CN115619127A

CN115619127A - Transportation capacity allocation method, electronic device, and storage medium

Info

Publication number: CN115619127A
Application number: CN202211169986.6A
Authority: CN
Inventors: 郑若辰
Original assignee: Beijing Kuangshi Robot Technology Co Ltd
Current assignee: Beijing Kuangshi Robot Technology Co Ltd
Priority date: 2022-09-22
Filing date: 2022-09-22
Publication date: 2023-01-17

Abstract

The embodiment of the application discloses a capacity allocation method, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring a transport capacity set and a task set in a target warehouse, wherein the task set comprises a plurality of target tasks to be transported, and the transport capacity set comprises a plurality of available transport capacities for transporting the target tasks; and distributing the target tasks in the task set for the available transport capacity in the transport capacity set according to the distance between the target tasks in the task set and the available transport capacity in the transport capacity set and the distance between the target tasks in the task set, so as to obtain a target distribution relation.

Description

Transportation capacity allocation method, electronic device, and storage medium

Technical Field

The present disclosure relates to the field of automated logistics technologies, and in particular, to a capacity allocation method, an electronic device, and a storage medium.

Background

In an automated logistics scenario, the use and scheduling of robots are important, and capacity allocation is one of the important links. Here, capacity allocation is to allocate available capacity for a task, where a task generally refers to a transport task, i.e., transport of an article or a container, and capacity generally refers to a transport robot or an Automated Guided Vehicle (AGV). In practical application, some robots such as four-way shuttles can only carry one container at the same time, corresponding to a single-vehicle single-task situation; some robots, such as box robots, can handle multiple containers simultaneously, corresponding to a single-car multitasking scenario, where single-car multitasking is a relatively more complex scenario.

At present, although the transportation capacity allocation method in the related art can ensure that tasks in a candidate task set are allocated, some problems also exist, for example, the transportation capacity is far away from a driving distance, the driving time is long, and the overall operation efficiency is low.

Disclosure of Invention

The embodiment of the application provides a transport capacity allocation method, electronic equipment and a storage medium, and aims to solve the technical problem that the overall transport capacity operation efficiency is low in the related technology when transport capacity allocation is performed on a single-vehicle multi-task scene.

According to a first aspect of the present application, a capacity distribution method is disclosed, the method comprising:

acquiring a transport capacity set and a task set in a target warehouse, wherein the task set comprises a plurality of target tasks to be transported, and the transport capacity set comprises a plurality of available transport capacities for transporting the target tasks;

and distributing the target tasks in the task set for the available transport capacity in the transport capacity set according to the distance between the target tasks in the task set and the available transport capacity in the transport capacity set and the distance between the target tasks in the task set, so as to obtain a target distribution relation.

According to a second aspect of the present application, there is disclosed a capacity distribution device, the device comprising:

the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring a transport capacity set and a task set in a target warehouse, the task set comprises a plurality of target tasks to be transported, and the transport capacity set comprises a plurality of available transport capacities for transporting the target tasks;

and the distribution module is used for distributing the target tasks in the task set to the available transport capacity in the transport capacity set according to the distance between the target tasks in the task set and the available transport capacity in the transport capacity set and the distance between the target tasks in the task set to obtain a target distribution relation.

According to a third aspect of the application, an electronic device is disclosed, comprising a memory, a processor and a computer program stored on the memory, the processor executing the computer program to implement the capacity allocation method as in the first aspect.

According to a fourth aspect of the present application, a computer-readable storage medium is disclosed, having stored thereon a computer program/instructions which, when executed by a processor, implement the capacity allocation method as in the first aspect.

According to a fifth aspect of the present application, a computer program product is disclosed, comprising computer programs/instructions which, when executed by a processor, implement the capacity allocation method as in the first aspect.

In the embodiment of the application, a transport capacity set and a task set in a target warehouse are obtained, wherein the task set comprises a plurality of target tasks to be transported, and the transport capacity set comprises a plurality of available transport capacities for transporting the target tasks; and allocating the target tasks in the task set for the available transport capacity in the transport capacity set according to the distance between the target tasks in the task set and the available transport capacity in the transport capacity set and the distance between the target tasks in the task set, so as to obtain a target allocation relationship. Therefore, in the embodiment of the application, when the transport capacity is allocated for a single-vehicle multi-task scene, the distance between the transport capacity and the task is considered, the distance between the task and the task is considered, the distribution relation between the transport capacity and the task is obtained by integrating the two factors, and the distance factor can be considered comprehensively on the global level, so that the running distance of the transport capacity is effectively reduced under the condition of meeting the requirement of completing the task, the comprehensive running distance of all the transport capacities is shortest, the running time is minimum, and the overall operation efficiency is improved.

