TW201928811A - Item picking method, and related apparatus - Google Patents
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Abstract
Description
本案涉及倉庫管理技術領域，更具體地，是物品揀選方法及相關設備。This case relates to the technical field of warehouse management, and more specifically, to an item selection method and related equipment.
在倉庫等物品儲存空間內，為了方便管理物品，通常將物品按種類分區域存放，即不同區域存放不同種類的物品。揀選是根據按照一定規則或要求，將一些物品從存放區域取出並運送到指定位置。其中存放區域也可以稱為揀選區域，從一個揀選區域揀選出所需物品的一次過程，可以稱為揀選子任務。
例如，物品訂單中包括3個種類的10件物品，種類1的物品包括2個，種類2的物品包括4個，種類3的物品包括4個，則需要從種類1對應的揀選區域揀選2個物品，從種類2對應的揀選區域揀選4個物品，從種類3對應的揀選區域揀選4個物品。
揀選子任務的先後執行順序，會影響揀選的完成情況。例如，上述種類1對應的揀選區域與種類2對應的揀選區域距離較近，但兩者與種類3對應的揀選區域距離均較遠，如果先揀選種類1的物品，再揀選種類3，最後揀選種類2的物品，會導致揀選距離較長，影響揀選效率。
目前，揀選子任務的執行順序是隨意的，揀選的完成情況通常不能滿足揀選目標需求。In an article storage space such as a warehouse, in order to facilitate the management of the articles, the articles are usually stored in different regions by type, that is, different types of articles are stored in different regions. Picking is in accordance with certain rules or requirements, some items are taken out of the storage area and transported to the designated location. The storage area can also be called a picking area. The process of picking out the required items from a picking area can be called a picking subtask.
For example, an item order includes 10 items in 3 categories, 2 items in category 1, 2 items in category 2, 4 items in category 3, and 2 items need to be picked from the picking area corresponding to type 1 For articles, 4 items are picked from the picking area corresponding to type 2 and 4 items are picked from the picking area corresponding to type 3.
The order of execution of the picking subtasks will affect the completion of the picking. For example, the picking area corresponding to category 1 and the picking area corresponding to category 2 are closer, but the picking areas corresponding to category 3 are both farther away. If you select the items of category 1 first, then select category 3, and finally pick Category 2 items will result in longer picking distances and affect picking efficiency.
At present, the execution order of the picking subtask is arbitrary, and the completion of the picking usually cannot meet the needs of the picking target.
有鑒於此，本案提供了一種物品揀選方法，以使揀選子任務的完成情況滿足預設的揀選目標。
為實現所述目的，本案提供的技術方案如下：
第一方面，本案提供了一種物品揀選方法，包括：
確定揀選目標以及所述揀選目標關聯的屬性項；
獲得多個揀選子任務，並確定所述揀選子任務與所述屬性項對應的屬性資料；
依據所述揀選目標及所述揀選子任務的屬性資料，確定所述多個揀選子任務的執行順序。
第二方面，本案提供了一種物品揀選設備，包括：處理器和記憶體，所述處理器透過執行儲存在所述記憶體內的軟體程式、調用儲存在所述記憶體內的資料，至少執行如下步驟：
確定揀選目標以及所述揀選目標關聯的屬性項；
獲得多個揀選子任務，並確定所述揀選子任務與所述屬性項對應的屬性資料；
依據所述揀選目標及所述揀選子任務的屬性資料，確定所述多個揀選子任務的執行順序。
第三方面，本案提供了一種物品揀選裝置，包括：
揀選目標確定單元，用於確定揀選目標以及所述揀選目標關聯的屬性項；
屬性資料確定單元，用於獲得多個揀選子任務，並確定所述揀選子任務與所述屬性項對應的屬性資料；
執行順序確定單元，用於依據所述揀選目標及所述揀選子任務的屬性資料，確定所述多個揀選子任務的執行順序。
由以上技術方案可知，本案提供的物品揀選方法，可以確定揀選目標以及揀選目標關聯的屬性項，獲得待排序的多個揀選子任務後確定揀選子任務與屬性項對應的屬性資料，進而依據揀選目標及揀選子任務的屬性資料，確定多個揀選子任務的執行順序。可見本案可以根據揀選目標對揀選子任務的揀選順序進行排班，以使揀選子任務的完成情況符合特定的揀選目標的要求。In view of this, this case provides a method for item selection, so that the completion of the selection sub-task meets the preset selection target.
To achieve the stated purpose, the technical solutions provided in this case are as follows:
In the first aspect, the case provides an article selection method, including:
Determining a picking target and attribute items associated with the picking target;
Obtaining a plurality of picking subtasks, and determining attribute data corresponding to the picking subtasks and the attribute items;
An execution order of the plurality of selection subtasks is determined according to the selection target and attribute data of the selection subtask.
In a second aspect, this case provides an item picking device, including a processor and a memory. The processor executes a software program stored in the memory, calls data stored in the memory, and performs at least the following steps: :
Determining a picking target and attribute items associated with the picking target;
Obtaining a plurality of picking subtasks, and determining attribute data corresponding to the picking subtasks and the attribute items;
An execution order of the plurality of selection subtasks is determined according to the selection target and attribute data of the selection subtask.
In a third aspect, this case provides an article picking device, including:
A picking target determining unit, configured to determine a picking target and an attribute item associated with the picking target;
An attribute data determining unit, configured to obtain multiple picking subtasks and determine attribute data corresponding to the picking subtasks and the attribute items;
