TW202336654A - Method for allocating slot for item transport machine according to request of item supplier and apparatus thereof - Google Patents

Abstract

According to an example embodiment, there may be provided a method for allocating a slot for an item transport machine according to a request for allocation of an item supplier terminal in a fulfillment center, the method including receiving an allocation request signal of a slot from the item supplier terminal, determining whether allocation is to be approved by comparing a threshold value output by a machine learning artificial intelligence (AI) model and a number of requested slots indicated in the allocation request signal, and transmitting approval information indicating whether the allocation is to be approved to the item supplier terminal. According to another example embodiment, there may be provided an apparatus for allocating a slot that performs the method for allocating a slot and a computer-readable recording medium for performing the method for allocating a slot.

Description

Method and equipment for allocating slots to article transport machines according to the requirements of article suppliers

The present invention relates to a method and equipment for allocating a slot for transporting an item provided from an item supplier terminal to perform an item transport procedure after arriving at a distribution center. Machine space.

As e-commerce is seriously implemented and many users purchase items through the Internet, various operations performed during loading and moving of items in a warehouse for sale and shipment of items may be performed by workers.

The items loaded in the warehouse may be items supplied by an item supplier terminal. Items supplied from the item supplier terminal may be stored in a distribution center via an item transport machine (such as a truck).

Due to the recent boom in e-commerce, items stocked in a distribution center can vary greatly in terms of type, quantity, volume, weight, etc., and in some cases, the number of item delivery machines required to deliver the items. But it's different.

Conventionally, the process of allocating a slot to an item delivery machine is based on a unified standard process determined without analysis from various aspects, or is manually managed individually by an administrator, resulting in reduced efficiency. .

Therefore, there is a need to appropriately allocate a space (ie, a slot) for these item transport machines transporting many items managed in a distribution center to implement a systematic item transport process.

[Technical Target]

The present invention is intended to provide a method for optimizing the number of slots allocated to an article transport machine for transporting articles in a distribution center according to a requirement of an article supplier terminal in a distribution center for effective management. Method and equipment for a limited number of slots used by an item transport machine for transporting one of many items.

The technical aspects of the present invention are not limited to the technical aspects mentioned above, and other technical aspects and advantages not mentioned can be understood by the following description, and will be understood more clearly by the following exemplary embodiments. In addition, it should be easily understood that the technical aspects and advantages that can be derived through the exemplary embodiments can be realized by the means described in the patent scope of the invention and their combinations. [Technical solutions]

According to an exemplary embodiment, a method for allocating a slot to an article transport machine according to an allocation request from an article supplier terminal in a distribution center is provided, the method comprising: receiving a slot from the article supplier terminal. An allocation request signal for a slot; determining whether to approve the allocation by comparing a threshold value output by a machine learning artificial intelligence (AI) model to a number of requested slots indicated in the allocation request signal; and transmit approval information indicating whether to approve the allocation to the item supplier terminal.

According to an exemplary embodiment, a method for allocating a slot is provided, wherein the threshold value is a plural number, and the determining whether to approve the allocation includes: if the number of requested slots is less than a plurality of the temporary If one of the first threshold values is one of the limits, the allocation of the number of requested slots is approved; and if the number of requested slots is equal to or greater than the first threshold value, then by A second threshold value that is one of the plurality of threshold values and is greater than the first threshold value is compared with the number of requested slots to determine whether to approve the allocation.

According to an exemplary embodiment, a method for allocating a slot is provided, wherein the first threshold value and the second threshold value are slots output from the AI model based on machine learning at different confidence levels. the predicted number.

According to an exemplary embodiment, a method for allocating a slot is provided, wherein the determining whether to approve the allocation by comparing the second threshold value with the number of requested slots further includes: if the requested slots are If the number of requested slots is less than the second threshold, then the allocation of the number of requested slots is approved; and if the number of requested slots is equal to or greater than the second threshold, based on The reliability of the item supplier terminal determined by using an item supplier reliability classification model determines whether allocation is approved.

According to an illustrative embodiment, a method for allocating a slot is provided, wherein the item supplier reliability classification model is configured to be processed as a no-show based on an allocated slot of the item supplier terminal being unused. (no-show; no-show) number of times to output the reliability of an item supplier terminal.

According to an exemplary embodiment, a method for allocating a slot is provided based on classification information including data obtained by classifying information about each of at least one type into at least one category and a value including numerical data Information to train the AI model.

According to an illustrative embodiment, a method is provided for allocating a slot based on an identifier selected from the group consisting of an identifier of a distribution center, a type of distribution center, and a largest portion of items occupying an item transport machine. an item type, at least one of a grouping of at least one classification based on a number of shipping locations for items transported by an article transport machine, and comprising a number selected from a type including temperature-sensitive items, The number of shipping positions of items transported by an article transport machine, the total volume of individual shipped inventory units (SKUs), the total weight of individual transported SKUs, the number of SKUs transported by a logistics transport machine, The numerical information of at least one of a group of a total volume of articles transported by an article conveying machine, a total weight of articles conveyed by an article conveying machine, and a weight per volume of articles of an article conveying machine Train the AI model.

According to an exemplary embodiment, a method for allocating a slot is provided, wherein the allocation request signal further includes information on an identifier of the item supplier terminal, information on an identifier of the distribution center and information on Information on expected shipping dates from one of the distribution centers.

According to an exemplary embodiment, a method for allocating a slot is provided, further comprising extracting the identifier for the AI model from the identifier of the item supplier terminal, the identifier of the distribution center, and the expected shipping date. Input data for machine learning; and perform machine learning on the AI model based on the input data.

According to another exemplary embodiment, an apparatus for allocating a slot to an article transport machine according to an allocation request from an article supplier terminal in a distribution center is provided, the apparatus comprising: a transceiver; and a processor A transceiver configured to respond by comparing a threshold value output by a machine-learned AI model to a threshold value indicated in an allocation request signal for a slot received from the item supplier terminal through the transceiver. A number of slots is required to determine whether allocation is approved, and the transceiver is controlled to transmit approval information indicating whether the allocation is approved to the item supplier terminal.

