CN117217644B - Resource allocation method and device for logistics operation task - Google Patents

Abstract

The invention relates to the technical field of logistics, in particular to a resource allocation method and a resource allocation device for a logistics operation task, which are used for determining whether a target optimal solution algorithm for carrying out resource allocation calculation is a local optimal solution algorithm or a global optimal solution algorithm according to a preset objective function; determining target constraint conditions according to task skill requirement data, resource skill attribute data and resource work skill data; and obtaining resource allocation data by utilizing a target optimal solution algorithm according to the task position data, the resource position data, the target function and the target constraint condition. Compared with the prior art, the method and the device can realize mixed scheduling of different types of execution resources, and can select a more reasonable optimal solution algorithm according to actual application requirements, so that manual participation is not needed in the resource allocation process of logistics operation, the logistics operation efficiency is improved, and the cost of the logistics operation can be reduced.

Description

Resource allocation method and device for logistics operation task

Technical Field

The application relates to the technical field of logistics, in particular to a resource allocation method and device for logistics operation tasks.

Background

In a logistics operation, the logistics operation on site can use different execution resources to execute the logistics operation tasks, and the execution resources can comprise forklift trucks, tractors, workers and the like. The forklift can be used for unloading, material warehouse entry, material selection and material warehouse exit, and the tractor can be used for carrying out delivery operation, namely, transporting and delivering materials to a production line warehouse. Each logistic job task needs to be allocated to the appropriate execution resources in combination with the required skills.

In the prior art, the main mode for solving the problems is that a task issuing system is combined with experience of field personnel, the field personnel automatically select logistics operation tasks, and the logistics operation tasks are independently executed off line. The method is too dependent on experience of field staff, so that staff training cost is increased, actual operation conditions and operation arrangement of execution resources of field executable logistics operation business are unknown, and logistics operation efficiency is low and related data cannot be statistically analyzed.

Disclosure of Invention

Accordingly, one of the technical problems to be solved by the embodiments of the present invention is to provide a resource allocation method and device for logistics task, which are used for solving the problems of high cost and low efficiency of logistics task caused by task allocation of field operators which are too dependent on experience in the prior art.

An embodiment of the present application discloses a resource allocation method for a logistics job task in a first aspect, where the method includes:

according to a preset objective function, determining a target optimal solution algorithm for carrying out resource allocation calculation as a local optimal solution algorithm or a global optimal solution algorithm;

determining target constraint conditions according to task skill requirement data, resource skill attribute data and resource work skill data; the task skill requirement data are used for representing skills required by carrying out logistics operation on materials corresponding to each task to be distributed; the resource skill attribute data is used for representing the skill possessed by each execution resource capable of executing the task to be distributed; the resource work skill data is used for representing the skill used by the execution resources capable of executing the tasks to be distributed to execute the tasks which are not completed at present; the target constraint condition at least comprises a first sub-condition, wherein the first sub-condition is that skills required for carrying out logistics operation on materials corresponding to each task to be allocated are matched with skills possessed by corresponding execution resources, and the skills required for carrying out logistics operation on the materials corresponding to each task to be allocated are matched with skills used by corresponding execution resources to execute tasks which are not completed currently;

Obtaining resource allocation data by utilizing the target optimal solution algorithm according to the task position data, the resource position data, the target function and the target constraint condition; the task position data are used for representing a starting point position and an end point position corresponding to each task to be distributed; the resource position data is used for representing a target position of an execution resource capable of executing the task to be distributed, wherein the target position is a real-time position or an end position for executing a task which is not completed currently; the resource allocation data is used for representing the execution resources corresponding to each task to be allocated.

A second aspect of the embodiments of the present application discloses a resource allocation device for a logistics operation task, where the device includes:

the first determining module is used for determining that a target optimal solution algorithm for carrying out resource allocation calculation is a local optimal solution algorithm or a global optimal solution algorithm according to a preset target function;

the second determining module is used for determining target constraint conditions according to the task skill requirement data, the resource skill attribute data and the resource work skill data; the task skill requirement data are used for representing skills required by carrying out logistics operation on materials corresponding to each task to be distributed; the resource skill attribute data is used for representing the skill possessed by each execution resource capable of executing the task to be distributed; the resource work skill data is used for representing the skill used by the execution resources capable of executing the tasks to be distributed to execute the tasks which are not completed at present; the target constraint condition at least comprises a first sub-condition, wherein the first sub-condition is that skills required for carrying out logistics operation on materials corresponding to each task to be allocated are matched with skills possessed by corresponding execution resources, and the skills required for carrying out logistics operation on the materials corresponding to each task to be allocated are matched with skills used by corresponding execution resources to execute tasks which are not completed currently;

The data acquisition module is used for acquiring resource allocation data by utilizing the target optimal solution algorithm according to the task position data, the resource position data, the target function and the target constraint condition; the task position data are used for representing a starting point position and an end point position corresponding to each task to be distributed; the resource position data is used for representing a target position of an execution resource capable of executing the task to be distributed, wherein the target position is a real-time position or an end position for executing a task which is not completed currently; the resource allocation data is used for representing the execution resources corresponding to each task to be allocated.

In the resource allocation method and device for the logistics operation task, according to a preset objective function, a target optimal solution algorithm for carrying out resource allocation calculation is determined to be a local optimal solution algorithm or a global optimal solution algorithm; determining target constraint conditions according to task skill requirement data, resource skill attribute data and resource work skill data; and obtaining resource allocation data by utilizing the target optimal solution algorithm according to the task position data, the resource position data, the target function and the target constraint condition. Compared with the prior art, the method and the device can realize mixed scheduling of different types of execution resources, and can select a more reasonable optimal solution algorithm according to actual application requirements, so that manual participation is not needed in the resource allocation process of logistics operation, the logistics operation efficiency is improved, and the cost of the logistics operation can be reduced.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic flow chart of a resource allocation method for a logistics task in accordance with an embodiment of the present application;

FIG. 2 is a schematic flow chart of a method for allocating resources for a logistics task disclosed in example II of the present application;

fig. 3 is a schematic block diagram of a resource allocation device for a logistics task disclosed in example three of the present application.