Drawings

Fig. 1 is an exemplary diagram of a capacity allocation result produced by a related-art capacity allocation method;

fig. 2 is a flowchart of a transportation capacity allocation method provided in an embodiment of the present application;

fig. 3 is an exemplary diagram of a capacity allocation result produced by the capacity allocation method according to the embodiment of the present application;

FIG. 4 is a flow chart of one implementation of step 2021 provided by an embodiment of the present application;

FIG. 5 is one of exemplary diagrams of an iterative operator provided by embodiments of the present application;

FIG. 6 is a second exemplary diagram of an iterative operator provided by an embodiment of the present application;

fig. 7 is a schematic structural diagram of a capacity distribution device provided in an embodiment of the present application;

fig. 8 is a block diagram of an electronic device according to an embodiment of the present application.

Detailed Description

In order to make the aforementioned objects, features and advantages of the present application more comprehensible, the present application is described in further detail with reference to the accompanying drawings and the detailed description.

It should be noted that, for simplicity of description, the method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the embodiments are not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the embodiments. Further, those skilled in the art will also appreciate that the embodiments described in the specification are presently preferred and that no particular act is required of the embodiments of the application.

With the development of Intelligent technologies such as internet of things, artificial intelligence and big data, the requirement for transformation and upgrading of the traditional Logistics industry by using the Intelligent technologies is stronger, and Intelligent Logistics (ILS) becomes a research hotspot in the Logistics field. The intelligent logistics utilizes artificial intelligence, big data, various information sensors, radio frequency identification technology, global Positioning System (GPS) and other Internet of things devices and technologies, is widely applied to basic activity links of material transportation, storage, delivery, packaging, loading and unloading, information service and the like, and realizes intelligent analysis and decision, automatic operation and high-efficiency optimization management in the material management process. The technology of the internet of things comprises sensing equipment, radio Frequency Identification (RFID) technology, laser infrared scanning, infrared induction Identification and the like, the internet of things can effectively connect materials in logistics with a network, the materials can be monitored in real time, environmental data such as humidity and temperature of a warehouse can be sensed, and the storage environment of the materials is guaranteed. All data in logistics can be sensed and collected through a big data technology, the data are uploaded to an information platform data layer, operations such as filtering, mining and analyzing are carried out on the data, and finally accurate data support is provided for business processes (such as links of transportation, warehousing, storing and taking, sorting, packaging, sorting, ex-warehouse, checking, distribution and the like). The application direction of artificial intelligence in logistics can be roughly divided into two types: 1) The method is characterized in that the artificial intelligence technology is used for endowing intelligent equipment such as an unmanned truck, an Automatic Guided Vehicle (AGV), an Autonomous Mobile Robot (AMR), a forklift, a shuttle car, a stacker, an unmanned distribution Vehicle, an unmanned aerial Vehicle, a service Robot, a mechanical arm, an intelligent terminal and the like to replace part of manpower; 2) The manual efficiency is improved through a software system such as a transportation equipment management system, a storage management system, an equipment scheduling system, an order distribution system and the like driven by technologies or algorithms such as computer vision, machine learning, operation and research optimization and the like. With the research and progress of intelligent logistics, the technology is applied to a plurality of fields, such as retail and electric commerce, electronic products, tobacco, medicine, industrial manufacturing, shoes and clothes, textile, food and the like.

Taking an automated logistics scenario as an example, in a single-vehicle multi-task scenario, when carrying out capacity allocation without considering other factors, it is generally desirable to allocate a task as close as possible to a certain capacity, so as to reduce the empty driving distance of the capacity and complete the transportation task as soon as possible. In the related art, when carrying out transport capacity allocation, according to the distance between a task set and a certain transport capacity, tasks are selected for the transport capacity one by one according to the principle from near to far until the upper limit of the transport capacity is reached.

Although the capacity allocation method in the related art can ensure that the tasks in the candidate task set are allocated, the overall operation efficiency is low because only the capacity and the distance between the tasks are considered, and the distance between the tasks is not considered, and a capacity allocation situation as shown in fig. 1 may occur, wherein a large circle with a dotted line represents that the tasks in the circle are allocated to the capacity in the circle, that is, a plurality of tasks allocated to a certain capacity may be closer to the capacity, but the tasks may be farther from each other. After the capacity is distributed to the tasks, the capacity is driven to the starting points of the tasks one by one and carries the containers corresponding to the tasks, and when the containers of all the tasks are carried, the capacity is driven to the terminal point, so that if the distance among the tasks is long, the driving time of the capacity is prolonged, and the whole operation efficiency cannot be ensured.

