WO2020263035A1 - Procédé et système pour fournir un service logistique pour fournir un produit à un client recherchant une réception directe sur la base d'une optimisation de commande en ligne - Google Patents

Procédé et système pour fournir un service logistique pour fournir un produit à un client recherchant une réception directe sur la base d'une optimisation de commande en ligne Download PDF

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Publication number
WO2020263035A1
WO2020263035A1 PCT/KR2020/008397 KR2020008397W WO2020263035A1 WO 2020263035 A1 WO2020263035 A1 WO 2020263035A1 KR 2020008397 W KR2020008397 W KR 2020008397W WO 2020263035 A1 WO2020263035 A1 WO 2020263035A1
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customer
service server
information
customers
vehicle
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PCT/KR2020/008397
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English (en)
Korean (ko)
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최중인
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최중인
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/083—Shipping
    • G06Q10/0832—Special goods or special handling procedures, e.g. handling of hazardous or fragile goods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/083—Shipping
    • G06Q10/0836—Recipient pick-ups
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]

Definitions

  • the present invention relates to a logistics service providing method and system, and specifically, after receiving an order online from a plurality of customers who wish to receive it directly, direct receipt is performed based on the expected arrival time and product information of the above customers. It relates to a method and a system for providing logistics services in an environment most suitable to go to.
  • the present invention relates to implementing a logistics environment so that customers who want to directly receive the ordered product can quickly receive the product, or to implement a warehouse-type store called Higgs mart.
  • a logistics environment so that customers who want to directly receive the ordered product can quickly receive the product, or to implement a warehouse-type store called Higgs mart.
  • An object of the present invention is to provide a logistics environment in which products can be quickly provided to customers who want to receive products directly after placing an online order.
  • the waiting time when the customer arrives at the receiving location by calculating the expected arrival time of customers who wish to directly receive the products that require low temperature storage, and the most efficient movement route for picking the products to be received in the warehouse. And it aims to minimize the time of receipt.
  • customers who will arrive at the receiving place at a similar time can be grouped, and products can be provided to customers included in the group collectively. It aims to increase efficiency.
  • the present invention shortens the time to provide products to customers by calculating a picking order so that products can be picked most efficiently in the warehouse by referring to the received product information of the above grouped customers, and resources in the warehouse. It aims to increase the work efficiency of people (picking workers, picking robots).
  • an object of the present invention is to enable the sharing of logistics transportation by allowing another customer's ordered product to be received and delivered on behalf of another customer at the choice of the customer among customers who wish to receive it directly.
  • the method of providing a logistics service includes the steps of: (a) receiving, by a service server, a direct receipt request from a customer from at least one or more arbitrary customer terminals; (b) calculating, by the service server, an expected arrival time of the customer's receiving location based on the information in the received direct receipt request; (c) transmitting, by the service server, the calculated expected arrival time and information on the received product of the customer to a local server corresponding to the receiving location; And (d) transmitting, by the local server, a picking order to a worker or a robot in the receiving place.
  • the direct receipt request may include information on a received product that a customer wants to receive, information on a departure time of the customer, and information on a departure location.
  • step (a) is a step in which the service server receives direct receipt requests from a plurality of customer terminals, and in step (b), the service server arrives at the receiving location of a plurality of customers. It is a step of calculating the expected time, and in step (c), the customers whose expected arrival time falls within a preset time range are classified into unit groups, and the expected arrival time of the customers included in each unit group and the received product It may be characterized in that the step of transmitting the information to the local server corresponding to the receiving place.
  • the step (c) is characterized in that the service server further transmits the picking order to the local server, wherein the picking order is received by a plurality of customers included in the same unit group. It may be characterized in that it is the order of picking products based on similar products classified from the information.
  • the distribution service providing method comprises the steps of: (e) receiving, by a service server, vehicle identification information from a vehicle identification device at the receiving location; (f) determining, by the service server, whether or not the vehicle can be directly received based on the vehicle identification information; may further include.