Detailed Description

The following description of the technical solutions in the embodiments of the present application will be made clearly and completely with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.

It should be noted that the terms "first," "second," "third," and "fourth," etc. in the description and claims of the present application are used for distinguishing between different objects and not for describing a particular sequential order. The terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, apparatus, article, or device that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or device.

Example one

As shown in fig. 1, fig. 1 is a schematic flowchart of a resource allocation method of a logistics task disclosed in an example of the present application, where the resource allocation method includes:

step S101, determining a target optimal solution algorithm for performing resource allocation calculation as a local optimal solution algorithm or a global optimal solution algorithm according to a preset target function.

In this embodiment, in the process of the logistics operation, according to whether the logistics operation task is allocated with the execution resources, the logistics operation task which is not yet executed and completed can be divided into two types, one type is the task which is already allocated with the execution resources, and the other type is the task which is not yet allocated with the execution resources, wherein the task to be allocated is the task which is not yet allocated with the execution resources. Further, tasks may be categorized into several types depending on the skill required to perform the task, for example, the types of tasks may include tallying, picking, unloading, warehousing, and the like.

In this embodiment, the execution resources are persons or things that can be used to execute the task to be allocated, and these persons and things need to be in a working state. There may be multiple execution resources during the logistic operation, each of which may include one or more skills, and the execution resources may complete the logistic operation task by using the corresponding skills. Wherein the different execution resources may be of the same or different types, and may be at least one of a forklift, a tractor, and a worker, for example.

In this embodiment, the objective function is used to characterize the objective condition that needs to be satisfied by the execution resource in the process of executing the task to be allocated. In the actual application process, different demands may be made on the resource allocation mode under different conditions, so that one or more objective functions can be preset, and the objective optimal solution algorithm is determined according to the preset objective functions, so that the execution resources corresponding to each finally determined task to be allocated are more reasonable.

In this embodiment, the target optimal solution algorithm is an optimal solution algorithm for executing resource allocation calculation, and may be one of a local optimal solution algorithm and a global optimal solution algorithm. The specific types of the local optimal solution algorithm and the global optimal solution algorithm are not limited, and can be reasonably selected according to actual application requirements. For example, to improve the efficiency and reliability of the solution, it may be preferable that the locally optimal solution algorithm is one of greedy algorithms; the globally optimal solution algorithm may preferably be one of an exact algorithm or one of a heuristic algorithm.

Optionally, when the target optimal solution algorithm is determined to be the global optimal solution algorithm, the target optimal solution algorithm type more meeting the actual application requirement may be determined according to the number of tasks to be allocated and the number of execution resources capable of executing the tasks to be allocated. Specifically, step S101 may include the following sub-steps S101a and S101b:

in sub-step S101a, when the target optimal solution algorithm is determined to be the global optimal solution algorithm, if the number of tasks to be allocated is smaller than the first number threshold and the number of execution resources capable of executing the tasks to be allocated is smaller than the second number threshold, the first optimal solution algorithm is determined to be the target optimal solution algorithm.

In sub-step S101b, if the number of tasks to be allocated is greater than or equal to the first number threshold and the number of execution resources capable of executing the tasks to be allocated is greater than or equal to the second number threshold, determining the second optimal solution algorithm as the target optimal solution algorithm.

The specific values of the first number threshold and the second number threshold are not limited, and can be reasonably selected according to actual application requirements.

The first optimal solution algorithm and the second optimal solution algorithm are global optimal solution algorithms of different types, the specific types of the algorithms are not limited, and the algorithms can be reasonably selected according to actual application requirements. For example, to improve the efficiency and reliability of the solution, it may be preferable that the first optimal solution algorithm is one of the exact algorithms, and that the second optimal solution algorithm is one of the heuristic algorithms.

Step S102, determining target constraint conditions according to task skill requirement data, resource skill attribute data and resource work skill data.

In this embodiment, task skill requirement data is used to characterize the skill required for carrying out a logistic operation on the material corresponding to each task to be allocated.

In this embodiment, the resource skill attribute data is used to characterize the skill possessed by the execution resource of each executable task to be allocated.

In this embodiment, the resource work skill data is used to characterize the skill used by the execution resources that can execute the task to be allocated to execute the task that is not currently completed.

In this embodiment, the target constraint condition is a constraint on a solution result when the solution is performed by using a target optimal solution algorithm. In the practical application process, the skills of the resources required for executing different types of logistics operation tasks may be different, and the tasks may be successfully executed only when the skills required for carrying out logistics operation on the materials corresponding to the logistics operation tasks are matched with the skills of the resources.

In addition, before the resource allocation method of the present embodiment is executed, it may be in an idle state for executing the resource, or may be in a state of executing the allocated logistics job task. When the execution resource is in a state of executing the allocated logistics job task, the currently used skill thereof has been determined, and if the skill required for the allocated task to be allocated to the execution resource is inconsistent with the currently used skill thereof when the resource allocation method of the embodiment performs the resource allocation calculation, the work efficiency thereof is likely to be affected. Thus, the target constraint may be determined based on the task skill requirement data, the resource skill attribute data, and the resource work skill data.

Specifically, the target constraint condition at least comprises a first sub-condition, wherein the first sub-condition is that skills required for carrying out logistics operation on materials corresponding to each task to be allocated are matched with skills of corresponding execution resources, and the skills required for carrying out logistics operation on the materials corresponding to each task to be allocated are matched with skills used by the corresponding execution resources to execute tasks which are not completed currently.

