of the constraints and the evaluation index; and the output part, which outputs the knowledge model to allow the plan formulation department to Using, the planning department uses the knowledge model to formulate plans.
Description
本發明涉及計畫制定輔助裝置和計畫制定輔助方法,例如適合應用於輔助計畫的制定的計畫制定輔助裝置和計畫制定輔助方法。 The present invention relates to a plan-making assisting device and a plan-making assisting method, for example, a plan-making assisting device and a plan-making assisting method suitable for assisting in making a plan.
產品的製造、大型系統的運營、管理等事前的計畫是重要的事項的情況有很多。在制定計畫時,需要制定遵守時間、空間、設備、人之類的資源等關於各種事項的限制條件,且考慮生產量的最大化、設備的運轉率的最大化、作業人員的人數的最小化等計畫的評價指標的計畫。在從開始手動進行計畫的制定的情況下,因為花費過多時間,所以使用電腦的情況也多。 There are many situations where advance planning is important, such as the manufacturing of products and the operation and management of large-scale systems. When formulating a plan, it is necessary to establish constraints that comply with various matters such as time, space, equipment, and resources such as people, and also consider maximizing production volume, maximizing equipment operation rate, and minimizing the number of workers. and other evaluation indicators for projects. When making a plan manually from the beginning, it takes too much time, so computers are often used.
另一方面,在實際環境中,限制條件經常複雜化,且即使使用電腦也有難以尋找遵守所有限制條件的解決辦法的情況。即使在這種情況下,計畫的制定者(下稱為計畫制定者)也能夠基於長年的知識來判斷應放寬多少限制條件,並制定放寬了數個限制條件的計畫。但是,電腦很難正確地定義制定計畫所需的限制條件,作為結果而獲得的計畫也難以滿足計畫制定者。 On the other hand, in real environments, constraints are often complicated, and it may be difficult to find a solution that adheres to all constraints even with the use of a computer. Even in this case, the planner of the plan (hereinafter referred to as the planner) can judge how much restriction conditions should be relaxed based on long-term knowledge, and formulate a plan that relaxes several restrictions. However, it is difficult for computers to correctly define the constraints required to formulate plans, and the resulting plans are difficult to satisfy the planners.
對於該問題,存在透過學習單元適當地學習限制條件的放寬量,對於類似的輸入資訊仿照同一限制條件的放寬量來制定計畫的技術。 Regarding this problem, there is a technology that appropriately learns the relaxation amount of the constraint through a learning unit, and formulates a plan based on the relaxation amount of the same constraint for similar input information.
作為這種技術,提出了透過使用最優處理順序來模擬事例,相對於所顯示的模擬的結果接受計畫制定者的限制條件的放寬量、優先度等修正,並學習接受的修正內容,由此再次設定限制條件的放寬量、優先度等知識參數,並再次模擬的模擬方法等(參照專利文獻1)。 As this technique, it is proposed to simulate cases using the optimal processing order, accept corrections such as the relaxation amount and priority of the planner's constraints with respect to the displayed simulation results, and learn the contents of the accepted corrections. This is a simulation method that re-sets knowledge parameters such as relaxation amount and priority of restriction conditions and simulates again (see Patent Document 1).
另外,提出了基於關於過去擬定的各產品的生產計畫的歷史資訊,考慮生產各產品時的各限制條件的放寬量,並且計算包含各產品的生產順序的計畫參數,根據計算出的計畫參數,列舉各產品的生產順序,制定關於各產品的生產計畫的數個計畫候選,基於相對於各限制條件的放寬量評價多個計畫候選,從多個計畫候選中選出最優的生產計畫的生產計畫制定方法等(參照專利文獻2)。 In addition, it is proposed to calculate the planning parameters including the production sequence of each product based on historical information about the production plans of each product that were drawn up in the past, taking into account the relaxation amount of each restriction condition when producing each product, and based on the calculated plan. Draw parameters, enumerate the production sequence of each product, develop several plan candidates for the production plan of each product, evaluate the plurality of plan candidates based on the amount of relaxation with respect to each constraint, and select the best plan candidate from the plurality of plan candidates A production plan formulation method for an excellent production plan, etc. (see Patent Document 2).
[專利文獻1]日本特開2005-339402號公報 [Patent Document 1] Japanese Patent Application Publication No. 2005-339402
[專利文獻2]國際公開第2018/220744號公報 [Patent Document 2] International Publication No. 2018/220744
在上述技術中,能夠基於計畫制定者的修正結果或過去擬定的計畫,進行放寬了限制條件的計畫的制定。 In the above-described technology, it is possible to formulate a plan with relaxed restrictions based on the plan maker's correction results or plans drawn up in the past.
但是,在專利文獻1中接受計畫制定者的修正為前提,例如,以訂單為100以上時可以放寬一天交貨限制,訂單為200以上時可以放寬兩天交貨限制的方式,限制條件的放寬量對於每個計畫的制定所需的輸入資訊(在此訂單數)而不同時,計畫制定者需要每次進行修正,且也有妨礙有效的制定計畫的情況。
However,
另外,在專利文獻1和專利文獻2中,以在將限制條件的放寬量的基準值、比率等確定為固定值後,制定使評價指標最小化或最大化的計畫為前提。例如,即使在利潤的最大化相對於評價指標的計畫利潤小時放寬生產能力的限制,也能夠增加利潤,但不能與以在利潤超出時不放寬生產能力的限制的方式根據評價指標的值使限制條件的放寬量動態變化的情況對應。
In addition,
本發明是考慮以上的點而完成的,其目的在於,提供能夠制定根據輸入資訊調整評價指標和限制條件的放寬量的計畫的計畫制定輔助裝置等。 The present invention has been made in consideration of the above points, and an object thereof is to provide a plan preparation assisting device and the like capable of formulating a plan that adjusts an evaluation index and a relaxation amount of a restriction condition based on input information.
為了解決該問題,在本發明中包括:生成部,其係從輸入歷史、以及計畫歷史,來生成知識模型,其中,該輸入歷史包含各計畫中的限制條件和評價指標,該計畫歷史包含有關放寬量的資訊,該放寬量表示違反了前述各計畫的前述限制條件的量,該知識模型表示前述限制條件的放寬量與前述評價指標的關係;以及輸出部,其係輸出前述 知識模型來讓計畫制定部能夠使用,該計畫制定部使用前述知識模型來制定計畫。 In order to solve this problem, the present invention includes: a generation unit that generates a knowledge model from input history and plan history, where the input history includes constraints and evaluation indicators in each plan, and the plan The history includes information on the amount of relaxation, which represents the amount of violation of the aforementioned constraints of each of the aforementioned plans, the knowledge model represents the relationship between the amount of relaxation of the aforementioned constraints and the aforementioned evaluation index; and an output unit outputs the aforementioned The knowledge model is used by the planning department, and the planning department uses the aforementioned knowledge model to formulate plans.
在上述結構中,生成表示評價指標與限制條件的放寬量的關係的知識模型。根據知識模型,例如,計畫制定部能夠掌握計畫制定者過去有多少價值時允許多少違反的關係(均衡)。因此,計畫制定部例如能夠根據輸入資訊制定與過去的計畫的均衡接近的計畫,即反映了評價指標與限制條件的放寬量的關係的計畫。 In the above structure, a knowledge model representing the relationship between the evaluation index and the relaxation amount of the restriction condition is generated. Based on the knowledge model, for example, the planning department can grasp the relationship (equilibrium) of how much violation is allowed based on how much value the planner has in the past. Therefore, for example, the planning department can formulate a plan that is close to the balance of past plans based on the input information, that is, a plan that reflects the relationship between the evaluation index and the relaxation amount of the restriction condition.
根據本發明,能夠輔助計畫的制定。 According to the present invention, plan formulation can be assisted.
100:計畫制定輔助裝置 100: Planning assistance device
111:生成部 111:Generation Department
112:輸出部 112:Output Department
113:計畫制定部 113: Planning Department
[圖1]是表示第一實施方式的計畫制定輔助裝置的結構的一例的圖。 [Fig. 1] Fig. 1 is a diagram showing an example of the structure of the planning support device according to the first embodiment.