An execution order determination unit is configured to determine an execution order of the plurality of selection subtasks according to the selection target and attribute data of the selection subtask.
It can be known from the above technical solutions that the article selection method provided in this case can determine the selection target and the attribute items associated with the selection target, obtain multiple selection subtasks to be sorted, determine the attribute data corresponding to the selection subtask and the attribute item, and then select the The attribute data of the target and the picking subtask determine the execution order of multiple picking subtasks. It can be seen that in this case, the picking order of the picking subtasks can be shifted according to the picking targets, so that the completion of the picking subtasks meets the requirements of the specific picking targets.
下面將結合本案實施例中的附圖，對本案實施例中的技術方案進行清楚、完整地描述，顯然，所描述的實施例僅僅是本案一部分實施例，而不是全部的實施例。基於本案中的實施例，本領域普通技術人員在沒有做出創造性勞動前提下所獲得的所有其他實施例，都屬於本案保護的範圍。
在物品存放空間內，通常設置有多個存放區域，用於存放不同種類的物品。在實際應用中，需要從存放區域中獲得滿足需求的物品，這個過程即揀選，存放區域也可以稱為揀選區域。
為實現自動化揀選工作，物品存放空間內還可以設置物品承載裝置及驅動裝置。其中，物品承載裝置用於承載從揀選區域揀選出的物品，驅動裝置用於驅動物品承載裝置進行運動，以使物品承載裝置從一個地點運動至另一地點。例如需要揀選多種物品，則驅動裝置需要將物品承載裝置從一個揀選區域牽引至另一揀選區域。物品承載裝置的一種具體形式為揀選車，驅動裝置的一種具體形式為移動機器人。
本案的物品揀選方法針對的對象是揀選子任務，揀選子任務與揀選區域具有關聯關係，物品承載裝置執行一個揀選子任務具體指的是，物品承載裝置裝載入從揀選子任務關聯的揀選區域中揀選出的物品。
在得到揀選子任務的基礎上，才可以執行物品揀選方法。為了便於理解，本案首先對揀選子任務的產生過程進行說明。具體地，揀選子任務的一種產生過程包括如下步驟A1~A2。
A1：獲得物品單並確定揀選目標，並依據物品單產生符合揀選目標的揀選單。
其中，物品單中包含有物品，該物品即需要揀選出的物品。物品單的一種具體形式為買家在電子商務平臺購買物品產生的物品訂單。獲得物品單的觸發條件信號可以包括：物品承載裝置空閒、驅動裝置空閒、需要從物品存放空間取走物品的運輸工具到達等。
通常地，物品單中的待揀選的物品為多個，且待揀選的物品單也為多個。為了對揀選過程進行控制，可以按照揀選目標從揀選區域中揀選這些物品。例如，揀選目標可以是物品單中所有物品的揀選時長最短，或者物品單中具有相同屬性的物品的揀選間隔時長最短等；其中相同屬性可以是物品的尺寸相同、物品的打包區域相同、物品的收貨地址相同或物品的運輸工具相同等等。
提供多個揀選目標，在揀選過程中可以根據實際需求在多個揀選目標中確定某個揀選目標作為該揀選過程使用的揀選目標。確定的方式可以是隨機選擇，也可以是根據用戶的選擇操作進行確定，或者也可以是根據物品單的屬性來確定揀選目標。例如物品單的屬性為加急，則可以將揀選目標確定為揀選時長最短。
確定揀選目標後，可以按照揀選目標將物品單聚合為揀選單，揀選單也可以稱為揀選任務。一個揀選單對應一個物品承載工具，一個物品承載工具需要承載一個揀選單中的所有物品才認為一個物品承載工具完成一個揀選任務。或者也可以說，一個揀選單中的所有揀選出的物品會被存放在同一物品承載工具中。如果將具有相同屬性的物品包含在同一揀選單中，這樣一個揀選車完成揀選任務時，就可以同時得到具有相同屬性的物品，即表示相同屬性的物品的揀選間隔時長最短。
產生揀選單的一種方式可以包括如下步驟A11~A14。
A11：確定與揀選目標關聯的屬性項。
其中，揀選目標關聯有屬性項，屬性項即影響揀選目標完成情況的屬性。例如，揀選目標為物品單中所有物品的揀選時長最短，則可以影響物品揀選時長的屬性項包括以下幾項中的任意一項或多項：物品所在的揀選區域的位置、物品所在揀選區域的工作負荷、物品所在揀選區域的工作效率、物品的揀選優先級等。又如，揀選目標為揀選單中物品單中收貨地址相同的物品的揀選間隔時長最短，則揀選目標對應的屬性項包括物品的收貨地址。
A12：獲得物品單，並將物品單中的物品組合後產生揀選單。
其中，可以將物品單中的物品進行排列組合，產生多種備選揀選單。組合可以包括所有數量形式的組合，例如，2個物品單中共包含5個物品，則將該5個物品進行1和4數量形式的組合，以及進行2和3數量形式的組合。當然，也可以設置組合的數量形式要求，如每種揀選單至少包括兩個物品，則將5個物品進行2和3數量形式的組合。
A13：確定揀選單中物品與屬性項對應的屬性資料，並依據屬性資料，計算揀選單的綜合得分。
其中，為揀選單中的物品確定在屬性項上的資料，該資料可以稱為屬性資料。例如，根據物品的種類確定物品所在的揀選區域，再確定揀選區域的所在位置、揀選區域的工作效率及揀選區域的工作負荷等；又如，根據物品單對應的收貨地址，確定物品單中物品的收貨地址。
為了計算備選揀選單的綜合得分，首先根據備選揀選單內物品的屬性資料，計算備選揀選單的屬性資料，再根據備選揀選單的屬性資料得到備選揀選單的綜合得分。
例如，根據備選揀選單內物品所在揀選區域的位置，計算備選揀選單內物品之間的間隔；根據備選揀選單內物品所在揀選區域的工作負荷，確定備選揀選單對應的負荷狀態。
又如，備選揀選單內滿足數量要求的物品所在揀選區域的工作負荷為高，則確定備選揀選單對應的負荷狀態為高；又如備選揀選單內滿足數量要求的物品所在揀選區域的工作負荷為中，則確定備選揀選單對應的負荷狀態為中；又如備選揀選單內滿足數量要求的物品所在揀選區域的工作負荷為低，則確定備選揀選單對應的負荷狀態為低。
為了計算得分，可以預先設置備選揀選單的屬性資料與分數之間的換算關係。
例如，備選揀選單內物品之間的間隔在[0,5)範圍內，則將其換算為1分，備選揀選單內物品之間的間隔在[5,10)範圍內，則將其換算為2分等；又如，備選揀選單對應的負荷狀態為高，則將其換算為3分，備選揀選單對應的負荷狀態為中，則將其換算為2分，備選揀選單對應的負荷狀態為低，則將其換算為1分。需要說明的是，上述對應的分數僅僅是示例說明，在實際應用中，可以是其他數值。
根據備選揀選單的屬性資料，以及揀選單的屬性資料與分數之間的換算關係，可以計算出揀選單對應的得分。由於揀選目標包含的屬性項可能為多項，則計算出的揀選單的屬性資料也可以包括多項，每項屬性資料均被換算為對應的分數，將各個分數求和便可以得到備選揀選單的分數，該分數可以稱為綜合得分。
例如，揀選目標為物品單中所有物品的揀選時長最短，則可以影響物品揀選時長的屬性項包括可以包括以下4項：物品所在的揀選區域的位置、物品所在揀選區域的工作負荷、物品所在揀選區域的工作效率、物品的揀選優先級。因此，備選揀選單中包括由4項的屬性資料換算後的分數，再將該4個分數進行求和後得到綜合得分。
當然，若揀選目標包含的屬性項為一項，則綜合得分是由一項分數得到的。
A14：選擇綜合得分符合揀選目標的揀選單。
其中，不同的揀選目標對綜合得分的要求也是不同的。例如，揀選目標為物品單中所有物品的揀選時長最短，且根據換算關係可以確定綜合分數越小表示揀選時長越短，因此可以確定綜合得分最小的備選揀選單為符合該揀選目標的揀選單。為了便於描述，可以將符合揀選目標的揀選單稱為目標揀選單。
以上根據物品單產生揀選單的過程可以稱為物品單的聚合。綜合以上技術方案可知，本案可以根據不同的揀選目標，將物品單中滿足揀選目標的物品聚合為同一揀選單。例如，若揀選目標為物品單中所有物品的揀選時長最短，則可以將物品單中位於相同或相近揀選區域的物品包含在同一揀選單中。
需要說明的是，以上物品單聚合為揀選單的過程看作全域優化問題，透過設置約束條件及約束目標可以求得優化問題的最佳解。具體地，用於求解全域優化問題的演算法可以為優化演算法，優化演算法包括但不限定於列產生演算法、遺傳演算法、貪婪演算法等。
物品單聚合對應的全域優化問題可以描述為以下內容。
設物品單的集合為，其中表示物品單。
假設約束目標為物品單中所有物品的揀選時長最短，則物品單聚合對應的目標函數為：。其中表示備選揀選單，j表示備選揀選單的序號，M表示備選揀選單的個數；表示備選揀選單對應的揀選時長，該揀選時長由該備選揀選單中的物品的數量、物品所在的揀選區域位置、物品所在揀選區域的工作負荷、物品所在揀選區域的工作效率、物品的揀選優先級等得到。
目標函數需要滿足的約束條件包括以下2項。
表示每個物品單只對應一個備選揀選單；其中i表示物品單的序號，表示物品單加入到備選揀選單中。
表示每個備選揀選單對應的物品單的數量在預設臨限值K以內。當然，也可以按照該形式設置每個備選揀選單包含的物品的重量、種類不能超過一定臨限值。
根據約束條件及約束目標，對上述目標函數進行求解後，可以得到滿足約束目標的揀選單。
A2：將揀選單中對應相同揀選區域的物品劃分為同一揀選子任務。
前已述及，不同種類的物品可以被存放在不同的揀選區域內。根據揀選單中包含的物品的所在揀選區域，將對應相同揀選區域的物品劃分為同一組。由於揀選單可以稱為揀選任務，因此根據揀選單劃分後的物品分組可以稱為揀選子任務。其中，揀選子任務表示從揀選區域中揀選物品。
透過上述說明可知，本案可以將各種物品單進行聚合得到各種揀選單，再揀選單劃分為各個揀選子任務。物品存放空間內的物品承載裝置及驅動裝置等工作設備的揀選能力是有限的，並不能保證揀選子任務同時執行，因此在得到揀選子任務後，需要對揀選子任務的執行過程進行排序，以保證揀選子任務的執行結果符合揀選目標的要求。