The matching of the skills required by the material to be allocated with the tasks and the skills of the corresponding execution resources means that the skills of the execution resources comprise the skills required by the material to be allocated with the tasks.

The fact that the skills required by the material corresponding to the task to be distributed for carrying out the logistics operation are matched with the skills required by the corresponding execution resources for executing the task which is not completed currently means that the skills required by the execution resources for executing the task which is not completed currently are the same as or similar to the skills required by the material corresponding to the task to be distributed for carrying out the logistics operation.

Furthermore, the number of sub-conditions included by the target constraint is not limited, i.e. several other sub-conditions may be included in addition to the first sub-condition.

Optionally, considering that one execution resource may have a plurality of different skills, there may be a plurality of execution resources available for executing the same task to be allocated, in order to simplify the complexity of the calculation in the subsequent step S103, to improve the calculation processing efficiency, step S102 may include:

and step S102a, obtaining resource target skill data according to the resource skill attribute data and the resource work skill data.

Sub-step S102b, determining a target constraint condition based on the task skill requirement data and the resource target skill data.

The resource target skill data is used for representing a skill corresponding to each execution resource executing task to be distributed. That is, in sub-step S102a, a target skill is determined from among the plurality of skills of each execution resource, so that in sub-step S102b, the target constraint condition is determined only according to the target skill and task skill requirement data of each execution resource.

When an execution resource executes a task that is not yet completed currently, determining in the substep S102a that a skill corresponding to the execution resource executing the task to be allocated is a skill for executing the task that is not yet completed currently; when an execution resource performs a task that has not yet been completed, in sub-step S102a, one skill corresponding to the task to be allocated by the execution resource may be selected from all the skills possessed by the execution resource according to other rules.

For example, if the skill required for performing the logistic operation on the material corresponding to the at least one task to be allocated matches the main skill of the execution resource, it may be determined in the substep S102a that one skill corresponding to the execution resource executing the task to be allocated is the main skill thereof; if the skills required for carrying out the logistic operation on the materials corresponding to all the tasks to be allocated do not match with the main skills of the execution resources, in the substep S102a, it may be determined that one of the skills corresponding to the execution resources for executing the tasks to be allocated is the auxiliary skill with the highest proficiency.

In this embodiment, the implementation sequence of step S102 and step S101 is not limited, and may be reasonably selected according to the actual application requirement.

And step S103, obtaining resource allocation data by utilizing a target optimal solution algorithm according to the task position data, the resource position data, the target function and the target constraint condition.

In this embodiment, the task position data is used to characterize a start position and an end position corresponding to each task to be allocated. The starting point position corresponding to the task to be allocated is the position of the material corresponding to the task to be allocated when the task to be allocated is not executed; the end position corresponding to the task to be distributed refers to the position of the material corresponding to the task to be distributed when the task to be distributed is completed.

In this embodiment, the resource location data is used to characterize a target location of an execution resource that can execute a task to be allocated, where the target location is a real-time location or an end location for executing a task that has not been completed at present. Under the condition that the task which is not completed currently by the execution resource is determined, the end position of the task which is not completed currently by the execution resource can be estimated according to the real-time position of the execution resource.

In this embodiment, the resource allocation data is used to characterize execution resources corresponding to each task to be allocated. After the resource allocation data is obtained, a corresponding task instruction can be sent to the corresponding execution resource according to the resource allocation data.

Optionally, in consideration of that in the actual application process, the determined execution resources corresponding to the tasks to be allocated may not necessarily be able to respond to and execute the tasks in time, in order to ensure that all the tasks to be allocated are executed smoothly and improve the logistics operation efficiency, after step S103, the embodiment further includes the following steps S104 and S105:

step S104, according to the resource allocation data, a task execution instruction is sent to the execution resource corresponding to each task to be allocated.

Step S105, when response data returned by the execution resource according to the task execution instruction is received within the preset response time, path planning data corresponding to the execution resource is obtained according to the response data, the task position data and the resource position data.

In step S104, the task execution instruction is used to characterize related information of at least one task to be allocated that needs to be executed by the execution resource. The relevant information included in the task execution instruction is not limited, and can be reasonably selected according to actual application requirements.

In step S105, the specific value and the setting mode of the preset response time are not limited, and may be reasonably selected according to the actual application requirement.

Wherein, in step S105, the response data is used to characterize at least one task to be allocated for executing the resource confirmation. The path planning data is used for characterizing the running path arrangement of each execution resource when all tasks to be allocated for confirmed execution are executed or all tasks which are not completed are executed. All tasks which are not completed in the execution resource comprise all tasks to be allocated for the execution resource at this time and tasks which are not completed currently.

Further, to facilitate management and monitoring of the logistics task, the present embodiment may further include at least one of the following steps a-C:

and step A, performing visual display according to path planning data corresponding to the execution resources.

And B, acquiring task execution data corresponding to the execution resources according to the preset time interval value, and performing early warning processing when the overtime phenomenon of the execution tasks of the execution resources is determined according to the task execution data. The task execution data is used for at least representing the starting time point and/or the finishing time point of the task executed by the execution resource.

And step C, analyzing and visually displaying the task execution condition of the execution resource according to the task execution data corresponding to the execution resource.

Optionally, in order to avoid that the task to be allocated that is not acknowledged by the execution resource is not executed from being missed, the present embodiment further includes the following step S106:

and S106, determining the next task to be allocated according to the response data.

Among all the tasks to be allocated for the current resource allocation, the task to be allocated which is not confirmed to be executed by the execution resource corresponding to the task to be allocated is the task to be allocated.

Alternatively, in order to improve the efficiency of the logistics operation, the material corresponding to the task to be allocated may be allocated to the execution resource closest to it. Specifically, the objective function includes a first sub-function, which is: the target position of the execution resource corresponding to each task to be allocated is closest to the starting point position of the material corresponding to each task to be allocated.