  • the vehicle recognition device may be characterized in that it induces a vehicle that can be directly received to a receiving position and a vehicle that cannot be directly received to a standby position according to the determination result of the service server.
  • step (a) is characterized in that the service server further receives a proxy receipt request from an arbitrary customer terminal, and in step (c), the received product information includes: It may be characterized by including information on products that the customer wants to receive on behalf of.
  • the distribution system for providing goods receives a direct receipt request from at least one or more arbitrary customer terminals, and is scheduled to arrive at the destination of the customer based on the information in the direct receipt request.
  • a service server that calculates a time and transmits the calculated expected arrival time and information of the product to be received to a local server corresponding to the receiving location; It may be characterized in that it includes a.
  • the distribution system may further include a local server that receives information about an expected arrival time and received product from the service server, and transmits a picking order to a worker or a robot in the receiving place.
  • the distribution system further includes a vehicle recognition device for recognizing a vehicle intended to enter the receiving place and transmitting vehicle identification information to the service server, wherein the service server recognizes the vehicle based on the vehicle identification information. It may be characterized in that it is determined whether or not the vehicle is a vehicle that can be directly received.
  • the above customers are grouped based on the expected arrival time, thereby reducing the time required for product provision and reducing the use of resources.
  • the system proposed by the present invention is suitable when an offline store (eg, Wal-Mart, etc.) is converted into an online-based store, and the wide display space of the conventional offline store is replaced with an online shopping platform, The space can be replaced with a drive-in pickup space.
  • an offline store eg, Wal-Mart, etc.
  • FIG. 1 shows a schematic configuration of a distribution system according to the present invention.
  • Figure 2 shows the actual implementation of the distribution warehouse proposed in the present invention.
  • FIG. 3 is a diagram illustrating a method of providing a logistics service for providing goods to a customer who visits a receiving place for direct collection by using the logistics system according to the present invention.
  • FIG. 4 is a diagram illustrating a process of calculating the estimated arrival time of each customer and grouping accordingly when a direct receipt request is received through a plurality of customer terminals.
  • FIG. 5 is a diagram illustrating a state in which a customer makes an online order and direct receipt request using a terminal.
  • FIG. 6 illustrates an embodiment in which a location of a customer approaching a receiving location is displayed in different colors according to distance.
  • FIG. 7 illustrates an embodiment in which orders ordered by a plurality of direct receiving customers are collected and a picking order is regenerated based on the ordered product.
  • FIGS. 8 to 12 are diagrams for helping understanding of an algorithm for obtaining an optimized path based on the position of a pallet when a plurality of products are to be picked up in a warehouse.
  • FIG. 13 illustrates an embodiment of controlling a circuit breaker by determining through vehicle recognition whether or not a vehicle can be directly received when a customer's vehicle arrives at a pickup location.
  • Fig. 1 shows a schematic configuration of a distribution system according to the present invention.
  • the entire system includes a cloud-based service server, and a plurality of local servers connected to the service server through a network as corresponding to each distribution warehouse or receiving place, and customer terminals are in accordance with the present invention.
  • it is not a direct configuration constituting the logistics system according to the present invention, since it is a configuration necessary for a customer to make an online order and direct receipt request to the service server, it is shown together to help understanding the implementation of the invention.
  • each configuration will be described.
  • the service server assumes that it has a central processing unit (CPU) and a memory in terms of hardware configuration, and the central processing unit is a controller, a microcontroller, a microprocessor, and It may be referred to as a microcomputer or the like, and may also be implemented by hardware or firmware, software, or a combination thereof.
  • the central processing unit is a controller, a microcontroller, a microprocessor, and It may be referred to as a microcomputer or the like, and may also be implemented by hardware or firmware, software, or a combination thereof.
  • ASIC application specific integrated circuit
  • DSP digital signal processor
  • DSPD digital signal processing device
  • PLD programmable logic device
  • FPGA field programmable gate array
  • firmware or software may be configured to include modules, procedures, or functions that perform the above functions or operations.
  • the memory is ROM (Read Only Memory), RAM (Random Access Memory), EPROM (Erasable Programmable Read Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, SRAM (Static RAM), It may be implemented as a hard disk drive (HDD) or a solid state drive (SSD).