According to an exemplary embodiment, an apparatus is provided for allocating a slot, wherein the threshold value is a plurality of threshold values; and if the number of requested slots is less than one of the plurality of threshold values first threshold value, the processor determines that it is approved to allocate as many slots as requested, and if the number of requested slots is equal to or greater than the first threshold value, then the processor determines as the plurality of temporary slots by comparison Whether one of the limits is greater than the first threshold, a second threshold and the number of requested slots determines whether to approve the allocation.

According to an exemplary embodiment, an apparatus for allocating a slot is provided, wherein the first threshold value and the second threshold value are slots output from the AI model based on machine learning at different confidence levels. the predicted number.

According to an exemplary embodiment, an apparatus is provided for allocating a slot, wherein the processor is further configured to: determine whether to approve the allocation by comparing the second threshold to the number of requested slots; If the number of requested slots is less than the second threshold, then allocation of the number of requested slots is approved; and if the number of requested slots is equal to or greater than the second threshold, then based on The reliability of the item supplier terminal is determined using an item supplier reliability classification model to determine whether allocation is approved.

According to an illustrative embodiment, an apparatus is provided for allocating a slot, wherein the item supplier reliability classification model is configured to be processed as a no-show based on an allocated slot of the item supplier terminal being unused. The reliability of the supplier terminal of an item is output a number of times.

According to an exemplary embodiment, an apparatus is provided for allocating a slot based on classification information including data obtained by classifying information about each of at least one type into at least one category and a value including numerical data Information to train the AI model.

According to an illustrative embodiment, an apparatus is provided for allocating a slot based on an identifier selected from the group consisting of an identifier of a distribution center, a type of distribution center, and a largest portion of items occupying an item transport machine. An article type, at least one classification based on a number of shipping locations for an article transported by an article transport machine, and at least one of a group of a number of shipping locations for the article transported by the article transport machine Information, and includes a quantity selected from the group consisting of types of temperature-sensitive items, a number of shipping locations for items transported by an item handling machine, the total volume of individual shipped stock keeping units (SKUs), the number of individual shipped SKUs A total weight, a number of SKUs transported by a logistics transport machine, a total volume of items transported by an article transport machine, a total weight of items transported by an article transport machine, and a total volume of items transported by an article transport machine The AI model is trained on the numerical information of at least one of the groups per volume weight.

According to an exemplary embodiment, an apparatus for allocating a slot is provided, wherein the allocation request signal further includes information on an identifier of the item supplier terminal, information on an identifier of the distribution center and information on Information on expected shipping dates from one of the distribution centers.

According to an exemplary embodiment, apparatus is provided for allocating a slot, wherein the processor is further configured to extract from the identifier of the item supplier terminal, the identifier of the distribution center, and the expected shipping date Input data used for machine learning of the AI model, and machine learning is performed on the AI model based on the input data.

According to another exemplary embodiment, a non-transitory computer-readable recording medium is provided that includes a computer program for executing a method for allocating a slot. [Effect]

The present invention guides an item supplier terminal to request the allocation of a slot less than a threshold value of an optimal number of slots determined through a slot allocation procedure, and approves the allocation, thereby effectively managing a limited number of slots. slot.

Specific effects that can be derived in conjunction with the above effects will be described below when specific details for practicing the invention are described.

Hereinafter, exemplary embodiments of the present invention are described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement the exemplary embodiments. The following illustrative embodiments may be implemented in various different forms and are not limited to the illustrative embodiments described herein.

The suffix "part" used for components in the following description is only for ease of writing instructions or for use, and does not itself have a different meaning or role from each other.

For clear explanation, throughout the specification, parts irrelevant to the description are omitted, and the same reference numerals are assigned to the same or similar elements. Furthermore, some exemplary embodiments will be described in detail with reference to illustrative drawings. When adding component symbols to components in each drawing, identical components may have the same component symbol whenever possible, even if they are drawn in different drawings. In addition, in describing the present invention, when it is determined that a detailed description of a related well-known configuration or function may make the gist of the exemplary embodiment unclear, the detailed description may be omitted.

When describing components of the illustrative embodiments, terms such as first, second, A, B, (a), (b), etc. may be used. These terms are only used to distinguish an element from other elements and are not limited by the terms as to the nature, order, sequence or number of the elements. When it is described as being "connected," "coupled," or "linked" between any components, the components can be directly connected or coupled, and it is understood that other components can be "interposed" between the components or that the components can be connected through Other components are "connected", "coupled" or "linked".

In the present invention, terms such as "comprising", "consisting of" or "having" are intended to specify that a feature, number, step, operation, component, part or combination thereof described in the specification exists and shall It is understood that this does not exclude the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts or combinations thereof.

In addition, when implementing the present invention, components may be subdivided for convenience, but these components may be implemented in one device or module, or one component may be implemented by being divided into a plurality of devices or modules.

FIG. 1 is a diagram illustrating an article transport machine using slots provided by a distribution center 100 according to an exemplary embodiment.

Referring to Figure 1, the distribution center 100 operates as many slots as reserved, and can allocate each slot to items traveling from the item supplier terminals 150a, 150b, and 150c according to the requirements of one of the item supplier terminals 150a, 150b, and 150c. Machines for transporting various items.

According to an exemplary embodiment, each of the item supplier terminals 150a, 150b, and 150c may load the item based on information related to an order of the item (eg, shipping address, inventory quantity, expected warehousing date, expected shipping date) An article transport machine is dispatched to the distribution center 100, and before dispatching the article transport machine, it is required to allocate a slot of the incoming article in the distribution center 100. According to an exemplary embodiment, the number of slots required to be allocated may vary depending on various information such as weight, volume, quantity, type, and SKU of the item. In other words, the number of item shipping machines from item supplier terminals 150a, 150b, and 150c may vary depending on each slot allocation requirement, and may vary depending on each of item supplier terminals 150a, 150b, and 150c.