Correspondingly, step S101 may include: when the objective function includes a first sub-function, the objective optimal solution algorithm is determined to be a local optimal solution algorithm.

The specific determination mode of the distance between the target position of the execution resource corresponding to each task to be allocated and the starting point position of the material corresponding to each task to be allocated is not limited, and reasonable selection can be performed according to actual application requirements.

Optionally, in order to make the accumulated working time length of the execution resources of all the executable tasks to be allocated more uniform, the objective function may include a second sub-function, where the second sub-function is: and the maximum difference value of the accumulated working time is the smallest from the preset time point to the time of executing the execution resources corresponding to all the tasks to be distributed.

Correspondingly, step S102 may include: and when the objective function comprises the second sub-function, determining the objective optimal solution algorithm as a global optimal solution algorithm.

Step S103 may include: and obtaining resource allocation data by utilizing a target optimal solution algorithm according to the task position data, the resource working time length data, the target function and the target constraint condition.

The resource working time length data are used for representing the accumulated working time length from a preset time point to the time of starting to execute the task to be distributed of execution resources of all executable tasks to be distributed.

The specific value-taking mode of the preset time point is not limited, and the specific value-taking mode can be reasonably selected according to actual application requirements.

For example, if according to a resource allocation scheme, execution resources corresponding to all tasks to be allocated are A, B and C, respectively, and when a finishes executing all tasks allocated to the tasks from a preset time point, the accumulated working time is 2 hours; b, accumulating the working time from a preset time point to the time of executing all tasks distributed to the time point, wherein the accumulated working time is 3 hours; and C, when all tasks distributed to the resource distribution scheme are executed from a preset time point to completion, the accumulated working time length is 1 hour, and the maximum difference value of the accumulated working time length from the preset time point to the completion of all tasks to be distributed by the execution resources corresponding to all tasks to be distributed in the resource distribution scheme is 2 hours, namely the difference value of the accumulated working time lengths of A and C.

Alternatively, when the number of tasks to be distributed is plural, in order to improve the efficiency of the logistics operation, the shortest time for executing the resource execution to complete all the tasks to be distributed may be taken as the target condition. Specifically, the objective function includes a third sub-function, which is: the total time consumed by execution of the execution resources to complete all of the tasks to be allocated is minimal.

The total time spent for executing and completing all the tasks to be distributed is the total time spent by a plurality of execution resources for executing and completing all the tasks to be distributed. The specific determination mode of time consumption for executing the resource to complete all tasks to be distributed is not limited, and reasonable selection can be performed according to actual application requirements. For example, the method can be calculated and determined according to a starting point position and an end point position corresponding to each task to be allocated, an end point position of execution resources capable of executing the task to be allocated for completing a task which is not completed currently, and an estimated speed value of each execution resource in combination with a preset path planning algorithm.

Alternatively, when the number of tasks to be distributed is plural, in order to save the energy consumption of the logistics job, the shortest distance for executing the resource to complete all the tasks to be distributed may be taken as the target condition. Specifically, the objective function includes a fourth sub-function, which is: the execution resource execution completes the shortest distance of all tasks to be allocated.

The distance for executing and completing all tasks to be distributed is the sum of the distance traveled when a plurality of execution resources execute and complete all tasks to be distributed. The specific determination mode of the distance for executing the resource to complete all the tasks to be distributed is not limited, and the distance can be reasonably selected according to the actual application requirements. For example, the final position of the task which is not completed at present can be calculated and determined according to the starting point position and the final position corresponding to each task to be allocated and the final position of the task which is not completed at present and is performed by the execution of the execution resources of the executable task to be allocated in combination with a preset path planning algorithm.

Optionally, in the practical application process, since the starting point positions and the end point positions corresponding to different logistics operation tasks may be located in different areas with a relatively long distance, in order to improve the efficiency of logistics operation, the matched execution resources may be selected according to the position areas corresponding to the tasks to be allocated. Specifically, the target constraint includes a second sub-condition that is: the region identifier corresponding to each task to be allocated is matched with the region identifier of the corresponding execution resource.

Correspondingly, step S102 may include: determining a region identifier corresponding to each task to be allocated according to the task position data, and determining a region identifier of an execution resource of each executable task to be allocated according to the resource position data; and determining the second sub-condition as that the region identifier corresponding to each task to be allocated is matched with the region identifier of the corresponding execution resource.

The area identifier corresponding to each task to be allocated can be determined according to the starting point position and/or the end point position of the material corresponding to each task to be allocated.

Wherein the region of execution resources that can execute the task to be allocated identifies the region for identifying the task that can execute it. The specific identifier and the setting mode of the area identifier of each executable execution resource of the task to be allocated are not limited, and the reasonable selection can be performed according to the actual application requirements. For example, the area identifier corresponding to the execution resource may be fixedly set, and the area identifier corresponding to the target location of the execution resource may also be determined to be the corresponding area identifier.

Optionally, in the practical application process, the types corresponding to different tasks to be allocated may be different, and for one execution resource, the efficiency of executing the tasks of the same type may be higher, so in order to improve the logistics operation efficiency, the target constraint condition may include a third sub-condition, where the third sub-condition is: all tasks to be allocated corresponding to each execution resource are the same in type.

Correspondingly, step S102 may include: and determining target constraint conditions according to the task skill requirement data, the resource skill attribute data, the task type data and the resource work skill data.

The task type data are used for representing the type corresponding to each task to be distributed.

Alternatively, since there may be one or more skills available for different execution resources during the actual application, if the skills are frequently switched during the work, a certain influence is exerted on the work efficiency and the work quality. Thus, in order to improve the efficiency and quality of the logistics operation, the target constraint may include a fourth sub-condition, which is: the skills used by each execution resource in executing the corresponding all tasks to be allocated are the same.

