RU2780386C1 - Dynamic trolley optimization system - Google Patents

Abstract

FIELD: computer systems.

SUBSTANCE: computer system for optimizing the selection of nodes and edges in the digital model of the graph accesses the digital model of the graph of the physical location of the warehouse. The digital graph model contains information indicating the location of a plurality of specific inventory items on racks in a physical warehouse location. The computer system identifies a set of orders, each containing one or more items, and an order priority. The computer system then maps each item in each order selected from the set of orders to a plurality of nodes in a digital graph model. The computer system identifies a ranking node from the plurality of nodes. The computer system then traverses along one or more edges that extend from the ranking node to identify the shortest path in the digital graph model to populate the digital model of the picking cart above the packing threshold level.

EFFECT: creation of computer system for optimizing the selection of nodes and edges in the digital model.

20 cl, 7 dwg

Description

Cross-reference to related applications

[0001] This application claims the priority of U.S. Provisional Patent Application No. 62/882,158, filed August 2, 2019, entitled DYNAMIC CART OPTIMIZATION SYSTEM, and U.S. Patent Application No. 16/942,541, filed July 29, 2020, entitled DYNAMIC CART OPTIMIZATION SYSTEM, which are expressly included herein by reference in its entirety.

PRIOR ART

[0002] The recent rapid growth of e-commerce has placed significant stress on the transportation and packaging industries. Not only is the volume of products shipped increasing significantly, but the speed at which products are expected to be delivered is also increasing significantly. Previously, it might have been acceptable for packages to be delivered 5-6 business days after an order was placed. However, current market conditions require packages to be delivered within two days, and in some cases the same day the order was placed.

[0003] To meet these rapidly growing needs, the packaging and transportation industry is required to innovate and develop new systems and machines to improve efficiency and speed. One area that has been improved in efficiency is the use of customized (custom) packaging systems that are able to create appropriately sized packages for a wide range of different orders. These customized packages reduce wasted space inside vehicles and storage equipment, allowing more orders to be shipped in the same vehicle.

[0004] The field of packaging and transportation, which requires technological improvements, is an area associated with bundling technologies. "Picking" is the process of gathering items within a warehouse for shipment. Typically, an order is received at the warehouse and a picking request is made to collect the items in the order so that they can be shipped to the recipient of the order. The processes and machines used in order picking can greatly affect the efficiency, cost, and speed with which an order is picked and prepared for shipment. There is a need in this area for technological solutions that improve picking systems.

[0005] The invention claimed herein is not limited to embodiments that overcome any disadvantages or that only work in environments such as those described above. Rather, these backgrounds are provided only to illustrate one exemplary technology area where some of the embodiments described herein may be practiced.

BRIEF DESCRIPTION OF THE INVENTION

[0006] The disclosed embodiments include a computer system for optimizing node and edge selection in a digital graph model. A computer system includes one or more processors and one or more computer-readable media having executable instructions stored thereon that, when executed by one or more processors, configure the computer system to perform various actions. For example, a computer system may access a digital model of a warehouse physical location (location) graph. The digital graph model contains information indicating the location of a plurality of specific inventory items among the shelves in a physical warehouse location. Additionally, the digital graph model contains a plurality of nodes and a plurality of edges, each of the plurality of nodes representing a different physical area within the physical location of the warehouse, and each of the plurality of edges representing a physical path between each node in the plurality of nodes and the nearest neighbor nodes of each node.

[0007] The computer system may further identify, in the order request database, a set of orders, with each order in the order set containing one or more items (items) and an order priority. The computer system then maps each item in each order selected from the set of orders to a plurality of nodes in a digital graph model. The computer system identifies a ranking node from the plurality of nodes. The ranking node contains the node with the highest number of displayed items. The computer system then traverses one or more edges that extend from the ranking node to identify the shortest path in the digital graph model to populate the digital model of the picking cart above the packing threshold level.

[0008] This Summary is intended to introduce a selection of concepts in a simplified form, which are further described below in the Detailed Description. This summary is not intended to identify key features or essential features of the claimed invention, nor is it intended to be used as an aid in determining the scope of the claimed invention.

[0009] Additional features and advantages will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the teachings herein. The features and advantages of the invention may be practiced and obtained using the tools and combinations specifically set forth in the appended claims. The features of the present invention will become more apparent from the following description and the appended claims, or may be learned by practice of the invention as set forth below.

BRIEF DESCRIPTION OF THE DRAWINGS

[0010] To describe the manner in which the above and other advantages and features can be obtained, a more specific description of the claimed invention briefly described above will be presented by reference to specific embodiments that are illustrated in the accompanying drawings. Understanding that these drawings depict only exemplary embodiments and, therefore, should not be construed as limiting the scope, the embodiments will be described and explained with additional specificity and detail through the use of the accompanying drawings.

