Scheduling method, scheduling system and computer program product for handling robots

Abstract

The present invention relates to the field of intelligent logistics. The present invention provides a scheduling method for a handling robot, the scheduling method comprising the following steps: S1: assigning a thermal value to each order in an order pool according to a picking friendliness index; S2: selecting an order whose thermal value meets a preset condition from the order pool in response to an order dispatching demand; and, S3: generating a scheduling instruction for at least one handling robot based on the selected order. The present invention also provides a scheduling system and a computer program product. In the scheduling strategy of the present invention, unordered orders are aggregated and dynamically classified according to picking friendliness, so that the order that is most conducive to saving manpower at the current moment can always be executed first, thereby realizing the rational scheduling of handling robots and pickers as a whole and improving picking efficiency.

Description

Technical Field

[0001] The invention relates to a scheduling method for a handling robot, a scheduling system and a computer program product. Background Art

[0002] With the rise of e-commerce, modern factories and other fields, more and more intelligent warehousing systems are used to complete the picking, handling, storage, etc. In the field of intelligent warehousing and logistics, in order to reduce the burden of manual pickers and improve the efficiency of picking operations, autonomous mobile robots (AMRs) are generally used in collaboration with humans to complete the picking and seeding of materials.

[0003] In the common scheduling modes known in the prior art, most orders are dispatched in sequence according to the order placement time, or the orders are reorganized according to the product category before dispatch, and then the handling robots and pickers complete the picking tasks in a "vehicle-to-person" or "goods-to-person" cooperative manner.

[0004] However, these solutions all have many limitations. In particular, assigning tasks purely according to the order in which orders arrive may seem simple and easy, but there are no rules at the task execution end. In many cases, pickers need to walk a long distance or wait for a long time at a fixed pick-up location for a handling robot, which cannot achieve optimal picking efficiency overall. The order processing method of disrupting and re-arranging orders will undoubtedly significantly increase the time cost of secondary sorting.

[0005] In this context, it is expected to provide an improved logistics robot scheduling method, which aims to achieve rational scheduling of orders, robots and personnel to improve picking efficiency. Summary of the invention

[0006] The object of the present invention is to provide a scheduling method for a handling robot, a scheduling system and a computer program product to at least solve some of the problems in the prior art.

[0007] According to a first aspect of the present invention, there is provided a scheduling method for a handling robot, the scheduling method comprising the following steps:

[0008] S1: Assign thermal value to each order in the order pool according to the picking friendliness index;

[0009] S2: In response to the order dispatching demand, select an order whose thermal value meets the preset conditions from the order pool; and

[0010] S3: Generate a scheduling instruction for at least one handling robot based on the selected order.

[0011] In the sense of the present invention, the picking-friendliness index can also be called a human efficiency value index, which is directly related to the overall manual picking efficiency. With the help of this index, the value of each order can be comprehensively measured in terms of the picker's walking distance, range of activities, waiting time, etc. The thermal value evaluated with the help of this indicator can especially reflect the friendliness of different orders to the pickers. In the above scheduling strategy, unordered orders can be aggregated and dynamically classified according to the picking-friendliness, so that the orders that are most conducive to saving manpower at the current moment can always be executed first, thereby realizing the rational scheduling of the handling robots as a whole and improving the picking efficiency.

[0012] Optionally, step S3 further includes: generating a dispatch instruction for at least one picker based on the selected order.

[0013] Optionally, the picking-friendliness index includes a static index and a dynamic index, wherein the static index does not change with the order content in the order pool, and the dynamic index changes with the order content in the order pool.

[0014] Here, the following technical advantages are achieved in particular: Due to the dynamic component contained in the picking friendliness index, it is possible that an order originally evaluated as not very friendly will be transformed into an order with high picking friendliness as new and old orders in the order pool are replaced and the types and quantities of goods change. It can be seen that as time goes by, this dynamic dispatching method can achieve an overall dispatching plan that tends to maximize labor efficiency.

[0015] Optionally, step S1 includes: obtaining the statistical distribution of the goods contained in each order in the order pool on the picking locations; screening out a certain number of picking locations based on the statistical distribution, and expanding at least one area outward as a hot zone based on the determined number of picking locations; and determining the thermal value based on the proportion of the picking locations corresponding to the goods in each order in the hot zone.