Alternatively, in order to ensure the success rate of the logistics operation while the efficiency of the logistics operation is improved, it is necessary that the total amount of tasks allocated for each executable task to be allocated for execution of the resources does not exceed the upper limit of the total amount of tasks that can be carried by the task. Specifically, the target constraint further includes a fifth sub-condition, which is: the number of all tasks to be allocated corresponding to each execution resource is smaller than a preset number threshold.

The specific value of the preset number of thresholds is not limited, and can be reasonably selected according to actual application requirements.

Optionally, in the practical application process, in order to ensure the success rate of the logistics operation while improving the efficiency of the logistics operation, the target constraint condition includes a sixth sub-condition that: the execution resource corresponding to each task to be allocated does not belong to the execution resource corresponding to the last time of resource allocation processing is performed on each task to be allocated.

Correspondingly, step S102 may include: and determining a sixth sub-condition according to the historical allocation data.

The historical allocation data are used for representing execution resources corresponding to each task to be allocated when the resource allocation processing is performed last time.

Further, in order to enable each task to be distributed to be processed in time, the logistics operation efficiency is improved, and the target constraint condition comprises a seventh sub-condition, wherein the seventh sub-condition is that: the estimated execution completion time point of each task to be allocated is not later than the task deadline time point corresponding to each task to be allocated. The estimated execution completion time point of one task to be allocated is the time point when the task to be allocated is executed to complete. The specific determination mode of the estimated execution completion time point is not limited, and reasonable selection can be performed according to actual application requirements.

Correspondingly, step S102 may include: a seventh sub-condition is determined based on the deadline data.

The deadline data are used for representing task deadline points corresponding to each task to be distributed.

Further, in the actual application process, there may be one or more tasks to be distributed that need to be processed urgently, so that in order to better meet the actual application requirement, relatively urgent tasks may be arranged to be processed preferentially. Specifically, the target constraint includes an eighth sub-condition that is: the start processing time point of the task to be allocated with high priority is earlier than the start processing time point of the task to be allocated with low priority. The priority setting mode of the tasks to be allocated is not limited, and the tasks can be reasonably set according to actual application requirements. For example, the priority of the task to be distributed can be set according to the corresponding related attribute of the material; the priority of the tasks to be distributed can be set or automatically adjusted according to the sequence of the task deadlines corresponding to the tasks to be distributed.

Correspondingly, step S102 may include: an eighth sub-condition is determined based on the priority data.

The priority data are used for representing the priority level corresponding to each task to be allocated.

Alternatively, in order to improve the efficiency of the logistics operation and better meet the actual application requirements, the main skill of using the execution resources may be prioritized. Specifically, the target constraint may further include a ninth sub-condition that: the skills selected when executing the task to be allocated to the execution resource are preferentially the main skills.

Further, when the execution resource does not have a task which is not completed yet, the skill selected when executing the task to be allocated to the execution resource is prioritized as the master skill. When the execution resource currently has a task which is not completed, the first sub-condition and the ninth sub-condition possibly conflict, and in order to avoid the influence of frequent switching skills on the logistics operation efficiency, the priority of the execution resource for executing the task which is not completed currently is set to be higher than the priority of the skills used by the main skill; or, in order to avoid that the execution resource is allocated to execute the task which is not completed by using the non-master skill for a long time, when the execution resource currently has the task which is not completed and the skill required by the task which is not completed is the auxiliary skill of the execution resource, the task to be allocated is not allocated to the execution resource at this time, and when the execution task completely completes the task which is not completed currently, the task to be allocated is allocated to the execution resource again.

Further, considering that an execution resource may have a plurality of auxiliary skills in addition to the main skills, if the execution resource is not allocated to use the main skills when performing the resource allocation calculation, the auxiliary skills with higher proficiency of the execution resource may be preferentially selected. Specifically, the ninth sub-condition may be adjusted to: when the execution resource does not have a task which is not completed yet, the skill selected when the execution resource executes the task to be allocated is the main skill first and the auxiliary skill with highest skill second.

As can be seen from the above embodiments of the present invention, in the embodiments of the present invention, a target optimal solution algorithm for performing resource allocation calculation is determined to be a local optimal solution algorithm or a global optimal solution algorithm according to a preset target function; determining target constraint conditions according to task skill requirement data, resource skill attribute data and resource work skill data; and obtaining resource allocation data by utilizing a target optimal solution algorithm according to the task position data, the resource position data, the target function and the target constraint condition. Compared with the prior art, the method and the device can realize mixed scheduling of different types of execution resources, and can select a more reasonable optimal solution algorithm according to actual application requirements, so that manual participation is not needed in the resource allocation process of logistics operation, the logistics operation efficiency is improved, and the cost of logistics operation can be reduced.

Example two

As shown in fig. 2, fig. 2 is a schematic flowchart of a resource allocation method of a logistics task disclosed in example two of the present application, where the resource allocation method includes:

step S201, determining a target optimal solution algorithm for performing resource allocation calculation as a local optimal solution algorithm or a global optimal solution algorithm according to a preset target function.

In this embodiment, the content of step S201 is substantially the same as or similar to that of step S101 in the first embodiment, and will not be described herein.

Step S202, determining target constraint conditions according to task skill requirement data, resource skill attribute data and resource work skill data.

In this embodiment, the content of step S202 is substantially the same as or similar to that of step S102 in the previous embodiment, and will not be described herein.

In step S203, when the number of objective functions is greater than 1, the objective score function is obtained according to all objective functions.

In this embodiment, the overall objective function includes at least two of the second sub-function, the third sub-function, and the fourth sub-function in the above example one. Since the dimensions of the results obtained by solving the objective functions are different, and in practical application, the importance of the objective conditions corresponding to the objective functions is different, when the number of objective functions is greater than 1, all objective functions can be normalized to obtain the objective score function.

