Task scheduling method and device, server, storage medium and warehousing system

Abstract

The invention relates to the technical field of task scheduling, and discloses a task scheduling method and device, a server, a storage medium and a warehousing system. The method comprises the following steps: dividing a reservoir area to obtain a plurality of subareas; determining a partition set in which each type of goods in the current wave order is located from the plurality of partitions, and determining task information of an order task corresponding to each type of goods; for each type of goods, determining input parameters of a linear programming model corresponding to the goods according to the partition set and the task information; solving the linear programming model according to the input parameters, and obtaining the flow distribution of the order task corresponding to each type of goods between each partition and each workstation in the partition set; and according to the flow distribution of each type of goods, a robot is scheduled to execute a workbin carrying task. The method and the device can adapt to various types of scenes and guarantee the balance of the flow of each subarea of the reservoir area.

Description

Technical Field

[0001] This application relates to the field of task scheduling technology, specifically to a task scheduling method, apparatus, server, storage medium, and warehousing system. Background Technology

[0002] A smart warehousing system is an intelligent system that integrates advanced information technology and automated equipment to achieve efficient storage, handling, identification, picking, and operation of goods. In a smart warehousing system, a scheduling server dispatches robots to handle the inbound and outbound movement of goods. Currently, robot scheduling schemes typically rely on engineers' experience to design scheduling strategies, such as using a greedy strategy that prioritizes the outbound workstation closest to the order's matching bin as the destination workstation for outbound goods, and prioritizes the storage location closest to the outbound workstation as the destination storage location for returning bins.

[0003] Intelligent warehousing systems have different map shapes and workstation locations in different deployment scenarios, resulting in different optimal task flow distributions. Relying solely on engineers' experience, such as greedy rule-based algorithms, makes it difficult to achieve good results in multiple scenarios. Furthermore, greedy strategies can lead to an uneven distribution of tasks within the warehouse, ultimately reducing closing efficiency. Summary of the Invention

[0004] In view of the above problems, this application provides a task scheduling method, apparatus, server, storage medium and warehousing system to solve the problem that existing robot scheduling schemes are difficult to achieve good results in various scenarios and lead to uneven task distribution in the warehouse area.

[0005] According to one aspect of the embodiments of this application, a task scheduling method is provided, the method comprising: The reservoir area is divided into multiple zones; Determine the set of partitions for each type of goods in the current wave of orders from the multiple partitions, and determine the task information for the order task corresponding to each type of goods; For each type of goods, the input parameters of the linear programming model corresponding to the goods are determined according to the partition set and the task information. The linear programming model includes an objective function and constraints. The objective function is to minimize the sum of the robot travel distances for performing the bin handling task. The bin handling task is generated based on all the order tasks corresponding to the goods. Solve the linear programming model based on the input parameters to obtain the traffic distribution of the order tasks corresponding to each type of goods among each partition and each workstation in the partition set; The robot is scheduled to perform the bin handling task according to the flow distribution of each type of goods.

[0006] In one optional approach, the types of order tasks include outbound tasks and inbound tasks, and the flow distribution includes outbound flow distribution and return flow distribution for outbound tasks corresponding to each type of goods, and retrieval flow distribution and inbound flow distribution for inbound tasks corresponding to each type of goods. The step of scheduling robots to perform the bin handling task based on the flow distribution of each type of goods includes: Based on the proportion of the first outbound flow from the first zone to the first outbound workstation in the expected outbound task flow, the robot is scheduled to transport the picking bins from the first zone to the first outbound workstation. Based on the proportion of the second return box flow from the second outbound workstation to the second zone in the total return box flow, the robot is scheduled to transport the picked boxes from the second outbound workstation or return them empty back to the second zone. Based on the proportion of the third box retrieval flow from the third zone to the third inbound workstation in the total box retrieval flow, the robot is scheduled to transport the boxes to be loaded or empty from the third zone to the third inbound workstation. Based on the proportion of the fourth inbound flow from the fourth inbound workstation to the fourth zone in the expected inbound task flow, the robot is scheduled to transport the same number of boxes to be inbound from the fourth inbound workstation to the fourth zone.

[0007] In one alternative approach, the traffic distribution also includes transfer traffic from each outbound workstation to each inbound workstation; The method of scheduling robots to perform the bin handling task according to the flow distribution of each type of goods further includes: Based on the proportion of the fifth transfer flow from the fifth outbound workstation to the fifth inbound workstation in the total transfer flow, the robot is scheduled to transport the full-case outbound picking boxes to the fifth outbound workstation, and then travel to the fifth inbound workstation to transport the full-case inbound picking boxes into the warehouse.

[0008] In one optional approach, the task information of the outbound task includes the expected outbound traffic, the workstation corresponding to the outbound task, and the proportion of full-case outbound; the task information of the inbound task includes the expected inbound traffic, the workstation corresponding to the inbound task, and the proportion of full-case inbound. The objective function is: ; in, It is a collection of outbound tasks. It is a collection of inbound tasks. It is the set of partitions, It is a collection of outbound workstations. It is a collection of inbound workstations. It is a partition To the outbound workstation distance, It is an outbound workstation To the warehouse workstation distance, It is a partition To the warehouse workstation distance, It is an order task From partition To the outbound workstation Traffic, It is an order task From the outbound workstation Return to partition Traffic, It is an order task From the outbound workstation Transferred to the warehouse workstation Traffic, It is an order task From the warehouse workstation To partition Traffic, It is an order task From partition To the warehouse workstation Traffic, , , , , , , , , are all input parameters of the objective function. , , , and The solution to the objective function that satisfies the aforementioned constraints; The constraints include: First constraint: , , , , ; Second constraint: , ,in, For order tasks Related partition sets, For order tasks The relevant outbound workstation collection, For order tasks The relevant collection of inbound workstations, For order tasks Expected traffic; Third constraint: , ,in, For order tasks The number of related outbound workstations For order tasks The number of related inbound workstations; Fourth constraint: If ,but ,otherwise ,in, For order tasks The splitting and picking ratio, For order tasks The proportion of multiple candidate positions; Fifth constraint: , ,in, For order tasks The proportion of sorting and picking to warehousing; Sixth constraint: , , , ; Seventh constraint: , ; Eighth constraint: , ,in, For order tasks The number of related partitions; Ninth constraint: , ; Tenth constraint: = , ; Eleventh constraint: , ,in, For order tasks The proportion of full-case warehousing; The step of determining the input parameters of the linear programming model corresponding to the goods based on the partition set and the task information includes: Determine the set of outbound tasks The set of inbound tasks The set of partitions The set of outbound workstations The set of warehouse entry workstations , which serves as the common input parameter for both the objective function and the constraints; According to the partition set The set of outbound workstations and the aforementioned collection of inbound workstations Determine the partition To the outbound workstation distance Outbound workstation To the warehouse workstation distance and partitions To the warehouse workstation distance , which serves as the input parameter of the objective function; Confirm Order Task The related set of partitions Order Task The related outbound workstation set Order Task The related collection of inbound workstations Order Task The relevant number of outbound workstations Order Task The relevant number of inbound workstations Order Task The splitting and picking ratio Order Task The ratio of the multiple candidate positions Order Task The ratio of splitting and picking to warehousing Order Task The relevant number of partitions and order tasks The full box warehousing ratio , which serves as the input parameter for the constraint condition; The expected outbound traffic volume is determined as the input parameter related to the outbound task in the constraints. The expected flow of the inbound task is determined as the input parameters related to the inbound task in the constraints. .