In order to solve the above technical problem, embodiments of the present application provide a capacity allocation method, an electronic device, and a storage medium.

First, a method for allocating the transport capacity provided in the embodiment of the present application will be described below.

Fig. 2 is a flowchart of a capacity allocation method provided in an embodiment of the present application, and as shown in fig. 2, the method may include the following steps: step 201 and step 202;

in step 201, a transportation capacity set and a task set in a target warehouse are obtained, wherein the task set includes a plurality of target tasks to be transported, and the transportation capacity set includes a plurality of available transportation capacities for transporting the target tasks.

In this embodiment, the total capacity of the available capacity in the capacity set is greater than or equal to the number of target tasks in the task set, where the capacity of the available capacity refers to the maximum number of target tasks that can be piggybacked by the available capacity at the same time.

In the embodiment of the application, the available transport capacity is a robot in warehouse logistics, the target task is a transport task for an article or a container, and in practical application, the robot can be a transport robot or an AGV.

In step 202, according to the distance between the target task in the task set and the available transportation capacity in the transportation capacity set and the distance between each target task in the task set, the target task in the task set is allocated to the available transportation capacity in the transportation capacity set, and a target allocation relationship is obtained.

In the embodiment of the application, the distance between the available transport capacity and the target task and the distance between the target tasks are both referred to as straight-line distances.

In this embodiment of the present application, the target allocation relationship may include: the target sequence of the assigned target tasks is executed by each available capacity.

In the embodiment of the application, each available transport capacity in the transport capacity set carries the target tasks in the task set according to the target distribution relation, so that the comprehensive travel distance of all the available transport capacities is shortest, and the travel time is shortest.

In an example, still taking the single-vehicle multi-task scenario shown in fig. 1 as an example, the task set includes: tasks 1-9, the capacity set includes: capacity 1 and capacity 2, the large circles in the figure with broken lines representing the capacity within the circle to which the task is allocated.

As can be seen from fig. 1, task 5 is allocated to capacity 2 because it is closer to capacity 2, but task 5 is far from other tasks 6-9 to which capacity 2 has been allocated, and capacity 2 carries over tasks 5-9, so this allocation is not the preferred allocation.

Through the processing in step 202 in the embodiment of the present application, the capacity allocation situation shown in fig. 3 can be obtained, as can be seen from fig. 3, task 5 is allocated to capacity 1, the distance between the tasks allocated by capacity 1 is relatively short, and the distance between the tasks allocated by capacity 2 is relatively short, so that the travel distance between capacity 1 and capacity 2 piggyback tasks is relatively short, that is, for the capacity allocation task, the distance between the capacity and the task and the distance between the tasks are considered, and the distance factor is considered comprehensively at the global level, so that the comprehensive travel distance of all capacities is shortest when the tasks are completed.

As can be seen from the above embodiment, in this embodiment, a transportation capacity set and a task set in a target warehouse are obtained, where the task set includes a plurality of target tasks to be transported, and the transportation capacity set includes a plurality of available transportation capacities for transporting the target tasks; and allocating the target tasks in the task set for the available transport capacity in the transport capacity set according to the distance between the target tasks in the task set and the available transport capacity in the transport capacity set and the distance between the target tasks in the task set, so as to obtain a target allocation relationship. Therefore, in the embodiment of the application, when the transport capacity is allocated for a single-vehicle multi-task scene, the distance between the transport capacity and the task is considered, the distance between the task and the task is considered, the distribution relation between the transport capacity and the task is obtained by integrating the two factors, and the distance factor can be considered comprehensively on the global level, so that the running distance of the transport capacity is effectively reduced under the condition of meeting the requirement of completing the task, the comprehensive running distance of all the transport capacities is shortest, the running time is minimum, and the overall operation efficiency is improved.

In another embodiment provided herein, for a single-vehicle multi-tasking scenario, the following three objectives can be optimized: goal one, all the targeted tasks assigned for a single available capacity are as close in distance as possible from the available capacity; target two, all target tasks allocated for a single available capacity are close in distance to each other; goal three, in a single capacity allocation, the cases of each available capacity for the above goal one and goal two are considered together, so that the overall distance cost is minimized.