After normalization processing is performed on all objective functions, different weight coefficients can be set in the objective score function according to actual application requirements for the corresponding part of each objective function.

In this embodiment, the objective score function is the highest or lowest score calculated according to the task skill requirement data and the resource skill attribute data. The specific mode for calculating and obtaining the score according to the task position data and the resource position data is not limited, and reasonable selection can be carried out according to actual application requirements.

And S204, obtaining resource allocation data by utilizing a target optimal solution algorithm according to the task position data, the resource position data, the target score function and the target constraint condition.

In this embodiment, the difference between the step S204 and the step S103 in the first embodiment is that the resource allocation data is obtained by replacing all objective functions with one objective score function, and the other contents are basically the same or similar, and are not described herein.

Compared with the previous embodiment, in the scheme of the embodiment, when the number of the objective functions is greater than 1, the objective score functions are obtained according to all the objective functions, and the resource allocation data are obtained according to the objective score functions, so that the allocation scheme obtained by solving according to the multiple objective functions can better meet the actual application requirements.

Example three

As shown in fig. 3, fig. 3 is a schematic structural diagram of a resource allocation device for a logistics task disclosed in a third embodiment of the present application, where the resource allocation device includes:

the first determining module is used for determining that a target optimal solution algorithm for carrying out resource allocation calculation is a local optimal solution algorithm or a global optimal solution algorithm according to a preset target function;

the second determining module is used for determining target constraint conditions according to the task skill requirement data, the resource skill attribute data and the resource work skill data; the task skill requirement data are used for representing skills required by carrying out logistics operation on materials corresponding to each task to be distributed; the resource skill attribute data is used for representing the skill possessed by each execution resource capable of executing the task to be distributed; the resource work skill data is used for representing the skill used by the execution resources capable of executing the tasks to be distributed to execute the tasks which are not completed at present; the target constraint condition at least comprises a first sub-condition, wherein the first sub-condition is that skills required for carrying out logistics operation on materials corresponding to each task to be allocated are matched with skills possessed by corresponding execution resources, and the skills required for carrying out logistics operation on the materials corresponding to each task to be allocated are matched with skills used by corresponding execution resources to execute tasks which are not completed currently;

The data acquisition module is used for acquiring resource allocation data by utilizing the target optimal solution algorithm according to the task position data, the resource position data, the target function and the target constraint condition; the task position data are used for representing a starting point position and an end point position corresponding to each task to be distributed; the resource position data is used for representing a target position of an execution resource capable of executing the task to be distributed, wherein the target position is a real-time position or an end position for executing a task which is not completed currently; the resource allocation data is used for representing the execution resources corresponding to each task to be allocated.

Optionally, when the target optimal solution algorithm is determined to be a global optimal solution algorithm, if the number of tasks to be allocated is smaller than a first number threshold and the number of execution resources capable of executing the tasks to be allocated is smaller than a second number threshold, determining the first optimal solution algorithm as the target optimal solution algorithm;

if the number of the tasks to be allocated is greater than or equal to the first number threshold and the number of the execution resources capable of executing the tasks to be allocated is greater than or equal to the second number threshold, determining a second optimal solution algorithm as the target optimal solution algorithm;

The first optimal solution algorithm and the second optimal solution algorithm are different types of global optimal solution algorithms.

Optionally, the device further includes a path planning module, where the path planning module is configured to send a task execution instruction to the execution resource corresponding to each task to be allocated according to the resource allocation data;

when response data returned by the execution resources according to the task execution instructions is received within a preset response time, path planning data corresponding to the execution resources are obtained according to the response data, the task position data and the resource position data; wherein the response data is used for characterizing at least one task to be allocated for the execution of the execution resource confirmation; the path planning data is used for characterizing the running path arrangement of each execution resource when all the tasks to be allocated for confirmed execution are executed or all tasks which are not completed are executed.

Optionally, the device further comprises a third determining module, wherein the third determining module is used for determining the task to be allocated next time according to the response data; among all the tasks to be allocated for the current resource allocation, the task to be allocated which is not confirmed to be executed by the execution resource corresponding to the task to be allocated is the task to be allocated.

Optionally, the objective function includes a first sub-function, where the first sub-function is: the target position of the execution resource corresponding to each task to be allocated is closest to the starting point position of the material corresponding to each task to be allocated;

correspondingly, the first determining module is further configured to determine that the target optimal solution algorithm is a local optimal solution algorithm when the target function includes the first sub-function.

Optionally, the objective function includes a second sub-function, the second sub-function being: the maximum difference value of the accumulated working time length is the smallest from the preset time point to the time of executing all the tasks to be distributed;

correspondingly, the data obtaining module is further used for obtaining resource allocation data by utilizing the target optimal solution algorithm according to the task position data, the resource working time length data, the target function and the target constraint condition; the resource working time length data is used for representing accumulated working time length from the preset time point to the time when the execution resources for executing the tasks to be distributed start.

Optionally, when the number of tasks to be allocated is a plurality, the objective function further includes a third sub-function, where the third sub-function is: the total time consumed by execution of the execution resources to complete all of the tasks to be allocated is minimal.

Optionally, when the number of tasks to be allocated is a plurality, the objective function further includes a fourth sub-function, where the fourth sub-function is: the execution resource execution completes the shortest distance of all tasks to be allocated.

Optionally, when the number of objective functions is greater than 1, the data obtaining module is further configured to obtain an objective score function according to all objective functions; the target score function is the highest or lowest score obtained by calculation according to the task position data and the resource position data;

and obtaining resource allocation data by utilizing a target optimal solution algorithm according to the task position data, the resource position data, the target score function and the target constraint condition.