  • the management server may further include a communication device for transmitting and receiving data to and from an external terminal or an external local server in addition to the central processing unit and memory.
  • the service server receives a direct receipt request from at least one or more arbitrary customer terminals, and is scheduled to arrive at the destination of the customer based on the information in the direct receipt request.
  • One feature may be that the time, that is, the expected time of arrival of the customer to the distribution warehouse for pickup of goods, is calculated, and the calculated expected arrival time and information of the received product of the customer are transmitted to a local server.
  • the service server may receive online orders from customer terminals before receiving a request for direct receipt from customer terminals. In this case, online order reception means that customers select the product they want to purchase, and It can be understood as including a series of processes of paying the cost.
  • the customer terminal referred to in this detailed description is understood to collectively refer to terminals carried by or owned by an unspecified customer, and includes, for example, a smartphone, a tablet PC, a laptop computer, and a desktop.
  • a central processing unit (CPU) for calculation, such as a computer, and a device including a memory may be included.
  • the customer terminal is required to have a network connection function so that data can be exchanged with other external components, that is, a service server.
  • the logistics system according to the present invention is not necessarily implemented in a form in which the customer must directly receive the product, but may be implemented to enable a delivery service for delivering the product to the customer in parallel.
  • the service server A special type of billing system can be provided to induce receipt.
  • the service server can induce payment of a predetermined annual fee instead of delivering the product to the customer every time an order is made. Instead, if the customer visits the warehouse and receives it directly, it is delivered by returning a certain amount per time. It is possible to provide services and induce direct receipt of customers.
  • the service server can provide a product delivery service for an online order corresponding to an annual payment of 36,000 won to a customer.
  • the service server provides such a billing system to customers so that it can induce customers to receive them directly, as well as to induce customers to consume more for free shipping benefits. There is an advantage that can enjoy improved commercial effect.
  • the service server may perform calculations based on information shared with a local server to be described later, or information in a direct receipt request received from a customer terminal. At this time, the service server performs calculations based on the information.
  • the meaning of thing can be understood as so-called big data processing that receives and processes a large amount of information.
  • the service server can process the data included in the management information by using a data extraction, conversion, and loading module (data ETL Sqoop), and this is a data waiting module (Data Que Kafka), data high-speed processing.
  • the data processing module can be implemented to first store the original file in the data distribution file system (Hadooop HDFS), purify it, and then store it in the big data storage device (Hadoop HBASE).
  • the accumulated big data can be utilized in various application programs through application program association (API).
  • the distribution system may further include a local server corresponding to each distribution warehouse or a receiving place.
  • the local server collects and stores general information on each receiving location, and may have a main feature of sharing this information with the service server through a network, and furthermore, the expected arrival time received from the service server, It may serve to transmit an order, including a product picking order, to a terminal or a picking robot of workers who pick products in a pickup location by referring to received product information or picking orders.
  • the above local server may also include a central processing unit and a memory similar to the service server described above.
  • Figure 2 shows the actual implementation of the logistics warehouse in the logistics system proposed in the present invention.
  • the distribution warehouse 1000 may have an outer wall for forming a space for storing products therein, an opening 1002 that can be seen from the outside, and a plurality of products can be stacked in the inner space. It may include a storage unit 1010 and a delivery unit 1020 through which the user can pick up products. In addition, the distribution warehouse 1000 may be provided with the aforementioned local server.
  • the outer wall constitutes the entire skeleton of the distribution warehouse 1000, and the outer wall is formed in the same manner as the outer wall of a general building including concrete and reinforced structures, and can be constructed on any site.
  • the outer wall may not necessarily be formed of concrete and reinforced structures, but may also be formed of a so-called container type made of metal panels.
  • the container type made of the outer wall of the metal panel it is relatively free to install and dismantle on an arbitrary site, so that the distribution warehouse can be easily operated according to the needs of the operator.
  • the space formed by the outer wall of the distribution warehouse 1000 may be a space that is significantly reduced compared to the existing distribution warehouse (mart) building.