According to an exemplary embodiment, the distribution center 100 may determine whether to allocate slots in response to slot allocation requests from the item supplier terminals 150a, 150b, and 150c, and transmit whether to allocate slots to the item supplier terminals 150a, 150b. and 150c. The item supplier terminals 150a, 150b, and 150c that have received whether slots are allocated may dispatch as many or fewer item delivery machines to the distribution center 100 as there are allocated slots.

According to an exemplary embodiment, the distribution center 100 may respond to slot allocation requests from the item supplier terminals 150a, 150b, and 150c to determine whether to allocate as many slots as requested, and if the distribution center 100 approves the allocation with the requested If the same number of slots are required, the item supplier terminals 150a, 150b and 150c can dispatch the item transport machines to the distribution center 100. Therefore, the distribution center 100 can decide whether to approve the slot allocation request by determining whether the number of slots requested by the item supplier terminals 150a, 150b, and 150c corresponds to the optimized number of slots.

Determining whether the number of requested slots corresponds to the optimal number of slots may be performed by comparing a value determined based on an artificial intelligence (AI) model trained with a plurality of criteria to the number of requested slots. Based on the comparison results, if it is determined that the number of requested slots is inappropriate, the allocation may ultimately be disapproved, and if it is determined that the number of requested slots is within an appropriate range, the allocation may be approved.

According to an exemplary embodiment, the item supplier terminals 150a, 150b, and 150c conventionally tend to require a large number of slots from the distribution center 100, and therefore considering this trend, the distribution center 100 needs to induce the item supplier terminals 150a, 150b, and 150c Requires the allocation of an appropriate number of slots. On the other hand, from the perspective of the distribution center 100, the need to prevent inefficiencies due to empty slots in operating slots should also be taken into consideration. Thus, the distribution center 100 needs to optimize slot operations and therefore, the present invention will be explained with respect to a procedure for determining whether to approve slot allocation requests from item supplier terminals 150a, 150b, and 150c to optimize slot operations. various exemplary embodiments.

FIG. 2 is a block diagram of a slot allocation device 200 according to an exemplary embodiment.

According to an exemplary embodiment, the slot allocation device 200 may include: a transceiver 210 configured to receive and respond to an allocation request signal from an item supplier terminal 250 including a requirement for allocating a slot. transmitting approval information in the allocation request signal indicating whether the allocation is approved; and a processor 220 configured to control the transceiver 210 and by comparing a plurality of threshold values output by the machine learning AI model with the allocation request signal. The number of requested slots indicated in the box will be used to determine whether the allocation is approved.

According to an exemplary embodiment, communication technologies used by transceiver 210 to transmit and receive information may include Global System for Mobile communications (GSM), Code Division Multiple Access (CDMA), Long Term Evolution (LTE), 5G, Wireless LAN ( WLAN), Wireless Fidelity (Wi-Fi), Bluetooth™, Radio Frequency Identification (RFID), Infrared Data Association (IrDA), ZigBee, Near Field Communication (NFC), etc.

According to an exemplary embodiment, the processor 220 may be included in the slot allocation device 200 and configured to control various components included in the slot allocation device 200 in order to perform various instances implemented by the slot allocation device 200 sexual examples. According to an exemplary embodiment, the transceiver 210 may be configured to transmit and receive predetermined information under the control of the processor 220.

According to an exemplary embodiment, the processor 220 may include at least one of a RAM, a ROM, a CPU, a graphics processing unit (GPU), and a bus. RAM, ROM, CPU and GPU and a bus can be connected to each other. According to an exemplary embodiment, the processor 220 may be configured to access a memory included in the slot allocation device 200 and perform various operations using various programs, data, and predetermined information stored in the memory.

According to an exemplary embodiment, the processor 220 may use a machine learning AI model to determine whether the number of slots required to be allocated included in the allocation request signal is within an allocable range, and whether to allocate. According to an exemplary embodiment, the AI model is trained based on various input data and may output a value of a predicted number of slots as output data corresponding to the input data. According to an exemplary embodiment, an output value of the predicted number of slots may be output as a different value depending on a preset reliability of training of the AI model. Processor 220 may determine whether to allocate by comparing at least one predicted number of output slots to the number of requested slots indicated by the allocation request signal. The training of the AI model will be described below through various exemplary embodiments.

FIG. 3 is a flowchart of a method for allocating a slot by the slot allocation device 200 according to an exemplary embodiment.

In operation S310, according to an exemplary embodiment, the slot allocation device 200 may receive a slot allocation request signal from the item supplier terminal 250. According to an exemplary embodiment, the allocation request signal may include information regarding the number of slots requested by the item supplier terminal 250. According to an exemplary embodiment, the allocation request signal may include logistics information, and the logistics information may include an expected shipping date to a distribution center, an identifier of a logistics provider (i.e., a merchant), and one of the distribution centers At least one piece of information about the identifier. According to an exemplary embodiment, even if there is no consumer order for the item, the item supplier terminal may still proceed with the process of warehousing the item to the distribution center 100 in consideration of inventory management, and therefore, an order number May not be included in the logistics data included in the distribution request signal.

In operation S320, according to an exemplary embodiment, the slot allocation device 200 may determine whether to approve by comparing a threshold value output by the machine learning AI model and the number of requested slots indicated in the allocation request signal. distribute. According to an exemplary embodiment, the processor 220 may use the AI model to output at least one threshold value to compare with the number of requested slots. According to another exemplary embodiment, the slot allocation device 200 may further include at least one separate dedicated processor capable of using the trained AI model. In the following, for convenience of description, it is assumed that the processor 220 uses an AI model.

According to an exemplary embodiment, the AI model used by processor 220 may be a previously machine-trained model. According to an exemplary embodiment, the AI model may be machine trained based on categorical information including data obtained by classifying information about at least one type into at least one category and numerical information including numerical data.