見圖1，其示出了本案提供的物品揀選方法的一種流程，具體包括步驟S101~S103。
S101：確定揀選目標以及揀選目標關聯的屬性項。
其中，有關揀選目標的說明參見上述說明，此處並不贅述。
S102：獲得多個揀選子任務，並確定揀選子任務與屬性項對應的屬性資料。
其中，物品揀選設備產生的揀選子任務的個數可以是多個，並且揀選子任務可以即時產生，也可以被執行完畢。因此，可以認為物品揀選設備維護有揀選子任務池，新產生的揀選子任務可以加入到揀選子任務池中，揀選子任務池中的揀選子任務也可以因為被執行完畢而被删除。揀選子任務池中的揀選子任務的數量可能是一個，也可能是多個，或者也有可能為0個。
獲得揀選子任務的時機可以包括以下幾種，如產生新的揀選子任務時，完成某揀選子任務時，接收到揀選子任務排序指令，或者預設的排序周期到達。為了便於描述，可以將這些時機稱為預設條件。
從物品揀選設備維護的揀選子任務中，獲得多個揀選子任務。可以理解的是，獲得的揀選子任務為未執行完畢的揀選子任務。
獲得的揀選子任務可以是任意的揀選子任務，也可以是指定的揀選子任務；獲得的揀選子任務的數量可以是預設數量，也可以是根據揀選狀態確定的數量。例如，設備的揀選狀態為空閒，則獲得數量較多的揀選子任務；設備的揀選狀態為忙碌，則獲得數量較少的揀選子任務。
揀選子任務也可以是與物品存放空間對應的，也可以是，獲得物品存放空間對應的全部揀選子任務。具體來講，針對一個物品存放空間，物品揀選設備維護有對應的揀選子任務，該物品存放空間當前對應有多少揀選子任務，則可以獲取全部的揀選子任務。這樣，便可以將全部的揀選子任務按照下述步驟進行整體排序，以獲得物品存放空間對應的所有揀選子任務的整體排序最佳效果。
或者也可以是，從物品存放空間對應的揀選子任務中，選擇符合篩選條件的多個揀選子任務。其中，篩選條件可以包括揀選子任務中物品的種類為特定種類、揀選子任務中物品的揀選預估時長在預設時長範圍內、揀選子任務中物品的數量在預設數量範圍內等等中的任意一個或多個。篩選條件可以是根據實際情況設置的任意條件，本案並不做具體限定。
獲得揀選子任務以及確定揀選目標後，需要確定揀選子任務在揀選目標的屬性項上的屬性資料。例如，揀選目標為物品單中所有物品的揀選時長最短，可以影響物品揀選時長的屬性項包括以下幾項中的任意一項或多項：揀選子任務包含的物品數量、揀選區域的工作負荷、揀選區域的工作效率、揀選優先級等。因此，需要確定揀選子任務包含的物品數量、揀選子任務對應的揀選區域的工作負荷、揀選子任務對應的揀選區域的工作效率、揀選子任務的優先級等。其中工作負荷可以具體體現為揀選區域尚未完成揀選的物品數量等。需要說明的是，確定的這些屬性資料為揀選子任務的屬性資料。
S103：依據揀選目標及揀選子任務的屬性資料，確定多個揀選子任務的執行順序。
其中，根據揀選子任務的屬性資料，可以確定出揀選子任務的揀選情況，進而根據揀選子任務的揀選情況可以安排揀選子任務的執行順序。
在具體實施中，本步驟的一種實現方式可以是：確定揀選子任務的多種備選執行順序；依據揀選子任務的屬性資料，預估每種備選執行順序的揀選情況；以及將滿足揀選目標的揀選情況對應的備選執行順序確定為揀選子任務的執行順序。
具體地，在確定揀選子任務的備選執行順序時，可以使用排列組合方法。但是，並非排列組合得到的所有執行順序在揀選場景中都能夠實現，這些不能實現的執行順序不可以作為備選執行順序。因此，可以使用約束條件對排列組合得到的執行順序進行篩選，選擇出滿足約束條件的執行順序作為備選執行順序。
具體地，由於一個物品承載工具不可能同時在不同的揀選區域進行揀選，因此對應同一物品承載工具的揀選子任務只能順序執行。另外，揀選區域的揀選能力通常是有限的，在揀選區域揀選能力有限的情況下，同一揀選區域只能順序執行揀選子任務中的物品揀選工作。使用以上兩個約束條件，可以將不滿足上述約束條件的執行順序删除，從而得到備選執行順序。
例如，獲得的揀選子任務包括3個，揀選子任務1及揀選子任務2對應同一物品承載工具，揀選子任務3對應另一物品承載工具。揀選子任務1及揀選子任務3對應的是揀選區域A，揀選子任務2對應的是揀選區域B。則可以得到圖2A及圖2B所示的兩種備選執行順序。其中，填充有左斜線的長形框表示揀選子任務1，填充有右斜線的長形框表示揀選子任務2，填充有竪線的長形框表示揀選子任務3。
揀選情況用於表示揀選子任務按照備選執行順序執行完成的情況。例如，揀選目標為物品的揀選時長最短，揀選情況表示的是揀選子任務按照備選執行順序執行完成後的揀選時長。又如，揀選目標為具有相同屬性的物品的揀選間隔時長最短，揀選情況表示的是揀選子任務按照備選執行順序執行完成後，具有相同屬性的物品的揀選完成間隔時長。
為了確定備選執行順序的揀選情況，可以首先根據揀選子任務的屬性資料，得到各個揀選子任務的揀選情況。然後，使用揀選子任務的揀選情況得到備選執行順序的揀選情況。
以揀選情況為揀選時長為例，對揀選子任務的備選執行順序的揀選情況的確定方法進行說明。參見圖2A和圖2B，假設揀選子任務1的執行時長為1分鐘，揀選子任務2的執行時長為2分鐘，揀選子任務3的執行時長為1.5分鐘。關於圖2A所示的備選執行順序，可以確定該備選執行順序的執行時長為4.5分鐘；關於圖2B所示的備選執行順序，可以確定該備選執行順序的執行時長為3分鐘。
根據備選執行順序對應的揀選情況，在備選執行順序中選擇揀選情況滿足揀選目標的備選執行順序作為揀選子任務的執行順序。仍以圖2A及圖2B所示的備選執行順序為例，明顯圖2B所示的備選執行順序的執行時長較短，因此將圖2B所示的執行順序作為目標執行順序。
以上主要使用物品揀選時長對應的揀選目標進行說明，以下說明滿足其他揀選目標的揀選子任務的執行順序。若揀選目標為物品單中具有相同屬性的物品的揀選間隔時長最短，則可以將具有相同屬性物品的揀選子任務在最短的時間間隔執行。
例如，獲得的揀選子任務包括5個，5個揀選子任務中的具有相同屬性的物品包括兩類，第一類揀選子任務包括揀選子任務1、揀選子任務2及揀選子任務3，第二類揀選子任務包括揀選子任務4及揀選子任務5。其中，揀選子任務1、揀選子任務2及揀選子任務3對應同一物品承載工具，揀選子任務4及揀選子任務5對應同一物品承載工具。揀選子任務1及揀選子任務4對應的是揀選區域A，揀選子任務3及揀選子任務5對應的是揀選區域B。揀選子任務2對應揀選區域C。
則所確定的一種執行順序可以如圖3所示，揀選區域C執行揀選子任務2，揀選區域A先執行揀選子任務1再執行揀選子任務4，揀選區域B先執行揀選子任務3再執行揀選子任務5。這樣，第一類揀選子任務包括的揀選子任務1、揀選子任務2及揀選子任務3可以儘量同時揀選完成；第一類揀選子任務包括的揀選子任務4及揀選子任務5可以儘量同時揀選完成。可見，在這種方式中具有相同屬性的物品的揀選間隔時長最短，滿足揀選目標。
需要說明的是，在實際應用中，以上為揀選子任務確定執行順序的過程可以看作批次處理作業調度的過程，可以使用批次處理作業調度演算法求解滿足揀選目標的執行順序。
以揀選目標為物品單中所有物品的揀選時長最短為例，介紹使用批次處理作業調度演算法為揀選子任務排序問題構建的數學模型及對數學模型的求解過程。
根據上述揀選目標，可以確定優化目標為最小化最遲揀選子任務的完成時間，因此構建的目標函數為：。
其中，i表示揀選區域集合，一個物品存放空間內所有的揀選區域作為一個揀選區域集合，或者一個物品存放空間包括多個揀選區域集合，每個揀選區域集合包含部分揀選區域；j表示揀選子任務；k表示揀選區域集合中的單個揀選區域；C表示完成時間；Cijk
為0或1的變量，表示揀選區域集合i的單個揀選區域k完成揀選子任務j的預估時長。
目標函數的約束條件包括但不限定於以下7個。
；該約束條件表示揀選區域集合i中的單個揀選區域k在一個時間片段t內，最多只能執行一個揀選子任務j。其中，n表示揀選子任務個數；Xijkt
為0或1的變量，表示在時間片段t揀選子任務j是否在揀選區域集合i中的單個揀選區域k執行。
；該約束條件表示在任何時間片段t，揀選子任務j只能在一個揀選區域執行。其中，s表示揀選區域集合數量；mi
表示揀選區域集合i中的揀選區域個數。
；該約束條件表示在任何時間片段t，揀選子任務j與揀選區域集合i中的單個揀選區域k對應的時間必須與預設執行時間Pijk
相同。其中，Ut
表示計劃時間周期；Pijk
表示揀選子任務j揀選區域集合i中的單個揀選區域k上的預設執行時間；Yijk
為0或1的變量，表示揀選子任務j是否在揀選區域集合i的單個揀選區域k執行。
；該約束條件表示如果揀選子任務j在揀選區域集合i中的單個揀選區域k上執行過一個周期，那麽這個揀選子任務必須在這個揀選區域i中的單個揀選區域k上執行完畢。其中，
；該約束條件表示對於每個揀選區域集合i，一個揀選子任務j只能在一個揀選區域k上執行。
；該約束條件表示揀選子任務不能被中斷。
；該約束條件為預估揀選子任務j在揀選區域集合i的單個揀選區域k的執行時間的方式。
根據以上約束條件，對目標函數進行求解後，可以獲得揀選子任務的執行順序。以下對未使用批次處理作業調度演算法求解前及使用批次處理作業調度演算法求解後揀選子任務的執行順序進行舉例說明。
見圖4A，其示出了未使用批次處理作業調度演算法求解前揀選子任務的執行順序；見圖4B，其示出了使用批次處理作業調度演算法求解後揀選子任務的執行順序。如圖所示，長形框內不同的填充內容，表示由不同的揀選任務產生的揀選子任務。填充有內容的長形框的長度表示揀選子任務在揀選區域的預估執行時長。每一行中的空白區域表示揀選區域的空閒時間段。每一行表示在一個揀選區域執行的各個揀選子任務的執行順序。可見，每個揀選區域同時只能進行一個揀選子任務。
對比圖4A及4B可知，揀選子任務的執行順序不同。使用批次處理作業調度演算法求解後，揀選子任務的執行總時長比未使用批次處理作業調度演算法求解前短，從而縮短了揀選子任務的執行總時長，且減少了揀選區域的閒置時長，也即揀選子任務的無效等待時長。
由以上技術方案可知，本案提供的物品揀選方法，可以確定揀選目標以及揀選目標關聯的屬性項，獲得待排序的多個揀選子任務後確定揀選子任務與屬性項對應的屬性資料，進而依據揀選目標及揀選子任務的屬性資料，確定多個揀選子任務的執行順序。可見本案可以根據揀選目標對揀選子任務的揀選順序進行排班，以使揀選子任務的完成情況符合特定的揀選目標的要求。
另外，本案提供的另一種物品揀選方法，該方法在圖4所示的物品揀選方法的基礎上還可以包括如下步驟B1及B2。
B1：根據揀選子任務對應的物品的屬性，為揀選子任務確定對應的物品承載裝置。
其中，揀選區域內可以設置機械臂等揀選作業設備，用於將存放在貨架上的物品取下並放入物品承載裝置內。物品承載裝置可能具有多種不同的類型，不同類型的物品承載裝置所能承載的物品的類型也是不同的。因此，可以具體根據揀選子任務中物品所需包裝類型與物品承載裝置的對應關係、物品的尺寸與物品承載裝置的對應關係等，為揀選子任務分配對應的物品承載裝置。