[0016] Here, the establishment of hot zones links the order pool with the warehouse map, realizing the mapping from the goods dimension to the picking location dimension. This can show which areas are more conducive to picking with a large number of people, and thus use this to more reasonably plan the picking path of the transport robot and the work area of ​​the picker.

[0017] Optionally, the hot zones are based on aisle areas containing picking locations, wherein different hot zones differ in size and / or shape.

[0018] Optionally, the statistical distribution is projected onto a warehouse map to generate a visual heat map, and the darkest area is selected from the heat map as a hot zone.

[0019] In particular, the following technical advantages are achieved here: by generating a heat map, the dynamic changes and development trends of the hot zones can be visualized. In addition, it can be more clearly seen whether the activity paths of personnel and handling robots perfectly coincide with each hot zone, and the size or shape of the hot zone can be adjusted in a timely manner when a large deviation occurs.

[0020] Optionally, the step S1 includes: determining the thermal value according to the degree of aggregation of picking locations corresponding to the goods in the order.

[0021] In particular, the following technical advantages are achieved here: the more concentrated the picking locations are, the more concentrated the goods are distributed on the adjacent shelves, and when completing the order task, there is no need to travel a long moving distance, so this also constitutes an important factor in measuring order-friendliness.

[0022] Optionally, the step S1 includes: determining the thermal value according to the quantity of goods included in the order.

[0023] Here, the following technical advantages are achieved in particular: By restricting the thermal value by the order length, it is possible to prevent all small orders from being dispatched in advance, thereby avoiding frequent changes in the hot zone. In addition, small orders are more flexible, so they do not need to occupy the main allocation share, but are filled into the task list in an interpolated manner when the task completion rate in the hot zone is high or the task gap of the picker is high, so that they can be executed without affecting the overall efficiency.

[0024] Optionally, the scheduling method further includes the following steps:

[0025] When the total thermal value of the unassigned orders is lower than a first threshold, redetermine the picking friendliness index for all unassigned orders, especially redefine the hot zones; and / or

[0026] When the remaining total thermal value of all assigned but uncompleted orders is lower than a second threshold, the uncompleted orders are returned to the order pool as new orders, and the picking friendliness index is redetermined for all current orders in the order pool, especially the hot zones.

[0027] Here, the following technical advantages are achieved in particular: as new orders are continuously added to the order pool, the originally determined evaluation index may become "outdated" as the order content changes and therefore no longer apply. It is also possible that part of a complete order reflects a high thermal value, while the thermal value of the remaining part is not high. In both cases, the picking friendliness index can be refreshed in a timely manner, so that the calculated thermal value is dynamically adapted to the order pool conditions at different stages.

[0028] Optionally, the scheduling method also includes the following steps: after sending the scheduling instructions to at least one handling robot and / or at least one picker, real-time monitoring of the picking efficiency, remaining order completion rate, congestion rate, the number of assigned handling robots and / or the ratio of the number of handling robots to the number of pickers in each work area, and updating the scheduling instructions for at least one handling robot and / or at least one picker based on the monitoring results.

[0029] As a result, tasks can be balanced in real time during the order execution process, so that the optimal picking efficiency can be met at any time.

[0030] Optionally, updating the scheduling instructions for at least one handling robot includes: updating the picking path; and / or, updating the scheduling instructions for at least one picker includes: instructing at least one picker to leave the current work area.

[0031] Optionally, step S2 includes: sorting the orders in the order pool according to the thermal value, and selecting the order with the largest thermal value in response to the order dispatching demand.

[0032] Optionally, step S3 includes: sending the picking list corresponding to the selected order and the information of the hot zone to a handheld communication device of at least one picker to guide the at least one picker to go to the hot zone and perform picking in the hot zone.

[0033] Here, sending the hot zone related information to the pickers can enable them to fully understand the dense areas of task distribution, so that they can consciously plan their walking paths when assisting the transport robot to complete the picking tasks, so as not to leave the core area.

[0034] Optionally, step S3 includes: additionally specifying for at least one picker based on the predicted walking distance and / or waiting time of a single picker: going to a predetermined picking location within the current hot zone, a walking path within the current hot zone and / or a docking sequence with a handling robot within the current hot zone; and / or, additionally specifying, based on the predicted walking distance and / or total waiting time of all pickers, specifying at least one other picker already in the current hot zone to go to a predetermined picking location outside the current hot zone.