[0011] FIG. 1 illustrates an embodiment of a packaging cell.

[0012] FIG. 2 illustrates a schematic representation of an embodiment of a warehouse location.

[0013] FIG. 3 illustrates a schematic representation of the warehouse location embodiment of FIG. 2 with nodes and elements of the order superimposed on a schematic representation.

[0014] FIG. 4 illustrates a digital model of the warehouse physical location graph shown in FIG. 2.

[0015] FIG. 5 illustrates a schematic of an embodiment of a computer system for identifying a pick request path and generating a pick request.

[0016] FIG. 6 illustrates a flowchart of an embodiment of steps in a method for generating a pick request.

[0017] FIG. 7 illustrates a flowchart of an embodiment of steps in a method for identifying a picking request path.

Detailed description

[0018] The disclosed embodiments are directed to systems for optimizing node and edge choices in a digital warehouse location graph model. In particular, the disclosed embodiments generate a digital graph model that contains nodes that represent the physical layout of a warehouse location. The digital model of the graph also contains edges passing between one or more nodes. In at least one embodiment, the digital graph model comprises a graph database. The computer system is configured to display the elements in one or more orders of the digital graph model and iteratively traverse along the edges in the model to generate an optimized selection of nodes and edges. As used herein, an "order" contains a group of items to be packaged in a single package. However, it should be appreciated that, in some embodiments, multiple orders may be packaged in the same package. Likewise, a customer may order a large number of items, which may be divided into multiple orders that are divided among multiple packages.

[0019] Optionally, the computer system may also use a cube processor (volume processor) to process the cube algorithm (volume algorithm) on elements in one or more orders while the computer system traverses along edges and nodes. The output of the sizing algorithm may be applied to a threshold packing level to determine when the optimal selection of nodes and edges has been identified. In at least one embodiment, a packing threshold is applied to the picking cart to determine when the selected items in one or more orders would sufficiently fill the picking cart.

[0020] It should be understood that the embodiments disclosed herein provide more efficient picking systems. Picking requests are dynamically generated within an optimized route to ensure the most efficient, best use of the picking cart within the warehouse location. In addition, the disclosed embodiments are highly customizable so that they can be implemented in a wide variety of locations, in various packaging systems, and with various packaging technologies.

[0021] Referring now to the drawings, in at least one embodiment, as shown in FIG. 1, a shipper (shipping company) may have one or more packing bins 100, which may include equipment for packing available orders and preparing them for shipment. For example, the packaging cell 100 may include a packaging system 110, an available order transportation system 120, a work area 130, a tracking code scanner 132, and a processed order transportation system 140. The packaging system 110 may include a converting machine 112 that may receive fanfold material 150 from one or more bales 152. The packaging system 110 may process the fanfold material 150 into customizable packaging templates 160. An operator can retrieve packaging templates 160 from packaging system 110 and form boxes 170 to ship available orders 180. As used herein, the term "available order" refers to any order (regardless of whether the order is single or multiple) that may be handled as one unit by the shipper.

[0022] The picking cart 120 can transport the various available orders 180 to the work area 130 for packaging and preparation for shipping. In some embodiments, picking cart 120 may be a conveyor system or a movable rack system that can transport available orders 180 to work area 130. When available orders 180 arrive at work area 130, an operator may request packaging templates 160 to be obtained. packaging system 110. In at least one embodiment, the operator requests packaging templates 160 by scanning, with the tracking code scanner 132, the code associated with each respective available order 180. As further described below, such packaging templates 160 can be configured with customizable sizes based on specific sizes available orders 180 to be packaged.

[0023] In addition to packaging the available orders 180, the operator can prepare the available orders 180 for shipment by attaching the required labels and other materials. Once an available order 180 has been processed (ie, packaged and/or prepared for shipping), such processed order 190 may be transported away from work area 130 via the processed order transport system 140. For example, the processed order transport system 140 may transport processed orders 190 to a shipping area. In some embodiments, the system for transporting processed orders 140 may be a conveyor belt that can connect the work area 130 and the desired location for processed orders 190. In other embodiments, the system for transporting processed orders 140 may be a mobile racking system that can transport processed 190 orders from 130 work area.

[0024] It will be understood that the packaging cell 100 in FIG. 1 is provided for example and context only. In various embodiments, various configurations and types of packaging systems may be used. For example, the packaging system may use standard die-cut boxes, dunnage systems, various workflows, or any number of other packaging systems and types. Similarly, packing cell 100 may use several different configurations of picking cart 120. For example, picking cart 120 may use automated picking carts, human-powered picking carts, portable container-based picking carts, or any number of other picking carts. Unless specifically stated otherwise, all embodiments of the dynamic cart optimization system disclosed herein may be used in any packaging configuration, picking configuration, or warehouse configuration. Thus, when specific examples of a packaging configuration, a picking configuration, or a storage configuration are used herein, they are provided by way of example and explanation only, and do not limit the invention to a specific embodiment.