從物品承載裝置的角度來看，一個物品承載裝置可能需要去往多個揀選區域執行揀選子任務。若多輛物品承載裝置在同一揀選區域內排隊等待執行揀選子任務，會對該揀選區域的揀選作業設備造成較重的揀選壓力。若等待的物品承載裝置還需要去往其他儲存區域進行揀選，恰好其他區域內並無物品承載裝置排隊，這樣排隊等待的情況會降低揀選效率。
因此，如果按照揀選目標確定了滿足揀選目標的揀選子任務的執行順序，則物品承載裝置並不容易在同一揀選區域排隊，從而不僅可以避免對同一揀選區域的揀選作業設備造成的揀選壓力，而且可以提高物品承載裝置的利用率，也可以提高物品的揀選效率。
B2：為物品承載裝置分配驅動裝置，以使驅動裝置將物品承載裝置驅動至揀選子任務對應的揀選區域所耗費的資源滿足資源要求。
其中，一個揀選單對應一個物品承載裝置，若一個揀選單被劃分為多個揀選子任務，則該物品承載裝置需要去往不同的揀選區域執行揀選子任務。驅動物品承載裝置進行移動的裝置稱為驅動裝置，驅動裝置如移動機器人。
物品存放空間內設置有至少一個驅動裝置，驅動裝置分布在物品存放空間的各個位置。不同位置的驅動裝置前去驅動同一物品承載裝置所耗費的資源是不同的，其中資源可以是時長或電量等。因此，針對步驟B1確定的物品承載裝置，計算分別由哪個驅動裝置前去驅動該物品承載裝置，以保證所有驅動裝置所耗費的總資源滿足資源要求。需要說明的是，本步驟可以將步驟B1確定出的物品承載裝置作為整體，計算如何使用最少的資源驅動所有的物品承載裝置。當然，本步驟還可以從步驟B1中選擇部分物品承載裝置，確定用於驅動所選擇的物品承載裝置的驅動裝置。
具體地，為物品承載裝置預分配驅動裝置，得到物品承載裝置與驅動裝置的多種預分配組合；計算每種預分配組合對應的資源耗費情況；選擇資源耗費情況滿足資源要求的預分配組合作為目標分配組合。
其中，計算物品承載裝置與驅動裝置的預分配組合對應的資源耗費情況前，需要確定資源要求對應的資源項。例如，資源項可以是時長，也可以是電量。以資源項為時長為例，確定資源項後則計算每個預分配組合中驅動裝置驅動該物品承載裝置從一個揀選子任務對應的揀選區域到達另一揀選子任務對應的揀選區域所耗費的時長。其中，時長的影響因素可以包括：驅動裝置與物品承載裝置之間的路徑距離，驅動裝置與物品承載裝置之間的路徑上的交通擁堵情況等。該一個揀選子任務到達另一揀選子任務指的是，該物品承載裝置對應的揀選子任務，按照揀選子任務的排序相鄰的兩個揀選子任務。
在實際應用中，為物品承載裝置分配驅動裝置的演算法包括但不限定於隱枚舉法、最大值分配法或匈牙利演算法等。這些演算法的執行過程如下所述。
假設A為驅動裝置集合，T為物品承載裝置集合。
；其中ai
表示驅動裝置集合中的一個驅動裝置；i表示驅動裝置序號；m表示驅動裝置集合中的驅動裝置數量。
；其中tj
表示物品承載裝置集合中的一個物品承載裝置；j表示物品承載裝置序號；n表示物品承載裝置集合中的物品承載裝置數量。
為分配代價矩陣，表示將一個物品承載裝置分配給一個驅動裝置後的代價，其中代價即所耗費的資源。具體地，表示將驅動裝置ai
分配給物品承載裝置tj
後，驅動裝置ai
驅動物品承載裝置tj
完成揀選子任務耗費的時長。
演算法的目標函數為：；其中為0或1的變量，為1表示將驅動裝置分配給物品承載裝置tj
。F表示各個分配組合的總耗費資源。
目標函數的約束條件包括：。其中，兩個約束條件分別表示為每個驅動裝置只分配一個物品承載裝置，每個物品承載裝置最多分配一個驅動裝置。
使用上述的演算法可以求解出滿足約束條件的最佳解，最佳解表示為每個物品承載裝置分配的是哪個驅動裝置。
需要說明的是，可以根據觸發條件執行上述分配演算法，觸發條件可以包括預設時間周期到達，接收到分配指令，或者揀選子任務完成。另外，對於等級較高的揀選子任務對應的物品承載裝置，可以優先為其分配驅動裝置，再為剩餘的物品承載裝置按照上述方式分配剩餘的驅動裝置。其中，等級較高的揀選子任務可以是等待時長超過一定時間臨限值的揀選子任務，設置有高優先級的揀選子任務等等。
由以上可知， 本案提供的技術方案，可以應用在倉庫等物品存放空間內進行揀選的情況。一般地，倉庫可以接收到大量的訂單，且訂單的長度（單個訂單包含的物品種類及數量）較長，倉庫的揀選壓力較大。本案並非根據簡單的篩選條件而是根據揀選目標，將訂單聚合為揀選單。揀選單可以劃分為揀選子任務，並且對揀選子任務進行排序，以使揀選子任務的揀選情況滿足揀選目標。在多個物品承載裝置及多個驅動裝置同時工作的情況下，本案不僅可以對揀選子任務進行排序，而且還可以對驅動裝置進行調度，以使驅動裝置驅動物品承載裝置耗費的資源可以滿足資源要求。
以下對本案提供的物品揀選設備的結構進行說明。如圖5所示，其示出了本案提供的物品揀選設備的一種結構，具體包括：記憶體501、處理器502及匯流排503。
記憶體501，用於儲存程式指令和/或資料。
處理器502，透過讀取所述記憶體501中儲存的指令和/或資料，用於執行以下操作：
確定揀選目標以及所述揀選目標關聯的屬性項；獲得多個揀選子任務，並確定所述揀選子任務與所述屬性項對應的屬性資料；以及依據所述揀選目標及所述揀選子任務的屬性資料，確定所述多個揀選子任務的執行順序。
匯流排503，用於將物品揀選設備的各個硬體組件耦合在一起。
在一個示例中，所述處理器用於依據所述揀選目標及所述揀選子任務的屬性資料，確定所述多個揀選子任務的執行順序，包括：處理器具體用於確定所述揀選子任務的多種備選執行順序；依據所述揀選子任務的屬性資料，預估每種所述備選執行順序的揀選情況；以及將滿足揀選目標的揀選情況對應的備選執行順序確定為揀選子任務的執行順序。
在一個示例中，所述處理器用於獲得至少一個揀選子任務，包括：處理器具體用於當滿足預設條件時，獲得至少一個揀選子任務；其中所述預設條件包括：產生揀選區域對應的揀選子任務，完成揀選區域對應的揀選子任務，接收到揀選子任務排序指令，或者排序周期到達。
在一個示例中，所述處理器用於獲得至少一個揀選子任務，包括：處理器具體用於從物品存放空間對應的揀選子任務中，選擇符合篩選條件的多個揀選子任務；其中所述篩選條件包括以下幾項中的任意一項或多項：揀選子任務中物品的種類為特定種類、揀選子任務中物品的揀選預估時長在預設時長範圍內、揀選子任務中物品的數量在預設數量範圍內。
在一個示例中，所述處理器確定的揀選目標包括以下幾項中的任意一項：揀選時長最短、相同屬性的物品的揀選間隔時長最短。
在一個示例中，處理器還用於獲得物品單，並依據所述物品單產生符合揀選目標的揀選單；以及將所述揀選單中對應相同揀選區域的物品劃分為同一揀選子任務。
在一個示例中，所述處理器用於獲得物品單，並依據所述物品單產生符合揀選目標的揀選單，包括：處理器具體用於獲得物品單，並將所述物品單中的物品組合後產生揀選單；確定所述揀選單中的物品與所述屬性項對應的屬性資料，並依據所述物品的屬性資料，計算所述揀選單的綜合得分；以及選擇綜合得分符合揀選目標的揀選單。
在一個示例中，處理器還用於根據所述揀選子任務對應的物品的屬性，為所述揀選子任務確定對應的物品承載裝置。
在一個示例中，處理器還用於為所述物品承載裝置分配驅動裝置，以使所述驅動裝置將所述物品承載裝置驅動至所述揀選子任務對應的揀選區域所耗費的資源滿足資源要求。
在一個示例中，所述處理器用於為所述物品承載裝置分配驅動裝置，以使所述驅動裝置將所述物品承載裝置驅動至所述揀選子任務對應的揀選區域所耗費的資源滿足資源要求，包括：處理器具體用於為所述物品承載裝置預分配驅動裝置，得到物品承載裝置與驅動裝置的多種預分配組合；計算每種所述預分配組合對應的資源耗費情況；以及選擇資源耗費情況滿足資源要求的預分配組合作為目標分配組合。
以下對本案提供的物品揀選裝置的結構進行說明。如圖6所示，其示出了本案提供的物品揀選設備的一種結構，具體包括：揀選目標確定單元601、屬性資料確定單元602及執行順序確定單元603。
揀選目標確定單元601，用於確定揀選目標以及所述揀選目標關聯的屬性項；
屬性資料確定單元602，用於獲得多個揀選子任務，並確定所述揀選子任務與所述屬性項對應的屬性資料；
執行順序確定單元603，用於依據所述揀選目標及所述揀選子任務的屬性資料，確定所述多個揀選子任務的執行順序。
需要說明的是，物品揀選裝置的各個單元在實現具體功能時，可以按照上述物品揀選方法中的相應步驟實現，此處並不贅述。
需要說明的是，本說明書中的各個實施例均採用遞進的方式描述，每個實施例重點說明的都是與其他實施例的不同之處，各個實施例之間相同相似的部分互相參見即可。
還需要說明的是，在本文中，諸如第一和第二等之類的關係術語僅僅用來將一個實體或者操作與另一個實體或操作區分開來，而不一定要求或者暗示這些實體或操作之間存在任何這種實際的關係或者順序。而且，術語“包括”、“包含”或者其任何其他變體意在涵蓋非排他性的包含，從而使得包括一系列要素的過程、方法、物品或者設備不僅包括那些要素，而且還包括沒有明確列出的其他要素，或者是還包括為這種過程、方法、物品或者設備所固有的要素。在沒有更多限制的情況下，由語句“包括一個……”限定的要素，並不排除在包括上述要素的過程、方法、物品或者設備中還存在另外的相同要素。
對所公開的實施例的上述說明，使本領域專業技術人員能夠實現或使用本案。對這些實施例的多種修改對本領域的專業技術人員來說將是顯而易見的，本文中所定義的一般原理可以在不脫離本案的精神或範圍的情況下，在其它實施例中實現。因此，本案將不會被限制於本文所示的這些實施例，而是要符合與本文所公開的原理和新穎特點相一致的最寬的範圍。In the following, the technical solutions in the embodiments of the present case will be clearly and completely described with reference to the drawings in the embodiments of the present case. Obviously, the described embodiments are only some of the embodiments of the case, not all of the embodiments. Based on the embodiments in the present case, all other embodiments obtained by a person of ordinary skill in the art without creative efforts shall fall within the protection scope of the present case.
