CN119228048A

CN119228048A - Picking review task management method, device, equipment, medium and product

Info

Publication number: CN119228048A
Application number: CN202411328402.4A
Authority: CN
Inventors: 胡可; 刘铮; 邹日佳; 赵磊
Original assignee: Beijing Jd Yuansheng Technology Co ltd
Current assignee: Beijing Jd Yuansheng Technology Co ltd
Priority date: 2024-09-23
Filing date: 2024-09-23
Publication date: 2024-12-31

Abstract

The embodiment of the invention discloses a method, a device, equipment and a medium for managing a picking and rechecking task, wherein the method comprises the steps of obtaining picking task information and picking person information corresponding to an in-progress picking task and working state information of rechecking stations, carrying out information prediction according to the obtained information to obtain residual picking time, average transportation time of goods to each rechecking station, first estimated time of each rechecking station for processing an unfinished picking and rechecking task and second estimated time of each rechecking station for completing the picking and rechecking task corresponding to the rechecking task to be distributed, and determining the target rechecking station corresponding to each rechecking task to be distributed according to the predicted information by taking the shortest rechecking completion estimated time of all the rechecking tasks to be distributed as a target task scheduling result so as to conduct task scheduling management. The technical scheme of the embodiment of the invention can replace manual scheduling of picking and checking tasks, reduce task scheduling cost and improve task scheduling efficiency and effect.

Description

Method, device, equipment, medium and product for managing order picking and rechecking tasks

Technical Field

The embodiment of the invention relates to the technical field of logistics management, in particular to a method, a device, equipment, a medium and a product for managing a goods picking and rechecking task.

Background

Before the goods transported by logistics are delivered out of the warehouse, the goods picking result needs to be checked again. The goods picking trolley for completing a goods picking task is parked at a position by a goods picking person, then the special goods picking task dispatcher dispatches the goods picking trolley to a rechecking platform, and finally the goods in the goods picking trolley is rechecked and delivered by the goods picking rechecking person.

However, in the process of implementing the present invention, it is found that at least the following technical problems exist in the prior art:

When the order picking task dispatcher dispatches the order picking and checking tasks corresponding to each order picking trolley, consideration factors are incomplete, and the overall capacity balance and the efficiency of the order picking and checking tasks cannot be considered. Moreover, the labor cost is high, and the negative influence of human factors in the dispatching process of the picking and checking task cannot be avoided.

Disclosure of Invention

The embodiment of the invention provides a method, a device, equipment, a medium and a product for managing a picking and rechecking task, which can replace manual dispatching of the picking and rechecking task, reduce task dispatching cost, rapidly analyze multidimensional information to determine a dispatching scheme of the picking and rechecking task, and improve task dispatching efficiency and effect.

In a first aspect, an embodiment of the present invention provides a method for managing a pick review task, where the method includes:

Acquiring order picking task information and order picker information corresponding to order picking tasks in a task in-progress state, and acquiring working state information of each review platform;

Information prediction is carried out according to the order picking task information, the order picker information and the working state information, so that residual order picking time of the order picking task, average transportation time for transporting the goods corresponding to the order picking task to each checking platform, first estimated time for each checking platform to process the unfinished order picking checking task and second estimated time for each checking platform to complete the order picking task to be distributed corresponding to the order picking task are obtained;

And determining a target review platform corresponding to each to-be-allocated order-picking review task according to the residual order-picking time length, the average transportation time consumption, the first estimated time consumption and the second estimated time consumption by taking the shortest estimated time for completing the order-picking review task to be allocated as a target task scheduling result so as to complete task scheduling management of the order-picking review task.

In a second aspect, an embodiment of the present invention provides a device for managing a picking review task, including:

The task information acquisition module is used for acquiring the order picking task information and the order picker information corresponding to the order picking task in the task in-progress state and acquiring the working state information of each review platform;

The task processing duration prediction module is used for carrying out information prediction according to the order picking task information, the order picker information and the working state information to obtain the residual order picking duration of the order picking task, the average transportation time for transporting the goods corresponding to the order picking task to each checking platform, the first estimated time for each checking platform to process the unfinished order picking checking task and the second estimated time for each checking platform to complete the order picking checking task to be distributed corresponding to the order picking task;

And the task scheduling processing module is used for determining a target review platform corresponding to each to-be-allocated order-picking review task according to the residual order-picking time length, the average transportation time consumption, the first estimated time consumption and the second estimated time consumption by taking the shortest estimated time for completing the order-picking review task to be allocated as a target task scheduling result so as to complete task scheduling management of the order-picking review task.

In a third aspect, an embodiment of the present invention further provides a computer apparatus, including:

One or more processors;

A memory for storing one or more programs;

when executed by one or more processors, the one or more programs cause the one or more processors to implement the method of order picking review task management as provided by any embodiment of the present invention.

In a fourth aspect, embodiments of the present invention further provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method for managing a pick review task as provided by any of the embodiments of the present invention.

In a fifth aspect, embodiments of the present disclosure also provide a computer program product comprising a computer program which, when executed by a processor, implements a method of order picking review task management as provided by any of the embodiments of the present invention.