[0035] Here, by planning the movement of people in the hot zone, a personalized scheduling plan can be formulated for each picker, thereby reducing the walking path of each picker and reducing waiting time. In addition, through dynamic cross-zone scheduling, vehicle allocation conflicts are resolved, and the efficiency between each work area can be balanced, achieving the global optimal picking efficiency.

[0036] Optionally, the scheduling instruction is additionally generated based on the moving speed of at least one handling robot, the picking speed and / or moving speed of at least one picker, the number of remaining picking tasks of at least one picker and / or at least one work area, and the scheduling instruction particularly includes instructing the next picking position of the handling robot and / or picker.

[0037] Here, the performance parameters of each candidate handling robot and the status parameters of each candidate picker are also taken into consideration when generating the scheduling instructions, thereby achieving a more reasonable allocation of order tasks.

[0038] Optionally, the scheduling instructions include assigning picking modes, wherein a non-binding picking mode is assigned to handling robots and pickers in a determined working area, especially a hot zone, and a binding picking mode is assigned to handling robots and pickers outside a determined working area, especially a hot zone.

[0039] Here, the following technical advantages are achieved in particular: In areas with dense orders, there are frequent transfer robots coming and going, and people generally do not have to wait for a long time for the car, so the pickers can flexibly cooperate with the robots to complete the picking in a one-person-multiple-carriage manner. In areas with sparse orders, there is a high probability that people will be idle or wait for a long time, so it is beneficial to change to the follow-up picking mode in these areas.

[0040] According to a second aspect of the present invention, a scheduling system is provided, the scheduling system being used to execute the scheduling method according to the first aspect of the present invention, the scheduling system comprising:

[0041] an analysis module configured to assign a thermal value to each order in the order pool according to a picking-friendliness index;

[0042] A selection module configured to select orders whose thermal values ​​meet preset conditions from an order pool in response to order dispatching requirements; and

[0043] The dispatching module is configured to generate a dispatching instruction for at least one handling robot based on the selected order.

[0044] According to a third aspect of the present invention, a computer program product is provided, comprising a computer program, wherein the computer program implements the scheduling method according to the first aspect of the present invention when executed on a computer. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The present invention will be described in more detail below with reference to the accompanying drawings, so that the principles, features and advantages of the present invention can be better understood. The accompanying drawings include:

[0046] Figure 1 A flow chart of a method for scheduling a handling robot according to an exemplary embodiment of the present invention is shown;

[0047] Figure 2 A flow chart showing one step of a scheduling method for a handling robot according to the present invention;

[0048] Figure 3 A flowchart showing another step of the scheduling method for a handling robot according to the present invention;

[0049] Figure 4 A flowchart showing two steps of a scheduling method for a handling robot according to the present invention is shown;

[0050] Figure 5 A schematic diagram showing the formation of a hot zone according to an exemplary embodiment of the present invention is shown;

[0051] Figure 6 A schematic diagram showing application of the scheduling method according to the present invention in a warehouse; and

[0052] Figure 7 A block diagram of a scheduling system according to an exemplary embodiment of the present invention is shown. DETAILED DESCRIPTION

[0053] In order to make the technical problems, technical solutions and beneficial technical effects to be solved by the present invention more clearly understood, the present invention will be further described in detail below in conjunction with the accompanying drawings and multiple exemplary embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the scope of protection of the present invention.

[0054] Figure 1 A flow chart of a scheduling method for a transport robot according to an exemplary embodiment of the present invention is shown.

[0055] In step S0, an order is obtained from a warehouse management system (WMS) and imported into an order pool.

[0056] In step S1, a thermal value is assigned to each order in the order pool according to the picking friendliness index. Here, this picking friendliness index is used to characterize the overall contribution of each order to the improvement of picking efficiency. This picking friendliness index includes, for example, static indicators and dynamic indicators. The static indicators do not change with the order content in the order pool, and the dynamic indicators change with the order content in the order pool. Due to the existence of dynamic indicators, as the state of the order pool changes continuously (new orders are added, old orders are removed), the same order in the order pool may be assigned different thermal values ​​at different time periods, and therefore also show different picking friendliness.