[0009] In an alternative approach, the method further includes: The minimum value of the objective function obtained when solving the linear programming model is determined as the total travel distance of the robot under the optimal task streamline distribution; Multiply the total travel distance of the robot by the scheduling efficiency reduction ratio to obtain the statistical value of the total travel distance of the robot; The hourly box-moving efficiency of the robot is determined based on the statistical values. The number of robots required to meet the hourly box handling capacity is determined based on the box handling efficiency.

[0010] In one alternative approach, the division of the storage area into multiple partitions includes: Determine the effective shelving area of ​​the warehouse area; Determine the outer envelope region of the reservoir area; The outer envelope region is divided into multiple rectangular regions; The intersection of the effective shelf area and each of the rectangular areas is calculated to obtain the multiple partitions.

[0011] In one alternative approach, determining the effective shelving area of ​​the warehouse includes: Determine the included and excluded areas of the storage area; Perform a graphical union calculation on the contained regions to obtain the total contained regions; The total included area is taken as the whole set, and the non-included area is taken as a subset. The complement of the subset is calculated to obtain the effective shelf area.

[0012] In one alternative approach, determining the included and excluded areas of the storage area includes: Based on the physical properties of the storage area, a geometric model of the storage area is established, wherein the physical properties include one or more building elements such as exterior walls, interior walls, columns, and elevator shafts, and the geometric model includes an element geometric model corresponding to each building element; The included region is determined based on the geometric model of the elements corresponding to the outer wall and the inner wall; The excluded area is determined based on the geometric model of the elements corresponding to the column and the elevator shaft.

[0013] According to another aspect of the embodiments of this application, a task scheduling apparatus is provided, comprising: The partitioning module is used to divide the storage area into multiple partitions; The first determining module is used to determine the set of partitions where each type of goods in the current wave of orders is located from the multiple partitions, and to determine the task information of the order task corresponding to each type of goods; The second determining module is used to determine the input parameters of the linear programming model corresponding to each type of goods, based on the partition set and the task information. The linear programming model includes an objective function and constraints. The objective function is to minimize the sum of the robot travel distances for performing the bin handling task. The bin handling task is generated based on all the order tasks corresponding to the goods. The solution module is used to solve the linear programming model based on the input parameters to obtain the traffic distribution of the order tasks corresponding to each type of goods between each partition and each workstation in the partition set; The scheduling module is used to schedule robots to perform the bin handling tasks according to the flow distribution of each type of goods.

[0014] According to another aspect of the embodiments of this application, a server is provided, including: a processor and a memory, wherein the memory stores executable instructions, and the processor is capable of executing the executable instructions to implement the task scheduling method as described in any of the above embodiments.

[0015] According to another aspect of the embodiments of this application, a computer-readable storage medium is provided, wherein executable instructions are stored therein, which, when executed on an electronic device, cause the electronic device to perform the task scheduling method as described in any of the preceding embodiments.

[0016] According to another aspect of the embodiments of this application, a warehousing system is provided, including a storage area, workstations, robots, and a server as described in the above embodiments; The storage area is used to store material bins; The workstation includes an outbound workstation and an inbound workstation; The server is used to schedule the robot to perform bin handling tasks between the warehouse area and the workstation.

[0017] This embodiment divides the warehouse area into multiple zones. For each type of goods in the current order, the input parameters of the corresponding linear programming model are determined based on the zone set where the goods belong and the task information of the order tasks corresponding to the goods. The linear programming model is then solved based on the input parameters, that is, the flow distribution between each zone and each workstation is minimized when the sum of the robot travel distances for the bin handling tasks generated from all the order tasks corresponding to the goods is minimized. Finally, the robots are scheduled to execute bin handling tasks according to the flow distribution of each type of goods. Because this method uses standardized calculation methods, compared to relying on engineers' experience to design scheduling strategies, such as greedy strategies, it can adapt to various types of scenarios. The optimal task flow distribution obtained can minimize the total robot travel distance while satisfying given constraints. Furthermore, through standardized mathematical modeling and constraints in the linear programming model, errors in engineers' experience-based strategies are reduced, ensuring balanced flow in each zone of the warehouse and avoiding reduced finishing efficiency.

[0018] The above description is merely an overview of the technical solutions of the embodiments of this application. In order to better understand the technical means of the embodiments of this application and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of this application more obvious and understandable, specific implementation methods of this application are described below. Attached Figure Description

[0019] The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A plan view of the warehousing system provided in an embodiment of this application is shown; Figure 2 A flowchart of the task scheduling method provided in an embodiment of this application is shown; Figure 3 This application provides a schematic diagram of the geometric model of the reservoir area, including the included region and the excluded region. Figure 4 This invention provides a schematic plan view of the total contained area of ​​the storage area according to an embodiment of the present application. Figure 5 This invention provides a schematic plan view of the effective shelving area and the outer enclosure area of ​​the warehouse area according to an embodiment of the present application. Figure 6 This illustration shows a planar schematic diagram of the outer envelope region after it has been divided into rectangular regions, as provided in an embodiment of this application. Figure 7 This diagram illustrates the intersection calculation of the effective shelving area and each rectangular area provided in the embodiments of this application. Figure 8A plan view of multiple partitions provided in an embodiment of this application is shown; Figure 9 A plan view of the partition set corresponding to each type of goods provided in the embodiments of this application is shown; Figure 10 This invention provides a schematic diagram of the overall task flow distribution of the warehouse area according to an embodiment of the present application. Figure 11 A schematic diagram of the structure of the task scheduling device provided in an embodiment of this application is shown; Figure 12 A schematic diagram of the server structure provided in an embodiment of this application is shown.

[0020] The reference numerals in the attached figures are as follows: Warehousing system 100; warehouse area R1; workstation area R2; shelving 10; workstation 20; robot 30; bin 40; server 50. Detailed Implementation

[0021] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein.