  • the opening 1002 may be formed on the front outer wall of the distribution warehouse 1000, and through the opening 1002, a user waiting for product pickup from the outside may You can check the transport status of goods.
  • the opening 1002 may preferably be closed with a panel made of a transparent material to prevent illegal intrusion from the outside, and for example, tempered glass may be installed in the opening 1002.
  • the opening 1002 may not be an essential component in implementing the distribution warehouse 1000, but may be included as one component for convenience of a user waiting for a product to be released.
  • a storage unit 1010 may be configured so that goods can be stored in a space formed by an outer wall in the distribution warehouse 1000, and this storage unit 1010 is preferably May be configured as a structure capable of placing a plurality of products, such as a shelf or a rack.
  • a structure constituting the storage unit 1010 will be referred to as a rack.
  • the rack constituting the storage unit 1010 may be installed in the remaining space excluding a space in which equipment for picking products, including a picking robot, can be installed within the space.
  • racks are installed inside the outer walls 1001 on both sides, respectively, and the picking robot can be moved in the space between the two racks.
  • a plurality of pallets that is, pallets that can contain products, may be further arranged in the rack, and these pallets are moved toward the warehousing part by the picking robot when new products are received through the warehousing part. It can be used for a purpose that can be contained.
  • a picking robot is mentioned as a means for picking products in the storage unit 1010, but this is only an example, and an internal employee, not the picking robot, directly picks the product and provides it to the delivery unit. There may also be a method of picking products.
  • the delivery unit 1020 refers to a space or structure in which a user who has ordered a product in advance can directly receive a product, and preferably, as shown in FIG. It may be formed in a protruding state on one side. Among the protruding portions, there may be at least a part of a flat surface so that the products picked by the picking robot can be placed, and the delivery unit 1020 is located at the bottom of the outer wall so that the user can easily receive the products. It may be formed to have a height of 1 m or less.
  • a drive through road may be provided in front of the delivery unit 1020 so that vehicles of customers can move for pickup.
  • FIGS. 1 and 2 a schematic configuration of a distribution system and a distribution warehouse according to the present invention has been described.
  • FIG. 3 the overall process from the step of the customer placing an online order to the step of directly receiving the product will be described.
  • the service server performs an online order reception and payment step (S101) in which product selection from a customer terminal and payment for selected products are performed.
  • S101 an online order reception and payment step
  • an application for online ordering and payment may be installed in the customer terminal, and the customer may select a product and make payment through this application.
  • the service server may receive a direct receipt request from the customer terminal to directly receive the ordered products (S102).
  • Direct pickup request can be made in various ways. For example, a customer can send a direct pickup request by pressing the'Departure' button on the customer terminal, or after the customer selects'Direct pickup' on the customer terminal on the radio box. You can also set the expected arrival time and send a request for pick up directly. It is understood that the above process of transmitting and receiving a direct receipt request is only one embodiment to aid understanding of the invention, and this can be accomplished in various ways. For reference, FIG.
  • FIG. 5 shows an embodiment of a screen in which a customer selects a plurality of products on a smartphone, a payment screen for the selected products, and a screen in which a'departure' button is displayed to transmit a direct receipt request, Further, an embodiment of a screen displaying the route to the pickup location and the expected arrival time after pressing the'departure' button is also shown.
  • the direct receipt request transmitted to the service server in step S102 may be understood to mean data including a series of information, and the information included at this time includes the item, size, quantity, and Receiving product information including information on at least one of a product number and a production (manufacturing) company may be included, and information on a receiving place, which is a location where the customer wants to receive the above products, may be further included. Further, departure time information for the time at which the customer departs, and departure position information for the departure position may be further included according to embodiments. On the other hand, the received product information may also include information on products that the customer intends to receive by proxy.
  • an arbitrary customer may receive a product on behalf of another customer, and for example, the random customer may request a proxy receipt together with at least one of the delivery location, delivery product, and delivery time.
  • the received product information may be transferred to the service server while the information on the products to be received by proxy is included.