[0057] In step S2, in response to the order dispatching demand, orders whose thermal values ​​meet the preset conditions are selected from the order pool. Here, the order dispatching demand may refer to a dispatching request received from a handling robot and / or a picker, but it is also possible that the dispatching platform observes that there are idle handling robots or pickers and thus actively generates an order dispatching demand. As an example, selecting orders whose thermal values ​​meet the preset conditions from the order pool includes: sorting the orders in the order pool according to the thermal value, and selecting the order with the largest thermal value in response to the order dispatching demand.

[0058] In step S3, a dispatch instruction for at least one handling robot is generated based on the selected order. Additionally, a dispatch instruction for at least one picker can also be generated based on the selected order. Here, for example, the selected order is first converted into a picking list containing at least a picking location, and then the picking list is respectively assigned to at least one handling robot and at least one picker. Here, the dispatch instruction for the handling robot includes at least a picking path, and the dispatch instruction for the picker includes at least a picking location.

[0059] In the optional step S4, it is checked whether order updates are obtained from the WMS, and new orders are added to the order pool when there are updates. Here, the newly added orders still participate in the thermal value sorting according to the predefined picking friendliness index.

[0060] In optional step S5, it is checked whether the total heat value of all currently unallocated orders in the order pool is lower than a first threshold.

[0061] If this is the case, it means that with the continuous addition of new orders, the preset picking friendliness index has become "outdated" and is therefore no longer applicable to the current order pool status. In order to improve this situation, for example, in optional step S7, the picking friendliness index can be re-determined for all orders in the order pool, and a thermal value can be assigned to each order based on the updated picking friendliness index.

[0062] If this is not the case, it is still possible to continue in optional step S6 to check whether the remaining total thermal value of the unfinished parts of all allocated orders is lower than a second threshold value.

[0063] If it is found that it is lower than the second threshold, it means that some orders have relatively high thermal values ​​overall, but the thermal value distribution is uneven within the order. Therefore, in the optional step S8, all unfinished orders can be returned to the order pool as new orders, and the picking friendliness index can be re-determined uniformly for the unassigned orders and unfinished orders.

[0064] If it is found in step S6 that it is higher than the second threshold, it means that the currently set picking friendliness index is still applicable, so the existing picking friendliness index can continue to be used in step S9.

[0065] Figure 2 FIG. 1 is a flow chart showing one step of a scheduling method for a handling robot according to the present invention. Figure 2 As shown, Figure 1 The method step S1 in the example includes sub-steps S11-S14, so as to illustrate how to comprehensively calculate the thermal value of each order through multiple evaluation indicators.

[0066] In step S11, the first thermal value component is calculated by constructing a hot zone. Here, for example, the statistical distribution of the goods contained in each order in the order pool on the picking position can be obtained, and then a certain number of picking positions can be screened out according to the statistical distribution, and at least one area can be expanded outwardly based on the picking position as a hot zone. As an example, the entire warehouse can be divided into multiple determined areas, and it is checked whether the number of positions to be picked or the cumulative number of traversals of the positions in each determined area exceeds a threshold. If it exceeds, the determined area is defined as a hot zone. As another example, all picking positions whose distance is less than a threshold can be aggregated first, and then it is checked whether the number of positions to be picked or the cumulative number of traversals of the positions in each determined area formed by the aggregation exceeds a threshold. If it exceeds, the determined area is defined as a hot zone. In addition, it is also conceivable that new hot zones are dynamically merged or split according to the geographical location and / or the proportion of the picking position in the hot zone. Finally, the thermal value component is determined based on the proportion of the picking positions corresponding to the goods in each order in the hot zone. Here, the higher the proportion of the picking positions of the goods in the order in the hot zone, the higher the thermal value component.

[0067] As an example, the first thermal value component determined in this way can be calculated by the following equation:

[0068]

[0069] Among them, cost mean Represents the first thermal value component, n in It indicates the number of cargo locations in all hot zones for the goods in the order, and n indicates the total number of cargo locations included in the order.