According to an illustrative embodiment, information regarding data available for training an AI model may be organized as shown in Table 1 below. [Table 1] Type material Classified information Distribution center identifier Item supplier terminal identifier Type of distribution center The item type that occupies the largest portion of the item transport machine's items At least one classification based on the number of shipping locations for items transported by the item transport machine numerical information Number of types of temperature sensitive items The number of shipping locations for items transported by the item transport machine Total volume of individual shipped SKUs Total weight of individual shipped SKUs Number of SKUs shipped by logistics delivery machines The total volume of items transported by the item transport machine The total weight of items transported by the item transport machine The weight per volume of an item in an item transporting machine The weight per volume of the article handling machine multiplied by the number of SKUs transported by the article handling machine

According to an illustrative embodiment, by inputting an identifier selected from the group consisting of an identifier of a distribution center, a type of a distribution center, an item type that occupies the largest portion of items of an item transport machine, Classification information for at least one of a group of at least one classification of a number of shipping locations for transported items, and including a number of shipping locations selected from a type including temperature-sensitive items, for items transported by an article transport machine A quantity, a total volume of individual transported SKUs, a total weight of individual transported SKUs, a number of SKUs transported by a logistics transport machine, a total volume of items transported by an article transport machine, transported by an article The AI model is trained using numerical information of at least one of a group of a total weight of items transported by the machine and a weight per volume of items of an item transporting machine.

According to an exemplary embodiment, processor 220 may determine whether the requested number of slots is allowable based on the predicted number of slots output from an AI model trained using various classification information and numerical information as input data.

In operation S330, according to an exemplary embodiment, the slot allocation device 200 may transmit the approval information indicating whether the allocation is approved determined in operation S320 to the item supplier terminal. According to an exemplary embodiment, the approval information may include information to approve or disapprove the allocation request. According to an exemplary embodiment, in addition to information on whether to approve or not, the approval information may further include information for requiring the item supplier terminal 250 to input predetermined information for slot allocation.

4 is a flowchart of a method for determining whether to allocate a slot by comparing a plurality of threshold values with a number of requested slots, according to an exemplary embodiment. Features of operations S410 and S430 of FIG. 4 may be the same as or similar to features of operations S310 and S330 of FIG. 3 , and therefore, detailed description will be omitted.

In operation S422, according to an exemplary embodiment, the slot allocation device 200 may determine whether the number of requested slots included in the allocation request signal received from the item supplier terminal 250 is less than a first threshold value.

According to an exemplary embodiment, the first threshold value may be a value output by the AI model (which may be used by the processor 220 of the slot allocation device 200). According to an exemplary embodiment, the processor 220 may extract information to be input to the AI model based on information included in the distribution request signal (for example, at least one of an identifier of a distribution center, an identifier of an item supplier terminal, and logistics information). information. Processor 220 may use the extracted data as data to be input to the AI model. The processor 220 may use the data output from the AI model when the extracted data is input to the AI model as one of the thresholds compared to the number of requested slots.

According to an exemplary embodiment, the processor 220 may extract a maximum portion of items selected from the group consisting of an identifier of a distribution center, a type of distribution center, and occupying an item transport machine based on information included in the distribution request signal. an item type, classification information for at least one of a group of at least one classification based on a number of shipping locations for items transported by an article transport machine, and including a number selected from a type including temperature-sensitive items, consisting of A number of shipping positions for items transported by an article transport machine, a total volume of individual transported SKUs, a total weight of individual transported SKUs, a number of SKUs transported by a logistics transport machine, transported by an article transport machine At least some numerical information of at least one of a group of a total volume of items, a total weight of items transported by an item transport machine, and a weight per volume of items of an item transport machine. According to an illustrative embodiment, processor 220 may output a number of allowable slots predicted from the AI model using the extracted data, and use the number of allowable slots predicted from the AI model as the number for the requested slot. A threshold value for number comparison. According to an illustrative embodiment, processor 220 may use subsequently determined information regarding whether to allocate with the extracted data to train the AI model.

According to an exemplary embodiment, when it is determined that the number of requested slots is less than the first threshold value, in operation S424, the slot allocation device 200 may determine that allocation of the number of requested slots is approved.

According to an exemplary embodiment, when it is determined that the number of requested slots is equal to or greater than the first threshold value, in operation S426, the slot allocation device 200 may compare a second value that is different from the first threshold value by comparing The threshold value and the number of requested slots are used to determine whether the allocation is approved. According to an exemplary embodiment, the second threshold value may be greater than the first threshold value.

In operation S430, according to an exemplary embodiment, the slot allocation device 200 may transmit the approval information indicating whether the allocation is approved determined through operation S424 or S426 to the item supplier terminal 250. According to an exemplary embodiment, the approval information may include information indicating approval or disapproval as a response to receiving the allocation request signal in operation S410.

According to an exemplary embodiment, in addition to information about whether to approve or not, the approval information transmitted to the item supplier terminal 250 as a response to receiving the allocation request signal may further include information about the item to be used by the item supplier terminal 250 according to Information on additional procedures to be taken for approval or disapproval. As an example, when the approval information includes information indicating approval, the slot allocation device 200 may further transmit information related to the allocated slot (slot location, when to move to the slot, slot type, etc.) to the item supply Business terminal 250. For another example, when the approval information includes information indicating disapproval, the slot allocation device 200 may further request the item supplier terminal 250 to input a message of additional information (for example, requesting the transmission of an allocation with fewer requested slots). A message requesting a signal, a message requesting modification of the slot usage time, a message requesting adjustment of the SKU, volume and/or weight of the items transported by the item transport machine) are transmitted to the item supplier terminal 250 .

Through operation S430, the item supplier terminal 250 having various information including the approval information can use the allocated slot and further execute a program related to the information indicated or requested by the transmitted information.

FIG. 5 is a flowchart illustrating a method of determining whether to allocate as many slots as required based on the reliability of the item supplier terminal 250 according to an exemplary embodiment. According to an exemplary embodiment, FIG. 5 is intended to specifically illustrate a process of determining whether to approve allocation by comparing the second threshold value with the number of requested slots in operation S426 of FIG. 4 .