In the article storage space, a plurality of storage areas are usually provided for storing different kinds of articles. In practical applications, it is necessary to obtain items that meet the requirements from the storage area. This process is picking, and the storage area can also be called a picking area.
In order to realize the automatic picking work, the article storage space can also be provided with an article carrying device and a driving device. The article carrying device is used to carry the articles picked out from the picking area, and the driving device is used to drive the article carrying device to move to move the article carrying device from one place to another. For example, if multiple items need to be picked, the driving device needs to pull the item carrying device from one picking area to another picking area. A specific form of the article carrying device is a picking car, and a specific form of the driving device is a mobile robot.
The object of the item picking method in this case is the picking subtask. The picking subtask has a relationship with the picking area. The item carrying device executes a picking subtask. Specifically, the article carrying device is loaded into the picking area associated with the picking subtask. Selected items.
Only after the picking subtask is obtained can the item picking method be performed. In order to facilitate understanding, this case first explains the generation process of the picking subtask. Specifically, a generation process of the selection subtask includes the following steps A1 to A2.
A1: Obtain the item list and determine the picking target, and generate a picking list that meets the picking target based on the item list.
Among them, the item list includes items, and the items need to be picked out. A specific form of the item list is an item order generated by a buyer who purchases an item on an e-commerce platform. The triggering condition signal for obtaining the item list may include: the item carrying device is idle, the driving device is idle, the transportation means that needs to take the item from the item storage space, etc. arrives.
Generally, there are multiple items to be picked in the item list, and there are also multiple items to be picked. In order to control the picking process, these items can be picked from the picking area according to the picking goals. For example, the picking target may be the shortest picking time of all the items in the list, or the shortest picking interval of items with the same attributes in the list, where the same attributes may be the same size of the items, the same packaging area of the items, The item's shipping address is the same or the item's transportation means is the same.
Provide multiple picking targets. During the picking process, a picking target can be determined from the multiple picking targets as the picking target used in the picking process. The determination method may be random selection, or may be determined according to a user's selection operation, or may also be determined according to the attributes of the item list. For example, if the attribute of the item list is urgent, the selection target can be determined as the shortest selection time.
After determining the picking target, the item list can be aggregated into a picking list according to the picking target. The picking list can also be called a picking task. One picking list corresponds to one item carrying tool, and one item carrying tool needs to carry all the items in one picking list before it is considered that one item carrying tool completes a picking task. Or it can be said that all the picked items in one picking list will be stored in the same item carrying tool. If items with the same attributes are included in the same picking list, such a picking cart can obtain items with the same attributes at the same time when the picking task is completed, that is, the shortest picking interval for items with the same attributes
One way to generate the picking list may include the following steps A11 to A14.
A11: Determine the attribute items associated with the picking target.
Among them, the picking target is associated with an attribute item, which is an attribute that affects the completion of the picking target. For example, if the picking target is the shortest picking time of all the items in the item list, the attribute items that can affect the picking time of the items include any one or more of the following: the location of the picking area where the item is located, the picking area where the item is located Workload, the efficiency of the picking area where the item is located, the priority of picking the item, etc. For another example, if the picking target is the shortest picking interval for items with the same delivery address in the pick list, the attribute item corresponding to the picking target includes the delivery address of the item.
A12: Obtain an item list and combine the items in the item list to generate a picking list.
Among them, the items in the item list can be arranged and combined to generate multiple alternative picking lists. The combination may include all combinations of quantity forms. For example, if there are 5 items in the 2 item lists, the 5 items are combined with 1 and 4 quantity forms, and the 2 and 3 quantity forms are combined. Of course, it is also possible to set a combination quantity form requirement. For example, if each picking list includes at least two items, 5 items are combined in the form of 2 and 3 quantities.
A13: Determine the attribute data corresponding to the items and attribute items in the picking list, and calculate the comprehensive score of the picking list based on the attribute data.
Among them, the data determined on the attribute items for the items in the picking list can be called attribute data. For example, the picking area where the item is located is determined according to the type of the item, and then the location of the picking area, the efficiency of the picking area, and the workload of the picking area are determined; for example, according to the delivery address corresponding to the item list, the The shipping address of the item.
In order to calculate the comprehensive score of the alternative picking list, first calculate the attribute data of the alternative picking list based on the attribute data of the items in the alternative picking list, and then obtain the comprehensive score of the alternative picking list based on the attribute data of the alternative picking list.
For example, calculate the interval between items in the alternative picking list based on the location of the picking area where the items in the alternative picking list are located; determine the load status corresponding to the alternative picking list based on the workload of the picking area where the items in the alternative picking list are located .
For another example, if the workload in the picking area where the items that meet the quantity requirements in the alternative picking list are high, then the load status corresponding to the alternative picking list is determined to be high; If the workload is medium, then determine the load status corresponding to the alternative picking list is medium; if the workload in the picking area where the items that meet the quantity requirements in the alternative picking list are low, determine the load status corresponding to the alternative picking list As low.
In order to calculate the score, a conversion relationship between the attribute data of the candidate picking list and the score can be set in advance.
For example, if the interval between the items in the alternative picking list is within the range of [0,5), it will be converted into 1 point, and if the interval between the items in the alternative picking list is within the range of [5,10), then It is converted to 2 points; for another example, if the load status corresponding to the alternative picking list is high, it is converted to 3 points, and the load status corresponding to the alternative picking list is medium, it is converted to 2 points. If the load status corresponding to the pick list is low, it is converted into 1 point. It should be noted that the above corresponding scores are merely examples, and in actual applications, they may be other values.
According to the attribute data of the alternative picking list, and the conversion relationship between the attribute data of the picking list and the score, the score corresponding to the picking list can be calculated. As the selection target may contain multiple attributes, the calculated attribute data of the picking list may also include multiple items. Each attribute data is converted into corresponding scores. The sum of the scores can be used to obtain the candidate picking list. A score, which can be called a composite score.
For example, if the picking target is the shortest picking time of all the items in the item list, the attribute items that can affect the picking time of the items include the following 4 items: the location of the picking area where the item is located, the workload of the picking area where the item is located, the item Work efficiency in the picking area and priority of picking items. Therefore, the candidate picking list includes the score converted from the attribute data of the four items, and then the four scores are summed to obtain a comprehensive score.
Of course, if the attribute item included in the selection target is one item, the comprehensive score is obtained from one item.
A14: Select a picking list with a comprehensive score that meets the picking goals.
Among them, different selection goals have different requirements for comprehensive scores. For example, the picking target is the shortest picking time of all the items in the item list, and according to the conversion relationship, it can be determined that the smaller the comprehensive score, the shorter the picking time. Therefore, the candidate picking list with the smallest comprehensive score can be determined to be in line with the picking target. Picking list. For the convenience of description, a picking list that meets a picking target may be referred to as a target picking list.
The above process of generating a picking list based on the item list can be referred to as the aggregation of the item list. It can be known from the above technical solutions that the items in the item list that meet the selection target can be aggregated into the same picking list according to different picking targets. For example, if the picking target is the shortest picking time of all the items in the item list, the items in the item list that are located in the same or similar picking area can be included in the same pick list.
It should be noted that the process of aggregating the above item list into a picking list is regarded as a global optimization problem. The best solution to the optimization problem can be obtained by setting constraints and constraints. Specifically, the algorithm used to solve the global optimization problem may be an optimization algorithm. The optimization algorithm includes, but is not limited to, a column generation algorithm, a genetic algorithm, a greedy algorithm, and the like.
The global optimization problem corresponding to the item single aggregation can be described as the following.
Let the collection of items be ,among them Represents the item list.
Assuming that the constraint target is the shortest picking time of all the items in the item list, the target function corresponding to the item list aggregation is: . among them Represents the alternative picking list, j represents the serial number of the alternative picking list, and M represents the number of alternative picking lists; Represents the picking time corresponding to the alternative picking list, which is determined by the number of items in the alternative picking list, the location of the picking area where the item is located, the workload of the picking area where the item is located, the work efficiency of the picking area where the item is located, Item selection priority is obtained.