[0070] In step S12, the second thermal value component is determined according to the degree of clustering of the picking locations corresponding to the goods in the order. As an example, the more clustered the picking locations are, the higher the thermal value component is. This degree of clustering can be particularly characterized by the degree of dispersion of the picking locations in different thermal zones. For example, the second thermal value component can be calculated by the following equation:

[0071]

[0072] Among them, cost dispersionrepresents the second thermal value component, k represents the number of hot zones, i belongs to [1, k], ni represents the number of cargo locations in the order in the i-th hot zone, and n represents the total number of cargo locations included in the order. From this equation, it can be seen that the fewer hot zones the cargo locations in a single order are concentrated in, the higher the thermal value.

[0073] In step S13, the third thermal value component is determined according to the order length, that is, the number of goods included in the order. As an example, the longer the order, the higher the thermal value component, thereby preventing shorter orders from being wrongly prioritized due to the evaluation indicators in steps S11 and S12, which actually leads to a reduction in overall picking efficiency. Therefore, the third thermal value component can be calculated by the following equation:

[0074]

[0075] Among them, cost length Represents the third thermal value component, and n represents the total number of cargo spaces included in the order.

[0076] Next, in step S14, the total thermal value of the order is comprehensively calculated. As an example, the total thermal value of the order can be calculated by the following equation:

[0077] cost=cost mean ×cost dispersion ×cost length

[0078] Here, the above calculation method of the total thermal value is only exemplary, and it is also conceivable to assign weights to each thermal value component and consider each thermal value component in a weighted manner. It is not intended to limit the specific formula for calculating the total thermal value.

[0079] Figure 3 FIG. 2 is a flow chart showing another step of the scheduling method for a handling robot according to the present invention. Figure 3 As shown, Figure 1 The method step S3 in exemplarily includes sub-steps S31-S38, so as to illustrate how to generate a scheduling instruction while additionally considering multiple factors.

[0080] In step S31, the user profile of each candidate picker is obtained, and the performance indicators of the candidate handling robots are obtained. Here, the candidate pickers and candidate handling robots are particularly understood as: pickers or robots that are idle at the time of dispatching or whose workload to be performed has not reached saturation. The user data profile includes, for example, summary statistics of the following ability dimensions of the picker: moving speed, picking speed, average picking efficiency, physical condition, and the number of remaining tasks currently to be performed. The performance indicators of the handling robot include, for example: the moving speed, version, intelligence level, and year of manufacture of the handling robot.

[0081] In step S32, the selected order is matched with the picker's capability dimensions and / or the robot's performance indicators.

[0082] In step S33, appropriate target pickers and target handling robots are selected based on the matching. For example, orders with a large number of tasks and frequent cross-region orders can be preferentially assigned to handling robots with fast movement speed and pickers with high picking efficiency, while orders with a small number of orders and concentrated picking locations can be assigned to handling robots and pickers with slower movement speeds.

[0083] In step S34 , for example, it may also be checked whether the selected order involves a task within the hot zone or a task outside the hot zone.

[0084] If the task involves a hot zone, the simulation algorithm can be used in step S35 to predict the walking distance and / or waiting time for the transport robot when the target picker docks with the transport robots already in the hot zone in different sequences.

[0085] Then in step S36, based on the traversed optimal solution, a predetermined picking location, walking path and / or docking sequence in the current hot zone is specified for the target picker.

[0086] If the task involves a task outside the hot zone, the walking distance and / or total waiting time of all pickers including the target picker can be predicted in step S37 with the help of a simulation algorithm.

[0087] Then, in step S38, a cross-zone picking route or picking location is assigned to the target picker based on the predicted results to achieve optimal global picking efficiency.

[0088] Figure 4 FIG. 2 is a flow chart showing two steps of a scheduling method for a handling robot according to the present invention. Figure 4 As shown, Figure 1 The method in the embodiment further exemplarily includes sub-steps S410-S460 after step S3, so as to illustrate how to update the allocated scheduling instructions based on the results of real-time monitoring after the scheduling instructions are allocated.

[0089] After the dispatch instruction is generated in step S3, a data dashboard that can reflect the picking situation in each hot zone in real time can be provided in step S410, for example. Such a data dashboard includes, for example: picking efficiency in each work area, remaining order completion rate, congestion rate, the number of assigned handling robots in each work area, and / or the ratio of the number of handling robots to the number of pickers.