[圖2]是表示第一實施方式的輸入歷史的資料結構例的圖。 [Fig. 2] Fig. 2 is a diagram showing an example of the data structure of the input history according to the first embodiment.
[圖3]是表示第一實施方式的計畫歷史的資料結構例的圖。 [Fig. 3] Fig. 3 is a diagram showing an example of the data structure of the plan history according to the first embodiment.
[圖4]是表示第一實施方式的計畫候選的資料結構例的圖。 [Fig. 4] Fig. 4 is a diagram showing an example of the data structure of plan candidates according to the first embodiment.
[圖5]是表示第一實施方式的計畫制定輔助方法的處理順序例的圖。 [Fig. 5] Fig. 5 is a diagram showing an example of the processing sequence of the plan preparation assisting method according to the first embodiment.
[圖6]是將第一實施方式的計畫歷史和計畫候選按每 個計畫資訊繪入於2個評價指標軸的影像圖。 [Fig. 6] shows the plan history and plan candidates according to the first embodiment The project information is plotted in an image chart with two evaluation index axes.
[圖7]是變更第一實施方式的計畫候選的計畫的標籤的值的影像圖。 [Fig. 7] Fig. 7 is an image diagram of changing the value of a label of a plan candidate in the first embodiment.
[圖8]是變更第一實施方式的計畫候選的計畫的標籤的值的影像圖。 [Fig. 8] is an image diagram of changing the value of a label of a plan candidate according to the first embodiment.
[圖9]是表示第一實施方式的計畫制定輔助方法的處理順序例的圖。 [Fig. 9] is a diagram showing an example of the processing sequence of the plan preparation assisting method according to the first embodiment.
[圖10]是表示關於第一實施方式的計畫資訊的各個最佳解概率的影像圖。 [Fig. 10] is an image diagram showing each optimal solution probability regarding the plan information of the first embodiment.
[圖11]是表示第一實施方式的知識模型生成畫面的一例的圖。 [Fig. 11] Fig. 11 is a diagram showing an example of the knowledge model generation screen according to the first embodiment.
[圖12]是表示第一實施方式的計畫制定畫面的一例的圖。 [Fig. 12] Fig. 12 is a diagram showing an example of the plan creation screen according to the first embodiment.
以下,關於附圖,對本發明的一實施方式進行詳細描述。本實施方式涉及在計畫的評價指標中反映出對於計畫制定者而言優選的解決辦法的、制定精度良好的計畫的技術。 Hereinafter, one embodiment of the present invention will be described in detail with reference to the accompanying drawings. This embodiment relates to a technology for formulating a plan with high accuracy by reflecting a solution preferred by the plan maker in the evaluation index of the plan.
圖1中,100整體上表示第一實施方式的計畫制定輔助裝置。
In FIG. 1 ,
圖1是表示計畫制定輔助裝置100的結構的一例的圖。
FIG. 1 is a diagram showing an example of the structure of the
計畫制定輔助裝置100是反映出根據輸入資訊調整評價指標和限制條件的放寬量的隱性知識的、能夠制定有效的計畫的電腦。作為實現計畫制定輔助裝置100的具體的結構,能夠假設主程序、個人電腦等。
The plan
本實施方式的計畫例如是指假設透過使用生產設備、工作人員等各種資源的一系列的工序來選擇在規定的生產能力的範圍內以使利潤最大的方式生產的商品(產品)的生產計畫。因此,該情況下的計畫制定輔助裝置100成為從對於商品製造過去制定的多個生產計畫中匯出如果是以前則被認為是隱性知識的評價指標和限制條件的放寬量的調整量,並將其應用於制定計畫的處理的裝置。
The plan of this embodiment is, for example, a production plan that assumes that goods (products) produced in a manner that maximizes profits within a predetermined production capacity range are selected through a series of processes using various resources such as production equipment and workers. Painting. Therefore, the
此外,計畫不限定於商品的生產計畫,也可以為從業人員的人員計畫、車的調配計畫等。 In addition, the plan is not limited to the production plan of the product, but may also be the personnel plan of the employees, the deployment plan of the vehicle, etc.
計畫制定輔助裝置100所具有的硬體例如是圖1所示的構件。即,計畫制定輔助裝置100具有:中央處理裝置110、儲存裝置120、記憶體130、輸入裝置140和輸出裝置150。
The hardware included in the
中央處理裝置110是CPU(Central Processing Unit)等處理器。中央處理裝置110進行計畫制定輔助裝置100自身的綜合控制,同時進行各種判斷、運算以及控制處理。儲存裝置120由SSD(Solid State Drive)等非揮發性記憶體件、硬碟驅動器等磁介質構成,在儲存裝置120中至少儲存有輸入歷史121(輸入歷史資訊)、計畫歷史122(計畫歷史資
訊)、計畫候選123(計畫候選資訊)、知識模型124(知識模型資訊)、程式125。
The
記憶體130由RAM(Random Access Memory)等揮發性儲存元件構成。輸入裝置140是鍵盤、指示裝置、麥克風等。輸入裝置140接受來自使用者的鍵輸入、聲音輸入等。輸出裝置150是可視資料終端、揚聲器等。輸出裝置150進行各種資訊的顯示、聲音輸出等。
The
中央處理裝置110透過將儲存於儲存裝置120的程式125讀出到記憶體130中並執行,安裝執行生成知識模型124的知識模型生成處理的生成部111、輸出由生成部111生成的知識模型124的輸出部112、基於由輸出部112輸出的知識模型124等進行計畫的制定的計畫制定部113之類的各功能。
The
另外,計畫制定輔助裝置100的功能(生成部111、輸出部112、計畫制定部113等)例如可以透過中央處理裝置110將儲存於儲存裝置120的程式125讀出到記憶體130中並執行(軟體)來實現,也可以透過專用的電路等硬體來實現,也可以將軟體和硬體組合來實現。另外,計畫制定輔助裝置100的功能的一部分也可以透過能夠與計畫制定輔助裝置100通訊的其它電腦來實現。
In addition, the functions (
用於安裝計畫制定輔助裝置100的功能的程式125除儲存於儲存裝置120的形式外,在執行時等需要時計畫制定輔助裝置100也可以經由規定的介質從其它裝置導入到儲存裝置120。規定的介質是指例如能夠裝拆於計畫制定輔
助裝置100的規定的介面的儲存介質。
In addition to being stored in the
另外,輸入歷史121是過去的輸入資訊的集合。輸入資訊是為了制定計畫而至少包含需要的限制條件和評價指標的資訊的集合。另外,計畫歷史122是表示由過去的計畫制定者制定的計畫的計畫資訊的集合。另外,計畫候選123是由計畫制定輔助裝置100輸出的計畫資訊的集合。1個計畫資訊由1個輸入資訊生成,因此,輸入歷史121的輸入資訊和計畫歷史122的計畫資訊及計畫候選123的計畫資訊處於對應關係。
In addition, the
本實施方式的計畫是如上述那樣選擇以在規定的生產能力的範圍內使利潤最大的方式生產的商品的生產計畫。此時,將輸入資訊中所含的限制條件、評價指標以及變數的例示於(式子1)。 The plan of this embodiment is a production plan in which products are selected to be produced in a manner that maximizes profits within a predetermined production capacity range as described above. At this time, examples of constraints, evaluation indicators, and variables included in the input information are shown in (Formula 1).
(式子1)中的I是在制定計畫時成為生產候選的商品的 集合。wi是為了生產生產候選中第i個商品而花費的成本。vi是透過生產候選中第i個商品並進行出售而獲得的利潤。W是生產能力的最大值,是在一個計畫內多個商品的生產成本的總計的極限值。xi是採用“0”或“1”的值的變數。xi的值“0”是指不生產生產候選中第i個商品,xi的值“1”是指要生產生產候選中第i個商品。在本實施方式中制定計畫是指決定xi的值。因此,輸入資訊中沒有xi的值,計畫資訊中的xi的值指定為“0”或“1”。 I in (Formula 1) is a set of products that become production candidates when planning is formulated. wi is the cost to produce the i-th product in the production candidate. vi is the profit obtained by producing the i-th product in the candidate and selling it. W is the maximum value of production capacity and the total limit value of the production costs of multiple commodities within a plan. x i is a variable that takes the value of "0" or "1". The value "0" of x i means that the i-th product in the production candidate will not be produced, and the value "1" of x i means that the i-th product in the production candidate will be produced. In this embodiment, making a plan means determining the value of x i . Therefore, there is no value for x i in the input information, and the value of x i in the plan information is specified as "0" or "1".