Accordingly, the step 202 includes the following steps: step 2021;

in step 2021, allocating a target task in the task set to the available capacity in the capacity set according to a distance between the target task in the task set and the available capacity in the capacity set, a distance between each target task in the task set, and a capacity allocation policy, to obtain a target allocation relationship;

wherein the capacity allocation strategy comprises: the distance between the target task assigned by the single available capacity and the single available capacity is smaller than the distance between the target task assigned by the single available capacity and other available capacities; and the distance between the target tasks allocated by the single available capacity is smaller than the distance between the target tasks allocated by the single available capacity and the target tasks allocated by other available capacities.

That is, when assigning a target task in the set of tasks to an available capacity in the set of capacities, all target tasks assigned to a single available capacity are close in distance to the single available capacity and all target tasks assigned to the single available capacity are close in distance to each other.

As can be seen, in the embodiment of the present application, for a single available capacity, the assigned target task is not only closer to the single available capacity, but also closer to each other, and the distance factors are comprehensively considered on a global level, so that the shortest comprehensive travel distance of all available capacities can be achieved when the target tasks are satisfied.

In another embodiment provided by the present application, an initial assignment relationship between available capacity and target task may be first generated based on a distance between the available capacity and the target task, and then individual target tasks that are farther from other target tasks and to which the available capacity has been assigned in the initial matching relationship may be reassigned among capacities based on a distance between the target tasks, so as to obtain a target assignment relationship between available capacity and target task, as shown in fig. 4, where step 202 may include the following steps: step 401 and step 402;

in step 401, for each available capacity in the capacity set, according to the distance between the target task and the available capacity in the task set, allocating the target tasks for the available capacity one by one according to the principle from near to far until the allocation of all the target tasks in the task set is completed, and obtaining an initial allocation relationship.

In the embodiment of the present application, the initial allocation relationship includes: the initial corresponding relation between each available transport capacity and the target task allocated to the available transport capacity, and the initial sequence of the target task allocated to each available transport capacity; the initial correspondence is generated based on the distance between the target task and the available capacity, and the initial sequence may be a random sequence or a sequence designated by the user.

In the embodiment of the application, the initial allocation relation of the available capacity-target tasks can be generated based on a heuristic algorithm or a graph model, and by taking the heuristic algorithm as an example, the problem of large-scale initial allocation of the capacity can be solved because the search cost of the heuristic algorithm is low.

Wherein, the heuristic algorithm can be defined as follows: an algorithm based on intuitive or empirical construction gives a feasible solution of each example of the combined optimization problem to be solved under the condition of acceptable cost (referring to calculation time and space), the deviation degree of the feasible solution and the optimal solution can not be predicted generally, and a heuristic algorithm mainly takes a natural body simulation algorithm and mainly comprises an ant colony algorithm, a simulated annealing method, a neural network and the like.

Optionally, in an embodiment, the step 401 may include the following steps: step 4011, step 4012, and step 4013;

in step 4011, a distance between each available capacity in the capacity set and each target task in the task set is calculated by a way-finding algorithm, so as to obtain a distance set.

In step 4012, starting from the shortest distance in the distance set, allocating the target task corresponding to the shortest distance to the available capacity corresponding to the shortest distance, after allocation is completed, allocating the target task corresponding to the next short distance in the distance set to the available capacity corresponding to the next short distance, and repeating the allocation process until allocation of all target tasks in the task set is completed.

In step 4013, a pre-and-post order of execution is randomly set for the target task to which each available capacity is assigned, resulting in an initial assignment.

Through the steps 4011 to 4013, the initial matching relationship between all target tasks and all available capacity and the sequence of executing the allocated target tasks by each available capacity can be obtained.

In step 402, the target tasks meeting the redistribution condition and allocated to each available transport capacity in the initial allocation relationship are redistributed among other available transport capacities until no target tasks meeting the redistribution condition exist in the target tasks allocated to each available transport capacity, and a target allocation relationship is obtained; and the distance between the target task which meets the redistribution condition and is allocated by the single available capacity and other target tasks which meet the redistribution condition is larger than the distance between the target task which meets the redistribution condition and other target tasks which are allocated by the single available capacity.

In the embodiment of the application, for a single available capacity, the target task which meets the redistribution condition and is distributed by the single available capacity is not close to other tasks which are distributed by the single available capacity in distance. That is, the target task satisfying the reassignment condition is closer to the single available capacity, but is farther from other target tasks of the single available capacity. For such target tasks, the target tasks are redistributed among other available capacity.