Optionally, the target constraint condition includes a second sub-condition, and the second determining module is further configured to determine, according to the task position data, an area identifier corresponding to each task to be allocated, and determine, according to the resource position data, an area identifier of each execution resource capable of executing the task to be allocated; and determining the second sub-condition to be that the region identifier corresponding to each task to be allocated is matched with the region identifier of the corresponding execution resource.

Optionally, the target constraint includes a third sub-condition, the third sub-condition being: all tasks to be allocated corresponding to each execution resource are the same in type;

Correspondingly, the second determining module is further used for determining a target constraint condition according to the task skill requirement data, the resource skill attribute data, the task type data and the resource work skill data; the task type data are used for representing the type of the material corresponding to each task to be distributed.

Optionally, the target constraint includes a fourth sub-condition, the fourth sub-condition being: the skills used by each execution resource in executing the corresponding all tasks to be allocated are the same.

Optionally, the target constraint includes a fifth sub-condition, the fifth sub-condition being: the number of all tasks to be allocated corresponding to each execution resource is smaller than a preset number threshold.

Optionally, the target constraint includes a sixth sub-condition that is: the execution resource corresponding to each task to be allocated does not belong to the execution resource corresponding to the last time of resource allocation processing is performed on each task to be allocated.

Correspondingly, the second determining module is further configured to determine a sixth sub-condition according to the historical allocation data. The historical allocation data are used for representing execution resources corresponding to each task to be allocated when the resource allocation processing is performed last time.

Optionally, the target constraint includes a seventh sub-condition: the estimated execution completion time point of each task to be allocated is not later than the task deadline time point corresponding to each task to be allocated.

Correspondingly, the second determining module is further configured to determine a seventh sub-condition according to the deadline data.

Optionally, the target constraint includes an eighth sub-condition that is: the starting processing time point of the task to be allocated with high priority is earlier than the starting processing time point of the task to be allocated with low priority;

correspondingly, the second determining module is further configured to determine an eighth sub-condition according to the priority data. The priority data are used for representing the priority level corresponding to each task to be allocated.

Optionally, the execution resource has at least one primary skill and any number of secondary skills. Correspondingly, the target constraint includes a ninth sub-condition, the ninth sub-condition being: the skills selected when executing the task to be allocated to the execution resource are preferentially the main skills.

Further, the ninth sub-condition may be adjusted to: when the execution resource does not have a task which is not completed yet, the skill selected when the execution resource executes the task to be allocated is the main skill first and the auxiliary skill with highest skill second.

Optionally, the second determining module is further configured to obtain resource target skill data according to the resource skill attribute data and the resource work skill data; the resource target skill data are used for representing a skill corresponding to each execution resource execution task to be distributed;

And determining target constraint conditions according to the task skill requirement data and the resource target skill data.

The resource allocation device for the logistics operation task of the embodiment can realize the resource allocation method for the corresponding logistics operation task in the method embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described herein.

Thus far, specific embodiments of the present application have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may be advantageous.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.

It will be apparent to those skilled in the art that embodiments of the present application may be provided as methods, apparatus. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer storage media (including, but not limited to, magnetic disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments in part.

The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.

Claims (12)

1. A resource allocation method for a logistics job task, the method comprising:

according to a preset objective function, determining a target optimal solution algorithm for carrying out resource allocation calculation as a local optimal solution algorithm or a global optimal solution algorithm;

determining target constraint conditions according to task skill requirement data, resource skill attribute data and resource work skill data; the task skill requirement data are used for representing skills required by carrying out logistics operation on materials corresponding to each task to be distributed; the resource skill attribute data is used for representing the skill possessed by each execution resource capable of executing the task to be distributed; the resource work skill data is used for representing the skill used by the execution resources capable of executing the tasks to be distributed to execute the tasks which are not completed at present; the target constraint condition at least comprises a first sub-condition, wherein the first sub-condition is that skills required for carrying out logistics operation on materials corresponding to each task to be allocated are matched with skills possessed by corresponding execution resources, and the skills required for carrying out logistics operation on the materials corresponding to each task to be allocated are matched with skills used by corresponding execution resources to execute tasks which are not completed currently;

Obtaining resource allocation data by utilizing the target optimal solution algorithm according to the task position data, the resource position data, the target function and the target constraint condition; the task position data are used for representing a starting point position and an end point position corresponding to each task to be distributed; the resource position data is used for representing a target position of an execution resource capable of executing the task to be distributed, wherein the target position is a real-time position or an end position for executing a task which is not completed currently; the resource allocation data is used for representing the execution resources corresponding to each task to be allocated.

2. The method for allocating resources according to claim 1, wherein determining, according to a preset objective function, whether the objective optimal solution algorithm for performing the resource allocation calculation is a local optimal solution algorithm or a global optimal solution algorithm comprises:

when the target optimal solution algorithm is determined to be a global optimal solution algorithm, if the number of tasks to be allocated is smaller than a first number threshold and the number of execution resources capable of executing the tasks to be allocated is smaller than a second number threshold, determining the first optimal solution algorithm as the target optimal solution algorithm;

If the number of the tasks to be allocated is greater than or equal to the first number threshold and the number of the execution resources capable of executing the tasks to be allocated is greater than or equal to the second number threshold, determining a second optimal solution algorithm as the target optimal solution algorithm;

the first optimal solution algorithm and the second optimal solution algorithm are different types of global optimal solution algorithms.

3. The resource allocation method according to claim 1, wherein the objective function comprises a first sub-function, the first sub-function being: the target position of the execution resource corresponding to each task to be allocated is closest to the starting point position of the material corresponding to each task to be allocated;

correspondingly, the determining, according to a preset objective function, that the objective optimal solution algorithm for performing the resource allocation calculation is a local optimal solution algorithm or a global optimal solution algorithm includes:

and when the objective function comprises a first sub-function, determining that the objective optimal solution algorithm is a local optimal solution algorithm.