In operation S500, according to an exemplary embodiment, the slot allocation device 200 may determine whether the number of requested slots indicated in the allocation request signal received from the item supplier terminal 250 is less than a second threshold value.

According to an exemplary embodiment, when the number of requested slots is equal to or greater than the second threshold value, in operation S510, according to an exemplary embodiment, the slot allocation device 200 may be based on an item supplier reliability classification The model determines whether the item supplier terminal 250 is reliable. According to an exemplary embodiment, the item supplier reliability classification model is a machine learning AI model, and may be a model for outputting the reliability of each item supplier terminal 250 . According to an exemplary embodiment, information indicating the reliability of the output of the item supplier terminal 250 may be in various forms, such as data in a numerical or categorical form indicating a degree of reliability or information indicating whether the item supplier terminal 250 is reliable. According to an exemplary embodiment, to train the item supplier reliability model, processor 220 may input information about whether item supplier terminal 250 has had no-shows for assigned slots in the past to the item supplier reliability classification model.

According to an exemplary embodiment, if the output data of the item supplier reliability classification model indicates whether the item supplier terminal 250 that has transmitted an allocation request signal is reliable, then in operation S510, the processor 220 may determine the item supplier reliability according to the item supplier reliability. The output data of the classification model determines whether the item supplier terminal 250 is reliable.

According to an exemplary embodiment, if the output data of the item supplier reliability classification model is in the form of a number or classification indicating the reliability of the item supplier terminal 250 that has transmitted the allocation request signal, then the corresponding output data can be determined by Whether the number or category indicated in is included in a reliable range according to a predetermined criterion determines whether the item supplier terminal 250 is reliable.

According to an exemplary embodiment, when it is determined that the item supplier terminal 250 that has transmitted an allocation request signal is reliable based on the output data of the item supplier reliability classification model, in operation S530, the slot allocation device 200 may determine that the request is approved. The slot allocation of the item supplier terminal 250 is not less than the slot allocation of the second threshold value.

According to an exemplary embodiment, when it is determined that the item supplier terminal 250 that has transmitted the allocation request signal is unreliable based on the output data of the item supplier reliability classification model, in operation S540, it may be determined that not as many as requested will be sent. The slots are allocated to the slot allocation device 200 .

According to an exemplary embodiment, when the number of requested slots is less than the second threshold value, according to an exemplary embodiment, in operation S520, the slot allocation device 200 may determine whether a transmitted allocation request signal is received. An additional request signal from the item supplier terminal 250.

According to an exemplary embodiment, a situation in which the number of slots required to be allocated by the item supplier terminal 250 is equal to or greater than a first threshold value and less than a second threshold value may be indicated (i.e., where allocation is required to be more than Information of a predetermined number of slots (eg, a first threshold value) of slots) is provided to the item supplier terminal 250 . In this exemplary embodiment, the item supplier terminal 250 may transmit a signal indicating that slot allocation is required to the slot allocation device 200 as an additional request signal, even if the number of slots required to be allocated is equal to or greater than the first temporary limit.

According to an exemplary embodiment, when the allocation of slots less than the first threshold is required, the slot allocation device 200 may decide to immediately approve the allocation. However, when it is required to allocate slots that are no less than the first threshold value and no more than the second threshold value, the item supplier terminal 250 is again requested to confirm whether allocation is required, and then after receiving an indication that one of the allocations is still required. Allocations may be approved following additional request signals. Therefore, if the number of slots required to be allocated is equal to or greater than the first threshold but less than the second threshold, the slot allocation device 200 may approve the allocation. In this case, the slot allocation device 200 can request the item supplier terminal 250 to confirm again whether the item supplier terminal 250 requires allocation of slots that are no less than the first threshold value, so that the AI model can learn this situation, and at the same time, Inefficient slot operation due to a habit of the item supplier terminal 250 requiring allocation of more slots than required may be prevented.

According to an exemplary embodiment, when receiving an additional request signal from the item supplier terminal that has transmitted the allocation request signal, in operation S530, the slot allocation device 200 may determine to approve allocation of the number of requested slots.

According to an exemplary embodiment, when an additional request signal is not received from the item supplier terminal 250 that has transmitted the allocation request signal, the slot allocation device 200 may determine not to allocate as many as required in operation S540. slot.

According to an exemplary embodiment, when it is determined not to allocate the number of requested slots requested by the item supplier terminal 250, in addition to the approval information, the slot allocation device 200 may also input the requested item supplier terminal 250 into a reservation. The information of the information is further transmitted to the item supplier terminal 250. According to an exemplary embodiment, the predetermined information may include various information such as a changed slot allocation time, a changed number of slots requiring allocation, a changed distribution center, and a specific reason why allocation is required. According to an exemplary embodiment, when the slot allocation device 200 receives the predetermined information from the item supplier terminal 250, the slot allocation device 200 may execute a process of determining whether to allocate based on the corresponding information again.

FIG. 6 illustrates a method of responding to a slot request and a model update function based on a slot prediction model and an item supplier reliability classification model, according to an exemplary embodiment. 6 is not intended to illustrate the inclusion relationship of the components included in the slot allocation device 200 of the present invention, but is to explain each function performed by the slot allocation device 200 and related to the item supplier terminal 250 block by block for the convenience of description. A block diagram. In other words, at least some of the components excluding an item supplier terminal 602 are included in the slot allocation device 200 and their functions may be performed by the processor 220 .

According to an exemplary embodiment, in operation S610, a system 601 may receive a slot allocation request from an item supplier terminal 602. The received slot allocation request signal may include information regarding the number of slots requested to be allocated by the item supplier terminal 602 ("Req" in Figure 6).