此外,(式子1)的限制條件是生產的商品的生產成本的總計沒有超過最大的生產能力的條件,但除此之外,也可以具有商品的種類的組合條件、庫存量條件、交貨期條件等多個限制條件。另外,(式子1)的評價指標是以生產的商品的價值的總計最大化的方式制定計畫的指標,但除此之外,也可以有商品的大小的種類數的最小化、生產成本的最小化等多個評價指標。 In addition, the restriction condition of (Formula 1) is that the total production cost of the goods produced does not exceed the maximum production capacity, but in addition, it may also have combination conditions of the type of goods, inventory conditions, and delivery conditions. period conditions and many other restrictions. In addition, the evaluation index of (Formula 1) is an index for formulating a plan so as to maximize the total value of the products produced, but in addition, it may also include minimizing the number of types of products, the size of the products, and the production cost. Minimization and other evaluation indicators.
另外,知識模型124是生成部111從輸入歷史121和計畫歷史122以及計畫候選123計算計畫制定者的隱性知識的資訊。
In addition, the
接著,對計畫制定輔助裝置100的功能進行說明。以下所說明的功能例如作為計畫制定輔助裝置100透過執行程式125而安裝的功能進行說明。
Next, the function of the
計畫制定輔助裝置100將由上述(式子1)所示的輸入資
訊(從輸入歷史121讀出的資訊)應用於規定的演算法而生成計畫候選123,從計畫歷史122提取與計畫候選123和輸入資訊共用的過去的計畫資訊,將過去的計畫資訊和計畫候選123應用於規定的演算法,生成調整限制條件的放寬量的知識模型124作為計畫制定者的隱性知識。這樣生成知識模型124的功能取決於生成部111。
The
另外,計畫制定輔助裝置100輸出生成的知識模型124,以使得計畫制定部113能夠使用。關於輸出的方法,不限定於特定的方法,例如可舉出儲存於儲存裝置120、通知計畫制定部113,發送至其它電腦、在輸出裝置150上顯示等。這樣輸出的功能取決於輸出部112。
In addition, the plan
另外,計畫制定輔助裝置100透過輸入裝置140從計畫制定者接受用於新的計畫的制定的新的輸入資訊,並將新的輸入資訊應用於規定的演算法(例如使用知識模型124)而制定反映了調整限制條件的放寬量的計畫制定者的隱性知識的新的計畫,並透過輸出裝置150進行輸出。制定這樣的計畫的功能取決於計畫制定部113。
In addition, the plan formulation
接著,對計畫制定輔助裝置100所使用的資料的具體例進行說明。首先,對輸入歷史121的具體例進行說明。
Next, a specific example of data used by the
圖2是表示輸入歷史121的資料結構例的圖。輸入歷史121是用於如上所述的過去的計畫制定的輸入資訊的集合體。
FIG. 2 is a diagram showing an example of the data structure of the
由圖2例示的輸入歷史121的各記錄是將計畫編號201、商品候選個數202、最大生產能力203、商品編號204、生產成本205、價值206的各值相關聯的記錄。
Each record of the
計畫編號201的值是唯一確定計畫資訊的識別資訊。作為同一計畫編號201的記錄表示說明該計畫編號201的計畫資訊。商品候選個數202的值是該計畫編號201中的生產的商品候選的數的值,相當於該計畫編號201的記錄的數。最大生產能力203的值是該計畫編號201中的多個商品的生產成本的總計的極限值。商品編號204的值是唯一確定該計畫編號201中的商品的識別資訊。生產成本205的值為了生產該商品而花費的成本。價值206的值是透過生產該商品並進行出售而獲得的利潤。
The value of
此外,在上述的輸入歷史121如以上所述儲存有該計畫編號201中的限制條件、評價指標以及變數(例如,(式子1)),例如,可以形成表示限制條件、評價指標等的式儲存於記錄的各行的表結構,也可以為其它的資料結構。另外,上述輸入歷史121除關於商品的資料外也可以包含計畫制定者的識別資訊、生產設備的運行狀態、氣溫、濕度、天氣等資料作為制定計畫所需的資料。
In addition, the
接著,對計畫歷史122的具體例進行說明。
Next, a specific example of the
圖3是表示計畫歷史122的資料結構例的圖。計畫歷史122是如上述基於上述的輸入資訊由計畫制定者在過去制定的計畫資訊的集合體。
FIG. 3 is a diagram showing an example of the data structure of the
圖3例示的計畫歷史122的各記錄是將計畫編號301、
商品候選個數302、最大生產能力303、商品編號304、生產成本305、價值306、選擇標誌307、利潤308、放寬量309、標識310的各值相關聯的記錄。計畫編號301、商品候選個數302、最大生產能力303、商品編號304、生產成本305、價值306的值是與上述的輸入歷史121的值相同的值。
Each record of the
選擇標誌307的值是表示計畫制定者選擇生產或不生產該商品的識別資訊。選擇標誌307的值為“1”時是指生產,選擇標誌307的值為“0”時是指不生產。利潤308的值是該計畫編號301的評價指標的值。利潤308的值是該計畫編號301的商品中生產的商品(選擇標誌307的值為“1”的商品)的價值306的值的總計值。利潤308的值也能夠透過以處於(式子1)的評價指標的方式總計將該計畫編號301的商品的價值306的值和選擇標誌307的值相乘的值而計算出。
The value of the
放寬量309的值是違反了該計畫編號301的限制條件的量(超過最大生產能力303的值的量)的值。放寬量309的值是從該計畫編號301的商品中生產的商品(選擇標誌307的值為“1”的商品)的生產成本305的值的總計值減去該計畫編號301的最大生產能力303的值的值。
The value of the
放寬量309的值以處於(式子1)的限制條件的方式,總計將該計畫編號301的商品的生產成本305的值和選擇標誌307的值相乘的值,透過減去該計畫編號301的最大生產能力303的值也能夠計算出。即,放寬量309的值為正的值時
是指違反了限制條件(超過最大生產能力303的值),放寬量309的值為負的值時是指遵守限制條件(未超過最大生產能力303的值)。
The value of the
標識310的值是該計畫資訊由計畫制定者制定的計畫歷史122,用於與由後述的計畫制定輔助裝置100輸出的計畫候選123進行區分的識別資訊。在計畫歷史122的標識310的值儲存有“0”。
The value of the
接著,對計畫候選123的具體例進行說明。
Next, a specific example of the
圖4是表示計畫候選123的資料結構例的圖。計畫候選123是如以上所述,基於上述的輸入資訊由計畫制定輔助裝置100輸出的計畫資訊的集合體。另外,計畫制定輔助裝置100從1個輸入資訊輸出成為多個候選的計畫資訊,因此,計畫候選123是計畫資訊的集合體的集合體。
FIG. 4 is a diagram showing an example of the data structure of the
圖4例示的計畫候選123的各記錄是將計畫編號401、商品候選個數402、最大生產能力403、商品編號404、生產成本405、價值406、選擇標誌407、利潤408、放寬量409、標識410的各值相關聯的記錄。
Each record of the
計畫編號401、商品候選個數402、最大生產能力403、商品編號404、生產成本405、價值406的值是與上述的輸入歷史121的值相同的值。
The values of the
選擇標誌407的值是表示計畫制定輔助裝置100選擇生產或不生產該商品的識別資訊。選擇標誌407的值為“1”時是指進行生產,選擇標誌407的值為“0”時是指不生產。利潤408和放寬量409的值是與上述的計畫歷史122的