4. The resource allocation method according to claim 1, wherein the objective function comprises a second sub-function, the second sub-function being: the maximum difference value of the accumulated working time length is the smallest from the preset time point to the time of executing all the tasks to be distributed;

Correspondingly, the determining, according to a preset objective function, that the objective optimal solution algorithm for performing the resource allocation calculation is a local optimal solution algorithm or a global optimal solution algorithm includes: when the objective function comprises a second sub-function, determining that the objective optimal solution algorithm is a global optimal solution algorithm;

the obtaining the resource allocation data by using the target optimal solution algorithm according to the task position data, the resource position data, the target function and the target constraint condition comprises the following steps: obtaining resource allocation data by utilizing the target optimal solution algorithm according to the task position data, the resource working time length data, the target function and the target constraint condition; the resource working time length data is used for representing accumulated working time length from the preset time point to the time when the execution resources for executing the tasks to be distributed start.

5. The resource allocation method according to claim 1, wherein when the number of the objective functions is greater than 1, the obtaining the resource allocation data using the objective optimal solution algorithm according to the task position data, the resource position data, the objective function, and the objective constraint condition comprises:

Obtaining a target score function according to all the target functions; the target score function is the highest or lowest score obtained by calculation according to the task position data and the resource position data;

and obtaining resource allocation data by utilizing the target optimal solution algorithm according to the task position data, the resource position data, the target score function and the target constraint condition.

6. The resource allocation method according to claim 1, wherein the target constraint comprises a second sub-condition, and wherein determining the target constraint based on the task skill requirement data, the resource skill property data, and the resource work skill data comprises:

determining a region identifier corresponding to each task to be allocated according to the task position data, and determining a region identifier of each execution resource capable of executing the task to be allocated according to the resource position data;

and determining the second sub-condition to be that the region identifier corresponding to each task to be allocated is matched with the region identifier of the corresponding execution resource.

7. The resource allocation method according to claim 1, wherein the target constraint condition includes a sixth sub-condition that the execution resource corresponding to each of the tasks to be allocated does not belong to the execution resource corresponding to when the resource allocation process was last performed on each of the tasks to be allocated;

Correspondingly, the determining the target constraint condition according to the task skill requirement data, the resource skill attribute data and the resource work skill data comprises:

determining the sixth sub-condition according to the historical allocation data; the history allocation data is used for representing the execution resources corresponding to each task to be allocated when the resource allocation processing is performed last time.

8. The resource allocation method according to claim 1, wherein the target constraint condition includes a seventh sub-condition, the seventh sub-condition being that an estimated execution completion time point of each of the tasks to be allocated is no later than a task deadline time point corresponding to each of the tasks to be allocated;

correspondingly, the determining the target constraint condition according to the task skill requirement data, the resource skill attribute data and the resource work skill data comprises:

determining the seventh sub-condition according to the deadline data; the deadline data are used for representing task deadline points corresponding to the tasks to be distributed.

9. The resource allocation method according to claim 1, wherein the target constraint condition includes an eighth sub-condition that a start processing time of the task to be allocated with a high priority is earlier than a start processing time of the task to be allocated with a low priority;

Correspondingly, the determining the target constraint condition according to the task skill requirement data, the resource skill attribute data and the resource work skill data comprises:

determining the eighth sub-condition according to the priority data; the priority data are used for representing the priority level corresponding to each task to be allocated.

10. The resource allocation method according to claim 1, characterized in that the method further comprises:

according to the resource allocation data, a task execution instruction is sent to the execution resource corresponding to each task to be allocated;

when response data returned by the execution resources according to the task execution instructions is received within a preset response time, path planning data corresponding to the execution resources are obtained according to the response data, the task position data and the resource position data; wherein the response data is used for characterizing at least one task to be allocated for the execution of the execution resource confirmation; the path planning data is used for characterizing the running path arrangement of each execution resource when all the tasks to be allocated for confirmed execution are executed or all tasks which are not completed are executed.

11. The resource allocation method according to claim 1, wherein determining the target constraint according to the task skill requirement data, the resource skill attribute data, and the resource work skill data comprises:

obtaining resource target skill data according to the resource skill attribute data and the resource work skill data; the resource target skill data are used for representing a skill corresponding to each execution resource executing the task to be distributed;

and determining target constraint conditions according to the task skill requirement data and the resource target skill data.

12. A resource allocation device for a logistics task, the device comprising:

the first determining module is used for determining that a target optimal solution algorithm for carrying out resource allocation calculation is a local optimal solution algorithm or a global optimal solution algorithm according to a preset target function;

the second determining module is used for determining target constraint conditions according to the task skill requirement data, the resource skill attribute data and the resource work skill data; the task skill requirement data are used for representing skills required by carrying out logistics operation on materials corresponding to each task to be distributed; the resource skill attribute data is used for representing the skill possessed by each execution resource capable of executing the task to be distributed; the resource work skill data is used for representing the skill used by the execution resources capable of executing the tasks to be distributed to execute the tasks which are not completed at present; the target constraint condition at least comprises a first sub-condition, wherein the first sub-condition is that skills required for carrying out logistics operation on materials corresponding to each task to be allocated are matched with skills possessed by corresponding execution resources, and the skills required for carrying out logistics operation on the materials corresponding to each task to be allocated are matched with skills used by corresponding execution resources to execute tasks which are not completed currently;

The data acquisition module is used for acquiring resource allocation data by utilizing the target optimal solution algorithm according to the task position data, the resource position data, the target function and the target constraint condition; the task position data are used for representing a starting point position and an end point position corresponding to each task to be distributed; the resource position data is used for representing a target position of an execution resource capable of executing the task to be distributed, wherein the target position is a real-time position or an end position for executing a task which is not completed currently; the resource allocation data is used for representing the execution resources corresponding to each task to be allocated.