In the embodiment of the application, the comprehensive driving distance of all available transport capacities corresponding to the target distribution relationship is smaller than the comprehensive driving distance of all available transport capacities corresponding to the initial distribution relationship.

In the embodiment of the application, the shortest comprehensive driving distance of all available transport capacity corresponding to the target distribution relation can be used as a search target based on a meta-heuristic search algorithm, and the distributed target tasks meeting the redistribution condition are redistributed among the available transport capacity to obtain the target distribution relation. The metaheuristic search algorithm has low search cost, so that the problem of large-scale task reallocation can be solved, and the search of the metaheuristic search algorithm has randomness, so that the task reallocation can be completed on the global level, and the overall operation efficiency of the transport capacity is improved.

The meta-heuristic search algorithm is an improvement of a heuristic algorithm, and is a product of combining a random algorithm and a local search algorithm, and the meta-heuristic algorithm can comprise a tabu search algorithm, a simulated annealing algorithm, a genetic algorithm, an ant colony optimization algorithm, a particle swarm optimization algorithm, an artificial fish swarm algorithm, an artificial bee colony algorithm, an artificial neural network algorithm and the like.

In an example, still taking the transportation capacity allocation result shown in fig. 1 and fig. 3 as an example, the allocation result shown in fig. 1, that is, the initial allocation relationship, may be obtained through the processing of step 401. Then, through the above processing of step 402, for example, through the continuous iteration of the meta-heuristic search framework, the distribution result may continuously converge to the distribution result shown in fig. 3, i.e., the target distribution relationship.

As can be seen, in the embodiment of the present application, for a single-vehicle multi-task transportation capacity allocation scenario, an initial allocation relationship between available transportation capacity and target tasks may be generated based on a distance between the available transportation capacity and the target tasks, and then, based on a distance between the target tasks, the target tasks to which the available transportation capacity has been allocated in the initial allocation relationship are reallocated among the available transportation capacities, so as to obtain a target allocation relationship. The distance between the transport capacity and the task is considered, the distance between the task and the task is considered, and the final distribution relation with the lowest distance cost between the transport capacity and the task is obtained by integrating two distance factors of the transport capacity-the task and the task-the task, so that the running distance of the transport capacity can be effectively reduced on the premise of ensuring the effective distribution of the task, and the overall operation efficiency is improved.

In another embodiment provided by the present application, when the allocated target task is re-allocated among the available capacity based on the initial allocation relationship, the re-allocation may be implemented by a tabu search method, and accordingly, the step 402 may include the following steps: step 4021 and step 4022;

in step 4021, taking the initial assignment relationship as an initial solution, and initializing a tabu table based on the initial solution; the tabu table is used for recording conversion information of the execution sequence of the target tasks in the process from the initial solution iteration to the target solution.

In the embodiment of the application, the tabu table is used for preventing the search from circulating, recording a plurality of previous walking points, directions or target values, and prohibiting the return. The tabu table is dynamically updated, namely the newest solution is recorded, the oldest solution is released from the tabu table, namely, the tabu table is continuously updated along with the iteration, and after a certain number of iterations, the movement which enters the tabu table at the earliest time is forbidden to exit from the tabu table.

In the embodiment of the present application, the tabu table is a matrix, where a row of the matrix corresponds to each task in the initial allocation relationship, a column of the matrix also corresponds to each task in the initial allocation relationship, and an element value in the matrix is related to whether the task in the row and the task in the column of the element are exchanged.

For example, if the initial assignment relationship includes 3 tasks: task 1, task 3, and task 3, the tabu table is a 3 × 3 matrix, rows of the matrix may correspond to task 1, task 2, and task 3, rows of the matrix may also correspond to task 1, task 2, and task 3, respectively, and after initializing the tabu table, values of elements in the tabu table are all 0. In the continuous iteration process, the values of the elements in the tabu table may also change, for example, for two tasks that have been exchanged recently, the values of the elements in the tabu table corresponding to the two tasks may be set to an integer greater than zero, and when the next iteration is performed, whether to perform the exchange of the execution order of the two tasks may be determined by looking up the values of the elements in the tabu table, if the value of the element is equal to 0, the exchange of the execution order of the two tasks may be performed, and if the value of the element is greater than 0, the exchange of the execution order of the two tasks is not performed, so as to obtain more search areas.