[0025] FIG. 2 illustrates a schematic representation of an embodiment of a warehouse location (location) 200. As used herein, "warehouse location 200" includes any physical location that is used to temporarily store goods for processing and shipping. The depicted warehouse location 200 includes a plurality of aisles 210 separated by a plurality of racks 220. It should be understood that, in a typical warehouse location 200, a plurality of racks 220 store items awaiting shipment.

[0026] FIG. 2 further shows arrows that indicate the normal flow of picking traffic across warehouse location 200. As indicated, conventional picking carts 120 are pushed along a common path through warehouse location 200. When the aisle between the racks where the items of interest are stored is reached, the human operator will push the picking cart 120 along the aisle and remove the desired products from the rack. Such a process would typically require each human operator and picking cart 120 to navigate a complete or nearly complete layout of the entire warehouse location 200 for each picking request that is generated.

[0027] FIG. 3 illustrates a schematic representation of an embodiment of the warehouse location 200 of FIG. 2 with nodes and other elements superimposed on the schematic. The nodes are shown as circles and are labeled A-R. The nodes represent different locations within warehouse location 200. For example, each pass is divided into a plurality of nodes based on the length of the pass. The depicted distance between nodes (A-R) is provided for example and explanation. In other embodiments, nodes (A-R) may be placed closer or further apart based on the required resolution within warehouse location 200.

[0028] Order elements are depicted as hexagons 300(a-c), triangles 310(a-c), squares 320(a-d), and diamonds 330(a-d). Order items are grouped in a form based on orders. As such, each element associated with a particular order is shown in a general form. For example, elements 300(a-c) are each associated with a hexagon and included in one order. In particular, element 300a is located on the rack near node c, element 300b is located on the shelf near node E, and element 300c is located on the shelf near node G. The various elements 300(a-c) in the order may contain any type or combination of items that are in warehouse locations 200.

[0029] FIG. 4 illustrates a digital graph model 400 of the physical warehouse location 200 shown in FIG. 2. The digital graph model 400 contains a computer representation of the nodes of FIG. 3 connected by ribs, which are depicted as arrows connecting different nodes. Edges represent physical paths within warehouse location 200 between different nodes. For example, the user and picking cart 120 may move directly between node A and node C, as indicated by the connecting rib.

[0030] FIG. 5 illustrates a schematic of an embodiment of a computer system 500 for optimizing nodes and edges for identifying a pick request path and generating a pick request. In at least one embodiment, one or more processors 510 in computer system 500 precalculate or compute shortest distances (also referred to as "hops") between each and every node. For example, computer system 500 determines that the shortest transition between node A and node B is one hop. In contrast, the computer system determines that the shortest distances between node A and node N are six hops. This process is continued until the shortest distance between each node is identified.

[0031] In at least one embodiment, the computer system 500 receives an order and stores it in a database 540. The order may be received via a network interface 520 from one or more clients 580(a-c) over a network connection 570, such as the Internet. In at least one embodiment, multiple users may request a cart at the same time. Additionally, in at least one embodiment, the computer system 500 is configured to process multiple cart fill requests at the same time.

[0032] In response to receiving an order, one or more processors 510 access the digital model 400 of the physical location graph 200 of the warehouse. As explained above and shown in FIG. 4, the digital graph model 400 contains information indicating the location of a plurality of specific inventory items among the racks in the physical warehouse location 200. In addition, the digital graph model 400 contains a plurality of nodes (A-R) and a plurality of edges connecting the nodes (A-R). Each of the plurality of nodes (A-R) represents a different physical area within the physical warehouse location 200, and each of the plurality of edges represents a physical path between each node within the plurality of nodes and each node's nearest neighbors.

[0033] The computer system 500 identifies, in the order request database 540, a set of orders, each containing one or more items, and an order priority. The order request database 540 contains a queue of orders that are ready for picking and packaging. In some embodiments, one or more orders may be associated with an order priority. Order priority indicates the urgency with which the order needs to be picked and shipped. For example, warehouse location 200 may offer same-day shipping on some orders. Such an order must be associated with a high priority level. Likewise, specific orders may be associated with specific shippers that arrive at warehouse location 200 for shipment at a specific time of day. In such a case, the orders associated with these shippers may become high priority if the shipper's arrival time approaches.

[0034] One or more processors 510 map each item in each order selected from the set of orders to a plurality of nodes in the digital graph model 400. One or more processors 510 use the information stored in the inventory map 560 to map items to the appropriate nodes. For example, inventory map 560 may include a database that associates each item in warehouse location 200 with its closest node (A-R). In at least one embodiment, the inventory map 560 and the order request database 540 are stored in the same database. Accordingly, computer system 500 is configured to search for each item in an order and determine which items are mapped to which nodes within warehouse location 200.