The constraints that the objective function needs to satisfy include the following two items.
Indicates that each item list corresponds to only one alternative picking list; where i represents the serial number of the item list, Indicates that the item list is added to the alternative pick list.
It indicates that the quantity of the item list corresponding to each candidate picking list is within the preset threshold K. Of course, the weight and type of the items included in each alternative picking list cannot be set in a certain threshold in this form.
After solving the above objective function according to the constraint condition and the constraint objective, a picking list that satisfies the constraint objective can be obtained.
A2: Divide the items in the picking list corresponding to the same picking area into the same picking subtask.
As mentioned earlier, different types of items can be stored in different picking areas. According to the picking area where the items included in the picking list are located, the articles corresponding to the same picking area are divided into the same group. Since the picking list can be called a picking task, the grouping of items divided according to the picking list can be called a picking subtask. Among them, the picking subtask means picking items from the picking area.
From the above description, it can be known that this case can aggregate various item lists to obtain various picking lists, and then the picking lists are divided into various picking subtasks. The picking capacity of working equipment such as the article carrying device and the driving device in the article storage space is limited, and the picking subtask cannot be performed at the same time. Therefore, after the picking subtask is obtained, the execution process of the picking subtask needs to be sorted to Ensure that the execution results of the picking subtask meet the requirements of the picking target.
As shown in FIG. 1, it illustrates a process of the article selection method provided in this case, which specifically includes steps S101 to S103.
S101: Determine a picking target and an attribute item associated with the picking target.
For the description of the selection target, refer to the above description, which will not be repeated here.
S102: Obtain multiple picking subtasks, and determine attribute data corresponding to the picking subtasks and attribute items.
Among them, the number of picking subtasks generated by the article picking equipment may be multiple, and the picking subtasks may be generated immediately or may be completed. Therefore, it can be considered that the item picking equipment maintains a picking subtask pool, a newly generated picking subtask can be added to the picking subtask pool, and the picking subtask in the picking subtask pool can also be deleted because it has been executed. The number of picking subtasks in the picking subtask pool may be one or more, or may be zero.
The timing for obtaining a picking subtask may include the following types, such as when a new picking subtask is generated, when a picking subtask is completed, a picking subtask sorting instruction is received, or a preset sorting cycle arrives. For ease of description, these timings may be referred to as preset conditions.
Multiple picking subtasks are obtained from the picking subtasks maintained by the item picking equipment. It can be understood that the obtained picking subtask is an unfinished picking subtask.
The obtained picking subtasks can be arbitrary picking subtasks or designated picking subtasks; the number of obtained picking subtasks can be a preset number or a number determined according to the picking status. For example, if the picking status of the device is idle, a large number of picking subtasks are obtained; when the picking status of the device is busy, a small number of picking subtasks are obtained.
The picking subtask may also correspond to the article storage space, or may obtain all the picking subtasks corresponding to the article storage space. Specifically, for an item storage space, the item picking equipment maintains a corresponding picking subtask. How many picking subtasks the item storage space currently corresponds to can obtain all the picking subtasks. In this way, all the picking subtasks can be sorted as a whole according to the following steps to obtain the best overall ranking of all the picking subtasks corresponding to the item storage space.
Alternatively, from the picking subtasks corresponding to the article storage space, a plurality of picking subtasks that meet the screening conditions may be selected. The screening conditions may include that the type of the items in the picking subtask is a specific type, the estimated length of the picking of the items in the picking subtask is within a preset length range, and the number of items in the picking subtask is within a preset number range, etc. Any one or more of them. The screening conditions may be any conditions set according to actual conditions, and this case is not specifically limited.
After obtaining the picking subtask and determining the picking target, the attribute data of the picking subtask on the attribute item of the picking target needs to be determined. For example, the picking target is the shortest picking time of all the items in the item list, and the attribute items that can affect the picking time of the items include any one or more of the following: the number of items included in the picking subtask, and the workload of the picking area , The efficiency of the picking area, the priority of picking, etc. Therefore, it is necessary to determine the number of items included in the picking subtask, the workload of the picking area corresponding to the picking subtask, the work efficiency of the picking area corresponding to the picking subtask, and the priority of the picking subtask. The workload can be embodied in the number of items that have not been picked in the picking area. It should be noted that the determined attribute data are attribute data of the picking subtask.
S103: Determine the execution order of multiple selection subtasks according to the selection data of the selection target and the selection subtask.
Among them, according to the attribute data of the picking subtask, the picking situation of the picking subtask can be determined, and then the execution order of the picking subtask can be arranged according to the picking situation of the picking subtask.
In specific implementation, an implementation of this step may be: determining multiple alternative execution orders of the picking subtask; estimating the picking situation of each alternative execution order based on the attribute data of the picking subtask; and the picking goal will be met The alternative execution order corresponding to the picking situation is determined to be the execution order of the picking subtask.
Specifically, when determining an alternative execution order of a picking subtask, a permutation and combination method may be used. However, not all execution orders obtained by permutation and combination can be realized in the picking scenario, and these unachievable execution orders cannot be used as alternative execution orders. Therefore, the constraint can be used to filter the execution order obtained by the permutation and combination, and the execution order satisfying the constraint condition can be selected as an alternative execution order.
Specifically, since it is impossible for one article carrying tool to be picked in different picking areas at the same time, the picking subtasks corresponding to the same article carrying tool can only be executed sequentially. In addition, the picking capacity of the picking area is usually limited. When the picking capacity of the picking area is limited, the same picking area can only perform the order picking work in the picking subtask. Using the above two constraints, the execution order that does not satisfy the above constraints can be deleted, and an alternative execution order can be obtained.
For example, the obtained picking subtask includes three, the picking subtask 1 and the picking subtask 2 correspond to the same article carrying tool, and the picking subtask 3 corresponds to another article carrying tool. The picking subtask 1 and the picking subtask 3 correspond to a picking area A, and the picking subtask 2 corresponds to a picking area B. Then, two alternative execution sequences shown in FIG. 2A and FIG. 2B can be obtained. Among them, the long box filled with the left oblique line represents the picking subtask 1, the long box filled with the right oblique line represents the picking subtask 2, and the long box filled with the vertical line represents the picking subtask 3.
The picking situation is used to indicate that the picking subtask is completed in the alternative execution order. For example, the picking target is the shortest picking time for the item, and the picking status indicates the picking time after the picking subtask is executed in the alternative execution order. For another example, the picking interval for items with the same attributes is the shortest, and the picking situation indicates that the picking interval for items with the same attributes is completed after the picking subtask is executed in the alternative execution order.
In order to determine the picking status of the alternative execution order, first, the picking status of each picking subtask can be obtained according to the attribute data of the picking subtask. Then, the picking situation of the picking subtask is used to obtain the picking situation of the alternative execution order.
Taking the selection situation as the selection duration as an example, the method for determining the selection situation of the alternative execution order of the selection subtask will be described. 2A and 2B, it is assumed that the execution time of the picking subtask 1 is 1 minute, the execution time of the picking subtask 2 is 2 minutes, and the execution time of the picking subtask 3 is 1.5 minutes. Regarding the alternative execution order shown in FIG. 2A, it can be determined that the execution time of the alternative execution order is 4.5 minutes; regarding the alternative execution order shown in FIG. 2B, it can be determined that the execution time of the alternative execution order is 3 minute.
According to the picking situation corresponding to the alternative execution order, an alternative execution order in which the picking situation satisfies the picking target is selected in the alternative execution order as the execution order of the picking subtask. Still taking the alternative execution order shown in FIG. 2A and FIG. 2B as an example, it is obvious that the execution time of the alternative execution order shown in FIG. 2B is shorter, so the execution order shown in FIG. 2B is taken as the target execution order.
The above mainly uses the picking target corresponding to the item picking time to explain, and the following describes the execution order of the picking subtasks that satisfy other picking targets. If the picking target is the shortest picking interval for items with the same attributes in the item list, the picking subtasks with the same attributes can be executed in the shortest time interval.
For example, the obtained picking subtask includes five, and the items with the same attributes among the five picking subtasks include two types. The first type of picking subtask includes the picking subtask 1, the picking subtask 2, and the picking subtask 3. The second type of selection subtask includes a selection subtask 4 and a selection subtask 5. Among them, the selection subtask 1, the selection subtask 2 and the selection subtask 3 correspond to the same item carrying tool, and the selection subtask 4 and the selection subtask 5 correspond to the same item carrying tool. The picking subtask 1 and the picking subtask 4 correspond to the picking area A, and the picking subtask 3 and the picking subtask 5 correspond to the picking area B. The picking subtask 2 corresponds to the picking area C.
Then, a certain execution order can be determined as shown in FIG. 3, the picking area C executes the picking subtask 2, the picking area A first performs the picking subtask 1 and then the picking subtask 4 and the picking area B first executes the picking subtask 3 and then executes Pick subtask 5. In this way, the selection subtask 1, the selection subtask 2 and the selection subtask 3 included in the first type of selection subtask can be completed as far as possible; the selection subtask 4 and the selection subtask 5 included in the first type of selection subtask can be performed simultaneously The selection is complete. It can be seen that the selection interval of items with the same attributes in this way is the shortest and meets the selection goal.
It should be noted that, in practical applications, the above process of determining the execution order for the picking subtask can be regarded as a batch processing job scheduling process, and a batch processing job scheduling algorithm can be used to solve the execution order that satisfies the selection goal.
Taking the picking target as the shortest picking time of all the items in the item list as an example, the mathematical model constructed using the batch processing job scheduling algorithm for the sorting problem of the picking subtask and the solving process of the mathematical model are introduced.