[0022] Intelligent warehousing systems are intelligent systems that integrate advanced information technology and automated equipment to achieve efficient storage, handling, identification, picking, and operation of goods. In an intelligent warehousing system, a scheduling server dispatches robots to handle the inbound and outbound movement of goods. Taking outbound goods as an example, the scheduling server dispatches robots to move boxes loaded with goods from the warehouse area to the outbound workstation for picking (outbound), then moves the picked boxes back to the warehouse area (return box), then retrieves another box from the warehouse area and moves it to the outbound workstation… thus forming a task loop, realizing the task flow from the warehouse area to the outbound workstation and back again. Currently, when designing robot scheduling schemes, engineers often rely on their experience to design scheduling strategies, such as using a greedy strategy, selecting the option that seems optimal at each step of the decision (local optimum) in order to achieve the optimal task flow distribution. For example, the scheduling server usually prioritizes the outbound workstation that is closest to the bin (i.e., inventory bin) that the order hits as the destination workstation for goods to be shipped out, and prioritizes the storage location that is closest to the outbound workstation as the destination storage location for returning the bin.

[0023] Intelligent warehousing systems have different map shapes and workstation locations in different deployment scenarios, resulting in different optimal task flow distributions. Relying solely on engineers' experience, such as greedy rule-based algorithms, makes it difficult to achieve good results in multiple scenarios. Furthermore, greedy strategies can lead to an uneven distribution of tasks within the warehouse, ultimately reducing closing efficiency.

[0024] This application provides a task scheduling method that can adapt to various types of scenarios. By using standardized methods, it can minimize the total travel distance of the robot while meeting given constraints, and ensure the balance of traffic flow in each partition of the warehouse area.

[0025] The task scheduling method of this application embodiment can be applied to warehousing systems. In logistics or manufacturing scenarios, the warehousing system can be used to process goods such as warehousing, picking, and inbound / outbound operations. Figure 1 A schematic plan view of the warehousing system provided in an embodiment of this application is shown, as follows: Figure 1 As shown, the warehousing system 100 includes a storage area R1 and a workstation area R2. The storage area is equipped with multiple shelving units 10, and the workstation area is equipped with multiple workstations 20. The warehousing system 100 also includes robots 30 and servers 50.

[0026] Shelves 10 within warehouse area R1 are used to store bins 40, which can be bins loaded with goods or temporarily empty bins. Multiple workstations 20 are used for order inbound and outbound picking. Robots 30 perform bin handling tasks between warehouse area R1 and workstations 20.

[0027] Server 50 is equipped with software systems from the warehouse system 100, such as the Intelligent Equipment Scheduling System (ESS), Order Management System (OMS), Warehouse Management System (WMS), Warehouse Control System (WCS), and Warehouse Execution System (WES).

[0028] Server 50 is used to schedule robots to perform bin handling tasks between the warehouse area and workstations. For example, for an outbound task, server 50 identifies a bin (inventory bin) loaded with goods in warehouse area R1, determines the destination workstation, and sends a bin handling task to robot 30 to move the identified bin to the destination workstation for outbound order picking. The picked bin is then moved back to warehouse area R1. As another example, for an inbound task, server 50 identifies an empty bin (order bin) in warehouse area R1, determines the destination workstation, and sends a bin handling task to robot 30 to move the identified empty bin to the destination workstation for inbound order picking. The picked bin is then moved back to warehouse area R1 for storage.

[0029] The warehousing system 100 may also include other facilities or equipment, which are not limited in this application.

[0030] The task scheduling method of this application embodiment can be applied to the server 50 described above. The flow of the task scheduling method executed by the server 50 is described in detail below.

[0031] Figure 2 A flowchart of a task scheduling method provided in an embodiment of this application is shown. Figure 2 As shown, the method includes the following steps: S110 divides the reservoir area into multiple zones.

[0032] This step involves creating the geometric objects of the storage area based on its physical properties and dividing the storage area into partitions, including the following steps: S111, determine the effective shelving area of ​​the warehouse.

[0033] In this step, we first determine the included and excluded areas of the warehouse area, then perform a graphical union calculation on the included areas to obtain the total included area. Next, we take the total included area as the whole set and the excluded areas as subsets, and calculate the complement of the subsets to obtain the effective shelving area.

[0034] Figure 3 This illustration shows a plan view of the geometric model of a storage area, including its included and excluded areas, provided in an embodiment of this application. When determining the included and excluded areas of the storage area, a geometric model of the storage area can be established based on its physical properties. These physical properties include one or more building elements such as exterior walls, interior walls, columns, and elevator shafts. The geometric model includes element geometric models corresponding to each building element, for example... Figure 1 The building elements of the shown storage area include exterior walls (60), columns (70), and elevator shafts (80). Building elements can be identified based on their vertex coordinates. The geometric model of the storage area can be constructed by calling a geometry engine. A geometry engine can be a low-level library capable of performing geometric operations, executing various spatial relationship judgments and calculations, performing spatial indexing, and checking whether geometric figures conform to geometric rules, such as GEOS (Geometry Engine, Open Source), JTS Topology Suite (JTS), and Shapely.

[0035] Then, the included area is determined based on the geometric model of the corresponding elements of the exterior and interior walls, for example... Figure 1 The contained region R i1 and R i2 For clarity, the dashed box containing the area in the diagram is slightly larger than the actual contained area. The areas not included are determined based on the geometric model of the elements corresponding to the columns and elevator shafts. Figure 1 The region R in the non-existent n1 and R n2Similarly, for clarity, the dashed box that does not contain the area in the figure is slightly larger than the actual contained area.

[0036] Figure 4 This application provides a schematic plan view of the total contained area of ​​the storage area, as shown in the embodiment. Figure 4 As shown, for the region R i1 and R i2 The total contained region R is obtained by performing a union calculation on the graphs. i3 .

[0037] Effective shelving area is the area that can be used to lay out shelving. Figure 5 This application provides a schematic plan view of the effective shelving area and the outer enclosure area of ​​the warehouse area, as shown in the embodiment. Figure 5 As shown, the total included region R i3 As a complete set, it will not include region R. n1 and R n2 As a subset, the complement of the subset is calculated to obtain the effective shelf area R. v (Pattern-filled area).

[0038] S112, Determine the outer envelope area of ​​the reservoir area.

[0039] In this step, first determine the maximum horizontal and vertical dimensions of the effective shelving area; then, use the maximum horizontal and vertical dimensions as the two side lengths of a rectangle to create a rectangular area that covers the entire effective shelving area, and define the rectangular area as the outer envelope area.

[0040] Please continue reading. Figure 5 The outer envelope region of the reservoir area is R. o For clarity, the dashed box around the outer enclosure area in the diagram is slightly larger than the actual outer enclosure area. This is because the effective shelving area R... v It is not a regular rectangle, therefore the determined outer envelope region is R. o Larger than the effective shelving area R v .