  • the above direct pickup request may include an online order number instead of the above received product information.
  • the online order number corresponds to the online order made by the customer earlier, and an online order number is created in the service server at the stage of online ordering. If so, it can be implemented so that the received product information is specified only with the corresponding online order number.
  • the departure time information may be directly used as the departure time information when the customer transmits a direct pickup request and the service server receives it.
  • the direct pickup request does not include separate departure time information.
  • the departure location information may be information generated by a GPS device or other location information device provided in the customer's terminal.
  • the service server receiving the direct receipt request calculates the estimated arrival time of the destination of the customer based on the information in the direct receipt request.
  • the information used to calculate the estimated arrival time may include the customer's departure time information, the customer's departure location information, and the pickup location information, and the service server further utilizes the current traffic information in addition to the above information. By doing so, it is possible to calculate the expected arrival time by calculating the route from the customer's starting point to the receiving place and the time required when moving along the route.
  • the current traffic information may be information that the service server can obtain by accessing an external institution server, or the service server may be information that can be obtained from a service company that provides current traffic information.
  • the service server directly calculates the expected arrival time of the customer's pickup location, but the service server receives only the corresponding value after being calculated on the customer's terminal. It can also be delivered in a way. That is, when the service server directly receives a request for receipt, the service server can acquire and manage it by simultaneously receiving information about the estimated arrival time that has been calculated from the corresponding customer terminal. That is, step S103 in the drawing is illustrated assuming that the operation subject is the service server, but the step is also modified and implemented in a manner in which only the expected arrival time is transmitted to the service server after being performed by the customer terminal. Understand.
  • a display device eg, a monitor of an operator managing the service server may display the customer access status in the form of FIG. 6.
  • FIG. 6 is a diagram showing an embodiment in which a plurality of customers who have made an online order and a direct receipt request to the service server display the current status of accessing each distribution warehouse or receiving location, whereby the estimated arrival time of the customers approaching the display device Each customer can be displayed by color according to the remaining time until.
  • a plurality of customers may be grouped and managed by customers with similar arrival times. In this case, customers within the same group may be displayed in the same color on the display device.
  • the service server calculates or receives the estimated arrival time and obtains the corresponding information
  • the service server sends the previously acquired estimated arrival time and the received product information to a local server corresponding to each warehouse or place of receipt. Deliver. (S104)
  • the local server can deliver the picking order to workers located in the warehouse or the receiving place, more precisely, to the terminals held by the workers, or if the distribution warehouse or the receiving place is a robot-automated place.
  • Picking orders can be delivered to each robot.
  • the picking order refers to a command for picking the ordered product, and the picking order includes data obtained by reprocessing the received product information previously received by the local server suitable for product picking. You can do it.
  • the local server rearranges the products ordered by the above customer in an order suitable for picking, based on information such as the storage location of various products in the distribution warehouse or the pick-up location, and the path for picking the product. Can be processed.
  • the above picking order depends on where the above three types of products are stored in the distribution warehouse or the receiving place.
  • the second product-the first product-the third product may be rearranged in order.
  • the above picking order may have been created by the local server through data processing as described above, but unlike this, the service server stores the product information of each customer that it has and the products in each distribution warehouse or collection location. It may be that a picking order is directly created based on information about the location and then delivered to the local server.
  • FIG. 4 is a schematic diagram assuming a case where three customers (customer A, customer B, and customer C) directly request receipt. For example, if Customer A, Customer B, and Customer C make a direct pickup request (S202A, S202B, S202C) to the service server at 12:54, 13:01, and 13:05, respectively, the service server The estimated arrival time of each customer is calculated (S203). In this case, the calculation subject of the expected arrival time may be a service server or a terminal of each customer as described above.
  • the estimated arrival time of each customer calculated by the service server may be 13:10, 13:14, and 13:40.
  • the service server has a predetermined time of arrival.