According to the above-mentioned picking goals, the optimization goal can be determined to minimize the completion time of the latest picking subtask, so the objective function constructed is: .
Among them, i represents the picking area set, and all the picking areas in one article storage space are regarded as one picking area set, or one article storage space includes multiple picking area sets, and each picking area set includes part of the picking area; j represents the picking subtask ; K represents a single picking area in the set of picking areas; C is the completion time; C ijk A variable of 0 or 1 represents the estimated time for a single picking area k of the picking area set i to complete the picking subtask j.
The constraints of the objective function include but are not limited to the following seven.
This constraint condition indicates that a single picking area k in the picking area set i can only perform at most one picking subtask j in a time segment t. Among them, n is the number of picking subtasks; X ijkt A variable of 0 or 1 indicates whether the picking subtask j in the time segment t is executed in a single picking area k in the picking area set i.
This constraint indicates that at any time segment t, the picking subtask j can only be executed in one picking area. Among them, s represents the number of sets in the picking area; m i Represents the number of picking areas in the picking area set i.
This constraint indicates that at any time segment t, the time corresponding to the picking subtask j and the single picking area k in the picking area set i must be equal to the preset execution time P ijk the same. Where U t Represents the planned time period; P ijk Represents a preset execution time on a single picking area k in the picking sub-task j picking area set i; Y ijk A variable of 0 or 1 indicates whether the picking subtask j is executed in a single picking area k of the picking area set i.
This constraint indicates that if the picking subtask j has been executed on a single picking area k in the picking area set i, then this picking subtask must be completed on a single picking area k in this picking area i. among them,
This constraint indicates that for each picking area set i, one picking subtask j can only be executed on one picking area k.
; This constraint indicates that the picking subtask cannot be interrupted.
This constraint is a way of estimating the execution time of the picking subtask j in a single picking area k of the picking area set i.
According to the above constraints, after the objective function is solved, the execution order of the picking subtask can be obtained. In the following, the execution order of the picking subtasks before the solution without the batch processing job scheduling algorithm and after the solution using the batch processing job scheduling algorithm is exemplified.
See FIG. 4A, which shows the execution order of the picking subtask before solving using the batch processing job scheduling algorithm; see FIG. 4B, which shows the execution order of the picking subtask after solving using the batch processing job scheduling algorithm . As shown in the figure, the different filling contents in the long box represent the picking subtasks generated by different picking tasks. The length of the long box filled with content indicates the estimated execution time of the picking subtask in the picking area. The blank area in each row indicates the free time period of the picking area. Each row represents the execution order of each picking subtask executed in a picking area. It can be seen that each picking area can only perform one picking subtask at a time.
Comparing FIGS. 4A and 4B, it can be seen that the execution order of the picking subtasks is different. After using the batch processing job scheduling algorithm to solve, the total execution time of the picking subtask is shorter than before the batch processing job scheduling algorithm is not used to solve, thereby reducing the total execution time of the picking subtask and reducing the picking area Of idle time, that is, the invalid waiting time of the pick subtask.
It can be known from the above technical solutions that the article selection method provided in this case can determine the selection target and the attribute items associated with the selection target, obtain multiple selection subtasks to be sorted, determine the attribute data corresponding to the selection subtask and the attribute item, and then select the The attribute data of the target and the picking subtask determine the execution order of multiple picking subtasks. It can be seen that in this case, the picking order of the picking subtasks can be shifted according to the picking targets, so that the completion of the picking subtasks meets the requirements of the specific picking targets.
In addition, another article selection method provided in this case may include the following steps B1 and B2 on the basis of the article selection method shown in FIG. 4.
B1: Determine the corresponding item bearing device for the picking subtask according to the attributes of the item corresponding to the picking subtask.
Among them, a picking operation device such as a robotic arm may be provided in the picking area to remove items stored on the shelf and put them into the item carrying device. There may be many different types of article carrying devices, and the types of articles that can be carried by different types of article carrying devices are also different. Therefore, the corresponding item carrying device can be allocated to the picking subtask according to the corresponding relationship between the packaging type required for the items in the picking subtask and the item carrying device, the corresponding relationship between the size of the item and the item carrying device, and so on.
From the perspective of the article carrying device, one article carrying device may need to go to multiple picking areas to perform the picking subtask. If multiple article-bearing devices are queuing up in the same picking area to perform picking subtasks, it will cause heavy picking pressure on the picking operation equipment in the picking area. If the waiting item carrying device needs to go to other storage areas for picking, there is no article holding device in the other area, so waiting in line will reduce the picking efficiency.
Therefore, if the execution order of the picking subtasks satisfying the picking target is determined according to the picking target, the article carrying devices are not easy to queue in the same picking area, so that not only can avoid picking pressure caused by picking operation equipment in the same picking area, but The utilization rate of the article carrying device can be improved, and the efficiency of article picking can also be improved.
B2: Allocate a driving device to the article carrying device, so that the resources consumed by the driving device to drive the article carrying device to the picking area corresponding to the picking subtask meet the resource requirements.
Among them, one picking list corresponds to one item carrying device. If one picking list is divided into multiple picking subtasks, the item carrying device needs to go to different picking areas to perform the picking subtask. The device that drives the article carrying device to move is called a driving device, such as a mobile robot.
At least one driving device is provided in the article storage space, and the driving devices are distributed at various positions in the article storage space. The resources consumed by the driving devices at different positions to drive the same article bearing device are different, and the resources may be time or electricity. Therefore, for the article carrying device determined in step B1, it is calculated which drive device drives the article carrying device to ensure that the total resources consumed by all the driving devices meet the resource requirements. It should be noted that, in this step, the article carrying device determined in step B1 as a whole can be used to calculate how to use the least resources to drive all the article carrying devices. Of course, in this step, a part of the article carrying device may be selected from step B1 to determine a driving device for driving the selected article carrying device.
Specifically, pre-allocate the driving device for the article carrying device to obtain multiple pre-allocation combinations of the article carrying device and the driving device; calculate the resource consumption situation corresponding to each pre-allocation combination; select the pre-allocation combination that meets resource requirements Assign a combination.
Wherein, before calculating the resource consumption situation corresponding to the pre-allocated combination of the article bearing device and the driving device, a resource item corresponding to the resource requirement needs to be determined. For example, the resource item can be a duration or a power amount. Take the resource item as an example. After determining the resource item, calculate the cost of the driving device in each pre-allocation combination to drive the item carrier from the picking area corresponding to one picking subtask to the picking area corresponding to another picking subtask. duration. Among them, the influencing factors of the duration may include: a path distance between the driving device and the article carrying device, a traffic congestion situation on a path between the driving device and the article carrying device, and the like. The fact that one picking subtask reaches another picking subtask means that the picking subtask corresponding to the item carrying device is based on the ordering of the picking subtask between two adjacent picking subtasks.
In practical applications, algorithms for allocating a driving device to an article carrying device include, but are not limited to, a hidden enumeration method, a maximum value allocation method, or a Hungarian algorithm. The execution of these algorithms is described below.
Suppose A is the set of driving devices, and T is the set of article carrying devices.
; Where a i Indicates a driving device in the driving device set; i indicates the serial number of the driving device; m indicates the number of driving devices in the driving device set.
; Where t j Represents one article carrying device in the article carrying device set; j represents the serial number of the article carrying device; n represents the number of article carrying devices in the article carrying device set.
To allocate a cost matrix, it means the cost after an item bearing device is allocated to a driving device, where the cost is the resource consumed. specifically, Means drive device a i Assigned to article carrier t j After, drive device a i Drive article carrying device t j How long it took to complete the picking subtask.
The objective function of the algorithm is: ;among them A variable of 0 or 1, 1 means drive device Assigned to article carrier t j . F represents the total consumed resources of each allocation combination.
The constraints of the objective function include: . Among them, the two constraints respectively indicate that only one article carrying device is allocated to each driving device, and each article carrying device is allocated to a maximum of one driving device.
The above algorithm can be used to solve the best solution that satisfies the constraint conditions. The best solution indicates which drive device is assigned to each article carrying device.
It should be noted that the above allocation algorithm may be executed according to a trigger condition, and the trigger condition may include a preset time period arrival, receipt of an allocation instruction, or completion of a picking subtask. In addition, for the item carrying device corresponding to the higher-level picking subtask, the driving device may be assigned first, and then the remaining driving devices may be allocated to the remaining item carrying devices in the manner described above. Among them, the picking subtask with a higher level may be a picking subtask waiting for a time exceeding a certain time threshold, a picking subtask having a high priority, and the like.
From the above, it can be known that the technical solution provided in this case can be applied to the case of picking in an article storage space such as a warehouse. Generally, the warehouse can receive a large number of orders, and the length of the order (the type and quantity of the items contained in a single order) is relatively long, and the warehouse's picking pressure is relatively large. In this case, the orders are not aggregated into a picking list based on simple selection criteria but based on the picking goals. The picking list can be divided into picking subtasks, and the picking subtasks are sorted so that the picking situation of the picking subtasks meets the picking goals. In the case where multiple article bearing devices and multiple driving devices work simultaneously, this case can not only sort the picking subtasks, but also schedule the driving devices so that the resources consumed by the driving device to drive the article bearing device can meet the resources Claim.
The structure of the article picking equipment provided in this case is described below. As shown in FIG. 5, it illustrates a structure of an article picking device provided in the present case, which specifically includes: a memory 501, a processor 502, and a bus 503.
The memory 501 is configured to store program instructions and / or data.