利潤308和放寬量309的值的計算方法相同。另外,如以上所述,計畫制定輔助裝置100從1個輸入資訊輸出成為多個候選的計畫資訊,因此,標識410的值是確定各計畫資訊的識別資訊。
The value of the
以下,使用圖5~圖12對本實施方式的計畫制定輔助方法的順序進行說明。與以下說明的計畫制定輔助方法對應的各種動作透過計畫制定輔助裝置100執行程式125來實現。此外,程式125由用於進行以下說明的各種動作的代碼構成。
Hereinafter, the procedure of the planning assistance method of this embodiment will be described using FIGS. 5 to 12 . Various operations corresponding to the plan-making assisting method described below are realized by the plan-making assisting
圖5是表示本實施方式的計畫制定輔助方法的處理順序例(主要是計畫制定輔助裝置100執行的處理的流程圖的一例)的圖。流程圖是大致例示計畫制定輔助裝置100的生成部111從積蓄於儲存裝置120的輸入歷史121和計畫歷史122生成調整限制條件的放寬量的知識模型124來作為計畫制定者的隱性知識,輸出部112將知識模型124儲存於儲存裝置120的處理的圖。此外,以下說明的各處理是至少在計畫制定部113進行的計畫的制定的執行前執行的處理,也可以按一定期間反復執行。
FIG. 5 is a diagram illustrating an example of a processing sequence (mainly an example of a flowchart of processing executed by the plan-making assisting device 100) of the plan-making assisting method according to this embodiment. The flowchart schematically illustrates how the
首先,作為前提,計畫制定者制定的計畫是滿足計畫制定者的計畫,計畫制定輔助裝置100的目的是輸出與計畫制定者制定的計畫類似的計畫。因此,首先,計畫制定者準備輸入歷史121和計畫歷史122(步驟S501)。例如,計
畫制定者準備作為教師資料的輸入資訊的集合(例如過去1年的1000件的輸入資訊)和基於輸入資訊調整作為計畫制定者的隱性知識的限制條件的放寬量而由計畫制定者制定的計畫資訊的集合。
First, as a premise, the plan formulated by the planner is a plan that satisfies the planner, and the purpose of the plan formulation
接著,計畫制定輔助裝置100的生成部111輸入輸入歷史121和計畫歷史122(步驟S502)。更具體而言,生成部111經由輸入裝置140從計畫制定者獲取在包含限制條件、評價功能、變數(例如(式子1))的步驟S501中準備的輸入資訊的集合作為輸入歷史121,並將其儲存於儲存裝置120。另外,生成部111獲取在步驟S501中準備的計畫制定者制定的計畫資訊的集合作為計畫歷史122,並將其儲存於儲存裝置120。
Next, the
接著,生成部111將在步驟S502中獲取的輸入歷史121以同一計畫編號201進行分類(步驟S503)。在此,將分類的各組在以下的步驟中稱為“輸入資訊”。
Next, the
接著,生成部111從在步驟S502中獲取的輸入歷史121中選擇到此為止未選擇的計畫編號201(步驟S504)。
Next, the
接著,生成部111判斷是否有在步驟S504中未選擇的計畫編號201(步驟S505)。生成部111判斷為存在步驟S504中未選擇的計畫編號201的情況下,將處理轉移到步驟S506,在判斷為沒有未選擇的計畫編號201的情況下,將處理轉移到步驟S509。即,將從步驟S506到步驟S508的處理僅反復輸入歷史121的計畫編號201的種類的數量。
Next, the
在存在上述步驟S505中未選擇的計畫編號201的情況
下,生成部111進行將該限制條件的放寬量作為罰則的評價指標化(步驟S506)。更具體而言,生成部111從輸入歷史121提取與該計畫編號201相等的輸入資訊中所含的限制條件、評價功能、變數(例如(式子1)),追加將該限制條件的放寬量設為罰則的評價指標,在以後的步驟中,視為多目的最優問題(例如下述(式子2))。這是因為在計畫制定者制定的計畫歷史122中存在一個以上放寬限制條件的計畫。
When there is a
接著,生成部111從與該計畫編號201相等的輸入資訊計算一個以上的成為候選的計畫資訊(步驟S507)。更具體而言,生成部111從與該計畫編號201相等的輸入資訊使用用於解決多目的最優問題(例如(式子2))的一般的方法(例如本地搜索法、遺傳演算法等)計算被稱為帕累托最優解
的候選的多個計畫(生成計畫資訊)。在多目的最優問題中,將不能同時改善多個評價指標的可執行的解稱為帕累托最優解,例如,將在沒有違反限制條件(放寬量為零)中使價值最大化的計畫、稍微違反且價值更高的計畫、價值更高但也違反大的計畫組稱為帕累托最優解。
Next, the
接著,生成部111將在步驟S507中計算出的各計畫資訊(帕累托最優解)中確定計畫資訊的識別資訊即標識410編號,作為計畫候選123儲存於儲存裝置120,處理返回步驟S504(步驟S508)。
Next, the
在沒有在上述步驟S505中未選擇的計畫編號201的情況下,生成部111從計畫歷史122對每個計畫資訊生成特徵向量和標籤的組(步驟S509)。更具體而言,生成部111從儲存裝置120讀出計畫制定者制定的計畫歷史122,對每個計畫編號301生成說明該計畫資訊的特徵向量(例如下述(式子3)),透過賦予標籤生成教師資料。期望一般構成特徵向量的要素是在計畫制定者制定計畫時,有意、無意地考慮的要素。將特徵向量的例示於(式子3)。
If there is no
例如,在輸入資訊的生產成本的平均值低,不均(標準差)小,商品價值的平均值高,不均(標準差)小時,計畫制定者制定與生產能力的限制條件的放寬量的最小化相比優先利潤的最大化的計畫。另外,例如,在輸入資訊的商品數多,生產成本的不均(標準差)大,商品價值的不均(標準差)大,計畫制定者制定與利潤的最大化相比優先生產能力的限制條件的放寬量的最小化的計畫。假設這樣的計畫制定者進行計畫的制定,如(式子3)所示,特徵向量包含加上輸入資訊的特徵的特徵向量和加上計畫資訊的特徵的特徵向量的2種類的特徵向量而構成。 For example, if the average value of the production cost of the input information is low and the unevenness (standard deviation) is small, and the average value of the product value is high and the unevenness (standard deviation) is small, the planner will set the amount of relaxation of the constraints on the production capacity. Minimization is prioritized over profit maximization. In addition, for example, when the number of products for which information is input is large, the unevenness (standard deviation) of production costs is large, and the unevenness (standard deviation) of product values is large, the planners develop a plan that prioritizes production capacity over maximizing profits. A plan that minimizes the amount of relaxation of constraints. Assume that such a planner formulates a plan. As shown in (Formula 3), the feature vector includes two types of features: a feature vector plus a feature of the input information and a feature vector plus a feature of the plan information. composed of vectors.