[0035] After one or more processors 510 map each item in each order to multiple nodes (A-R) using the digital graph model 400, one or more processors 510 identify the nodes with which each order within the order set is associated. For example, one or more processors 510 identify that order 300 (depicted by hexagons) is associated with nodes C, E, and G, order 310 (depicted by triangles) is associated with nodes C, G, and D, order 320 (depicted by squares) is associated with nodes B , G, J, and Q, and order 330 (depicted by diamonds) is associated with nodes J, M, I, and K.

[0036] One or more processors 510 then identify a ranking node from the plurality of nodes. The ranking node contains the node with the most items displayed. For example, as shown in FIG. 4, node G is a ranking node because it is associated with three different elements (300c, 310b and 320c), which is more than the number of elements associated with any other node. It should be understood that any number of methods can be used to identify a ranking node when a subset of nodes has the same largest number of associated elements. For example, in such a case, a ranking node may be randomly selected from a subset of nodes.

[0037] Once a ranking node (e.g., node G) is identified, one or more processors 510 traverse along one or more edges that extend from the ranking node to identify the shortest path in the digital graph model to populate the digital cart model above a threshold packaging level. The packing threshold may be determined from the results of a volume algorithm that determines the volume of items ordered relative to the volume available in the picking cart 120. The packing threshold may additionally and/or alternatively comprise a weight metric that ensures that the picking cart 120 does not exceed a predetermined the weight.

[0038] In at least one embodiment, traversing one or more edges that extend from a ranking node G to identify the shortest path in the digital graph model 400 comprises accessing a ranking node in the digital graph model 400 and then identifying a subset of orders in one or more orders, each of which is associated with at least one element located in the ranking node G. For example, as shown in FIG. 4, order 300 (ie, items 300a, 300b, 300c, and 300d) has item 300c associated with a ranking node G. In addition, order 310 (ie, items 310a, 310b, and 310c) has item 310b associated with a ranking node G. Similarly, order 320 (ie, items 320a, 320b, 320c, and 320d) has item 320c associated with a ranking node G. As such, each subset of orders 300, 310, and 320 has an element associated with a ranking node G.

[0039] The computer system 500 then identifies a set of associated nodes that are associated with orders having items in node G. For example, order 300 item 300c is associated with node G. The remaining nodes associated with order 300 are nodes C and E. In addition, , element 310b of order 310 is associated with node G. The remaining nodes associated with order 310 are nodes C and D. In addition, element 320c of order 320 is associated with node G. The remaining nodes associated with order 320 are nodes B, J, and Q. Accordingly, in the example shown in FIG. 4, nodes B, C, D, E, G, J, and Q are considered "associated nodes".

[0040] Once the associated nodes are identified, one or more processors 510 traverse along each edge extending from the ranking node G to one or more associated nodes. As computer system 500 traverses along the edges between ranking node G and each associated node (B, C, D, E, J, and Q), computer system 500 identifies additional items associated with orders in the subset of orders. For example, at node E, computer system 500 identifies item 300b that is associated with order 300. At node J, computer system 500 identifies item 320b that is associated with order 320. When making a pass to node Q, at node P, computer system 500 does not identifies any elements associated with a subset of orders.

[0041] The computer system 500 identifies one or more completed orders by continuing to traverse along each edge extending from a ranking node to each of one or more associated nodes until one or more completed orders have been fully counted among the traversed nodes. As used herein, a "completed order" is an order in which each item in the order has been identified along the traversal path from the ranking node to the associated nodes.

[0042] The computer system 500 then generates a picking request based on the shortest path in the digital graph model to populate the digital model of the picking cart 120 above a packing threshold level. The pick request contains one or more orders selected from a set of orders that are located along the shortest path. The shortest path contains the shortest physical travel distance, as represented by the edges in the digital graph model 400, required to populate the digital cart model above the packing threshold level.

[0043] In at least one embodiment, determining that the digital picking cart model is filled above a packaging threshold with one or more completed orders comprises applying a volume determination algorithm to the one or more completed orders. Any of a number of conventional sizing algorithms may be used to determine if the identified items in one or more completed orders can fit into the available shelf space of the picking cart 120. In addition, the packing threshold may include a volume usage limit and/or a weight limit. . For example, picking cart 120 may be configured to only hold a predetermined maximum weight. In addition, the individual shelves in the picking cart 120 can be configured to hold a predetermined maximum weight. Likewise, each shelf in the picking cart 120 may be associated with a specific volume. Using this information, the volume determination algorithm is adapted to determine whether items within a particular completed order are capable of being accommodated on the picking cart 120 based on the use of weight and/or volume. In addition, the volume detection algorithm is able to determine if the picking cart 120 has been underused and only filled below a desired pack level threshold. For example, the picking cart 120 can only be filled so that it uses half of its available capacity. In contrast, a packing level threshold may indicate that picking trolleys must be filled to use at least eighty percent of their volume. Accordingly, in at least one embodiment, the packing level threshold may comprise both a lower and an upper bound.