In an illustrative embodiment, the system 601 may determine the use of data based on data output from an AI model ("slot prediction models 603 and 604" in Figure 6) that is machine trained to output a predicted number of slots required to be allocated. At a plurality of threshold values compared to the number of requested slots ("P1" and "P2" in Figure 6). According to an exemplary embodiment, the slot prediction model may be trained based on different confidence levels, and the data output from the slot prediction model (i.e., the "predicted number of allocable slots" in Figure 6) is determined by the user The values output by slot prediction models trained with different confidence levels, and therefore can have different values. According to an exemplary embodiment, the slot prediction model may output an upper end value of a range of values calculated according to a preset confidence level as the predicted number of allocable slots. As an example, the slot prediction model 603 outputting the first threshold value P1 may be trained based on a confidence level of 78%, and the second threshold value P2 may be trained based on a confidence level of 95%. Slot prediction model 604. However, the exemplary embodiments of the present invention need not be construed as being limited to a specific value set to a confidence level for training a slot prediction model, and may be free to output a second threshold value that is greater than the first threshold value. A preset confidence level in a relationship can be used to train the viewpoint understanding of the slot prediction model.

According to an exemplary embodiment, if the number of requested slots is less than the first threshold by comparing the first threshold P1 of the plurality of thresholds to the number of requested slots (Req), the system 601 As many slots as requested may be approved for allocation. The process of approving allocation by comparing the number of requested slots with the first threshold has been described above with reference to operation S424 of FIG. 4 , and therefore a detailed description of the process will be omitted.

According to an exemplary embodiment, when the number of requested slots is equal to or greater than the first threshold, the system 601 may compare the second threshold P2, which is greater than the first threshold, to the number of requested slots. In operation S640, when the number of requested slots is less than the second threshold, a procedure for approving allocation of as many slots as requested ("automatic approval procedure" in FIG. 6) may be executed. The "automatic approval process" for approving allocation by comparing the number of requested slots with the second threshold has been described above with reference to operation S520 of FIG. 5, and therefore a detailed description will be omitted.

According to an exemplary embodiment, in operation S650, when the number of requested slots is equal to or greater than the second threshold value, the system 601 may perform a method for indicating an item based on an item output by an item supplier reliability classification model 605. The supplier terminal 250 determines whether to distribute the information based on reliable information (the "manual approval process" in Figure 6). The "manual approval process" for determining whether to allocate when the number of requested slots is equal to or greater than the second threshold value has been described above with reference to operation S510 of FIG. 5 , and therefore a detailed description will be omitted.

According to an exemplary embodiment, the system 601 may transmit approval information indicating whether to approve based on a result of comparing at least one threshold value P1 and/or P2 with the number of required slots (Req) to the item supplier terminal 602 .

FIG. 7 illustrates a procedure for reserving a slot through the item supplier terminal 250 communicating with the slot allocation device 200 according to an exemplary embodiment.

According to an exemplary embodiment, the item supplier terminal 250 may use an application required to request slot allocation from the slot allocation device 200, and FIG. 7 illustrates a UI of such an application as an example.

According to an exemplary embodiment, the item supplier terminal 250 may transmit information including a truck name (an identifier), storage time, reservation, etc. to the slot allocation device 200 to request slot allocation. Here, a truck name may be understood to refer to a group including at least one article transport machine requiring slot allocation, and need not be limitedly interpreted as information for identifying each individual article transport machine.

Referring to FIG. 7 , according to an exemplary embodiment, if an object 700 for requesting slot allocation is selected on the UI displayed on the screen of the item supplier terminal 250 , the item supplier terminal 250 may specify the request by specifically describing the slot allocation. The number of allocated slots, storage time and distribution center information are used to complete the allocation requirements.

According to an exemplary embodiment, when the number of slots required to be allocated is less than the first threshold, the item supplier terminal 250 may receive approval information indicating that as many slots as required have been allocated from the slot allocation device 200 , and based on the approval information, the booking result information can be displayed.

According to an exemplary embodiment, when the slot allocation requested to a specific distribution center is completed, the screen of the item supplier terminal 250 may display one or more objects for providing an additional slot allocation request function to the corresponding distribution center. 710 and 720. According to an exemplary embodiment, when there are additional slots that the item supplier terminal 250 can additionally request allocation from the distribution center, the item supplier terminal 250 can display information about the number of slots that can additionally request allocation (for example, FIG. 7 component symbol 712).

According to an exemplary embodiment, the number of slots that may additionally be requested to be allocated may be shown as at least one value obtained by subtracting the number of reserved slots from the above-mentioned first threshold value and/or the second threshold value. .

According to an exemplary embodiment, when the number of slots that can additionally be requested to be allocated is displayed as a value obtained by subtracting the number of slots that have been reserved from the first threshold value, even according to the request from the item supplier terminal For an additional allocation request of 250, slots less than the first threshold value are allocated to the item supplier terminal 250, and therefore, the slot allocation device 200 can also approve the allocation of the additional allocation request without special restrictions.

According to an exemplary embodiment, when the number of slots that can additionally be requested to be allocated is displayed as a value obtained by subtracting the number of reserved slots from the second threshold value, if the number of slots received from the item supplier terminal is 250 additional allocation requirements, and slots that are still less than the first threshold value are allocated to the item supplier terminal 250, then the slot allocation device 200 can approve the allocation of the additional allocation requirements without special restrictions. According to another exemplary embodiment, when an additional allocation request comes from the item supplier terminal 250, slots that are not less than the first threshold value but less than the second threshold value are allocated to the item supplier terminal 250 At this time, the slot allocation device 200 may perform an allocation approval procedure for one of the additional allocation requirements according to the above-described exemplary embodiment.

According to an exemplary embodiment, the item supplier terminal 250 may display on the screen one of the items for which additional slots are required to be allocated no less than a number shown as being additionally allocated (e.g., component symbol 712 in FIG. 7 ). (For example, symbol 714 in Figure 7). According to an exemplary embodiment, the item supplier terminal 250 may additionally display a guidance message 716 for this additional allocation request.