[0090] In step S420, for example, based on the monitoring results provided by the above data dashboard, it is determined whether the task load in each hot zone is balanced.

[0091] If the order distribution is uneven, it is easy for the pickers in some high-frequency work areas to be very busy, while the pickers in the low-frequency work areas have long waiting times and long moving distances. Therefore, if it is determined that there is an imbalance, the scheduling instructions can be updated in step S460. Here, for example, more pickers or handling robots can be dispatched to work areas with a large amount of tasks, and the work mode of the pickers and handling robots in the work areas with less task distribution can be adjusted (for example, from a non-binding picking mode to a binding picking mode).

[0092] If no problem with task balancing is found, it may be possible to continue to check in step S430 whether the congestion rate in each hot zone is lower than the congestion threshold.

[0093] If the congestion rate in some hot zones is higher than the congestion threshold, while the congestion rate in other hot zones is much lower than the congestion threshold, there may be an imbalance problem in the traffic flow density in the working area. Therefore, for example, the scheduling instruction can be updated again in step S460. As an example, the picking path of the transport robot in the working area with a higher congestion rate can be updated so that it can perform the picking tasks in other hot zones first, or the picker in the current hot zone can be assigned another picking position outside the hot zone so that it can temporarily leave the current hot zone, thereby alleviating the congestion in the current hot zone.

[0094] If there is no congestion problem, it can also be determined in step S440 based on the monitoring results: whether the workload of each picker is balanced.

[0095] Unbalanced workload means, in particular, that pickers with low work efficiency or poor physical condition are assigned workloads that exceed their tolerance limits, while some pickers with high work efficiency may not have tasks saturated. Therefore, in this case, the scheduling instructions can also be updated in step S460. As an example, the orders or part of the orders originally assigned to the first picker with an overloaded workload can be transferred to the second picker with an underloaded workload, thereby achieving a dynamic balance in the workload, which is conducive to achieving higher picking efficiency overall.

[0096] If the monitoring results do not reflect any imbalance, the current scheduling instructions may be kept unchanged in step S450 to prompt the picker or handling robot to continue to perform tasks according to the assigned scheduling instructions.

[0097] It should be noted here that this exemplary embodiment is not intended to limit the execution order of steps S420 to S440, and it is also possible that these steps are executed in parallel or in other orders.

[0098] Figure 5 A schematic diagram showing the formation of a hot zone according to an exemplary embodiment of the present invention is shown.

[0099] like Figure 5 As shown, the warehouse 50 includes six aisles A, B, C, D, E, and F. Each aisle includes two rows of shelves 51 on the left and right, and each row of shelves 51 has 10 picking positions 52. In order to form a hot zone, firstly, the statistical distribution of the goods contained in each order in the order pool on the picking positions 52 is requested. Then, for example, this statistical distribution is projected onto the warehouse map to generate Figure 5 The visual heat map shown.

[0100] As an example, the threshold for forming a hot zone may be set to 5, which means, for example, that if there are at least 5 cargo locations to be picked on both sides of an aisle, the aisle is defined as a hot zone.

[0101] exist Figure 5 In the embodiment shown, the positions to be picked are marked in dark colors, and it can be seen that channels A to F include 5, 1, 0, 7, 3, and 2 positions to be picked, respectively. Therefore, based on the above-mentioned hot zone formation criteria, it can be determined that channels A and D are hot zones.

[0102] As another example, the initial heat and the heat increment of each traversal of the cargo position can also be defined for each cargo position on the shelf. When the goods in multiple orders repeatedly involve the same cargo position, the initial heat of the cargo position is accumulated with the heat increment. Here, the number of times a cargo position becomes a cargo position to be picked can be represented by the depth of color, so it can also be thought of that these picking positions are assigned weight scores according to the depth of color, so that when determining the hot zone, not only the number of cargo positions involved but also the repetition rate of cargo positions can be considered. In this way, more accurate determination of the hot zone can be achieved.

[0103] Figure 6 A schematic diagram showing application of the scheduling method according to the present invention in a warehouse is shown.

[0104] In this embodiment, channels A' and C' have been identified as hot zones. Here, the distribution of handling robots and pickers in each channel can be seen.