The embodiments of the above invention have the following advantages or benefits:

According to the embodiment of the invention, the information prediction is carried out according to the information of the picking task, the information of the picking person and the information of the working state, the remaining picking time of the picking task, the average transportation time for transporting the goods corresponding to the picking task to each rechecking station, the first estimated time for processing the unfinished picking rechecking task by each rechecking station and the second estimated time for completing the picking rechecking task to be distributed corresponding to the picking task by each rechecking station are obtained, the estimated time for completing rechecking of all the rechecking tasks to be distributed is used as a target task scheduling result, and the target rechecking station corresponding to each rechecking task to be distributed is determined according to the remaining picking time, the average transportation time, the first estimated time and the second estimated time, so as to complete task scheduling management of the picking rechecking task. The technical scheme of the embodiment of the invention solves the problems of low efficiency of manually dispatching the order picking and checking task and low task dispatching income caused by incomplete information analysis, can replace the manual dispatching of the order picking and checking task, reduces the task dispatching cost, rapidly analyzes the multidimensional information to determine the dispatching scheme of the order picking and checking task, and improves the task dispatching efficiency and effect.

Drawings

FIG. 1 is a flow chart of a method for managing a pick review task provided by an embodiment of the present invention;

FIG. 2 is a schematic diagram of a process for implementing dispatch management of a pick review task in the prior art according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of a process for managing order picking and review tasks according to an embodiment of the present invention;

FIG. 4 is a flowchart of a method for managing a pick review task according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of a process for managing a pick review task according to an embodiment of the present invention;

FIG. 6 is a flowchart illustration of a method for managing order picking review tasks provided by an embodiment of the present invention;

FIG. 7 is a schematic view of an application example of a method for managing a pick review task according to an embodiment of the present invention;

fig. 8 is a schematic structural diagram of a sorting review task management device according to an embodiment of the present invention;

fig. 9 is a schematic structural diagram of a computer device according to an embodiment of the present invention.

Detailed Description

The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.

Fig. 1 is a flowchart of a method for managing a picking and rechecking task according to an embodiment of the present invention, where the embodiment is applicable to a shipment management scenario of a logistics transported cargo during shipment, and particularly, a situation of picking and rechecking a cargo after picking is completed. The method may be performed by a pick review task management device, which may be implemented in software and/or hardware, integrated into a computer device having application development functionality, such as a warehouse management system (Warehouse MANAGEMENT SYSTEM, WMS).

The current in-warehouse delivery process mainly comprises the processes of order receiving and delivering in warehouse, order positioning, order grouping and order picking task list, picking, rechecking, packaging, delivering and the like, as shown in fig. 2. The goods picking and rechecking process is carried out after a plurality of goods pickers pick up the goods picking tasks corresponding to the picked-up task list, the goods picking trolley filled with the goods is pushed to the outlet of the goods picking storage area, and the goods picking trolley is pushed to a relatively proper rechecking platform by a dispatcher in the warehouse according to the busyness of each rechecking platform. The review station will then combine the goods present in the picking cart in order dimensions and transfer to the following packaging label and out of stock.

In the process, the picking staff can directly stop and disorder the picking trolley after picking the task list, the situation that a plurality of picking trolleys are particularly disordered in the stacking site can be caused in the peak period, and a special dispatcher in a warehouse is required to push the picking trolleys to the rechecking platform manually, so that the labor cost is increased, and the production efficiency is also reduced. Moreover, the pushing of the dispatcher to the checking platform is completely dependent on the busyness of the current checking platform, and factors of different checking efficiency of different checking platforms and different trolley distances are ignored, so that multi-dimensional information can not be rapidly analyzed to realize overall productivity balance and efficiency guarantee. In the current ex-warehouse process, the cheating actions of dispatchers and recheckers in a warehouse cannot be avoided, so that the workload and the counting and lifting in the rechecking link are unbalanced, and the phenomenon of loss of rechecking personnel caused by phase change is caused. The picking trolley for picking abnormal goods of the pickers is unclear in responsibility and cannot effectively restrain the pickers.

In order to improve the current warehouse-out working condition, the technical scheme of the embodiment is provided to perfect the working flow of warehouse-out work and improve the warehouse-out efficiency of goods.

As shown in fig. 1, the method for managing a picking review task in this embodiment includes the following steps:

s110, acquiring order picking task information and order picker information corresponding to order picking tasks in the task in-progress state, and acquiring working state information of each review platform.

The picking task may be a picking task corresponding to at least one order received in the warehouse management system. The target pickles in a pickles task may be determined after a plurality of order groups.

The picking task in the task in-progress state can be any picking task corresponding to the to-be-picked task list after being picked up by the picker. Each pick task in the on-going state of the task is bound to a corresponding picker.

The order picking task information corresponding to the order picking task in the task in-progress state can be information which is related to the order to be picked and has influence on the order picking task completion time, and can also be information representing the order picking task difficulty. For example, the pick job information may be information of quantity of pickles, volume, weight, total weight, warehouse location distribution, number of cross-shelf aisles, and pick job type.