[0041] S113 divides the outer envelope region into multiple rectangular regions.

[0042] When dividing a region into rectangular areas, you can define the size of the rectangular area and divide the outer envelope region into multiple rectangular areas based on this size. Alternatively, you can define the number of rectangular areas to be divided horizontally and the number of rectangular areas to be divided vertically, and divide the outer envelope region into multiple rectangular areas based on these numbers.

[0043] Figure 6 This illustration shows a planar schematic diagram of the outer envelope region after dividing it into rectangular regions, as provided in an embodiment of this application. Figure 6 As shown, the outer envelope region of the reservoir area is R.o It is divided into multiple rectangular regions r. Figure 6 The outer envelope area is divided based on the dimensions of a predefined rectangular region. Since the effective shelf area R... v The horizontal dimension is not an integer multiple of the predefined horizontal dimension of the rectangular area, therefore the dimension of the rectangular area in the rightmost column horizontally is not equal to the dimensions of the other rectangular areas.

[0044] S114, calculate the intersection of the effective shelf area and each rectangular area to obtain multiple partitions.

[0045] Figure 7 This illustration shows a schematic diagram after calculating the intersection of the effective shelving area and each rectangular area provided in the embodiments of this application, as shown below. Figure 7 As shown, among the partitions obtained by calculating the intersection of the effective shelf area and each rectangular area, some partitions are the same as the original rectangular areas (e.g., partition d1), while the area of ​​other partitions is smaller than the original rectangular areas (e.g., partition d2). Figure 8 The diagram shows a plan view of multiple partitions provided in the embodiments of this application. Partitions d2 and d3 have smaller areas and may or may not have shelves. It is necessary to obtain their actual shelf layout.

[0046] The above method achieves the partitioning of the effective shelving area, which is equivalent to discretizing the warehouse area using multiple partitions. Each partition can include multiple complete shelving units, or each of the included multiple shelving units can be a part of a complete shelving unit. In subsequent inbound and outbound flow calculations, directly calculating on a single shelving unit would result in low computational efficiency due to the large number of shelving units. Partitioning, compared to using individual shelving units, increases the volume of a single calculation unit, thereby improving computational efficiency.

[0047] S120: Determine the set of partitions for each type of goods in the current wave of orders from multiple partitions, and determine the task information for the order task corresponding to each type of goods.

[0048] The SKUs for each type of goods are the same. In this embodiment, the task flow distribution is optimized for each type of goods. Figure 9 This application provides a schematic diagram of the partition set corresponding to each type of goods, as shown in the embodiments of this application. Figure 9 As shown in the figure, different patterns are used to indicate the partition sets corresponding to different types of goods.

[0049] Order tasks can be categorized into outbound tasks and inbound tasks. Figure 10 This diagram illustrates the overall task flow distribution of the warehouse area according to an embodiment of this application. The diagram only shows three partitions, one outbound workstation, and one inbound workstation. For example... Figure 10As shown, in the case of split picking for inbound and outbound operations, when performing outbound tasks, the robot transports the boxes to be picked from each zone in the warehouse area to each outbound workstation (outbound), and transports the picked boxes from each outbound workstation back to each zone (outbound return box); when performing inbound tasks, the robot transports the boxes used to receive inbound goods (e.g., empty boxes) from each zone to each inbound workstation (box retrieval and inbound), and transports the picked boxes from the inbound workstation back to each zone (inbound).

[0050] Order tasks also include full-case outbound and full-case inbound scenarios. A typical full-case outbound operation involves the robot transporting boxes from various zones to the respective outbound workstations. The boxes are shipped out as a whole without needing to be picked, and the robot can then return empty to the zone to await the next box transport task. A typical full-case inbound operation involves the robot traveling empty from the zone to the inbound workstation and transporting the boxes requiring full-case inbound back to the respective zones.

[0051] Based on the above scenarios, when a robot returns from the outbound workstation to the warehouse area, it may be carrying picked boxes (splitting and picking) or it may be empty (full box outbound). This paper refers to both types of flow between the outbound workstation and the warehouse area as "box return flow." Similarly, when a robot returns from the warehouse area to the inbound workstation, it may be carrying boxes containing incoming goods (splitting and picking) or it may be empty (full box inbound). This paper refers to both types of flow between the warehouse area and the inbound workstation as "box retrieval flow."

[0052] For both outbound and inbound scenarios involving full cases, a full case outbound / inbound connection can be implemented to reduce robot travel distance and improve handling efficiency. A full case outbound / inbound connection means that after transporting the boxes to the outbound workstation, the robot does not return to the warehouse area but goes directly to the inbound workstation assigned to full case inbound tasks, transporting the boxes to be inbound back to the warehouse area. For example, Figure 10 The route shown as ③ (the full-case inbound / outbound connection route) is shorter than the total distance of routes ① and ② (the conventional full-case inbound route). This article also refers to the full-case inbound / outbound connection as the robot's transfer from the outbound workstation to the inbound workstation.

[0053] The task information for outbound tasks includes the expected outbound traffic, the workstation corresponding to the outbound task, and the proportion of full-case outbound tasks. The task information for inbound tasks includes the expected inbound traffic, the workstation corresponding to the inbound task, and the proportion of full-case inbound tasks.

[0054] The expected outbound traffic refers to the number of boxes that need to be processed per hour for each outbound task.

[0055] Each outbound task may correspond to a single workstation or multiple workstations, such as a group of workstations with the same type. When the outbound task corresponds to a single workstation, it is a difference-based picking workstation; when the outbound task corresponds to multiple workstations, it is a non-difference-based picking workstation, meaning that any of the multiple workstations can perform the picking for that outbound task. Determining the workstation corresponding to an outbound task also yields the proportion of multiple candidate locations, which is the proportion of boxes that can be picked at a non-difference-based workstation out of all boxes corresponding to that outbound task.

[0056] The full case shipment ratio refers to the proportion of full case shipments in each shipment task out of all the full case shipments for that task.

[0057] The expected flow rate of an inbound task refers to the number of bins that need to be processed per hour for each inbound task.

[0058] The workstations corresponding to the inbound task are usually multiple workstations, such as a workstation group.

[0059] The full case warehousing ratio refers to the proportion of full case warehousing boxes in each warehousing task out of all boxes corresponding to that warehousing task.

[0060] This step enables the statistical analysis and aggregation of task information based on warehouse area partitions. For example, the first type of SKU is stored in warehouse area A, which consists of multiple partitions such as A01 and A02; the first type of SKU corresponds to various inbound and outbound tasks, namely the first outbound task, the second outbound task, and the first inbound task.

[0061] S130. For each type of goods, determine the input parameters of the corresponding linear programming model based on the partition set and task information.