[0105] Here, there are four handling robots and two pickers in the first hot zone (i.e., channel A'), which shows that the channel is relatively crowded. The actual task volume of the second hot zone (i.e., channel C') is higher than that of the first hot zone, but fewer handling robots and pickers are assigned. In addition, it can be seen that although channel B' has a smaller task volume (only one cargo position to be picked), there is no handling robot or picker dispatched to perform tasks in this channel.

[0106] In this case, for example, the task completion status in the first hot zone can be checked through the data dashboard. As an example, according to the data dashboard, it is found that the remaining task amount of the transport robot 601 in the hot zone is not much, then the picking path of the transport robot 601 originally located in channel A' can be updated to make it give priority to complete the picking task in channel B', and dispatch idle pickers to channel B'. As another example, according to the data dashboard, it is found that the to-be-picked position of the transport robot 602 also includes several cargo locations in channel C', so in order to alleviate the congestion in channel A', the picking path of the transport robot 602 originally located in channel A' can also be updated to make the transport robot 602 leave the current hot zone and go to the second hot zone (i.e., channel C'). Through this real-time updated scheduling method, task balance between each work area is achieved and congestion is alleviated.

[0107] Figure 7 A block diagram of a scheduling system according to an exemplary embodiment of the present invention is shown.

[0108] like Figure 7 As shown, the scheduling system 1 includes an analysis module 10, which is connected to the warehouse management system 2 and can therefore receive orders from there. After the corresponding orders are merged into the order pool, the analysis module 10 assigns thermal values ​​to each order in the order pool according to a preset picking friendliness index. Next, this thermal value information is transmitted from the analysis module 10 to the selection module 20, so that orders whose thermal values ​​meet preset conditions are selected from the order pool in response to the order dispatching requirements. The selection module 20 is also connected to the dispatching module 30, so that the dispatching module 30 generates a dispatch instruction for at least one handling robot 3 and at least one picker 4 based on the selected order.

[0109] Although specific embodiments of the present invention are described in detail herein, they are provided for the purpose of explanation only and should not be considered to limit the scope of the present invention. Various substitutions, changes and modifications may be conceived without departing from the spirit and scope of the present invention.

Claims

1. A scheduling method for a handling robot (3), the scheduling method comprising the following steps: S1: Allocate a thermal value to each order in the order pool according to a picking-friendly index, where the picking-friendly index is used to measure the contribution of each order to improving manual picking efficiency. The thermal value is allocated to each order according to the picking-friendly index in the following manner: Find the statistical distribution of the goods contained in each order in the order pool at the picking location, A certain number of picking locations are selected according to the statistical distribution, and at least one area is expanded outwards based on the certain number of picking locations as a hot zone. Determine the first thermal value component based on the proportion of the picking locations corresponding to the goods in each order in the hot zone, Calculating the thermal value based on the first thermal value component; S2: In response to the order dispatching demand, select an order whose thermal value meets the preset conditions from the order pool; and S3: Generate a dispatch instruction for at least one handling robot (3) based on the selected order.

2. The scheduling method according to claim 1, wherein step S3 further comprises: Dispatch instructions for at least one picker (4) are generated based on the selected order.

3. The scheduling method according to claim 1 or 2, wherein: The picking-friendliness index includes a static index and a dynamic index. The static index does not change with the order content in the order pool, and the dynamic index changes with the order content in the order pool.

4. The scheduling method according to claim 1, wherein: The hot zone is based on the channel area containing the picking location.

5. The scheduling method according to claim 1 or 4, wherein: The statistical distribution is projected onto a warehouse map to generate a visual heat map, and the darkest area is selected from the heat map as a hot zone.

6. The scheduling method according to claim 1 or 2, wherein: The step S1 includes: determining a second thermal value component according to the degree of aggregation of picking locations corresponding to the goods in the order, and calculating a thermal value based on the second thermal value component.

7. The scheduling method according to claim 1 or 2, wherein: The step S1 includes: determining a third thermal value component according to the quantity of goods included in the order, and calculating the thermal value based on the third thermal value component.

8. The scheduling method according to claim 1 or 2, wherein: The scheduling method further comprises the following steps: When the total thermal value of all unassigned orders is lower than the first threshold, redetermine the picking friendliness index for all unassigned orders; and / or When the remaining total thermal value of all assigned but uncompleted orders is lower than the second threshold, the uncompleted orders are returned to the order pool as new orders, and the picking friendliness index is re-determined for all current orders in the order pool.