在加上輸入資訊的特徵的特徵向量設定商品數、商品生產成本的平均值和標準差、商品價值的平均值和標準差,在加上計畫資訊的特徵的特徵向量設定評價指標的值。構成特徵向量的要素可以選擇計畫制定者等,也可以預先準備每個要素而使用多元回歸分析、聚類分析等將計畫制定者制定的計畫歷史122的計畫資訊的集合基於類似性的指標進行分類時計算有效的要素並選擇這些的要素。另外,生成部111對於標籤的值設定表示計畫制定者滿足的計畫“1”。
The number of products, the average and standard deviation of product production costs, and the average and standard deviation of product values are set to the feature vector that adds the characteristics of the input information, and the value of the evaluation index is set to the feature vector that adds the features of the plan information. The elements constituting the feature vector can be selected by the planner, etc., or each element can be prepared in advance and a collection of plan information of the
接著,生成部111從計畫候選123對每個計畫資訊生成
特徵向量和標籤的組(步驟S510)。更具體而言,生成部111從儲存裝置120讀取在步驟S507中計算出的計畫候選123,透過對每個計畫編號301和標識410生成說明該計畫資訊的特徵向量(例如(式子3)),賦予標籤生成教師資料。構成特徵向量的要素與在上述的步驟S509中設定的要素相同。
Next, the
在此,圖6是將計畫制定者制定的計畫歷史122和在步驟S507中計算出的計畫候選123對每個計畫資訊繪入(式子2)的2個評價指標軸的影像圖。
Here, FIG. 6 is an image of two evaluation index axes plotted in (Equation 2) for each plan information, including the
圖6中的左圖表示第一計畫資訊,右圖表示第i個計畫資訊。橫軸是作為(式子2)的第一評價指標的利潤(商品價值的總計)的倒數,越向左走利潤越大。縱軸是作為(式子2)的第二評價指標的放寬量(從生產成本的總計減去最大生產能力的值),越向下走放寬量越小。該2個評價指標處於權衡關係,存在多個最佳的計畫(帕累托最優解)。這是計畫候選123,圖6中由黑圓點表示。相對於計畫候選123,將選擇計畫制定者最優選的計畫(計畫制定者制定的計畫)稱為最佳解,圖6中由白圓點表示。
The left picture in Figure 6 represents the first plan information, and the right picture represents the i-th plan information. The horizontal axis is the reciprocal of profit (total product value), which is the first evaluation index of (Equation 2). The further to the left, the greater the profit. The vertical axis represents the relaxation amount (the value obtained by subtracting the maximum production capacity from the total production cost) as the second evaluation index of (Equation 2), and the relaxation amount decreases as the value goes downward. These two evaluation indicators are in a trade-off relationship, and there are multiple optimal plans (Pareto optimal solutions). This is
在上述的步驟S510中,生成部111對每個生成的特徵向量賦予標籤,生成教師資料,此時,設定標籤的值表示不滿足計畫制定者的計畫的“0”。
In step S510 described above, the
在此,為了解決標籤的值為“1”的教師資料的數比標籤的值為“0”的教師資料的數壓倒性地少的問題,如圖7或圖8所示,也可以變更計畫候選123的計畫的標籤的
值。
Here, in order to solve the problem that the number of teacher materials with a tag value of “1” is overwhelmingly smaller than the number of teacher materials with a tag value of “0”, as shown in FIG. 7 or 8 , the calculation may be changed. Draw labels for
圖7是將計畫候選123的計畫的標籤的值從“1”變更為“0”的影像圖。例如,生成部111獲取在上述步驟S509中得到的最佳解,與最佳解接近的計畫候選123的計畫(近傍解)的標籤的值與最佳解同樣設定表示滿足計畫制定者的計畫的“1”,設定除此以外的計畫候選123的計畫的標籤的值為表示不滿足計畫制定者的計畫的“0”。此外,關於近傍解,例如,能夠為距最佳解處於一定的範圍內(例如預先規定的規定距離內)的計畫候選123的計畫。
FIG. 7 is an image diagram in which the value of the label of the plan of the
圖8是將計畫候選123的計畫的標籤的值從“1”變更為“(無)”的影像圖。例如,生成部111獲取在上述步驟S509中得到的最佳解,在與最佳解接近的計畫候選123的計畫(近傍解)中排除該教師資料(特徵向量和標籤的組)(包含刪除。)(例如除機器學習的物件外的資料),設定除此以外的計畫候選123的計畫的標籤的值為表示不滿足計畫制定者的計畫的“0”。
FIG. 8 is an image diagram in which the value of the label of the plan of the
接著,生成部111機器學習在步驟S509和步驟S510中生成的特徵向量和標籤的組作為教師資料,生成回歸模型作為知識模型124(步驟S511)。在此,機器學習使用通常的方法(例如邏輯回歸、隨機森林、深層學習等)。(式子4)是表示知識模型124的一例的式。
Next, the
[式子4]最佳解概率:P=f(y)f:知識模型y:特徵向量 …(式子4) [Formula 4] Optimal solution probability: P = f ( y ) f: knowledge model y: feature vector ...(Formula 4)
知識模型124是當輸入特徵向量時輸出作為最佳解的概率(下稱為最佳解概率)的函數。機器學習是指生成該函數。最佳解概率是從“0”到“1”的實數,是指當接近“0”時作為最佳解的概率低,當接近“1”時作為最佳解的概率高。即,期待當將與從計畫制定者制定的計畫資訊生成的特徵向量接近的特徵向量輸入到知識模型124時,輸出與“1”接近的值。另一方面,當將距從計畫制定者制定的計畫資訊生成的特徵向量遠的特徵向量輸入到知識模型124時,輸出與“0”接近的值。
The
在此,某2個不同的特徵向量接近是指例如2個特徵向量之間的馬氏距離短,2個不同的特徵向量遠是指例如2個特徵向量之間的馬氏距離長。 Here, when two different eigenvectors are close, it means that the Mahalanobis distance between the two eigenvectors is short, for example, and when two different eigenvectors are far apart, it means that the Mahalanobis distance between the two eigenvectors is long, for example.
接著,輸出部112將在步驟S511中生成的回歸模型作為知識模型124儲存於儲存裝置120(步驟S512),結束處理。此外,在正常生成知識模型124並儲存於儲存裝置120後,計畫制定輔助裝置100也可以經由輸出裝置150通知正常地結束了處理。
Next, the
在此,使用圖9和圖10,在利用知識模型124說明制定計畫的處理前,在從上述步驟S501至步驟S512的一系列處理時,計畫制定者透過輸出裝置150進行閱覽,使用圖11對透過輸入裝置140進行適當的資訊輸入的畫面進行說明。
Here, using FIGS. 9 and 10 , before using the
圖11是表示知識模型生成畫面1100的一例的圖。
FIG. 11 is a diagram showing an example of the knowledge
知識模型生成畫面1100包括:文本輸入區1101、輸入歷史讀取按鈕1102、計畫歷史讀取按鈕1103、知識模型生成按鈕1104、教師資料分布輸出區1105。
The knowledge
文本輸入區1101是計畫制定者輸入上述的限制條件、評價指標以及變數的區。輸入歷史讀取按鈕1102是計畫制定者用於選擇記載有輸入歷史121的內容的文字檔的按鈕。計畫歷史讀取按鈕1103是計畫制定者用於選擇記載有計畫歷史122的內容的文字檔的按鈕。知識模型生成按鈕1104是計畫制定者用於指示知識模型124的生成的按鈕。教師資料分布輸出區1105是表示由生成部111生成的教師資料(特徵向量和標籤的組)以評價指標為軸分布的模式的區。
The