[0044] As the computer system 500 processes each completed order through a sizing algorithm, orders that contain items that do not fit in the picking cart 120 are removed from the order subset and left in place for picking at a subsequent picking request. As such, as the computer system 500 traverses along the edges in the digital model 400 of the warehouse 200 location graph, a volume algorithm and a packing threshold are used to filter out orders that are not suitable for a given picking request. In at least one embodiment, orders that are outside the subset of orders, but that can be picked based on a path through the digital graph model 400, can be added to the subset of orders.

[0045] In at least one embodiment, computer system 500 is also capable of dynamically adapting to order priority. As mentioned above, an order priority may be associated with one or more items in the order request database 540 . In at least one embodiment, if an order has a high enough priority, computer system 500 includes, in the pick request, a specific order that is associated with a particular order priority that exceeds a priority threshold. For example, a particular storage location 200 may set a priority threshold such that same-day deliveries are placed in the pick request as soon as possible. At such warehouse location 200, computer system 500 can identify an order with same day priority. In response to identifying that an order's priority exceeds a priority threshold, the order may be added to the pick request even though the particular order does not fall on the shortest path.

[0046] In at least one embodiment, once a pick request is generated, computer system 500 displays one or more visual indications on the user interface for moving the user within physical warehouse location 200 to complete the pick request. For example, a mobile computing device held by a user may visually render a map with instructions for moving within warehouse location 200 to complete a picking request. Likewise, instructions may be played on a device built into a picking cart 120, built into racks at warehouse location 200, or otherwise played to a user. Further, in at least one embodiment, the computer system 500 may be configured to generate one or more computer instructions for communicating with the automated picker, the one or more computer instructions causing the automated picker to navigate the shortest path.

[0047] The following discussion now relates to a number of methods and method steps that can be performed. While the steps of the method may be considered in a particular order or illustrated in the flowchart as occurring in a particular order, a particular ordering is not required, unless specifically stated or required, since the action is dependent on another action completing before the action is performed.

[0048] FIG. 6 illustrates a flowchart of an embodiment of the steps in a method 600 for generating a pick request. Method 600 includes an act 610 of selecting a subset of orders. In at least one embodiment, this subset is selected based on which orders should ship sooner and how many bundles they have.

[0049] The method 600 also includes a replenishment step 620 . As described above, in at least one embodiment, computer system 500 can perform order replenishment by adding to the subset of orders those orders that are in the search path but not in the most urgent picking area. Such a system allows the picking trolley 120 to be more fully loaded and faster to collect for other areas.

[0050] In addition, method 600 includes a step 630 of setting starter areas. In at least one embodiment, computer system 500 will select the smallest set of nodes to start from. For example, in at least one embodiment, computer system 500 can identify a large number of orders associated with a ranking node. In such a case, computer system 500 may dynamically limit the number of orders to be searched by randomly selecting a specific number of orders from a large number of orders. Additionally or alternatively, computer system 500 may only select orders with the highest order priorities.

[0051] The method 600 then includes step 640 of filling the picking cart 120. Step 640 includes using a volume detection algorithm to determine that the picking cart 120 is full by using volume and/or weight. As shown at step 650, if the picking cart 120 is not full at the selected nodes, the computer system 500 executes step 660 and expands the picking areas by adding a new node to the selection of associated nodes and attempts to fill the picking cart 120 again. It will repeat this process until until the picking cart 120 is full. After the picking cart 120 is full, step 670 provides setup statistics and returns to the first step 610. The statistics provided may provide information about the picking cart 120 that was created, such as feet per unit, usage, and more.

[0052] FIG. 7 illustrates a flowchart of an embodiment of the steps in a method 700 for identifying a pick request path. The method 700 includes the step 710 of accessing the digital model 400 of the graph of the physical location 200 of the warehouse. Act 710 contains access to the digital model of the graph of the physical location of the warehouse. The digital graph model contains information indicating the location of a plurality of specific inventory items among racks in a physical warehouse location. Additionally, the digital graph model contains a plurality of nodes and a plurality of edges, each of the plurality of nodes representing a different physical area within the physical location of the warehouse, and each of the plurality of edges representing a physical path between each node in the plurality of nodes and the nearest neighbor nodes of each node. For example, as shown and described with reference to FIG. 2-4 and in the associated description, physical warehouse location 200 can be converted into a digital graph model 400 that depicts nodes representing picking areas at warehouse location 200 and edges representing paths between nodes.