For example, when the exchange of the execution order of task 1 and task 3 is effective, the numerical value of the element in the third row of the first column in the tabu table may be set from 0 to 10. Furthermore, each iteration of the solution is performed, the value of the element in the tabu table is reduced by one, to gradually release the exchange that occurred before.

In step 4022, performing iterative processing based on the initial solution, the tabu table and the iterative operator until the iterative cost of the iterative processing for M times is kept unchanged to obtain a target solution; the iteration operator is a function used for exchanging execution orders between two target tasks, M is an upper limit value of iteration times, and a target solution is a target distribution relation.

In an embodiment of the present application, the iterative operator may include at least one of: a first function for exchanging execution order between target tasks across capacity, such as shown in fig. 5, and a second function for exchanging execution order between target tasks within a single available capacity, such as shown in fig. 6.

Optionally, in one embodiment, the process of each iteration process includes the following steps (not shown in the figure): step 301, step 302, step 303, step 304, and step 305;

in step 301, an iterative operator is selected.

In step 302, any two target tasks in the current solution are traversed, and whether the two target tasks in the latest M times of iteration processing are exchanged according to the execution order of the iteration operators is determined according to the tabu table.

In the embodiment of the application, if the fact that the execution orders of two target tasks in the latest M times of iteration processing are exchanged according to the iteration operators is determined according to the tabu table, the execution orders of the two target tasks are not exchanged.

In step 303, if not, calculating a cost increment and a cost decrement generated by exchanging the two target tasks according to the execution order of the iterative operators, and calculating a first iteration cost of exchanging the two target tasks according to the execution order of the iterative operators according to the cost increment and the cost decrement.

In this embodiment of the present application, when the iterative operator selected in step 301 is the first function, the cost increment generated by exchanging the execution order of the two target tasks according to the iterative operator is:

the cost reduction is as follows:

in this embodiment of the present application, when the iterative operator selected in step 301 is the second function, the cost increment generated by exchanging the execution order of the two target tasks according to the iterative operator is:

the cost reduction is as follows:

wherein, the first and the second end of the pipe are connected with each other,

and

respectively, two target tasks, d (A, B) represents the distance from A to B, p (A) represents the parent node of A, and k (A) represents the child node of A.

In step 304, if the first iteration cost is lower than the target iteration cost, the current solution is updated according to the execution order of the two target tasks exchanged by the iteration operator, the update information is recorded in the tabu table, and the target iteration cost is updated to the first iteration cost.

In the embodiment of the present application, updating the current solution refers to generating a solution after exchange according to the solution before exchange and the exchange situation of two target tasks where exchange occurs.

In the embodiment of the present application, the recording of the update information in the tabu table means that the values of the elements corresponding to the two target tasks that are exchanged in the tabu table are set to an integer greater than zero, for example, 10, so as to avoid the exchange of the execution orders of the two target tasks corresponding to the elements during the subsequent iterations.

In step 305, a summation operation is performed on all the first iteration costs for performing the order exchange, so as to obtain the iteration cost of the current iteration process.

In the embodiment of the application, if the iteration cost of the iteration processing for M consecutive times remains unchanged, the solution obtained by the iteration processing for the M-th time is determined as the final target solution, that is, the target assignment relationship.

In order to facilitate understanding of the above steps 301 to 305, the reassignment of the tasks is described below with reference to an example.

Firstly, initializing tabu table, presetting a shaping parameter tabu horizon as 10 for example, and presetting an upper limit M of iteration times as 50 for example;

then, when the number of iterations does not exceed the upper limit:

1. selecting an iterative operator;

2. traverse arbitrary 2 task point pairs

And

a) If any one task is searched in the round, skipping;

b) If it is not

And

if any one element value corresponding to the tabu table is greater than 0, skipping;

c) Computing

And

a cost increment addpost and a cost decrement minusCost at which execution order exchange occurs;

d) Computing

And

the cost swapCost = addpost-minusCost at which the execution order swap occurs;

e) If swapCost is less than bestCost, the exchange is effective, and bestCost = swapCost;

3. subtracting one from all element values in the tabu table;

4.

and

adding tabuHorizon to the corresponding element value in the tabu table;

5. and if the unchanged iteration number of the bestCost exceeds the upper limit M, terminating, and determining the solution obtained by the last iteration processing as the target solution.

Therefore, in the embodiment of the application, task reallocation can be performed by applying meta-heuristic search based on the initial solution, the advantage of the solution is guaranteed by designing an accurate search paradigm, the final solution with the lowest distance cost is obtained, the driving distance of the transport capacity is effectively reduced on the premise of guaranteeing effective task allocation, and therefore the overall operation efficiency is improved.