The picker information corresponding to the picking task in the task in-progress state may be information describing that the picking task completion time is affected from the picker dimension, or may be feature information indicating picking efficiency of the picker. For example, information such as age, post, and labor attribute corresponding to the order picker information.

The working state information of each review station is information which has influence on the time efficiency of the picking review task from the dimension of the review station, and comprises information such as business type, span of a review person, backlog condition and the like which indicate the review efficiency of the review station.

The above-mentioned information about the in-progress status of the processing task regarding the pick task takes into account the effect on the pick review task efficiency from multiple dimensions, so as to consider from multiple dimensions how to schedule the in-progress status of the pick task for the corresponding guardian review task after the pick is completed.

S120, carrying out information prediction according to the order picking task information, the order picker information and the working state information to obtain the remaining order picking time of the order picking task, the average transportation time for transporting the goods corresponding to the order picking task to each rechecking platform, the first estimated time for each rechecking platform to process the unfinished order picking rechecking task and the second estimated time for each rechecking platform to complete the order picking task to be allocated corresponding to the order picking task.

The remaining picking time of the picking task, the average transportation time for transporting the goods corresponding to the picking task to each rechecking platform, the first estimated time for each rechecking platform to process the unfinished picking rechecking task and the second estimated time for each rechecking platform to complete the picking task to be allocated corresponding to the picking task are respectively predicted, and the prediction can be performed according to the information data which has influence on the corresponding time in the picking task information, the picker information and the working state information acquired in the previous step.

The specific time length prediction process can be calculated by adopting a corresponding time length prediction model algorithm, and corresponding information in the history order picking task practice process can be matched according to the order picking task information, the order picker information and the corresponding information in the working state information so as to determine the residual order picking time length, the average transportation time consumption, the first estimated time consumption and the second estimated time consumption of a closest history order picking task to be used as corresponding prediction results.

The total time from one picking task to the completion of the corresponding picking and rechecking task includes the sum of the remaining picking time, the time for transporting to the corresponding rechecking station after the picking is completed, the time for waiting for rechecking after the rechecking station and the time for being compounded. The time length waiting for rechecking after the rechecking platform corresponds to the first estimated time consumption of the rechecking platform for processing the unfinished picking rechecking task, and the time length being compounded corresponds to the second estimated time consumption of the picking rechecking task to be distributed, corresponding to the completion of the picking task by the rechecking platform.

S130, determining a target review platform corresponding to each to-be-allocated order-picking review task according to the residual order-picking time length, the average transportation time consumption, the first estimated time consumption and the second estimated time consumption by taking the shortest estimated time for completing the order-picking review task as a target task scheduling result, so as to complete task scheduling management of the order-picking review task.

The scheduling result of the task taking the shortest estimated time for checking the all to-be-allocated picking checking tasks as the target considers the scheduling efficiency of the global picking checking task.

The time consumption of the whole of each possible picking and rechecking task scheduling and distributing scheme can be calculated according to the remaining picking time length, the average transportation time consumption, the first estimated time consumption and the second estimated time consumption, and the scheme with the shortest overall time consumption is taken as a final target scheme.

Suppose there are currently 4 pick review tasks corresponding to the pick tasks, and a total of 3 review stations. Each pick review task may be assigned to any one of the 3 review stations, and then the corresponding allocable scheme is 3 x 3. For each allocable scheme, the estimated time for checking completion of all the picking checking tasks to be allocated can be calculated. The estimated time for rechecking completion of one to-be-allocated picking rechecking task can be calculated and accumulated according to the corresponding residual picking time length, average transportation time consumption, first estimated time consumption and second estimated time consumption, and the estimated time for rechecking completion of the to-be-allocated picking rechecking task is accumulated.

In the scheme that the estimated time for checking the complete of all the to-be-allocated picking and checking tasks is shortest, the corresponding relation between each to-be-allocated picking and checking task and the checking platform is used as the dispatching and allocation result of the final picking and checking task.

Further, the identification information and/or the position information of the target review platform corresponding to each to-be-allocated order picking review task may be sent to the target handheld terminal determined according to the order picker information. So that the corresponding picking person can directly push the picking trolley filled with the picked goods to the target review platform after seeing the target review platform information. This process saves the role of the dispatcher and the decision-making thinking process of the dispatcher. The scheduling of the picking and rechecking tasks is replaced by manual operation, so that the labor cost is reduced, the connection of picking and rechecking is realized, and the task scheduling efficiency is improved.

For example, reference may be made to the comparison of different task scheduling processes shown in FIG. 3. In this embodiment, the whole scheme is that a traditional dispatcher pushes a disordered picking trolley to a current proper rechecking platform to enable the mobility of a picking person to be changed into a rechecking platform pushed by an algorithm, the next picking task list can be picked after the picking person pushes, and otherwise, an account cannot participate in picking. Compared with the original scheme, the method has the advantages that the role of a dispatcher is omitted, the labor cost is saved, unbalance distribution of rechecking tasks is avoided, fraud is avoided, the on-site rechecking production scene is changed from unordered to ordered, and the whole production time is consumed in the optimization stage time.