  • Each customer within the range can be classified into one unit group. For example, assuming that the preset time range is 5 minutes, the estimated time of arrival of customer A and customer B will be 4 minutes different from the estimated time of arrival of customer A, so the service server sets the customer A and customer B as one. It can be classified as Group I, which is a unit group of, and the rest of Customer C can be classified as Group II.
  • the service server After being classified into a unit group in this way, the service server can divide and deliver the information for each unit group above when delivering the estimated arrival time and received product information to the local server. (S204)
  • the local server may generate a new picking order based on the estimated arrival time and received product information transmitted for each unit group, and then transmit it to a worker terminal or a robot to perform product picking.
  • Fig. 7 shows the information generated based on the received product information of customers A and B in the above group I. An embodiment of a picking order is shown.
  • customer A ordered 2L of milk and 300g of Korean beef, and customer B ordered 600g of pork belly and 300g of Korean beef, and the picking order created based on this information is ordered as seen from the right side of FIG.
  • the order may be rearranged based on the product.
  • the order product order may be the order in which the products stored in the distribution warehouse or the receiving place can be picked in the shortest time or by the shortest route.
  • the picking order may include location information where each product is stored, that is, a pallet ID, and an operator or robot receiving the picking order may perform product picking with reference to the above information.
  • 8 to 12 are for helping understanding of an algorithm for generating an optimized picking order.
  • a path function F may be defined.
  • the path function is defined based on a product storage location defined by three-dimensional spatial coordinates. For example, if you define the location of a pallet where goods are stored as rows (A, B, C %), columns (1, 2, 3, ...), and layers (I, II, III, 7), the path function is Starting from the reference point, it may be determined as an accumulated sum of unit distances passing through each pallet for product picking.
  • T the time it takes to pick and pack all three customers' products, T (P1+P2+P3), picks each customer's products individually. And T(P1)+T(P2)+T(P3) which is the sum of the times it takes to pack.
  • the service server or the local server will obtain the optimized value of the path function or the pavement time function through machine learning-based deep learning, but will perform learning and calculations according to the above prerequisites.
  • the optimal value on the path function or the packaging time function it may be considered to group customers who have placed similar orders into one similar order customer when there are a plurality of online order customers.
  • the optimal picking route is calculated based on the online order information of multiple customers and the logistics service is provided within, the similar ordering customers are united so that packaging and product provision can be completed within a set time with only minimal movement (or time).
  • There is a need to bind it it will be very difficult to optimize modeling because various cases may occur depending on product type, product order quantity, and storage location.
  • this modeling can be implemented to be improved when the service server or the local server gradually generates picking orders by performing machine learning based on big data.
  • FIG. 9 conceptually illustrates an algorithm for implementing the optimization of a path function or a packing time function by grouping customers who have placed similar orders into one similar order customer when a plurality of online order customers exist.
  • the service server or local server is the optimal picking route and number of workers or robots.
  • the minimum moving route by grouping the items of 9 customers as indicated by the three squares on the right side of Fig. 9, and the combination that can pick all the products within a given time of 10 minutes is a neuralman-based engine. It can be found through. That is, the positions of each online ordered product are set as an input set, the value of the moving route is output, a route that optimizes it within a limited time is selected, and an optimal picking order is obtained based on this.
  • FIG. 10 shows how variables for calculating the optimization value can be expressed from the order when a customer A, which is an arbitrary customer, orders 27 products.
  • a customer A which is an arbitrary customer
  • the path for picking all of these products is P1
  • the path for picking 27 ordered products can be transformed into a vector or tensor variable based on the position of the pallet described above.
  • P1 (R1(A,3,II), R2(B,7,III),... , G1(A,7,V),... B1(A,5,I),... It can be expressed as ⁇ .
  • the indication of the P1 path above is represented by variables having 27 three-dimensional spatial coordinates, but is not necessarily limited thereto, and as long as an arbitrary path can be specified and expressed, there is no limitation on the type and expression method of the variable.
  • a neural network structure consisting of an input layer, a plurality of hidden layers, and an output layer by using the R, G, and B variables included in the order of one customer as three different input variables (P1, P2, P3) as shown in FIG. It is possible to implement a deep learning program in the service server of or a local server.