In another embodiment provided by the present application, in consideration of the allocation of prioritized tasks, the step 201 may include the following steps: step 2011, step 2012, step 2013 and step 2014;

in step 2011, a capacity set is obtained.

In step 2012, the total capacity N of available capacity corresponding to the capacity set is calculated.

For example, the capacity of 3 transporting forces is 3, 4 and 5 respectively, and the total capacity of the transporting force N =3+4+5=12.

In step 2013, when the number of the tasks to be carried is greater than N, all the tasks to be carried are sorted in the order of the priority from high to low.

In the embodiment of the present application, the priority of the task may be represented by a numerical value, for example, the lower the numerical value, the higher the priority. Higher priority tasks are assigned capacity prior to lower priority tasks.

In step 2014, the first N tasks are obtained, and the task set corresponding to the transportation capacity set is obtained, where the target task in the task set is the first N tasks.

For example, the total capacity N of the transportation capacity set is 12, the number of tasks to be transported is 15, and since 15 is greater than 12, the 15 tasks to be transported are sorted from high to low according to the priorities of the 15 tasks to be transported, and the top 12 tasks are selected as the target tasks in the task set.

Therefore, in the embodiment of the application, the task with the priority has a higher priority, and it can be ensured that the task with the high priority is preferentially allocated under the condition that the transport capacity is less than that of the task, that is, the transport capacity is preferentially allocated to the task with the high priority than to the task with the low priority, so that the task with the high priority can be preferentially processed.

Fig. 7 is a schematic structural diagram of a capacity distribution device according to an embodiment of the present disclosure, and as shown in fig. 7, a capacity distribution device 700 may include: an acquisition module 701 and a distribution module 702;

an obtaining module 701, configured to obtain a transport capacity set and a task set in a target warehouse, where the task set includes a plurality of target tasks to be transported, and the transport capacity set includes a plurality of available transport capacities for transporting the target tasks;

an allocating module 702, configured to allocate the target tasks in the task set to the available capacity in the capacity set according to the distance between the target task in the task set and the available capacity in the capacity set and the distance between the target tasks in the task set, so as to obtain a target allocation relationship.

Optionally, as an embodiment, the allocating module 702 includes:

the distribution submodule is used for distributing the target tasks in the task set to the available transport capacity in the transport capacity set according to the distance between the target tasks in the task set and the available transport capacity in the transport capacity set, the distance between the target tasks in the task set and a transport capacity distribution strategy, so that a target distribution relation is obtained;

wherein the capacity allocation policy comprises: the distance between the target task assigned by the single available capacity and the single available capacity is smaller than the distance between the target task assigned by the single available capacity and other available capacities; and the distance between the target tasks allocated by the single available capacity is smaller than the distance between the target tasks allocated by the single available capacity and the target tasks allocated by other available capacities.

Optionally, as an embodiment, the allocation submodule includes:

the initial allocation unit is used for allocating target tasks for each available transport capacity in the transport capacity set one by one according to the distance between the target task and the available transport capacity in the task set and the principle of from near to far until the allocation of all the target tasks in the task set is completed to obtain an initial allocation relation;

the redistribution unit is used for redistributing the target tasks meeting the redistribution condition and distributed to each available transport capacity in the initial distribution relation among other available transport capacities until the target tasks meeting the redistribution condition do not exist in the target tasks distributed to each available transport capacity, so that a target distribution relation is obtained;

wherein the distance between the target task meeting the redistribution condition and the other target tasks to which the single available capacity is distributed is larger than the distance between the target task meeting the redistribution condition and the other target tasks to which the single available capacity is distributed.

Optionally, as an embodiment, the reallocation unit includes:

the initialization subunit is used for taking the initial distribution relationship as an initial solution and initializing a tabu table based on the initial solution;

the iteration subunit is used for carrying out iteration processing on the basis of the initial solution, the tabu table and the iteration operator until the iteration cost of continuous M times of iteration processing is kept unchanged to obtain a target solution;

the tabu table is used for recording transformation information of an execution order of target tasks in the process from the initial solution iteration to the target solution, the iteration operator is a function for exchanging the execution order between the two target tasks, M is an upper limit value of the iteration times, and the target solution is a target distribution relation.