例如,計畫制定者在上述步驟S502中將作為限制條件、評價指標以及變數的(式子1)輸入到文本輸入區1101。另外,例如,在步驟S502開始前,計畫制定者點擊輸入歷史記錄讀取按鈕1102,從輸入儲存裝置120選擇記載有輸入歷史121的內容的文字檔。另外,例如,計畫制定者點擊計畫歷史讀取按鈕1103,從儲存裝置120選擇記載有計畫歷史122的內容的文字檔。
For example, the planner inputs (Formula 1) as the restriction condition, evaluation index, and variable into the
然後,計畫制定者點擊知識模型生成按鈕1104。由此執行以下的步驟,作為其結果,知識模型124積蓄於儲存裝置120,作為所有特徵向量和標籤的組的教師資料在教師資料分布輸出區1105中顯示。在存在多個現有的評價指標、承認違反的限制條件的情況下,在教師資料分布輸出
區1105中從現有的評價指標、承認違反的限制條件中使2個或3個計畫制定者選擇,並以被選擇的評價指標、限制條件為軸顯示教師資料的分布。
Then, the planner clicks the knowledge
接著,如上所述,對利用調整限制條件的放寬量的知識模型124作為計畫制定者的隱性知識,基於新的輸入資訊制定新的計畫的處理進行說明。
Next, as described above, the process of formulating a new plan based on new input information using the
圖9是表示本實施方式的計畫制定輔助方法的處理順序例(主要是計畫制定輔助裝置100執行的處理的流程圖的一例)的圖。本流程圖是例示計畫制定部113根據由輸出部112輸出的知識模型124,生成與計畫制定者所制定的計畫資訊類似的新的計畫資訊的處理的圖。
FIG. 9 is a diagram illustrating an example of a processing sequence (mainly an example of a flowchart of processing executed by the plan-making assisting device 100) of the plan-making assisting method according to this embodiment. This flowchart is a diagram illustrating a process in which the
本流程圖中,例如,假設計畫制定輔助裝置100在輸入裝置140中觸發從計畫制定者接受用於新的計畫的制定的輸入資訊,起動計畫制定部113,開始處理。另外,從計畫制定者接受新的輸入資訊相當於具有與圖2例示的輸入歷史121的記錄相同的專案,以同一計畫編號匯總的資訊。
In this flowchart, for example, it is assumed that the plan
首先,計畫制定輔助裝置100的計畫制定部113輸入輸入資訊(步驟S901)。更具體而言,計畫制定部113經由輸入裝置140從計畫制定者接受包含限制條件、評價功能、變數(例如(式子1))的新的輸入資訊。
First, the
接著,計畫制定部113從輸入資訊使限制條件的放寬量評價指標化作為罰則(步驟S902)。更具體而言,計畫制定部113對新的輸入資訊中所含的限制條件、評價功能、
變數(例如,(式子1)),追加將該限制條件的放寬量設為罰則的評價指標,在以下的步驟中,視為多目的最優問題(例如(式子2))。
Next, the
接著,計畫制定部113從輸入資訊計算1個以上成為候選的計畫資訊(步驟S903)。更具體而言,計畫制定部113從新的輸入資訊使用用於解決多目的最優問題(例如(式子2))的通常的方法(例如本地搜索法、遺傳的演算法等)計算稱為帕累托最優解的候選的多個計畫資訊。
Next, the
接著,計畫制定部113對每個成為候選的計畫資訊生成特徵向量(步驟S904)。更具體而言,計畫制定部113生成在步驟S903中計算出的成為候選的多個計畫資訊和對每個計畫資訊說明該計畫資訊的特徵向量(例如(式子3))。構成特徵向量的要素與在上述步驟S509中設定的要素相同。
Next, the
接著,計畫制定部113使用知識模型124對每個成為候選的計畫資訊計算最佳解概率(步驟S905)。更具體而言,計畫制定部113從儲存裝置120讀出知識模型124,將對每個在上述步驟S904中成為候選的計畫資訊生成的特徵向量輸入到知識模型124(例如(式子4))並計算最佳解概率。
Next, the
圖10是對成為候選的5個計畫資訊的各個表示最佳解概率的影像圖。圖10中,5個計畫資訊中利潤最高的(f1的值最小)P1為“0.1”,放寬量最小的(f2的值最小)P5為“0.3”,最佳解概率低,利潤高且放寬量小的P3為“0.9”,最佳解概率變高。 FIG. 10 is an image diagram showing the optimal solution probability for each of the five candidate plan information pieces. In Figure 10, among the five plan information, the one with the highest profit (the value of f 1 is the smallest) P 1 is "0.1", and the one with the smallest relaxation (the value of f 2 is the smallest) P 5 is "0.3", and the probability of the best solution is low. , the profit is high and the amount of relaxation is small, P 3 is "0.9", and the probability of the best solution becomes higher.
接著,計畫制定部113輸出最佳解概率最高的計畫資
訊(步驟S906)。更具體而言,計畫制定部113選擇在上述步驟S905中計算出的最佳解概率中數值最大的計畫資訊,經由輸出裝置150將選擇了的計畫資訊輸出到計畫制定者,結束計畫制定部113的處理。
Next, the
在此,說明在從上述步驟S901至步驟S906的一系列處理時,計畫制定者透過輸出裝置150來閱覽,透過輸入裝置140進行適當的資訊輸入的畫面。
Here, during the series of processes from step S901 to step S906 described above, the planner views the screen through the
圖12是表示計畫制定畫面1200的一例的圖。
FIG. 12 is a diagram showing an example of the
計畫制定畫面1200包括:輸入資訊讀取按鈕1201、輸入資訊文本輸出欄位1202、知識模型讀取按鈕1203、計畫資訊生成按鈕1204、計畫資訊輸出區1205、教師資料分布輸出區1206。
The
輸入資訊讀取按鈕1201是用於計畫制定者選擇記載有為了制定計畫所需的輸入資訊的內容的文字檔的按鈕。輸入資訊文本輸出欄位1202是顯示所讀取的輸入資訊的欄位。知識模型讀取按鈕1203是用於計畫制定者選擇由生成部111生成的知識模型124的按鈕。計畫資訊生成按鈕1204是用於計畫制定者起動計畫制定部113而指示計畫資訊的生成(計畫的制定)的按鈕。計畫資訊輸出區1205是計畫制定部113以表格形式顯示輸出的計畫資訊的區。教師資料分布輸出區1206是表示與計畫制定部113輸出的計畫資訊類似的教師資料(特徵向量與標籤的組)以評價指標為軸分布的模式的區。
The input information read
例如,計畫制定者在上述步驟S901時,透過點擊輸入
資訊讀取按鈕1201來從儲存裝置120選擇記載有輸入資訊的內容的文字檔而獲取用於制定新的計畫的輸入資訊。另外,透過知識模型讀取按鈕1203的點擊從儲存裝置120呼出用於新的計畫的制定的知識模型124,點擊計畫資訊生成按鈕1204。
For example, in the above step S901, the planner inputs by clicking
The information read
由此,執行以下的步驟,作為其結果新生成的計畫資訊在計畫資訊輸出區1205中顯示。另外,作為與所生成的計畫資訊類似的特徵向量和標籤的組的教師資料在教師資料分布輸出區1206上顯示。在存在多個現有的評價指標、承認違反的限制條件的情況下,也可以在教師資料分布輸出區1206,從現有的評價指標、承認違反的限制條件中選擇2個或3個計畫制定者,以所選擇的評價指標、限制條件為軸顯示教師資料的分布。
As a result, the following steps are executed, and the plan information newly generated as a result is displayed in the plan
以上,在本實施方式中,從輸入歷史和計畫歷史生成調整限制條件的放寬量的知識模型,作為計畫制定者的隱性知識,透過反映到以後的計畫的制定,能夠輸出滿足度高的計畫。由此,能夠進行根據輸入資訊反映調整評價指標和限制條件的放寬量的隱性知識的有效的計畫的制定。 As mentioned above, in this embodiment, a knowledge model that adjusts the relaxation amount of the restriction condition is generated from the input history and the plan history. As the tacit knowledge of the plan maker, it is reflected in the formulation of future plans, and the satisfaction level can be output. High plans. This makes it possible to formulate an effective plan that reflects tacit knowledge that adjusts the evaluation index and the relaxation amount of the restriction conditions based on the input information.