According to the technical scheme, the picking task information and the picking person information corresponding to the picking tasks in the task in-progress state are obtained, the working state information of each review platform is obtained, information prediction is carried out according to the picking task information, the picking person information and the working state information, the remaining picking time of the picking tasks, average transportation time for transporting goods corresponding to the picking tasks to each review platform, first estimated time for each review platform to process incomplete picking review tasks and second estimated time for each review platform to complete picking tasks to be distributed corresponding to the picking tasks are obtained, the shortest estimated time for completing review of all picking tasks to be distributed is used as a target task scheduling result, and the target review platform corresponding to each picking task to be distributed is determined according to the remaining picking time, the average transportation time, the first estimated time and the second estimated time, so that task scheduling management of the picking tasks to be distributed is completed. The technical scheme of the embodiment of the invention solves the problems of low efficiency of manually dispatching the order picking and checking task and low task dispatching income caused by incomplete information analysis, can replace the manual dispatching of the order picking and checking task, reduces the task dispatching cost, rapidly analyzes the multidimensional information to determine the dispatching scheme of the order picking and checking task, and improves the task dispatching efficiency and effect.

Fig. 4 is a flowchart of a method for managing a picking and rechecking task according to an embodiment of the present invention, where the method for managing a picking and rechecking task in this embodiment and the method for managing a picking and rechecking task in the foregoing embodiment belong to the same inventive concept, and further describe a process for determining a task scheduling scheme. The method can be executed by a goods picking and rechecking task management device, and the device can be realized by a software and/or hardware mode and is integrated into computer equipment with an application development function.

As shown in fig. 4, the method for managing a picking review task in this embodiment includes the following steps:

s210, acquiring picking task information and picking person information corresponding to the picking task in the task in-progress state, and acquiring working state information of each review platform.

S220, carrying out information prediction according to the order picking task information, the order picker information and the working state information to obtain the remaining order picking time of the order picking task, the average transportation time for transporting the goods corresponding to the order picking task to each rechecking platform, the first estimated time for each rechecking platform to process the unfinished order picking rechecking task and the second estimated time for each rechecking platform to complete the order picking task to be allocated corresponding to the order picking task.

The method comprises the steps of inputting order picking task information and order picker information into an order picking time length prediction model to conduct information prediction to obtain the remaining order picking time length of an order picking task, inputting information related to transportation time in the order picking task information and the order picker information into an average transportation time consumption prediction model to obtain average transportation time consumption of transporting goods corresponding to the order picking task to each review platform, inputting work state information into an backlog review task time consumption prediction model to obtain first estimated time consumption of each review platform for processing incomplete order picking review tasks, and inputting the order picking task information and the work state information into a single review task time consumption prediction model to obtain second estimated time consumption of each review platform for completing the order picking task to be distributed.

The order picking time length prediction model, the average transportation time consumption prediction model, the backlog review task time consumption prediction model and/or the single review task time consumption prediction model can be a machine learning model or a deep learning network. The order picking time length prediction model, the average transportation time consumption prediction model, the backlog rechecking task time consumption prediction model and/or the single rechecking task time consumption prediction model can be obtained by performing model training based on order picking task information and order picker information corresponding to order picking tasks in a dispatching process of the order picking rechecking tasks corresponding to historical order picking tasks, working state information of each rechecking platform, and residual order picking time length, average transportation time consumption, first estimated time consumption and second estimated time consumption data determined by the order picking rechecking tasks corresponding to the historical order picking tasks.

In an alternative embodiment, the process of training the pick duration prediction model, the average transportation time consumption prediction model, the backlog review task time consumption prediction model, and/or the single review task time consumption prediction model may include the steps of:

Firstly, acquiring picking task information, picking person information corresponding to each historical picking and rechecking task in the task scheduling process of the historical picking and rechecking task and time consuming duration data of each link in the task scheduling and distributing process, and acquiring working state information of each rechecking platform. And then, respectively constructing a picking time length prediction model, an average transportation time consumption prediction model, a backlog rechecking task time consumption prediction model and/or model training samples of a single rechecking task time consumption prediction model according to the picking task information, the picking person information, the time consumption time length data of each link in the task scheduling and distributing process and the working state information.

In a specific implementation, historical data can be divided into barrels from the dimensions of a picking task sheet, a picking person and a review desk, and the characteristics adopted by the barrels are prior experience input. For example, the data for measuring the difficulty of the picking task comprises the total picking number of the task list, the total channel number, the total weight, the order quantity, the type of the task list and the like, the characteristics for measuring the efficiency of the pickers such as the span, the post and the labor attribute, the characteristics for measuring the rechecking efficiency of the rechecking platform such as the business type, the rechecking personnel span and the backlog condition, and the characteristic data for measuring the difficulty of the picking task comprise the characteristics for measuring the transportation time consumption, such as the storage area crowding degree, the goods distribution and the like, which are included in the information of the picking task. Equations can be constructed in different data dimensions according to the obtained sample data, so as to describe corresponding information. For example:

pick task information f (α _i)＝θ₁(qtys)+θ₂(weight)+θ₃ (taskType) +.