[0062] The linear programming model includes an objective function and constraints. The objective function minimizes the sum of robot travel distances for performing bin handling tasks, which are generated based on all order tasks corresponding to the goods. The robot travel distance can be Manhattan distance or Euclidean distance. The objective function is given by the following formula: ;Formula 1 in, It is a collection of outbound tasks. It is a collection of inbound tasks. It is a partitioned set. It is a collection of outbound workstations. It is a collection of inbound workstations. It is a partition To the outbound workstation distance, It is an outbound workstation To the warehouse workstation distance, It is a partition To the warehouse workstation distance, It is an order task From partition To the outbound workstation Traffic, It is an order task From the outbound workstation Return to partition Traffic, It is an order task From the outbound workstation Transferred to the warehouse workstation Traffic, It is an order task From the warehouse workstation To partition Traffic, It is an order task From partition To the warehouse workstation Traffic, , , , , , , , All of these are input parameters of the objective function. , , , and To find the solution (decision variables) of the objective function under the constraints.

[0063] The constraints include: First constraint: , , , , The first constraint is a non-negativity constraint, which requires all decision variables (flow rates) to be greater than or equal to zero, because flow rate is a physical quantity and cannot be negative.

[0064] Second constraint: , ,in, For order tasks Related partition sets, For order tasks The relevant outbound workstation collection, For order tasks The relevant collection of inbound workstations, For order tasks The first constraint is the expected flow rate; the second constraint is the total task volume constraint, which requires that the total flow rate of each task must be equal to its expected flow rate, to ensure that the total amount of outbound tasks flowing out of the warehouse area and the total amount of inbound tasks flowing into the warehouse area are consistent with the planned task volume, and to avoid flow loss or redundancy.

[0065] Third constraint: , ,in, For order tasks The number of related outbound workstations For order tasks The third constraint is the equal workload constraint for each workstation, which requires that the traffic processed by each workstation (either outbound or inbound) is equal. This constraint achieves workload balancing, prevents some workstations from being overloaded while others are idle, and improves system efficiency.

[0066] Fourth constraint: If ,but ,otherwise ,in, For order tasks The splitting and picking ratio, For order tasks The ratio of multiple candidate locations; the fourth constraint is that outbound equals return or "return + transfer", requiring that the outbound volume of the outbound task must be equal to the volume of returned boxes (returned to the warehouse area) or the sum of the returned box volume and the volume transferred to the inbound workstation. To make... The following conditions must be met simultaneously: =1: Outbound tasks consist entirely of splitting and picking bins; there are no bins shipped out as whole boxes.

[0067] =0: Outbound tasks do not have multiple candidate workstations (undifferentiated workstations), meaning all split picking boxes are fixedly assigned to a single workstation (no flexibility in workstation selection).

[0068] therefore, At this time, it belongs to single-loop mode, corresponding to the conventional splitting and picking operation: take out the bin → pick → return to the original storage area. According to the principle of flow conservation, all items left by the robot return to the storage area. .

[0069] like This is not true; the outbound task may include full cases of outbound containers. <1), and / or there may be multiple candidate workstations ( If the value is greater than 0, then the system enters a dual-loop mode. In dual-loop mode, some robots may not return directly to the storage area, but instead transfer to the inbound workstation, which is the whole-box inbound / outbound connection mentioned earlier. At this time, the outbound flow is no longer entirely returned to the storage area, but is divided into two parts: "return to the storage area" and "transfer to the inbound workstation". It should be understood that what is transferred to the inbound workstation is not the tote box, but the robot. According to the law of conservation of flow, .

[0070] Fifth constraint: , ,in, For order tasks The fifth constraint is the transfer ratio, which controls the upper and lower limits of the flow from the outbound workstation to the inbound workstation. Specifically, this is achieved through parameters (such as the split-picking ratio). Multiple candidate positions ratio (etc.) Limit the transfer range to ensure that the system strikes a balance between configurable flexibility and stability.

[0071] Sixth constraint: , , , The sixth constraint is to ensure that each partition receives the same amount of task traffic, requiring all partitions related to the same task to receive equal amounts of return traffic or inbound traffic, so as to avoid uneven distribution of inventory traffic and ensure that each partition bears a balanced load in task processing.

[0072] Seventh constraint: , The seventh constraint is the "Do Not Return to Irrelevant Partitions" constraint, which requires that the return traffic of outbound tasks can only return to the partitions related to the task, and irrelevant partitions should receive zero traffic. This constraint prevents task mismatch, ensures the accuracy of logistics paths, and reduces unnecessary handling.

[0073] Eighth constraint: , ,in, For order tasks The number of relevant partitions; the eighth constraint is the undifferentiated task constraint, which requires that even if a task can be assigned to multiple outbound workstations, each relevant outbound workstation must bear a certain minimum traffic. This constraint ensures that multiple candidate workstations are effectively utilized, avoids resource idleness, and improves system robustness.

[0074] Ninth constraint: , The ninth constraint is the inbound workstation flow balance constraint, which requires that the inflow (from outbound transfers and inbound tasks) of each inbound workstation equals the outflow (sent to the storage area). This constraint ensures the flow of inbound workstations is conserved, preventing congestion or idleness and maintaining continuous operation.

[0075] Tenth constraint: = , The tenth constraint is the warehouse area flow balance constraint, which requires that the total outflow (outbound + box pickup and pickup) of each warehouse area equals the total inflow (inbound + box pickup and pickup). This constraint ensures the dynamic balance of inventory in the warehouse areas, avoids backlog or shortage, and maintains the stability of the warehousing system.

[0076] Eleventh constraint: , ,in, For order tasks The eleventh constraint is the proportion of full-case inbound traffic; it requires each inbound workstation to handle at least a certain proportion of full-case inbound traffic. This constraint ensures that full-case inbound tasks are met, optimizes inbound efficiency, and reduces splitting operations.

[0077] Step S130 includes the following steps: S131, Determine the set of outbound tasks Collection of Inbound Tasks Partition set Outbound workstation collection Collection of warehouse entry workstations , which serves as the common input parameter for both the objective function and the constraints; S132, based on the partition set Outbound workstation collection Collection of warehouse entry workstations Determine the partition To the outbound workstation distance Outbound workstation To the warehouse workstation distance and partitions To the warehouse workstation distance , as the input parameter of the objective function; S133, Confirm Order Task Related partition sets Order Task Related outbound workstation collection Order Task Related inbound workstation collection Order Task The number of related outbound workstations Order Task The relevant number of inbound workstations Order Task Split picking ratio Order Task Multiple candidate position ratio Order Task The ratio of splitting and picking to warehousing Order Task Number of related partitions and order tasks Full box warehousing ratio , as input parameters for constraints; S134, define the expected outbound task flow as the input parameter related to the outbound task in the constraints. The expected flow of the inbound task is determined as the input parameter related to the inbound task in the constraints. .