[0053] The method 700 also includes the step 720 of identifying a set of orders. Step 720 includes identifying, in the order request database 540, a set of orders, where each order within the set of orders contains one or more elements, and an order priority. As shown and described with reference to FIG. 3-5 and in the corresponding description, computer system 500 can access, from order request database 540, orders that have been received from customer computers 580(a-c).

[0054] Additionally, method 700 includes an act 730 of displaying items in node orders in a digital graph model. Step 730 includes mapping each item in each order selected from the set of orders to a plurality of nodes in a digital graph model. For example, as shown and described with reference to FIG. 3-5, computer system 500 uses inventory map 560 to display items in orders for assemblies at warehouse location 200.

[0055] Next, the method 700 includes the step 740 of identifying a ranking node. Step 740 comprises identifying a ranking node from among the plurality of nodes, where the ranking node contains the node with the largest number of displayed items. For example, as shown in FIG. 4 and 5, computer system 500 identifies node G as a ranking node because node G is associated with the largest number of items from orders.

[0056] In addition, method 700 includes step 750 of walking along one or more edges to identify the shortest path. Step 750 includes walking along one or more edges that extend from the ranking node to identify the shortest path in the digital model of the graph to populate the digital model of the picking cart above a packing threshold level. For example, as shown and described with reference to FIG. 4 and 5, the computer system 500 iteratively travels along the edges extending from the ranking node until a picking request is generated based on the filling of the picking cart 120 to a threshold.

[0057] In addition, the methods can be implemented using a computer system including one or more processors and computer-readable media such as computer memory. In particular, computer memory may store computer-executable instructions that, when executed by one or more processors, cause various functions to be performed, such as the actions indicated in the embodiments.

[0058] The functionality of a computing system can be enhanced by allowing the computing system to interact with other computing systems via network connections. Network connections may include, but are not limited to, wired or wireless Ethernet connections, cellular connections, or even computer-to-computer connections via serial, parallel, USB, or other connections. Connections allow a computing system to access services on other computing systems and to quickly and efficiently receive application data from other computing systems.

[0059] The interconnection of computing systems has facilitated the creation of distributed computing systems, such as so-called "cloud" computing systems. In this description, "cloud computing" can be systems or resources for providing ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, services, etc.) , which can be provisioned and released with less administration or service provider interaction. The cloud model can be composed of various characteristics (e.g., on-demand self-service, broad network access, resource pooling, agility, measurable service, etc.), service models (e.g., software as a service ("SaaS"), platform as a service ("PaaS"), infrastructure as a service ("IaaS"), and deployment models (e.g. private cloud, community cloud, public cloud, hybrid cloud, etc.)

[0060] Cloud and remote service applications dominate. Such applications are hosted on public and private remote systems such as clouds and typically offer a set of web-based services for two-way communication with clients.

[0061] Many computers are designed to be used with direct user interaction with the computer. Essentially, computers have input hardware and software user interfaces to facilitate user interaction. For example, a modern general purpose computer may include a keyboard, mouse, touchpad, camera, and so on. to allow the user to enter data into the computer. In addition, various software user interfaces may be available.

[0062] Examples of software user interfaces include graphical user interfaces, text command line based user interfaces, function key or hotkey user interfaces, and the like.

[0063] The disclosed embodiments may comprise or use a special purpose or general purpose computer, including computer hardware, as described in more detail below. The disclosed embodiments also include physical and other computer-readable media for carrying or storing computer-executable instructions and/or data structures. Such computer-readable media can be any available media that can be accessed by a general purpose or special purpose computer system. Computer-readable media that stores computer-executable instructions are physical storage media. Computer-readable media that carry computer-executable instructions are transmission media. Thus, by way of example, and not limitation, embodiments of the invention may comprise at least two distinctly different kinds of computer-readable media: physical computer-readable storage media and computer-readable transmission media.

[0064] Physical computer-readable storage media includes RAM, ROM, EEPROM, CD-ROM, or other optical disc storage device (such as CD, DVD, etc.), magnetic disk storage device, or other magnetic storage devices. or any other medium that can be used to store the desired program code means in the form of computer executable instructions or data structures and that can be accessed by a general purpose or special purpose computer.

[0065] A "network" is defined as one or more data links that allow electronic data to be transported between computer systems and/or modules and/or other electronic devices. When information is transmitted or provided over a network or other communication connection (wired, wireless, or a combination of wired or wireless connection) to a computer, the computer properly considers the connection as a transmission medium. Communication media may include a network and/or data lines that can be used to carry program code in the form of computer executable instructions or data structures and that can be accessed by a general purpose or special purpose computer. Combinations of the above are also included within the scope of computer-readable media.