Picker information, g (β _j)＝σ₁(age)+σ₂(experience)+σ₃ (job) +.

Checking the working state information of the table, namely h (gamma _k)＝δ₁(busy)+δ₂(age)+δ₃ (type) +.

Transportation is time-consuming:

Wherein qtys represents quatity, weight represents cargo weight, taskType represents task type, age represents age, experience represents age, job represents post, busy represents busyness, and the number of backlog review tasks can be used to measure, group represents service type grouping of review tasks.

And training each model or equation constructed in advance through a training sample to obtain a corresponding picking time length prediction model, an average transportation time consumption prediction model, a backlog review task time consumption prediction model and/or a single review task time consumption prediction model.

The models trained from the sample data of the above dimensions can be expressed as:

A pick duration prediction model e _ij＝U(f(α_i),g(β_j)), predicts the remaining pick times of the pick task i by the current pick person j.

And an average transportation time consumption prediction model, namely a cost (mu _ik), for predicting the average transportation time consumption of the picking trolley i to the rechecking platform k. Wherein the order picking trolley corresponds to an order picking task.

And the backlog review task time consumption prediction model is busy _k＝V(h(γ_k))*busyQtys_k for predicting the processing backlog time consumption of the review platform.

A single review task time-consuming prediction model: real _ik＝W(f(α_i),h(γ_k)) for predicting the estimated time consumption of the review current task i of the review station k.

S230, calculating estimated time consumption results of each sorting and rechecking task to be distributed from sorting to each rechecking platform according to the remaining sorting time length, the average transportation time consumption, the first estimated time consumption and the second estimated time consumption.

Predicting the time spent by each pick review task to be assigned from completing the pick to assigning to each review station looks at the process shown with reference to fig. 5, and calculates the possible time spent by the pick cart to each review station, including the accumulation of the three parts u _i、f_i and e _ij, respectively. Where u _i corresponds to the sum of the remaining pick time and the average shipping time, f _i corresponds to the first estimated time, and e _ij corresponds to the second estimated time.

S240, calculating the rechecking completion estimated time of all the to-be-allocated picking rechecking tasks corresponding to each feasible task scheduling scheme according to the estimated time consumption result.

The picking and rechecking task is to distribute the picking trolleys generated in the upstream picking link to the relevant rechecking platform, and the time for picking the picking trolleys by the picker can be estimated in advance due to the pre-distribution mode, so that the matching relationship between the picking trolleys and the rechecking platforms can be realized. The concept of a 0-1 integer programming model can be adopted, and the shortest estimated rechecking completion time of all the current task sheets is taken as an objective function, and the method specifically comprises the following steps:

the decision variable is x _ik =0or 1, and represents whether the picking and rechecking task corresponding to the picking task i is rechecked by the rechecking platform k.

The shadow variable y _ij =0or 1, which indicates whether the pick task i was picked by the pick person j, is a known variable.

Objective function:

the constraint conditions for solving the above objective function may be:

S250, determining a target review platform corresponding to each to-be-allocated picking review task according to the corresponding relation between the to-be-allocated picking review task and the review platform in the task scheduling scheme corresponding to the shortest time in the review completion estimated time.

And solving the objective function according to the constraint condition to obtain a solution of the objective function, and then determining the objective review platform corresponding to each sorting review task to be distributed.

According to the technical scheme, the picking task information and the picking person information corresponding to the picking tasks in the task in-progress state are obtained, the working state information of each review platform is obtained, information prediction is carried out according to the picking task information, the picking person information and the working state information and a pre-trained information prediction model, the remaining picking time of the picking tasks, the average transportation time spent on transporting the cargoes corresponding to the picking tasks to each review platform, the first estimated time spent on processing the unfinished picking tasks by each review platform and the second estimated time spent on completing the picking tasks corresponding to the picking tasks by each review platform are obtained, the time spent on completing the review tasks corresponding to the tasks to be distributed according to the remaining picking time, the average transportation time spent, the first estimated time spent on completing the picking tasks and the second estimated time spent on completing the review tasks are calculated respectively, the time spent on completing the review tasks corresponding to the tasks to be distributed according to the pre-estimated time spent on completing the review tasks of each feasible scheduling scheme is calculated according to the estimated time spent on completing the review tasks corresponding to the tasks to be distributed to the review platform. The technical scheme of the embodiment of the invention solves the problems of low efficiency of manually dispatching the order picking and checking task and low task dispatching income caused by incomplete information analysis, can replace the manual dispatching of the order picking and checking task, reduces the task dispatching cost, rapidly analyzes the multidimensional information to determine the dispatching scheme of the order picking and checking task, and improves the task dispatching efficiency and effect.

Fig. 6 is a flowchart of a method for managing a picking and rechecking task according to an embodiment of the present invention, where the method for managing a picking and rechecking task in this embodiment and the method for managing a picking and rechecking task in the foregoing embodiment belong to the same inventive concept, and further illustrate a process for determining a dispatching and allocation scheme of a picking and rechecking task. The method can be executed by a goods picking and rechecking task management device, and the device can be realized by a software and/or hardware mode and is integrated into computer equipment with an application development function.