[0078] This step determines the input parameters of the corresponding linear programming model for each type of goods.

[0079] S140, solve the linear programming model based on the input parameters to obtain the traffic distribution of order tasks corresponding to each type of goods between each partition and each workstation in the partition set.

[0080] This step solves a separate linear programming model for each type of goods. Since the objective function is to minimize the sum of robot travel distances for handling bins, this embodiment optimizes the allocation of outbound and inbound tasks across different warehouse areas and workstations from the perspective of robot travel distance, ensuring optimal distribution of robot task flow lines. Based on a given series of tasks, the task flow lines for all robots can be generated, minimizing the total distance of task execution.

[0081] The resulting flow distribution includes the outbound flow distribution and return flow distribution for each type of goods' outbound task, as well as the retrieval flow distribution and inbound flow distribution for each type of goods' inbound task.

[0082] The S150 schedules robots to perform bin handling tasks based on the flow distribution of each type of goods.

[0083] Based on the flow distribution obtained in step S140, step S150 may include the following steps: S151, based on the proportion of the first outbound flow from the first zone to the first outbound workstation in the expected outbound task flow, the scheduling robot moves the boxes to be picked from the first zone to the first outbound workstation. For example, for task t, if the first outbound flow from the first zone to the first outbound workstation is 100 boxes / hour, and the expected outbound task flow is 1000 boxes / hour, then the proportion of the first outbound flow from the first zone to the first outbound workstation in the expected outbound task flow is 10%. If outbound task t contains 2000 boxes, then the scheduling robot moves 200 (2000 * 10%) boxes to be picked from the first zone to the first outbound workstation. The calculation method for the number of boxes to be picked that need to be moved between any zone and any outbound workstation is the same as the above method.

[0084] S152, based on the proportion of the second return flow from the second outbound workstation to the second zone in the total return flow, the robot is scheduled to move picked boxes or return empty boxes from the second outbound workstation to the second zone. The total return flow equals the expected outbound task flow minus the transfer flow. If the transfer flow is 0, then the total return flow equals the expected outbound task flow. When a robot arrives at the second outbound workstation and moves split-picked boxes, the robot moves the picked boxes to the second zone; when a robot arrives at the second outbound workstation and moves full-case outbound boxes, the robot returns empty boxes to the second zone. Taking the robot moving all picked boxes to the second zone as an example, for task t, the second return flow from the second outbound workstation to the second zone is 40 boxes / hour, and the total return flow is 500 boxes / hour, then the proportion of the second return flow from the second outbound workstation to the second zone in the total return flow is 8%. If outbound task t contains 2000 boxes, then the dispatch robot will move 160 (2000 * 8%) picked boxes from the second outbound workstation to the second partition. The calculation method for the number of picked boxes to be moved between any outbound workstation and any partition is the same as the above method.

[0085] S153, based on the proportion of the third-level picking flow from the third zone to the third inbound workstation in the total picking flow, the robot is scheduled to transport boxes to be loaded or empty from the third zone to the third inbound workstation. The total picking flow equals the expected inbound task flow minus the transfer flow. If the transfer flow is 0, then the total picking flow equals the expected inbound task flow. When a robot needs to transport a split-picked box back to the warehouse area, it will be transporting a box to be loaded when it arrives at the third inbound workstation; when a robot needs to transport a full box of inbound boxes back to the warehouse area, it will be empty when it arrives at the third inbound workstation. Taking the robot transporting all boxes to be loaded to the third inbound workstation as an example, for task t, the third-level picking flow from the third zone to the third inbound workstation is 120 boxes / hour, and the total picking flow is 400 boxes / hour, then the proportion of the third-level picking flow from the third zone to the third inbound workstation in the total picking flow is 30%. If the number of bins in the inbound task t is 2000, then the scheduling robot will move 600 (2000 * 30%) bins to be loaded from the third zone to the third inbound workstation. The calculation method for the number of bins to be loaded that need to be moved between any zone and any inbound workstation is the same as the above method.

[0086] S154, based on the proportion of the fourth inbound flow from the fourth inbound workstation to the fourth zone in the expected inbound task flow, the scheduling robot moves the same number of boxes to be inbound from the fourth inbound workstation to the fourth zone. For example, for task t, the fourth inbound flow from the fourth inbound workstation to the fourth zone is 60 boxes / hour, and the expected inbound task flow is 300 boxes / hour, then the proportion of the fourth inbound flow from the fourth inbound workstation to the fourth zone in the expected inbound task flow is 20%. If inbound task t contains 2000 boxes, then the scheduling robot moves 400 (2000 * 20%) boxes to be inbound from the fourth inbound workstation to the fourth zone. The calculation method for the number of boxes to be inbound that need to be moved between any inbound workstation and any zone is the same as the above method.

[0087] For each partition, the server records a list of traffic proportions from that partition to each workstation; for each workstation, the server records a list of traffic proportions from that workstation to each partition. Traffic can be allocated based on these traffic proportion lists. Taking workstation A as an example, the server records a list of its traffic proportions to each partition. ],in To reach the reservoir area from workstation A The ratio can be used to determine which zones the robot should return the bins to after it completes the outbound picking task at workstation A.

[0088] The above method enables the robot to complete the material box handling task based on the optimal task flow distribution scheduling.

[0089] In some embodiments, if the traffic distribution also includes transfer traffic from each outbound workstation to each inbound workstation, then S150 further includes the following steps: S155, based on the proportion of the fifth transfer flow from the fifth outbound workstation to the fifth inbound workstation in the total transfer flow, the scheduled robots, after transporting the outbound boxes to the fifth outbound workstation, proceed to the fifth inbound workstation to transport the inbound boxes into the warehouse. For example, for task t, if the fifth transfer flow from the fifth outbound workstation to the fifth inbound workstation is 20 boxes / hour and the total transfer flow is 100 boxes / hour, then the proportion of the fifth transfer flow from the fifth outbound workstation to the fifth inbound workstation in the total transfer flow is 20%. If the total number of robots required for transfer is 200, then 40 (200 * 20%) robots are scheduled to transport the outbound boxes to the fifth outbound workstation and then proceed to the fifth inbound workstation to transport the inbound boxes into the warehouse. The calculation method for the number of robots transferring between any outbound workstation and any inbound workstation is the same as described above.