[0066] In addition, upon reaching the various components of the computer system, the program code means in the form of computer-executable instructions or data structures can be automatically transferred from computer-readable transmission media to physical computer-readable storage media (or vice versa). For example, computer-executable instructions or data structures received over a network or data link may be buffered in RAM in a network interface module (e.g., "NIC") and then transferred to the computer system's RAM and/or to less volatile computer-readable physical storage media in a computer system. Thus, computer-readable physical storage media can be included in computer system components that also (or even primarily) use a transmission medium.

[0067] Computer-executable instructions comprise, for example, instructions and data that cause a general purpose computer, a special purpose computer, or a special purpose processing device to perform a specific function or group of functions. The computer-executable instructions may be, for example, binary data, instructions in an intermediate format, such as assembly language, or even source code. While the subject matter has been described in terms specific to structural features and/or methodological acts, it should be understood that the scope of the invention as defined by the appended claims is not necessarily limited to the features or acts described above. Rather, the described features and activities are disclosed as exemplary forms of implementation of the claims.

[0068] Those skilled in the art will appreciate that the invention may be practiced in networked computing environments with many types of computer system configurations, including personal computers, desktop computers, laptop computers, message processors, portable devices, multiprocessor systems, microprocessor or programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile phones, PDAs, pagers, routers, switches, and the like. The invention may also be practiced in distributed system environments where local and remote computer systems that are connected (by wired data links, wireless data links, or a combination of wired and wireless data links) over a network both perform tasks. In a distributed system environment, program modules may be located in both local and remote storage devices.

[0069] Alternatively or additionally, the functionality described herein may be performed at least in part by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that may be used include field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), software-based off-the-shelf products (ASSPs), single-chip systems (SOCs), complex programmable logic devices (CPLD), etc.

[0070] The present invention may be embodied in other specific forms without departing from its spirit or characteristics. The described embodiments are to be considered in all respects only as illustrative and not restrictive. Therefore, the scope of the invention is defined by the appended claims and not by the preceding description. All changes that fall within the meaning and equivalence range of the claims are to be included within their scope.

Claims (68)