As shown in fig. 6, the method for managing a picking review task in this embodiment includes the following steps:

s310, acquiring the order picking task information and the order picker information corresponding to the order picking task in the task in-progress state, and acquiring the working state information of each review platform.

S320, inputting the order picking task information and the order picker information into an order picking time length prediction model for information prediction to obtain the remaining order picking time length of the order picking task, inputting the information related to the transportation time in the order picking task information and the order picker information into an average transportation time consumption prediction model to obtain the average transportation time consumption of transporting the goods corresponding to the order picking task to each review platform, inputting the working state information into a backlog review task time consumption prediction model to obtain the first estimated time consumption of each review platform for processing the incomplete order picking review task, and inputting the order picking task information and the working state information into a single review task time consumption prediction model to obtain the second estimated time consumption of each review platform for completing the order picking task to be allocated.

S330, calculating estimated time consumption results of each sorting and rechecking task to be distributed from sorting to each rechecking platform according to the remaining sorting time length, the average transportation time consumption, the first estimated time consumption and the second estimated time consumption.

S340, calculating the estimated time for checking completion of all the sorting and checking tasks to be allocated corresponding to each feasible task scheduling scheme according to the estimated time consumption result, and calculating the coefficient of the busyness of each checking platform in each task scheduling scheme.

The smaller the coefficient of the foundation is used for representing the busyness difference of each review platform, the more fair the distribution of the sorting review tasks of each review platform is.

The process of the coefficient of kunity calculation may be expressed as gini _k(∑_i∈Ix_ik*busy_k).

S350, comprehensively analyzing the estimated time of review completion and the coefficient of the foundation, and determining a target review platform corresponding to each to-be-allocated order picking review task by taking the minimum value of the corresponding comprehensive analysis result as a target task scheduling result.

In this embodiment, the objective function of determining the objective review table corresponding to each pick review task to be allocated may be expressed as:

The multi-objective planning can be performed through the functions, the former term is that the estimated time consumption of the total task list is shortest, the latter term is that the coefficient of the busyness of each review platform is the coefficient, the smaller the description is, the more fair, the balance (trade-off) is performed on two objectives through lambda _i, and the balance of efficiency priority and fair assignment is achieved. The constraint of the multi-objective planning is the same as that in the previous embodiment.

S360, the identification information and/or the position information of the target review platform are sent to the target handheld terminal determined according to the information of the pickers.

And sending the identification information and/or the position information of the target review platform corresponding to each to-be-allocated picking review task to the target handheld terminal determined according to the picker information. So that the corresponding picking person can directly push the picking trolley filled with the picked goods to the target review platform after seeing the target review platform information. This process saves the role of the dispatcher and the decision-making thinking process of the dispatcher. The scheduling of the picking and rechecking tasks is replaced by manual operation, so that the labor cost is reduced, the connection of picking and rechecking is realized, and the task scheduling efficiency is improved.

According to the technical scheme, the method comprises the steps of obtaining the order picking task information and the order picker information corresponding to the order picking task in a task in-progress state, obtaining the work state information of each checking platform, inputting the order picking task information and the order picker information into an order picking time prediction model for information prediction to obtain the residual order picking time of the order picking task, inputting the information related to the transportation time in the order picking task information and the order picker information into an average transportation time prediction model to obtain the average transportation time consumption of the goods corresponding to the order picking task to each checking platform, inputting the work state information into a backcheck task time consumption prediction model to obtain the first estimated time consumption of each checking platform for processing the incomplete order picking task, inputting the order picking task information and the work state information into a single backcheck task time consumption prediction model to obtain the second estimated time consumption of each checking platform for completing the order picking task, calculating the estimated time consumption of each order picking task to be distributed according to the residual order picking time, the average transportation time consumption, calculating the first estimated time consumption and the second estimated time consumption, calculating the coefficient of each checking task to be distributed to the base and the complete task to the complete scheduling coefficient of each checking task, and analyzing the time coefficient of each checking platform to be the complete according to the estimated time coefficient of each checking platform, and transmitting the identification information and/or the position information of the target review station to the target handheld terminal determined according to the information of the pickers. The technical scheme of the embodiment of the invention solves the problems of low efficiency of manual dispatching, picking and rechecking of tasks and low task dispatching income caused by incomplete information analysis, and can realize global efficiency improvement on the basis of the efficiency of batch picking and rechecking of tasks and a constraint dispatching algorithm with fairness and optimal fairness, so that in-bin rechecking of the tasks can be realized on the basis of fairness. An application example of the method for managing the order picking and rechecking task is shown in fig. 7, which shows that after the scheme of the embodiment of the invention is used, the total rechecking time is reduced from 14.4h to 12.1h, and the single bin is changed from 5.3 rechecking stations which provide rechecking capability on average to 6.6, and furthermore, the average labor cost for one year without intervention of a dispatcher is 2.3h×365×25 (time salary) ×750 (medium and small parts warehouse number) +0.7 (average dispatcher number) ×8500 (month wage) ×12×750 (medium and small parts warehouse number) of the dispatcher labor cost is approximately 6929w. In addition, there are potential benefits such as reduced personnel loss and increased equipment utilization.