The processor 502 is configured to perform the following operations by reading instructions and / or data stored in the memory 501:
Determining a picking target and an attribute item associated with the picking target; obtaining a plurality of picking subtasks and determining attribute data corresponding to the picking subtask and the attribute item; and according to the picking target and the picking subtask The attribute data determines the execution order of the plurality of picking subtasks.
The bus bar 503 is used for coupling the hardware components of the article picking equipment together.
In an example, the processor is configured to determine an execution order of the plurality of picking subtasks according to the picking target and attribute data of the picking subtasks, including: the processor is specifically configured to determine the picking subtasks A plurality of alternative execution orders; based on the attribute data of the picking subtask, estimating the picking situation of each of the alternative execution orders; and determining the alternative execution order corresponding to the picking situation that meets the picking target as the picking subtask Execution order.
In an example, the processor is configured to obtain at least one picking subtask, including: the processor is specifically configured to obtain at least one picking subtask when a preset condition is satisfied; wherein the preset condition includes: generating a picking area corresponding The picking subtask, complete the picking subtask corresponding to the picking area, receive a sorting order for the picking subtask, or the sorting cycle arrives.
In one example, the processor is configured to obtain at least one picking subtask, including: the processor is specifically configured to select a plurality of picking subtasks that meet a screening condition from the picking subtasks corresponding to the article storage space; wherein the screening The conditions include any one or more of the following: the type of the items in the picking subtask is a specific type, the estimated length of the picking of the items in the picking subtask is within a preset duration, and the number of items in the picking subtask Within the preset number range.
In one example, the picking target determined by the processor includes any one of the following items: the shortest picking time and the shortest picking interval of items of the same attribute.
In one example, the processor is further configured to obtain an item list, and generate a pick list that meets the picking target according to the item list; and divide the items in the pick list corresponding to the same picking area into the same picking subtask.
In an example, the processor is configured to obtain an item list and generate a picking list according to the item list according to the item list, including: the processor is specifically configured to obtain the item list and combine the items in the item list Generating a picking list; determining attribute data corresponding to the items in the picking list and the attribute item, and calculating a comprehensive score of the picking list based on the item's attribute data; and selecting a picking list with a comprehensive score that matches the picking target .
In one example, the processor is further configured to determine a corresponding item bearing device for the picking subtask according to the attributes of the item corresponding to the picking subtask.
In an example, the processor is further configured to allocate a driving device to the article bearing device, so that the resource consumed by the driving device to drive the article bearing device to a picking area corresponding to the picking subtask satisfies resource requirements. .
In an example, the processor is configured to allocate a driving device to the article bearing device, so that the resource consumed by the driving device to drive the article bearing device to a picking area corresponding to the picking subtask satisfies resource requirements. Including: the processor is specifically configured to pre-allocate the driving device for the article bearing device to obtain multiple pre-allocation combinations of the article carrying device and the driving device; calculate the resource consumption situation corresponding to each of the pre-allocation combinations; and select the resource consumption The pre-allocation combination that meets the resource requirements is the target allocation combination.
The structure of the article picking device provided in this case is described below. As shown in FIG. 6, it illustrates a structure of an item picking device provided in this case, and specifically includes a picking target determination unit 601, an attribute data determination unit 602, and an execution order determination unit 603.
A picking target determining unit 601, configured to determine a picking target and an attribute item associated with the picking target;
An attribute data determining unit 602, configured to obtain multiple picking subtasks, and determine attribute data corresponding to the picking subtasks and the attribute items;
An execution order determination unit 603 is configured to determine an execution order of the plurality of selection subtasks according to the selection target and attribute data of the selection subtask.
It should be noted that, when each unit of the article picking device implements a specific function, it can be implemented according to the corresponding steps in the above-mentioned article picking method, which is not repeated here.
It should be noted that each embodiment in this specification is described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same and similar parts between the various embodiments refer to each other. can.
It should also be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply these entities or operations There is any such actual relationship or order among them. Moreover, the terms "including", "comprising", or any other variation thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or device that includes a series of elements includes not only those elements but also those that are not explicitly listed Or other elements inherent to such a process, method, article, or device. Without more restrictions, the elements defined by the sentence "including a ..." do not exclude the existence of other identical elements in the process, method, article or equipment including the above elements.
The above descriptions of the disclosed embodiments enable those skilled in the art to implement or use the case. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features disclosed herein.
S101‧‧‧步驟S101‧‧‧step
S102‧‧‧步驟 S102‧‧‧step
S103‧‧‧步驟 S103‧‧‧step
501‧‧‧記憶體 501‧‧‧Memory
502‧‧‧處理器 502‧‧‧ processor
503‧‧‧匯流排 503‧‧‧Bus
601‧‧‧揀選目標確定單元 601‧‧‧Selection target determination unit
602‧‧‧屬性資料確定單元 602‧‧‧Attribute data determination unit
603‧‧‧執行順序確定單元 603‧‧‧execution order determination unit
為了更清楚地說明本案實施例或現有技術中的技術方案，下面將對實施例或現有技術描述中所需要使用的附圖作簡單地介紹，顯而易見地，下面描述中的附圖僅僅是本案的實施例，對於本領域普通技術人員來講，在不付出創造性勞動的前提下，還可以根據提供的附圖獲得其他的附圖。In order to more clearly explain the embodiments of the present case or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present case. For those skilled in the art, other embodiments may also obtain other drawings according to the provided drawings without paying creative labor.
圖1為本案提供的物品揀選方法的一種流程圖； FIG. 1 is a flowchart of an article selection method provided by the present case;
圖2A及圖2B為本案提供的揀選子任務的兩種執行順序示意圖； 2A and 2B are schematic diagrams of two execution sequences of a picking subtask provided by the present case;
圖3為本案提供的揀選子任務的一種執行順序示意圖； 3 is a schematic diagram of an execution sequence of a picking subtask provided by the present case;
圖4A及圖4B為本案提供的使用優化演算法及未使用優化演算法得到的兩種揀選子任務執行順序示意圖； 4A and 4B are schematic diagrams of the execution order of two sorting subtasks obtained by using the optimization algorithm and not using the optimization algorithm provided in the present case;
圖5為本案提供的物品揀選設備的一種結構示意圖； FIG. 5 is a schematic structural diagram of an article picking device provided in this case;
圖6為本案提供的物品揀選裝置的一種結構示意圖。 FIG. 6 is a schematic structural diagram of an article picking device provided by the present application.
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| CN201711401064.2A CN109961244A (en) | 2017-12-22 | 2017-12-22 | Item sorting method and related apparatus |
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| TW201928811A true TW201928811A (en) | 2019-07-16 |
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| TWI741957B (en) * | 2020-09-17 | 2021-10-01 | 大陸商深圳市海柔創新科技有限公司 | Order processing method, device, control equipment, storage system and storage medium |
| TWI783332B (en) * | 2020-02-14 | 2022-11-11 | 南韓商韓領有限公司 | Computerized system and computer-implemented method for determining item groupings for packaging |
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| CN112651680B (en) * | 2019-10-12 | 2023-11-07 | 北京京东振世信息技术有限公司 | Multitasking method and apparatus, computer readable storage medium |
| CN112801437A (en) * | 2019-11-13 | 2021-05-14 | 顺丰科技有限公司 | Sorting equipment scheduling method and device, electronic equipment and storage medium |
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| CN111846726B (en) * | 2020-07-30 | 2022-04-19 | 重庆惠科金渝光电科技有限公司 | Transportation equipment and carrying method thereof |
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| US8571700B2 (en) * | 2010-08-02 | 2013-10-29 | Brightstar Corp. | Robotic picking line for serialized products |
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| US10229383B2 (en) * | 2012-02-05 | 2019-03-12 | Matthews International Corporation | Perpetual batch order fulfillment |
| CA2874456C (en) * | 2012-05-22 | 2018-07-03 | Wynright Corporation | System, method, and apparatus for picking-and-putting product |
| CN104809606B (en) * | 2015-04-29 | 2018-05-04 | 上海交通大学 | There is the warehouse management system of guiding car dispatching distribution more |
| CN106228302A (en) * | 2016-07-21 | 2016-12-14 | 上海仙知机器人科技有限公司 | A kind of method and apparatus for carrying out task scheduling in target area |
| CN106447186B (en) * | 2016-09-21 | 2018-05-04 | 广东工业大学 | The method and device that transporting equipment task is distributed in a kind of intelligent storage |
| CN106682788A (en) * | 2017-01-09 | 2017-05-17 | 郑州云海信息技术有限公司 | Method and device for selection sequence determination |
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| CN107274246A (en) * | 2017-05-03 | 2017-10-20 | 浙江工商大学 | The Automated Sorting System order processing method of optimisation strategy is cooperateed with based on subregion |
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2017
- 2017-12-22 CN CN201711401064.2A patent/CN109961244A/en active Pending
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- 2018-10-15 TW TW107136176A patent/TW201928811A/en unknown
- 2018-12-17 WO PCT/CN2018/121446 patent/WO2019120158A1/en active Application Filing
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI783332B (en) * | 2020-02-14 | 2022-11-11 | 南韓商韓領有限公司 | Computerized system and computer-implemented method for determining item groupings for packaging |
| TWI741957B (en) * | 2020-09-17 | 2021-10-01 | 大陸商深圳市海柔創新科技有限公司 | Order processing method, device, control equipment, storage system and storage medium |
| US11276036B1 (en) | 2020-09-17 | 2022-03-15 | Hai Robotics Co., Ltd. | Order processing method, apparatus, device, system, and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2019120158A1 (en) | 2019-06-27 |
| CN109961244A (en) | 2019-07-02 |
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