1. A computer system for optimizing the selection of nodes and edges in a digital graph model, containing: one or more processors; and one or more computer-readable media that store executable instructions that, when executed by one or more processors, configure a computer system to do at least the following: access to the digital model of the graph of the physical location of the warehouse, while: the digital model of the graph contains information indicating the location of a plurality of specific items of inventory on racks in the physical location of the warehouse, and the digital graph model comprises a plurality of nodes and a plurality of edges, each of the plurality of nodes representing a different physical area within the physical location of the warehouse, and each of the plurality of edges representing a physical path between each node in the plurality of nodes and nearest neighbor nodes of each node; identifying, in the order request database, a set of orders, each order in the order set containing one or more elements, and an order priority; mapping each item in each order selected from the set of orders to a plurality of nodes in the digital graph model; identifying a ranking node from the plurality of nodes, the ranking node containing the node with the largest number of displayed items; and traversing one or more edges that extend from the ranking node to identify the shortest path in the digital graph model to populate the digital model of the picking cart above a packing threshold level. 2. The computer system of claim 1, wherein the executable instructions include instructions executable to configure the computer system to generate a pick request based on the shortest path, the pick request comprising one or more orders selected from a set of orders that are arranged along the shortest path. 3. The computer system of claim 2, wherein the executable instructions include instructions executable to configure the computer system to display one or more visual indications in a user interface for moving a user within a physical warehouse location to complete a picking request. 4. The computer system of claim 2, wherein the executable instructions include instructions executable to configure the computer system to generate one or more computer instructions to communicate with the automated picker, wherein the one or more computer instructions cause the automated picker to travel along a shortest path. . 5. The computer system of claim 2, wherein traversing one or more edges that extend from a ranking node to identify the shortest path in the digital graph model comprises: accessing the ranking node in the digital graph model; identifying a subset of orders in one or more orders, each of which is associated with at least one element located in the ranking node; identifying a set of associated nodes that are associated with elements from the subset of orders; passing along each edge from the ranking node to one or more associated nodes of the ranking node; at these one or more associated nodes, identifying additional elements associated with orders in the subset of orders; identifying one or more completed orders by continuing along each edge from the ranking node to each of said one or more associated nodes until said one or more completed orders are fully accounted for in the set of associated nodes; and generating a picking request upon determining that the digital model of the picking cart is filled above a packing threshold with completed orders. 6. The computer system of claim 5, wherein determining that the digital model of the picking cart is filled above a packaging threshold with one or more completed orders comprises: applying a sizing algorithm to said one or more completed orders; and determining, based on the results of the volume determination algorithm, that the digital model of the picking trolley is filled above a threshold level of the package. 7. The computer system of claim 5, wherein generating the pick request comprises including, in the pick request, a particular order that is associated with a particular order priority that exceeds a priority threshold, where that particular order does not fall on the shortest path. 8. The computer system of claim 5, wherein the packing threshold comprises a weight. 9. The computer system of claim 5, wherein the packaging threshold comprises volume usage. 10. A computer-implemented method for optimizing selections of nodes and edges in a digital graph model, wherein the computer-implemented method is executed on a computer system containing one or more processors and computer-readable media, the computer-implemented method comprising the steps of: access the digital model of the graph of the physical location of the warehouse, while: the digital model of the graph contains information indicating the location of a plurality of specific items of inventory on racks in the physical location of the warehouse, and the digital graph model comprises a plurality of nodes and a plurality of edges, each of the plurality of nodes representing a different physical area within the physical location of the warehouse, and each of the plurality of edges representing a physical path between each node in the plurality of nodes and nearest neighbor nodes of each node; identifying, in the order request database, a set of orders, each order in the order set containing one or more elements and an order priority; displaying each item in each order selected from the set of orders to a plurality of nodes in the digital graph model; identifying a ranking node from a plurality of nodes, and the ranking node contains the node with the largest number of displayed elements; and traverse along one or more edges that extend from the ranking node to identify the shortest path in the digital graph model to populate the digital model of the picking cart above a packing threshold level. 11. The computer-implemented method of claim 10, further comprising generating a pick request based on the shortest path, the pick request comprising one or more orders selected from a set of orders that are located along the shortest path. 12. The computer-implemented method of claim 11, further comprising rendering one or more visual indications in the user interface for moving the user within the physical location of the warehouse to fulfill a pick request. 13. The computer-implemented method of claim 11, further comprising generating one or more computer instructions for communicating with the automated picker, the one or more computer instructions causing the automated picker to move along a shortest path. 14. The computer-implemented method of claim 11, wherein traversing one or more edges that extend from the ranking node to identify the shortest path in the digital graph model comprises: accessing a ranking node in the digital graph model; identifying a subset of orders in one or more orders, each of which is associated with at least one element located in the ranking node; identifying a set of associated nodes that are associated with elements from the subset of orders; traverse along each edge from the ranking node to one or more associated nodes of the ranking node; at these one or more associated nodes, identify additional elements associated with orders in the subset of orders; identifying one or more completed orders by continuing along each edge from the ranking node to each of said one or more associated nodes until said one or more completed orders are fully accounted for in the set of associated nodes; and generating a picking request upon determining that the digital model of the picking cart is filled above a threshold packing level with completed orders. 15. The computer-implemented method of claim 14, wherein determining that the digital model of the picking cart is filled above a packaging threshold level with completed orders comprises the steps of: applying the volume determination algorithm to said one or more completed orders; and determining, based on the results of the volume determination algorithm, that the digital model of the picking trolley is filled above a threshold level of the package. 16. The computer-implemented method of claim 14, wherein when generating the pick request, include in the pick request a particular order that is associated with a particular order priority that exceeds a priority threshold, where that particular order does not fall on the shortest path. 17. The computer-implemented method of claim 14, wherein the packing threshold comprises a weight. 18. The computer-implemented method of claim 14, wherein the packing threshold comprises the use of volume. 19. A computer-readable medium comprising one or more physical computer-readable storage media storing machine-executable instructions that, when executed by a processor, cause a computer system to perform a method for creating a packaging template after an order has been assembled, the method comprising: access to the digital model of the graph of the physical location of the warehouse, while: the digital model of the graph contains information indicating the location of a plurality of specific items of inventory on racks in the physical location of the warehouse, and the digital graph model comprises a plurality of nodes and a plurality of edges, each of the plurality of nodes representing a different physical area within the physical location of the warehouse, and each of the plurality of edges representing a physical path between each node in the plurality of nodes and nearest neighbor nodes of each node; identifying, in the order request database, a set of orders, each order in the order set containing one or more elements, and an order priority; mapping each item in each order selected from the set of orders to a plurality of nodes in the digital graph model; identifying a ranking node from the plurality of nodes, the ranking node containing the node with the largest number of displayed items; and traversing one or more edges that extend from the ranking node to identify the shortest path in the digital graph model to populate the digital model of the picking cart above a packing threshold level. 20. The computer-readable medium of claim 19, wherein traversing one or more edges that extend from a ranking node to identify a shortest path in a digital graph model comprises: accessing the ranking node in the digital graph model; identifying a subset of orders in one or more orders, each of which is associated with at least one element located in the ranking node; identifying a set of associated nodes that are associated with items from the subset of orders; passing along each edge from the ranking node to one or more associated nodes of the ranking node; at those one or more associated nodes, identifying additional elements associated with the orders in the subset of orders; identifying one or more completed orders by continuing along each edge from the ranking node to each of said one or more associated nodes until said one or more completed orders are fully accounted for in the set of associated nodes; and generating a picking request upon determining that the digital model of the picking cart is filled above a packing threshold with completed orders.

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