對本實施方式進行說明。其中,主要對與第一實施方式不同的點進行說明。本實施方式是圖1所示的計畫制定輔助裝置100在有多個根據輸入資訊調整評價指標和限制條件的放寬量的隱性知識的種類的情況下,有效地制定反
映這些隱性知識的不同的計畫的形式。隱性知識的不同例如也可以是計畫制定者有多人的情況下的每個計畫制定者的隱性知識的不同、或者隨時間經過而不同的一個計畫制定者的隱性知識的不同。
This embodiment will be described. Among these, differences from the first embodiment will be mainly described. In this embodiment, the
本實施方式的計畫制定輔助裝置100的生成部111以與第一實施方式的生成部111的處理(圖5)相比除一部分以外執行同樣的處理。因此,以下,對本實施方式的生成部111的處理中的與第一實施方式不同的內容進行說明。
The
本實施方式的生成部111在圖5中已例示的處理中,在步驟S501中準備輸入資訊的集合和計畫資訊的集合時,對每個計畫制定者進行劃分並準備。或者,也可以劃分為最近1年和最近1個月分和最近1周來準備。
In the process illustrated in FIG. 5 , when preparing the set of input information and the set of plan information in step S501 , the
接著,本實施方式的生成部111在圖5中已例示的處理中,在步驟S502中經由輸入裝置140輸入在上述步驟S501中劃分後的輸入資訊的集合和計畫資訊的集合,以劃分後的數量執行生成部111的處理。
Next, in the process illustrated in FIG. 5 , the
透過以上處理,本實施方式的生成部111以在步驟S501中劃分的數量生成知識模型124。
Through the above processing, the
另外,本實施方式的計畫制定部113以與第一實施方式的計畫制定部113的處理(圖9)相比除一部分以外執行同樣的處理。因此,以下,對本實施方式的計畫制定部113的處理中的與第一實施方式不同的內容進行說明。
In addition, the
本實施方式的計畫制定部113在圖9中已例示的處理中,在步驟S905中以在上述步驟S501中劃分後的數量從儲
存裝置120讀出知識模型124,將對在上述步驟S904中成為候選的計畫資訊生成的特徵向量輸入到知識模型124(例如(式子4))來以劃分的數量計算最佳解概率。
In the process illustrated in FIG. 9 , the
接著,本實施方式的計畫制定部113在圖9中已例示的處理中,在步驟S906中,以在上述步驟S501中劃分後的數量選擇在上述步驟S905中計算出的最佳解概率中數值最大的計畫資訊,經由輸出裝置150將這些計畫資訊輸出到計畫制定者,完成計畫制定部113的處理。
Next, in the process illustrated in FIG. 9 , in step S906 , the
以上,根據本實施方式,例如,在如計畫制定者有多人的情況下的每個計畫制定者的隱性知識的不同、或者隨時間經過而不同的一個計畫制定者的隱性知識的不同那樣有多個隱性知識的種類的情況下,從輸入歷史和計畫歷史生成多個調整限制條件的放寬量的知識模型,作為計畫制定者的隱性知識,透過將這些隱性知識的不同反映到以後的計畫的制定,能夠向每個知識模型輸出滿足度高的計畫。由此,能夠進行反映根據輸入資訊調整評價指標和限制條件的放寬量的隱性知識的有效的計畫制定。 As described above, according to this embodiment, for example, when there are multiple planners, the tacit knowledge of each planner is different, or the tacit knowledge of one planner changes with the passage of time. When there are multiple types of tacit knowledge such as the difference in knowledge, multiple knowledge models that adjust the relaxation amount of the restriction conditions are generated from the input history and the plan history, and these tacit knowledge are used as the tacit knowledge of the planner. The difference in sexual knowledge is reflected in the formulation of future plans, and plans with a high degree of satisfaction can be output to each knowledge model. This enables effective planning reflecting tacit knowledge that adjusts the evaluation index and the relaxation amount of the restriction conditions based on the input information.
以上,對用於實施本發明的優選方式等進行了具體地說明,但本發明不限定於此,在不脫離其宗旨的範圍內可進行各種變更。 As mentioned above, the preferable mode etc. for carrying out this invention were demonstrated concretely, However, this invention is not limited to this, Various changes are possible within the range which does not deviate from the summary.
此外,在上述實施方式中,對將本發明應用於計畫制定輔助裝置的情況進行了說明,但本發明不限定於此,能 夠廣泛應用於其它各種的系統、裝置、方法、程式。 In addition, in the above-mentioned embodiment, the case where the present invention is applied to a planning assistance device has been described, but the present invention is not limited to this and may It can be widely used in various other systems, devices, methods, and programs.
另外,在上述實施方式中,各表的結構為一例,1個表可以劃分為2個以上的表,2個以上的表的全部或一部分也可以為1個表。 In addition, in the above embodiment, the structure of each table is an example, and one table may be divided into two or more tables, and all or part of two or more tables may be one table.
另外,在上述的實施方式中,為了方便說明,使用XX表、XX資料夾說明了各種資料,但資料結構沒有限定,也可以表現為XX資訊等。 In addition, in the above-mentioned embodiment, for convenience of explanation, various data are described using XX tables and XX folders, but the data structure is not limited and may also be expressed as XX information, etc.
另外,在上述的說明中,實現各功能的程式、表、資料夾等資訊能夠存放於記憶體、硬碟、SSD等儲存裝置、IC卡、SD卡、DVD等記錄介質。 In addition, in the above description, information such as programs, tables, and folders that implement each function can be stored in storage devices such as memory, hard disks, and SSDs, and recording media such as IC cards, SD cards, and DVDs.
上述的實施方式例如具有以下的特徵結構。 The above-described embodiment has, for example, the following characteristic structures.
計畫制定輔助裝置(例如計畫制定輔助裝置100)包括:生成部(例如生成部111),其基於包含各計畫中的限制條件和評價指標的輸入歷史(例如輸入歷史121)以及包含關於表示違反了上述各計畫的上述限制條件的量的放寬量的資訊(例如放寬量309、作為用於計算放寬量309的資訊的最大生產能力303、生產成本305、選擇標誌307等)的計畫歷史(例如計畫歷史122),生成表示上述限制條件的放寬量與上述評價指標的關係的知識模型(例如知識模型124、(式子4));和輸出部(例如輸出部112),其輸出上述知識模型,使得要使用上述知識模型來制定計畫的計畫制定部(例如計畫制定部113)能夠使用(例如儲存於儲存裝置120、通知到計畫制定部113、發送到其它電腦、在輸出裝置150上顯示等)上述知識模型。 The plan preparation assistance device (for example, the plan preparation assistance device 100) includes a generation unit (for example, the generation unit 111) based on an input history (for example, the input history 121) including constraints and evaluation indicators in each plan and information on Information indicating the relaxation amount of the amount that violates the above-mentioned restriction conditions of each of the above plans (for example, the relaxation amount 309, the maximum production capacity 303 as the information used to calculate the relaxation amount 309, the production cost 305, the selection flag 307, etc.) drawing history (for example, plan history 122), generating a knowledge model (for example, knowledge model 124, (Formula 4)) representing the relationship between the relaxation amount of the restriction condition and the above evaluation index; and an output unit (for example, output unit 112), It outputs the above-mentioned knowledge model so that the plan-making part (for example, the plan-making part 113) who wants to use the above-mentioned knowledge model to make a plan can use it (for example, store it in the storage device 120, notify it to the plan-making part 113, and send it to other computer, display on the output device 150, etc.) the above knowledge model.
在上述結構中,生成表示評價指標和限制條件的放寬量的關係的知識模型。根據知識模型,例如,計畫制定部能夠掌握計畫制定者在過去有多少價值時允許多少違反的關係(均衡)。因此,計畫制定部例如能夠制定根據輸入資訊反映了與過去的計畫的均衡接近的計畫、即反映評價指標和限制條件的放寬量的關係的計畫。 In the above structure, a knowledge model representing the relationship between the evaluation index and the relaxation amount of the restriction condition is generated. Based on the knowledge model, for example, the planning department can grasp the relationship (equilibrium) of how much violation the planner allowed based on how much value there was in the past. Therefore, for example, the planning department can formulate a plan that reflects the equilibrium close to the past plan based on the input information, that is, a plan that reflects the relationship between the evaluation index and the relaxation amount of the restriction condition.
上述生成部,基於上述計畫歷史,對上述各計畫生成上述各計畫的特徵向量(例如(式子3))和表示是由計畫制定者創建的情事的第一標籤(例如“1”)的組,基於上述輸入歷史,對上述各計畫進行上述評價指標和使得上述放寬量最小化的評價指標的優化來生成計畫候選(例如計畫候選123),並生成上述計畫候選的各計畫的特徵向量和表示是計畫候選的情事的第二標籤(例如“0”)的組,對所生成的特徵向量和標籤的組進行機器學習而生成上述知識模型。 The above-mentioned generation unit generates, for each of the above-mentioned plans, based on the above-mentioned plan history, the feature vector of each of the above-mentioned plans (for example, (Formula 3)) and the first label indicating that it was created by the plan maker (for example, "1 ”) group, based on the above-mentioned input history, the above-mentioned each plan is optimized with the above-mentioned evaluation index and the evaluation index that minimizes the above-mentioned relaxation amount to generate a plan candidate (for example, plan candidate 123), and generate the above-mentioned plan candidate A set of feature vectors of each plan and a second label (for example, “0”) indicating that the plan is a candidate, and machine learning is performed on the generated set of feature vectors and labels to generate the above-mentioned knowledge model.