Fig. 8 is a schematic structural diagram of a sorting and rechecking task management device according to an embodiment of the present invention, where the present embodiment may be suitable for a shipment management scenario of a logistics transported cargo during shipment, and particularly for a situation of sorting and rechecking a cargo after sorting is completed, where the sorting and rechecking task management device may be implemented by software and/or hardware, and is integrated in a computer terminal device with an application and development function.

As shown in fig. 8, the order review task management device includes a task information acquisition module 410, a task processing duration prediction module 420, and a task scheduling processing module 430.

The task information acquiring module 410 is configured to acquire picking task information and picking person information corresponding to a picking task in a task in progress state, and acquire working state information of each review platform; the task processing duration prediction module 420 is configured to predict information according to the order picking task information, the order picker information and the working state information, obtain a remaining order picking duration of the order picking task, an average transportation time for transporting the goods corresponding to the order picking task to each review platform, a first estimated time for each review platform to process the unfinished order picking review task, and a second estimated time for each review platform to complete the order picking task to be allocated corresponding to the order picking task, and the task scheduling processing module 430 is configured to determine a target review platform corresponding to each order picking review task according to the remaining order picking duration, the average transportation time, the first estimated time, and the second estimated time by taking the shortest estimated time for the review completion of all order picking review tasks as a target task scheduling result, so as to complete task scheduling management of the order picking review task.

In an alternative embodiment, the task scheduling processing module 430 is specifically configured to:

According to the remaining picking time length, the average transportation time consumption, the first estimated time consumption and the second estimated time consumption, respectively calculating the estimated time consumption result from the completion of picking to the completion of rechecking of each rechecking platform for each picking rechecking task to be distributed;

Calculating the rechecking completion estimated time of all the to-be-allocated picking rechecking tasks corresponding to each feasible task scheduling scheme according to the estimated time consumption result;

And determining a target review platform corresponding to each to-be-allocated picking review task according to the corresponding relation between the to-be-allocated picking review task and the review platform in the task scheduling scheme corresponding to the shortest time in the review completion estimated time.

In an alternative embodiment, the task scheduling processing module 430 may be further specifically configured to:

Calculating a coefficient of the busyness of each review station in each task scheduling scheme;

And determining a target review platform corresponding to each to-be-allocated picking review task by taking the minimum comprehensive analysis result value corresponding to the coefficient of the foundation and the review completion estimated time as a target task scheduling result.

In an optional embodiment, the order picking and rechecking task management device further includes a task scheduling management information sending module, configured to:

And sending the identification information and/or the position information of the target review platform to the target handheld terminal determined according to the information of the pickers.

In an alternative embodiment, the task processing duration prediction module 420 is specifically configured to:

Inputting the order picking task information and the order picker information into an order picking time length prediction model for information prediction to obtain the residual order picking time length of the order picking task;

inputting the information related to the transportation time in the order picking task information and the order picking person information into an average transportation time consumption prediction model to obtain average transportation time consumption for transporting the goods corresponding to the order picking task to each review platform;

inputting the working state information into a backlog review task time consumption prediction model to obtain first estimated time consumption of each review station for processing the incomplete picking review task;

and inputting the order picking task information and the working state information into a single review task time consumption prediction model to obtain second estimated time consumption of the order picking review task to be allocated, wherein the second estimated time consumption is corresponding to each review platform for completing the order picking task.

In an alternative embodiment, the order picking and review task management device further comprises a model training module for:

training the picking time length prediction model, the average transportation time consumption prediction model, the backlog review task time consumption prediction model and/or the single review task time consumption prediction model, wherein the training process comprises the following steps of:

Acquiring picking task information, picking person information and time-consuming duration data of each link in the task scheduling and distributing process of each historical picking and rechecking task in the task scheduling process of the historical picking and rechecking task, and acquiring working state information of each rechecking platform;

Respectively constructing a picking time length prediction model, an average transportation time length prediction model, a backlog review task time length prediction model and/or a model training sample of a single review task time length prediction model according to picking task information, picking person information, time length data of each link in a task scheduling and distributing process and working state information;

and performing model training based on the model training sample to obtain a picking time length prediction model, an average transportation time consumption prediction model, a backlog review task time consumption prediction model and/or a single review task time consumption prediction model.

The order picking and rechecking task management device provided by the embodiment of the invention can execute the order picking and rechecking task management method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

Fig. 9 is a schematic structural diagram of a computer device according to an embodiment of the present invention. Fig. 9 illustrates a block diagram of an exemplary computer device 12 suitable for use in implementing embodiments of the present invention. The computer device 12 shown in fig. 9 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention. The computer device 12 may be any terminal device with computing power, such as an intelligent controller, a server, a mobile phone, and the like.

As shown in fig. 9, the computer device 12 is in the form of a general purpose computing device. Components of computer device 12 may include, but are not limited to, one or more processors or processing units 16, a system memory 28, and a bus 18 that connects the various system components, including system memory 28 and processing units 16.

Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

Computer device 12 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by computer device 12 and includes both volatile and nonvolatile media, removable and non-removable media.