  • an optimal deep learning program by performing machine learning that measures the travel distance and time required by inputting the order data of multiple customers and adjusts the weight of each layer (layer).
  • machine learning that measures the travel distance and time required by inputting the order data of multiple customers and adjusts the weight of each layer (layer).
  • W11, W12, ..., W5 the function value of the hidden node is obtained, and this is repeated.
  • the outputs F and T are multiplied by the weights V11, V12, ... of the last hidden layer.
  • the deep learning-based program created in this way improves intelligence through learning based on input and output field data.
  • Each variable is statistically pre-tuned for fast convergence and accuracy.
  • an algorithm that binds orders of similar customers that minimizes travel distance and can complete packaging within a target time becomes one of the core functions of the present invention.
  • the service server or the local server in the present invention may be implemented to predict the type and number of products to be purchased by customers in the next purchase step through deep learning based on big data and machine learning. For example, if the milk sales forecast is 90 or 110, but the actual number of milk ordered by the customer is 100, if the product is expected to be 90, damage may occur due to premature sellout. If the product was picked up by predicting the number of items, 10 items may remain, resulting in damage due to product disposal. Therefore, an algorithm that predicts the customer's purchase pattern (type and quantity) based on big data on the past purchase history of customers can be said to be essential in a logistics service system such as the present invention.
  • the management server 200 or the local server 100 inputs normalization for each customer and normalization for each product based on the customer purchase history data table, and then performs a deep learning process based on a neural network to interact with each customer's product. It can be made to predict the correlation index.
  • a purchase pattern of customers calculated through this is calculated, similarity between customers based on this can be obtained through the following equation, and customers having similarities higher than a preset value can be grouped into a group.
  • a service server or a local server may learn an index on which customers and products have relevance based on past purchase history data.
  • FIG. 13 is a diagram for explaining a system capable of determining through a vehicle recognition device whether a vehicle can be directly received when a customer arrives at a distribution warehouse or a pickup location, and taking appropriate follow-up measures accordingly. .
  • a service server and a vehicle recognition device are mainly included, and the vehicle recognition device first recognizes a vehicle that has arrived at a distribution warehouse or a pickup location (S1001).
  • the vehicle recognition device includes all devices that can identify the vehicle, such as the license plate of the vehicle or the exterior of the vehicle, and preferably further includes a circuit breaker operated according to the vehicle recognition result. Can be.
  • the vehicle recognition device transmits the vehicle recognition information to the service server (S1002).
  • the transmitted vehicle recognition information may include at least one of a number and a photograph of the vehicle.
  • the service server receiving the vehicle recognition information determines whether the vehicle is a vehicle that can be directly received (S1003). In this case, whether the vehicle can be directly received is determined whether the vehicle is the vehicle of the customer who requested the direct collection. It is determined based on whether the vehicle has arrived at the expected arrival time and whether the products requested by the customer are ready.
  • the service server transmits the determination result back to the vehicle recognition device (S1004) so that the vehicle recognition device can guide the arriving vehicle to a predetermined location. (S1005) At this time, if the vehicle can be directly received, the vehicle recognition device can guide the vehicle to the receiving position so that the vehicle can receive the product. If the vehicle is in a state where direct collection is not possible, the circuit breaker is cut off It is possible to induce the waiting of the vehicle while leaving the vehicle, or it is possible to guide the vehicle to a separate waiting space.

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Abstract

La présente invention concerne un procédé et un système pour fournir un service logistique et, plus précisément, un procédé de fourniture d'un service logistique dans l'environnement le plus approprié pour la réception directe, sur la base d'informations d'heure d'arrivée attendue et de produits de réception d'une pluralité de clients, après la réception de commandes en ligne de la pluralité de clients qui souhaitent recevoir des produits directement, ainsi qu'un système associé.
PCT/KR2020/008397 2019-06-28 2020-06-26 Procédé et système pour fournir un service logistique pour fournir un produit à un client recherchant une réception directe sur la base d'une optimisation de commande en ligne WO2020263035A1 (fr)

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