根據上述結構,透過對正解的教師資料(計畫歷史的計畫的特徵向量和第一標籤的組)和非正解的教師資料(計畫候選的計畫的特徵向量和第二標籤的組)同時進行學習,能夠提高知識模型的精度。由此,計畫制定部能夠制定更滿足計畫制定者的計畫。 According to the above structure, by comparing the teacher data of the correct solution (the set of feature vectors of the project history and the first label) and the teacher data of the incorrect solution (the set of feature vectors of the project candidates and the second tag) Learning at the same time can improve the accuracy of the knowledge model. This allows the planning department to formulate plans that are more satisfying to the planners.
此外,在不使用計畫候選的計畫而使用計畫歷史的計畫生成知識模型的情況下,能夠使知識模型的生成簡化,使知識模型的生成時間縮短。另外,也可以從計畫歷史的計畫的特徵向量和第一標籤的組的分布的偏斜決定用於生 成知識模型時的資訊。例如,在分布的標準差超過閾值的情況下,決定為使用計畫歷史和計畫候選生成知識模型,在分布的標準差未超過閾值的情況下,決定為使用計畫歷史生成知識模型。 In addition, when the knowledge model is generated using the plans of the plan history instead of the plans of the plan candidates, the generation of the knowledge model can be simplified and the generation time of the knowledge model can be shortened. In addition, the generation method may also be determined based on the skewness of the distribution of the project feature vector in the project history and the first label group. information when forming a knowledge model. For example, when the standard deviation of the distribution exceeds the threshold, it is decided to generate a knowledge model for the usage plan history and plan candidates, and when the standard deviation of the distribution does not exceed the threshold, it is decided to generate a knowledge model for the usage plan history.
上述生成部將位於上述第一標籤的組附近的上述第二標籤的組的標籤變更為上述第一標籤,來生成上述知識模型(例如參照圖7)。 The generation unit generates the knowledge model by changing the label of the second label group located near the first label group to the first label (see, for example, FIG. 7 ).
根據上述結構,透過將正解的教師資料(計畫歷史的計畫的特徵向量和第一標籤的組)附近的非正解的教師資料(計畫候選的計畫的特徵向量和第二標籤的組)變更為正解的教師資料,能夠避免正解的教師資料壓倒性少的事態。由此,例如,生成部能夠確保正解的教師資料,因此能夠提高知識模型的精度。 According to the above structure, by combining the teacher data of the correct answer (the set of feature vectors of the project history and the first label) to the teacher data of the incorrect answer (the set of feature vectors of the project candidates and the second tag) ) to correct teacher materials can avoid a situation where there are overwhelmingly few correct teacher materials. This allows, for example, the generation unit to secure correct answer teacher materials, thereby improving the accuracy of the knowledge model.
上述生成部對於上述各計畫排除位於上述第一標籤的組附近的上述第二標籤的組,來生成上述知識模型(例如參照圖8)。 The generation unit generates the knowledge model by excluding the second label group located near the first label group for each of the plans (see, for example, FIG. 8 ).
根據上述結構,透過排除正解的教師資料(計畫歷史的計畫的特徵向量和第一標籤的組)附近的非正解的教師資料(計畫候選的計畫的特徵向量和第二標籤的組),能夠避免正解的教師資料壓倒性地少的事態。由此,例如,生成部能夠採用正解的教師資料和非正解的教師資料的資料數的均衡,因此能夠提高知識模型的精度。 According to the above structure, by excluding the teacher data of the correct answer (the set of feature vectors of the project history and the first label) that are nearby the non-correct answer teacher data (the set of feature vectors of the project candidate and the second label) ), it is possible to avoid a situation in which there are overwhelmingly few teacher materials with correct answers. This allows, for example, the generation unit to balance the number of correct teacher materials and incorrect teacher materials, thereby improving the accuracy of the knowledge model.
上述生成部根據預先指定的條件(例如氣溫、濕度、天氣、氣壓、風的強度等現象、季節、時間帶等期間、日 期時間等時間、星期、休息日、工作日等天的區分等),對上述各計畫基於上述計畫歷史生成上述各計畫的特徵向量和表示是由計畫制定者制定的情事的第一標籤的組,對所生成的特徵向量和標籤的組進行機器學習來生成上述知識模型。 The above-mentioned generating unit generates data based on pre-specified conditions (such as temperature, humidity, weather, air pressure, wind intensity and other phenomena, seasons, time zones and other periods, days). (period time and other time, days of the week, holidays, working days, etc.), for each of the above-mentioned plans, the feature vector of each of the above-mentioned plans is generated based on the above-mentioned plan history, and the first feature vector representing the situation set by the plan maker is generated. A group of labels, and machine learning is performed on the generated feature vector and the group of labels to generate the above knowledge model.
在上述結構中,根據條件表示的知識模型生成評價指標和限制條件的放寬量的關係,因此,計畫制定部根據預先確定的條件劃分輸入資訊,由此,能夠制定與輸入資訊對應的計畫。例如,計畫制定部從輸入資訊中所含的日期時間的資料中判別季節(夏、冬等),能夠制定與季節對應的計畫。 In the above structure, the relationship between the evaluation index and the relaxation amount of the restriction condition is generated based on the knowledge model expressed by the condition. Therefore, the plan formulation unit divides the input information according to the predetermined conditions, thereby making it possible to formulate a plan corresponding to the input information. . For example, the planning department can determine the season (summer, winter, etc.) from the date and time data included in the input information, and can formulate a plan corresponding to the season.
上述生成部按每個預先指定的觀點(預先指定的觀點是計畫制定者計畫所需的觀點。例如表示隱性知識的種類,計畫制定者、1年、1個月、一周的期間等),對上述各計畫基於上述計畫歷史生成上述各計畫的特徵向量和表示計畫制定者創建的第一標籤的組,對所生成的特徵向量和標籤的組進行機器學習而生成上述知識模型。 The above-mentioned generation unit generates data for each pre-specified point of view (the pre-specified point of view is a point of view required for the plan maker's plan. For example, it indicates the type of tacit knowledge, the plan maker, the period of one year, one month, one week etc.), for each of the above-mentioned projects, a feature vector of each of the above-mentioned projects and a group representing the first label created by the plan maker are generated for each of the above-mentioned projects based on the above-mentioned project history, and the generated set of feature vectors and labels are generated by machine learning. The above knowledge model.
在上述結構中,針對每個預先指定的觀點生成知識模型。例如,在針對1年、1個月、一周的期間生成有知識模型的情況下,計畫制定者透過選擇任一個期間的知識模型,能夠得到反映了1年、1個月、一周的期間的傾向的計畫。另外,例如,在對每個計畫制定者生成了知識模型的情況下,計畫制定者選擇自身的知識模型,能夠得到反映了自身的隱性知識的計畫,或者透過選擇技術人員的知識 模型,能夠得到反映了技術人員的隱性知識的計畫。 In the above structure, a knowledge model is generated for each pre-specified viewpoint. For example, when a knowledge model is generated for a period of one year, one month, or one week, the planner can obtain a knowledge model that reflects the period of one year, one month, or one week by selecting the knowledge model for any one period. Tendency plan. In addition, for example, when a knowledge model is generated for each planner, the planner can select his or her own knowledge model to obtain a plan that reflects his or her own tacit knowledge, or by selecting the knowledge of technicians. Models can be used to obtain plans that reflect the tacit knowledge of technicians.
另外,在上述結構中,在不脫離本發明的宗旨的範圍內,也可以進行適當變更、重排、組合、省略。 In addition, in the above structure, appropriate changes, rearrangements, combinations, and omissions may be made within the scope that does not deviate from the gist of the present invention.
100:計畫制定輔助裝置 100: Planning assistance device
111:生成部 111:Generation Department
112:輸出部 112:Output Department
113:計畫制定部 113: Planning Department
120:儲存裝置 120:Storage device
121:輸入歷史 121:Input history
122:計畫歷史 122:Project History
123:計畫候選 123:Project candidate
124:知識模型 124:Knowledge model
125:程式 125:Program
130:記憶體 130:Memory
140:輸入裝置 140:Input device
150:輸出裝置 150:Output device
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