The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. The computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 9, commonly referred to as a "hard disk drive"). Although not shown in fig. 9, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. The system memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of the embodiments of the invention.

A program/utility 40 having a set (at least one) of program modules 42 may be stored in, for example, system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods of the embodiments described herein.

The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with the computer device 12, and/or any devices (e.g., network card, modem, etc.) that enable the computer device 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. Moreover, computer device 12 may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through network adapter 20. As shown, network adapter 20 communicates with other modules of computer device 12 via bus 18. It should be appreciated that although not shown in FIG. 9, other hardware and/or software modules may be used in connection with computer device 12, including, but not limited to, microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.

The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, for example, implementing the order picking review task management method provided by the present embodiment, the method includes:

Carrying out information prediction according to the order picking task information, the order picker information and the working state information to obtain the residual order picking time of the order picking task, the average transportation time for transporting the goods corresponding to the order picking task to each rechecking platform, the first estimated time for each rechecking platform to process the unfinished order picking rechecking task and the second estimated time for each rechecking platform to complete the order picking task to be allocated;

And determining a target review platform corresponding to each to-be-allocated order-picking review task according to the residual order-picking time length, the average transportation time consumption, the first estimated time consumption and the second estimated time consumption by taking the shortest estimated time for completing the order-picking review task as a target task scheduling result so as to complete task scheduling management of the order-picking review task.

The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the method for managing a pick review task provided by any embodiment of the invention, the method comprising:

The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations of the present invention may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

It will be appreciated by those of ordinary skill in the art that the modules or steps of the invention described above may be implemented in a general purpose computing device, they may be centralized on a single computing device, or distributed over a network of computing devices, or they may alternatively be implemented in program code executable by a computer device, such that they are stored in a memory device and executed by the computing device, or they may be separately fabricated as individual integrated circuit modules, or multiple modules or steps within them may be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.

The disclosed embodiments also provide a computer program product comprising a computer program which, when executed by a processor, implements a method of order picking review task management as provided by any of the embodiments of the present disclosure.

Computer program product in an implementation, computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.

Claims

1. The method for managing the goods picking and rechecking tasks is characterized by comprising the following steps of:

2. The method according to claim 1, wherein the determining, with the shortest estimated time for review completion of all the pick-up tasks to be allocated as a target task scheduling result, the target review station corresponding to each pick-up task to be allocated according to the remaining pick-up duration, the average transportation time consumption, the first estimated time consumption, and the second estimated time consumption includes:

According to the remaining picking time length, the average transportation time consumption, the first estimated time consumption and the second estimated time consumption, respectively calculating estimated time consumption results from the completion of picking to the completion of rechecking of each rechecking platform for each picking task to be distributed;

Calculating the rechecking completion estimated time of all the rechecking tasks to be distributed corresponding to each feasible task scheduling scheme according to the estimated time consumption result;

3. The method according to claim 2, wherein the method further comprises:

And determining a target review platform corresponding to each to-be-allocated picking review task by taking the minimum comprehensive analysis result value corresponding to the coefficient of the foundation and the review completion estimated time as the target task scheduling result.

4. The method according to claim 1, wherein the method further comprises:

and sending the identification information and/or the position information of the target review platform to a target handheld terminal determined according to the picker information.

5. The method according to any one of claims 1 to 4, wherein the performing information prediction according to the order picking task information, the order picker information, and the working state information, to obtain a remaining order picking time of the order picking task, an average transportation time for transporting the goods corresponding to the order picking task to each of the review platforms, a first estimated time for each of the review platforms to process an incomplete order picking review task, and a second estimated time for each of the review platforms to complete an order picking review task to be allocated corresponding to the order picking task, includes:

Inputting the working state information into a backlog review task time consumption prediction model to obtain first estimated time consumption of each review platform for processing the incomplete picking review task;

And inputting the order picking task information and the working state information into a single review task time consumption prediction model to obtain second estimated time consumption of each review platform for completing the order picking task to be allocated corresponding to the order picking task.

6. The method of claim 5, wherein training the pick duration prediction model, the average transportation time consumption prediction model, the backlog review task time consumption prediction model, and/or the single review task time consumption prediction model comprises:

Respectively constructing the order picking time length prediction model, the average transportation time length prediction model, the backlog review task time length prediction model and/or model training samples of the single review task time length prediction model according to the order picking task information, the order picker information, the time length data of each link in the task scheduling and distributing process and the working state information;

And performing model training based on the model training sample to obtain the picking time length prediction model, the average transportation time consumption prediction model, the backlog review task time consumption prediction model and/or the single review task time consumption prediction model.

7. A pick review task management device, comprising:

8. A computer device, the computer device comprising:

One or more processors;

A memory for storing one or more programs;

When executed by the one or more processors, causes the one or more processors to implement the pick review task management method of any of claims 1-6.

9. A computer readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements a method of order picking review task management as claimed in any one of claims 1 to 6.

10. A computer program product comprising a computer program which, when executed by a processor, implements a method of order picking review task management as claimed in any one of claims 1 to 6.