9. The scheduling method according to claim 2, wherein: The scheduling method further comprises the following steps: After sending the scheduling instructions to at least one transport robot and / or at least one picker, the picking efficiency, remaining order completion rate, congestion rate, the number of allocated transport robots and / or the ratio of the number of transport robots to the number of pickers in each work area are monitored in real time, and the scheduling instructions for at least one transport robot and / or at least one picker are updated based on the monitoring results.

10. The scheduling method according to claim 9, wherein: Updating the scheduling instructions for at least one handling robot includes: updating the picking path; and / or Updating the scheduling instructions for at least one picker includes instructing at least one picker to leave a current work area.

11. The scheduling method according to claim 1 or 2, wherein: The step S2 comprises: The orders in the order pool are sorted according to the thermal value, and the order with the largest thermal value is selected in response to the order dispatch demand.

12. The scheduling method according to claim 1, wherein: The step S3 comprises: The picking list corresponding to the selected order and the information of the hot zone are sent to a handheld communication device of at least one picker to guide the at least one picker to go to the hot zone and perform picking in the hot zone.

13. The scheduling method according to claim 12, wherein: The step S3 comprises: Additionally, based on the predicted walking distance and / or waiting time of the individual pickers, at least one picker is assigned: a predetermined picking location within the current hot zone, a walking path within the current hot zone, and / or a docking sequence with a handling robot within the current hot zone; and / or Additionally, at least one other picker already in the current hot zone is assigned to go to a predetermined picking location outside the current hot zone based on the predicted walking distance and / or total waiting time of all pickers.

14. The scheduling method according to claim 1 or 2, wherein: The step S3 includes: additionally generating the scheduling instruction based on the moving speed of at least one handling robot, the picking speed and / or moving speed of at least one picker, and the number of remaining picking tasks of at least one picker and / or at least one work area.

15. The scheduling method according to claim 1 or 2, wherein: The dispatch instruction includes assigning a picking mode, wherein a non-binding picking mode is assigned to a transport robot and a picker in a determined work area, and a binding picking mode is assigned to a transport robot and a picker in another determined work area.

16. The scheduling method according to claim 4, wherein: Different hot zones differ in size and / or shape.

17. The scheduling method according to claim 8, wherein: The scheduling method further comprises the following steps: When the total thermal value of all unassigned orders is lower than a first threshold, redetermine the thermal zones for all unassigned orders; and / or When the remaining total heat value of all allocated but uncompleted orders is lower than the second threshold, the uncompleted orders are returned to the order pool as new orders, and the hot zones are re-determined for all current orders in the order pool.

18. The scheduling method according to claim 14, wherein: The dispatch instruction includes instructing the next picking location of the transport robot and / or the picker.

19. The scheduling method according to claim 15, wherein: The dispatch instruction includes allocating a picking mode, wherein a non-binding picking mode is allocated to the transport robots and pickers in the hot zone, and a binding picking mode is allocated to the transport robots and pickers outside the hot zone.

20. A scheduling system (1), the scheduling system (1) being used to execute the scheduling method according to any one of claims 1 to 19, the scheduling system (1) comprising: An analysis module (10) is configured to allocate a thermal value to each order in the order pool according to a picking friendliness index, wherein the picking friendliness index is used to measure the contribution of each order to improving manual picking efficiency, wherein the thermal value is allocated to each order according to the picking friendliness index in the following manner: Find the statistical distribution of the goods contained in each order in the order pool at the picking location, A certain number of picking locations are selected according to the statistical distribution, and at least one area is expanded outwards based on the certain number of picking locations as a hot zone. Determine the first thermal value component based on the proportion of the picking locations corresponding to the goods in each order in the hot zone, Calculating the thermal value based on the first thermal value component; A selection module (20) configured to select, in response to an order dispatching demand, an order whose thermal value meets a preset condition from an order pool; An order dispatching module (30) is configured to generate a dispatching instruction for at least one transport robot (3) based on the selected order.

21. A computer program product, comprising a computer program, wherein the computer program implements the scheduling method according to any one of claims 1 to 19 when the computer executes the computer program.

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