According to an exemplary embodiment, when a value obtained by subtracting the number of reserved slots from the first threshold value and/or the second threshold value is zero, the item supplier terminal 250 may display that it is impossible. Allocate more slots than the first threshold and/or the second threshold (eg, element symbol 722 in FIG. 7 ). According to an exemplary embodiment, the item supplier terminal 250 may display that it is impossible to allocate more slots than the first threshold value and/or the second threshold value, and at the same time, further display corresponding information and for additional request allocation. A guidance message for a program (for example, element symbol 726 in Figure 7). According to an exemplary embodiment, a user of the item supplier terminal 250 may select an additional allocation request function displayed on the screen for providing slots with more than the first threshold value and/or the second threshold value. An object (eg, element symbol 724 of FIG. 7 ) to request an additional slot allocation may be a procedure corresponding to the allocation procedure performed through operation S426 of FIG. 4 .

FIG. 8 illustrates an example of a UI for requesting allocation of slots with more than a first threshold and/or a second threshold, according to an exemplary embodiment.

Referring to FIG. 8 , in response to a selection of an object (eg, element 800 in FIG. 8 ) for providing additional allocation requirement functionality for slots that are more than the first threshold and/or the second threshold. The screen displayed on the item supplier terminal 250 (eg, element symbol 850 in FIG. 8 ) may include various information (such as a logistics center (distribution center), expected storage date, number of trucks available for reservation, number of reserved trucks a number, a number of trucks for which additional reservations are desired, a recipient's email address and other requirements) as the information required for the additional slot allocation request. According to an exemplary embodiment, the item corresponding to "the number of trucks available for reservation" may display the first threshold value and/or the second threshold value previously determined by the slot allocation device 200, which is the item supply The information in the business terminal 250 cannot be modified. According to an exemplary embodiment, the item for "number of trucks reserved" may display the number of trucks that have been requested and approved for allocation, which is unmodifiable information in the item supplier terminal 250. According to an exemplary embodiment, if as many slots have been allocated as the first threshold and/or the second threshold displayed on the screen after requesting additional allocation, then the "number of trucks available for reservation" and the "number of trucks booked" may be directed to be the same number.

According to an exemplary embodiment, a user of item supplier terminal 250 may request additional allocations by revealing the number of slots for which additional allocations are requested in "Number of Trucks Desiring Additional Reservations."

According to an exemplary embodiment, when "the number of trucks available for reservation" is the first threshold value, the slot allocation device 200 that has received the additional allocation request may be based on "the number of trucks that wish to make additional reservations" and " The above slot allocation procedure will be executed based on the determination of whether the sum of the number of reserved trucks is less than the first threshold value and/or the second threshold value. In this regard, it has been described above with reference to FIGS. 4 and 5 , and therefore a detailed description will be omitted.

According to an exemplary embodiment, when "the number of trucks available for reservation" is the second threshold value, the slot allocation device 200 that has received the additional allocation request may be based on "the number of trucks that wish to make additional reservations" and " The above slot allocation procedure is executed based on the determination of whether the sum of the "number of reserved trucks" is less than the second threshold value. In this regard, it has been described above with reference to FIG. 5, and therefore a detailed description will be omitted.

According to an exemplary embodiment, the item supplier terminal 250 may transmit a modification request to the slot allocation request or an additional slot allocation request transmitted to the slot allocation device 200 . After receiving the modification request, the slot allocation device 200 can determine whether to approve or disapprove the modification request according to predetermined conditions or based on a manager's confirmation, and the item supplier terminal 250 can display information about whether it is approved, information about whether it is approved, or not. Information on the reasons for the determination, etc.

The method of the present invention described above may be provided as a program to be executed by a computer and recorded in a computer-readable recording medium. The method according to the invention can be executed through software. When executed as software, the building blocks of the invention are segments of program code that perform the required tasks. The program or program code fragment may be stored on a non-transitory computer (processor) readable medium.

Computer-readable recording medium includes any type of recording device in which data can be stored, which can be read by a computer system. Examples of computer-readable recording devices include ROM, RAM, CD-ROM, DVD±ROM, DVD-RAM, magnetic tape, floppy disk, hard disk and optical data storage devices. Additionally, the computer-readable recording medium can be distributed throughout network-connected computer devices so that the computer-readable program code can be stored and executed in a distributed fashion.

For those skilled in the art related to the present invention, various substitutions, modifications and changes are feasible within the scope of the exemplary embodiments without departing from the technical spirit of the exemplary embodiments. Therefore, the above disclosure is not limited to the exemplary embodiments and accompanying drawings. limit.

100:Distribution center 150a: Item Supplier Terminal 150b: Item Supplier Terminal 150c: Item Vendor Terminal 200: Slot allocation equipment 210:Transceiver 220: Processor 250:Item supplier terminal 601:System 602:Item supplier terminal 603: Slot prediction model 604: Slot prediction model 605: Item supplier reliability classification model 700:Object 710:Object 712:Information 714:Object 716: Guidance message 720:Object 722:Information 724:Object 726: Guidance message 800:Object 850:Screen S310: Operation S320: Operation S330: Operation S410: Operation S422: Operation S424: Operation S426: Operation S430: Operation S500: Operation S510: Operation S520: Operation S530: Operation S540: Operation S610: Operation S640: Operation S650: Operation

FIG. 1 is a diagram illustrating an article transport machine using slots provided by a distribution center according to an exemplary embodiment.

FIG. 2 is a block diagram of a slot allocation device according to an exemplary embodiment.

Figure 3 is a flow chart of a slot allocation method according to an exemplary embodiment.

4 is a flowchart of a method for determining whether to allocate a slot by comparing a plurality of threshold values with a number of requested slots, according to an exemplary embodiment.

FIG. 5 is a flowchart illustrating a method for determining whether to allocate as many slots as required based on the reliability of an item supplier terminal according to an exemplary embodiment.

FIG. 6 illustrates a method of responding to a slot request and a model update function based on a slot prediction model and an item supplier reliability classification model, according to an exemplary embodiment.