[0090] This embodiment divides the warehouse area into multiple zones. For each type of goods in the current order, the input parameters of the corresponding linear programming model are determined based on the zone set where the goods belong and the task information of the order tasks corresponding to the goods. The linear programming model is then solved based on the input parameters, that is, the flow distribution between each zone and each workstation is minimized when the sum of the robot travel distances for the bin handling tasks generated from all the order tasks corresponding to the goods is minimized. Finally, the robots are scheduled to execute bin handling tasks according to the flow distribution of each type of goods. Because this method uses standardized calculation methods, compared to relying on engineers' experience to design scheduling strategies, such as greedy strategies, it can adapt to various types of scenarios. The optimal task flow distribution obtained can minimize the total robot travel distance while satisfying given constraints. Furthermore, through standardized mathematical modeling and constraints in the linear programming model, errors in engineers' experience-based strategies are reduced, ensuring balanced flow in each zone of the warehouse and avoiding reduced finishing efficiency.

[0091] Furthermore, by establishing the task flow distribution as a linear programming model, the solution speed is fast and the time consumption is short. Even for large-scale warehouse maps, the flow distribution results can be obtained in a very short time, supporting real-time scheduling. This linear programming model is a general-purpose model, adaptable to various scenarios, including but not limited to irregular maps, multiple types of SKUs, SKU popularity partitioning, multiple destination selection for tasks, and full-box inbound and outbound scenarios.

[0092] In intelligent warehousing systems, the hourly handling capacity is a core indicator for measuring the system's throughput and operational efficiency. When designing an intelligent warehousing system, based on a greedy strategy to design the optimal task flow, the robot's travel distance is typically estimated according to the warehouse size, thereby estimating the robot's hourly handling capacity and calculating the number of robots required to meet the system's hourly handling capacity. However, estimating robot travel distance based on warehouse size is prone to distortion under different workstation locations and irregular map conditions. In some embodiments, the number of robots required to meet the system's hourly handling capacity can be determined based on the minimum value of the objective function obtained from the task scheduling method described above. Specifically, the number of robots required to meet the system's hourly handling capacity can be determined through the following steps: S210, the minimum value of the objective function obtained when solving the linear programming model is determined as the total travel distance of the robot under the optimal task streamline distribution.

[0093] S220: Multiply the total travel distance of the robot by the scheduling efficiency reduction ratio to obtain the statistical value of the total travel distance of the robot.

[0094] S230 determines the robot's hourly box-moving efficiency based on statistical values.

[0095] When calculating the hourly box-moving efficiency of the robot based on statistical values, the robot's driving speed, the total number of boxes being moved, and the total number of robots involved in the box-moving process are also considered to obtain the average hourly box-moving efficiency of each robot.

[0096] S240 determines the number of robots needed to meet the hourly box handling capacity requirement based on box handling efficiency.

[0097] Dividing the required hourly box handling volume by the box handling efficiency calculated in step S230 yields the number of robots needed to meet the hourly box handling volume requirement.

[0098] By employing the methods described above, we can more accurately estimate the overall route distance of robots in actual warehouse scheduling, improving the accuracy of robot quantity estimation in warehouse solution design. On the other hand, it also allows us to assess whether the efficiency of on-site scheduling of the warehouse system has reached the ideal value.

[0099] Figure 11 A schematic diagram of the task scheduling device provided in an embodiment of this application is shown. Figure 11 As shown, the task scheduling device 300 includes: The partitioning module 310 is used to divide the storage area into multiple partitions; The first determining module 320 is used to determine the set of partitions where each type of goods in the current wave of orders is located from multiple partitions, and to determine the task information of the order task corresponding to each type of goods; The second determining module 330 is used to determine the input parameters of the linear programming model corresponding to each type of goods based on the partition set and task information. The linear programming model includes an objective function and constraints. The objective function is to minimize the sum of the robot travel distances for performing the bin handling task. The bin handling task is generated based on all order tasks corresponding to the goods. Solver module 340 is used to solve the linear programming model based on the input parameters to obtain the traffic distribution of order tasks corresponding to each type of goods between each partition and each workstation in the partition set; The scheduling module 350 is used to schedule robots to perform bin handling tasks based on the flow distribution of each type of goods.

[0100] The task scheduling device 300 of this application embodiment also includes other modules for performing the steps of the above method embodiments, which will not be described in detail here.

[0101] Figure 12 A schematic diagram of the server structure provided in an embodiment of this application is shown, as follows: Figure 12 As shown, the server 400 may include a processor 402 and a memory 404.

[0102] The memory 404 is used to store the computer program 406. The memory 404 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk drive. The computer program 406 may include computer-executable instructions.

[0103] The processor 402 is used to execute computer programs 3=406 to implement the above-described task scheduling method embodiment.

[0104] Processor 402 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. Server 400 may include one or more processors of the same type, such as one or more CPUs; or it may include processors of different types, such as one or more CPUs and one or more ASICs.

[0105] This application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described task scheduling method embodiment.

[0106] This application provides a computer program that can be executed by a processor to implement the above-described task scheduling method embodiments.

[0107] This application provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described task scheduling method embodiment.

[0108] In the several embodiments provided in this application, any function, if implemented as a software functional module / unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the technical solution of this application can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or other electronic device) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing computer program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0109] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, the embodiments of this application are not directed to any particular programming language. It should be understood that the content of this application described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of this application.

[0110] It should be noted that the above embodiments are illustrative of this application and not restrictive, and those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In claims enumerating several means, several units or modules of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

[0111] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A task scheduling method, characterized in that, The method includes: The reservoir area is divided into multiple zones; Determine the set of partitions for each type of goods in the current wave of orders from the multiple partitions, and determine the task information for the order task corresponding to each type of goods; For each type of goods, the input parameters of the linear programming model corresponding to the goods are determined according to the partition set and the task information. The linear programming model includes an objective function and constraints. The objective function is to minimize the sum of the robot travel distances for performing the bin handling task. The bin handling task is generated based on all the order tasks corresponding to the goods. Solve the linear programming model based on the input parameters to obtain the traffic distribution of the order tasks corresponding to each type of goods among each partition and each workstation in the partition set; The robot is scheduled to perform the bin handling task according to the flow distribution of each type of goods.

2. The method according to claim 1, characterized in that, The order tasks include outbound tasks and inbound tasks. The flow distribution includes the outbound flow distribution and return flow distribution of the outbound tasks corresponding to each type of goods, and the pick-up flow distribution and inbound flow distribution of the inbound tasks corresponding to each type of goods. The step of scheduling robots to perform the bin handling task based on the flow distribution of each type of goods includes: Based on the proportion of the first outbound flow from the first zone to the first outbound workstation in the expected outbound task flow, the robot is scheduled to transport the picking bins from the first zone to the first outbound workstation. Based on the proportion of the second return box flow from the second outbound workstation to the second zone in the total return box flow, the robot is scheduled to transport the picked boxes from the second outbound workstation or return them empty back to the second zone. Based on the proportion of the third box retrieval flow from the third zone to the third inbound workstation in the total box retrieval flow, the robot is scheduled to transport the boxes to be loaded or empty from the third zone to the third inbound workstation. Based on the proportion of the fourth inbound flow from the fourth inbound workstation to the fourth zone in the expected inbound task flow, the robot is scheduled to transport the same number of boxes to be inbound from the fourth inbound workstation to the fourth zone.

3. The method according to claim 2, characterized in that, The flow distribution also includes the transfer flow from each outbound workstation to each inbound workstation; The method of scheduling robots to perform the bin handling task according to the flow distribution of each type of goods further includes: Based on the proportion of the fifth transfer flow from the fifth outbound workstation to the fifth inbound workstation in the total transfer flow, the robot is scheduled to transport the full-case outbound picking boxes to the fifth outbound workstation, and then travel to the fifth inbound workstation to transport the full-case inbound picking boxes into the warehouse.

4. The method according to claim 3, characterized in that, The task information for the outbound task includes the expected outbound traffic, the workstation corresponding to the outbound task, and the proportion of full-case outbound. The task information for the inbound task includes the expected inbound traffic, the workstation corresponding to the inbound task, and the proportion of full-case inbound. The objective function is: ； in, It is a collection of outbound tasks. It is a collection of inbound tasks. It is the set of partitions, It is a collection of outbound workstations. It is a collection of inbound workstations. It is a partition To the outbound workstation distance, It is an outbound workstation To the warehouse workstation distance, It is a partition To the warehouse workstation distance, It is an order task From partition To the outbound workstation Traffic, It is an order task From the outbound workstation Return to partition Traffic, It is an order task From the outbound workstation Transferred to the warehouse workstation Traffic, It is an order task From the warehouse workstation To partition Traffic, It is an order task From partition To the warehouse workstation Traffic, , , , , , , , , are all input parameters of the objective function. , , , and The solution to the objective function that satisfies the aforementioned constraints; The constraints include: First constraint: , , , , ; Second constraint: , ,in, For order tasks Related partition sets, For order tasks The relevant outbound workstation collection, For order tasks The relevant collection of inbound workstations, For order tasks Expected traffic; Third constraint: , ,in, For order tasks The number of related outbound workstations For order tasks The number of related inbound workstations; Fourth constraint: If ,but ,otherwise ,in, For order tasks The splitting and picking ratio, For order tasks The proportion of multiple candidate positions; Fifth constraint: , ,in, For order tasks The proportion of sorting and picking to warehousing; Sixth constraint: , , , ; Seventh constraint: , ; Eighth constraint: , ,in, For order tasks The number of related partitions; Ninth constraint: , ; Tenth constraint: = , ; Eleventh constraint: , ,in, For order tasks The proportion of full-case warehousing; The step of determining the input parameters of the linear programming model corresponding to the goods based on the partition set and the task information includes: Determine the set of outbound tasks The set of inbound tasks The set of partitions The set of outbound workstations The set of warehouse entry workstations , which serves as the common input parameter for both the objective function and the constraints; According to the partition set The set of outbound workstations and the aforementioned collection of inbound workstations Determine the partition To the outbound workstation distance Outbound workstation To the warehouse workstation distance and partitions To the warehouse workstation distance , which serves as the input parameter of the objective function; Confirm Order Task The related set of partitions Order Task The related outbound workstation set Order Task The related collection of inbound workstations Order Task The relevant number of outbound workstations Order Task The relevant number of inbound workstations Order Task The splitting and picking ratio Order Task The ratio of the multiple candidate positions Order Task The ratio of splitting and picking to warehousing Order Task The relevant number of partitions and order tasks The full box warehousing ratio , which serves as the input parameter for the constraint condition; The expected outbound traffic volume is determined as the input parameter related to the outbound task in the constraints. The expected flow of the inbound task is determined as the input parameters related to the inbound task in the constraints. .

5. The method according to claim 1, characterized in that, The method further includes: The minimum value of the objective function obtained when solving the linear programming model is determined as the total travel distance of the robot under the optimal task streamline distribution; Multiply the total travel distance of the robot by the scheduling efficiency reduction ratio to obtain the statistical value of the total travel distance of the robot; The hourly box-moving efficiency of the robot is determined based on the statistical values. The number of robots required to meet the hourly box handling capacity is determined based on the box handling efficiency.

6. The method according to claim 1, characterized in that, The reservoir area is divided into multiple partitions, including: Determine the effective shelving area of ​​the warehouse area; Determine the outer envelope region of the reservoir area; The outer envelope region is divided into multiple rectangular regions; The intersection of the effective shelf area and each of the rectangular areas is calculated to obtain the multiple partitions.

7. The method according to claim 6, characterized in that, Determining the effective shelving area of ​​the warehouse includes: Determine the included and excluded areas of the storage area; Perform a graphical union calculation on the contained regions to obtain the total contained regions; The total included area is taken as the whole set, and the non-included area is taken as a subset. The complement of the subset is calculated to obtain the effective shelf area.

8. The method according to claim 7, characterized in that, Determining the included and excluded areas of the storage area includes: Based on the physical properties of the storage area, a geometric model of the storage area is established, wherein the physical properties include one or more building elements such as exterior walls, interior walls, columns, and elevator shafts, and the geometric model includes an element geometric model corresponding to each building element; The included region is determined based on the geometric model of the elements corresponding to the outer wall and the inner wall; The excluded area is determined based on the geometric model of the elements corresponding to the column and the elevator shaft.

9. A task scheduling device, characterized in that, include: The partitioning module is used to divide the storage area into multiple partitions; The first determining module is used to determine the set of partitions where each type of goods in the current wave of orders is located from the multiple partitions, and to determine the task information of the order task corresponding to each type of goods; The second determining module is used to determine the input parameters of the linear programming model corresponding to each type of goods based on the partition set and the task information. The linear programming model includes an objective function and constraints. The objective function is to minimize the sum of the robot travel distances for performing the bin handling task. The bin handling task is generated based on all the order tasks corresponding to the goods. The solution module is used to solve the linear programming model based on the input parameters to obtain the traffic distribution of the order tasks corresponding to each type of goods between each partition and each workstation in the partition set; The scheduling module is used to schedule robots to perform the bin handling tasks according to the flow distribution of each type of goods.

10. A server, characterized in that, include: A processor and a memory, wherein the memory stores executable instructions, and the processor is capable of executing the executable instructions to implement the task scheduling method as described in any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, The storage medium stores executable instructions, which, when executed on the electronic device, cause the electronic device to perform the task scheduling method as described in any one of claims 1-8.

12. A warehousing system, characterized in that, Includes a storage area, workstations, robots, and the server as described in claim 10; The storage area is used to store material bins; The workstation includes an outbound workstation and an inbound workstation; The server is used to schedule the robot to perform bin handling tasks between the warehouse area and the workstation.

Images