Figure 7 illustrates a process for reserving a slot by an item supplier terminal communicating with a slot allocation device, according to an exemplary embodiment.

8 illustrates an example of a user interface (UI) for requesting allocation of slots with more than a first threshold and/or a second threshold, according to an exemplary embodiment.

200: Slot allocation equipment

210:Transceiver

220: Processor

250:Item supplier terminal

Claims (19)

A method for allocating a slot to an article transport machine based on an allocation request from an article supplier terminal in a distribution center, the method comprising: Receive an allocation request signal from one of the slots of the item supplier terminal; Determine whether to approve the allocation by comparing a threshold value output by a machine-learned artificial intelligence (AI) model to a number of requested slots indicated in the allocation request signal; and Approval information indicating whether the allocation is approved is transmitted to the item supplier terminal. If the method of request item 1 is used, where the threshold is a complex number, and The determination of whether to approve allocation includes: If the number of requested slots is less than a first threshold value that is one of a plurality of such threshold values, approving the allocation of the number of requested slots; and If the number of requested slots is equal to or greater than the first threshold value, then by comparing a second threshold value that is one of the plurality of threshold values and is greater than the first threshold value and The allocation will be determined based on the number of requested slots. The method of claim 2, wherein the first threshold value and the second threshold value are predicted numbers of slots output from the AI model based on machine learning at different confidence levels. The method of claim 2, wherein the determining whether to approve the allocation by comparing the second threshold value with the number of requested slots further includes: If the number of requested slots is less than the second threshold, approve the allocation of the number of requested slots; and If the number of requested slots is equal to or greater than the second threshold value, then whether to approve the allocation is determined based on the reliability of the item supplier terminal determined by using an item supplier reliability classification model. The method of claim 4, wherein the item supplier reliability classification model is configured to output an item supplier terminal based on a number of times that an allocated slot of the item supplier terminal is unused and is processed as a no-show. of reliability. The method of claim 1, wherein the AI model is trained based on classification information including data obtained by classifying information about each of at least one type into at least one category and numerical information including numerical data. The method of claim 6, wherein the method is based on an identifier selected from the group consisting of an identifier of a distribution center, a type of a distribution center, an item type that occupies a largest portion of an item of an item transport machine, at least one of a group of at least one category of a number of shipping locations for transported items, and includes shipments of items selected from a number of types including temperature-sensitive items, transported by an item transport machine A number of locations, a total volume of an individual shipped inventory unit (SKU), a total weight of an individual shipped SKU, a number of SKUs transported by a logistics delivery machine, one of items transported by an article delivery machine The AI model is trained on the numerical information of at least one of a group of a total volume, a total weight of items transported by an article transport machine, and a weight per volume of items of an article transport machine. The method of claim 1, wherein the distribution request signal further includes information about an identifier of the item supplier terminal, information about an identifier of the distribution center, and information about an expected shipping date to the distribution center. . The method of claim 8 further includes: Extract input data for machine learning of the AI model from the identifier of the item supplier terminal, the identifier of the distribution center, and the expected shipping date; and Perform machine learning on the AI model based on this input data. An equipment for allocating a slot to an article transport machine based on an allocation request from an article supplier terminal in a distribution center, the equipment includes: a transceiver; and a processor configured to compare a threshold value output by a machine-learned AI model to an allocation request signal for a slot received from the item supplier terminal through the transceiver It is determined whether the allocation is approved based on the number of requested slots, and the transceiver is controlled to transmit approval information indicating whether the allocation is approved to the item supplier terminal. Such as the equipment of claim 10, wherein the threshold value is a plural number; If the number of requested slots is less than a first threshold of one of a plurality of such thresholds, the processor determines that allocation of as many slots as requested is authorized, and if the requested The number of slots is equal to or greater than the first threshold, by comparing a second threshold that is one of the plurality of thresholds greater than the first threshold with the number of requested slots. This number will be used to determine whether to approve the allocation. The device of claim 11, wherein the first threshold value and the second threshold value are predicted numbers of slots output from the AI model based on machine learning at different confidence levels. The device of claim 11, wherein the processor is further configured to: Determine whether to approve the allocation by comparing the second threshold value with the number of requested slots; If the number of requested slots is less than the second threshold, approve the allocation of the number of requested slots; and If the number of requested slots is equal to or greater than the second threshold value, it is determined whether to approve the allocation based on the reliability of the item supplier terminal determined by using an item supplier reliability classification model. The apparatus of claim 13, wherein the item supplier reliability classification model is configured to output an item supplier terminal based on a number of times that an allocated slot of the item supplier terminal is unused and is processed as a non-occurrence of reliability. The apparatus of claim 10, wherein the AI model is trained based on classification information including data obtained by classifying information about each of at least one type into at least one category and numerical information including numerical data. The device of claim 15, wherein the basis includes an identifier selected from a distribution center, a type of a distribution center, an item type that occupies a largest portion of an item of an item transport machine, based on an item transport machine The classification information of at least one of a number of shipping locations for the items being transported and a group of a number of shipping locations for the items being transported by the item transporting machine, and includes information selected from the group consisting of temperature-sensitive items A number of types, a number of shipping locations for items transported by an item transport machine, a total volume of individual shipped inventory units (SKUs), a total weight of individual transported SKUs, transported by a logistics transport machine At least one of the number of SKUs, the total volume of items transported by an article transport machine, the total weight of items transported by an article transport machine, and the weight per volume of items of an article transport machine One uses the numerical information to train the AI model. The device of claim 10, wherein the distribution request signal further includes information about an identifier of the item supplier terminal, information about an identifier of the distribution center, and information about an expected shipping date to the distribution center. . The apparatus of claim 17, wherein the processor is further configured to extract input for machine learning of the AI model from the identifier of the item supplier terminal, the identifier of the distribution center, and the expected shipping date data, and perform machine learning on the AI model based on the input data. A computer-readable recording medium includes a computer program for executing the method for allocating a slot as claimed in claim 1.