Optionally, as an embodiment, the process of each iteration processing of the iteration subunit includes the following steps:

selecting the iterative operator;

traversing any two target tasks in the current solution, and determining whether the two target tasks in the latest M times of iteration processing are exchanged according to the execution order of the iteration operators according to the tabu table;

if not, calculating cost increment and cost decrement generated by the two target tasks according to the execution order exchange of the iterative operator, and calculating a first iteration cost of the two target tasks according to the execution order exchange of the iterative operator according to the cost increment and the cost decrement;

if the first iteration cost is lower than the target iteration cost, exchanging the execution sequence of the two target tasks according to the iteration operator, updating the current solution, recording the updating information into the tabu table, and updating the target iteration cost into the first iteration cost;

and summing all the first iteration costs of the execution sequence exchange to obtain the iteration cost of the iteration processing.

Optionally, as an embodiment, the iterative operator includes at least one of:

a first function for exchanging execution order between target tasks across capacity, and a second function for exchanging execution order between target tasks within a single available capacity.

Optionally, as an embodiment, when the iterative operator is a first function, a cost increment generated by exchanging execution orders of two target tasks according to the iterative operator is:

the cost reduction is as follows:

when the iterative operator is a second function, the cost increment generated by exchanging the execution order of the two target tasks according to the iterative operator is as follows:

the cost reduction is as follows:

wherein the content of the first and second substances,

and

Optionally, as an embodiment, the initial allocation unit includes:

the calculation subunit is used for calculating the distance between each available transport capacity in the transport capacity set and each target task in the task set through a routing algorithm to obtain a distance set;

the allocation subunit is configured to, starting from a shortest distance in the distance set, allocate the target task corresponding to the shortest distance to the available capacity corresponding to the shortest distance, after allocation is completed, allocate a target task corresponding to a next short distance in the distance set to the available capacity corresponding to the next short distance, and repeat the allocation process until allocation of all target tasks in the task set is completed;

and the setting subunit is used for randomly setting the execution sequence of the target tasks allocated to each available transport capacity to obtain the initial allocation relation.

Optionally, as an embodiment, the target allocation relationship includes: the target sequence of the assigned target tasks is executed by each available capacity.

Optionally, as an embodiment, the obtaining module 701 includes:

the first acquisition submodule is used for acquiring a transport capacity set;

the calculation submodule is used for calculating the total available capacity N corresponding to the transport capacity set;

the sequencing submodule is used for sequencing all the tasks to be carried according to the sequence of the priority levels from high to low under the condition that the number of the tasks to be carried is greater than N;

and the second obtaining submodule is used for obtaining the tasks arranged at the front N positions and obtaining a task set corresponding to the transport capacity set, wherein the target task in the task set is the task arranged at the front N positions.

Any one step and specific operation in any one step in the embodiments of the capacity allocation method provided by the present application can refer to the process of the corresponding operation described in the embodiments of the capacity allocation method in the process of the corresponding operation performed by each module in the capacity allocation device being performed by the corresponding module in the capacity allocation device.

For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.

Fig. 8 is a block diagram of an electronic device according to an embodiment of the present application. The electronic device includes a processing component 822, which further includes one or more processors, and memory resources, represented by memory 832, for storing instructions, such as application programs, that are executable by the processing component 822. The application programs stored in memory 832 may include one or more modules that each correspond to a set of instructions. Further, the processing component 822 is configured to execute instructions to perform the above-described methods.

The electronic device may also include a power component 826 configured to perform power management of the electronic device, a wired or wireless network interface 850 configured to connect the electronic device to a network, and an input/output (I/O) interface 858. The electronic device may operate based on an operating system stored in memory 832, such as Windows Server, macOS XTM, unixTM, linuxTM, freeBSDTM, or the like.

According to yet another embodiment of the present application, there is also provided a computer-readable storage medium having stored thereon a computer program/instructions which, when executed by a processor, implement the steps in the capacity allocation method according to any one of the above embodiments.

According to yet another embodiment of the present application, there is also provided a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps in the capacity allocation method according to any one of the above embodiments.

The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.

As will be appreciated by one of skill in the art, embodiments of the present application may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of 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, embodiments of the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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 terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

While preferred embodiments of the present application have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the true scope of the embodiments of the application.

Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal 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 terminal. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or terminal apparatus that comprises the element.

The foregoing detailed description is directed to a capacity allocation method, an electronic device, and a storage medium, which are provided by the present application, and specific examples are applied in the present application to explain the principles and implementations of the present application, and the descriptions of the foregoing examples are only used to help understand the method and the core ideas of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, the specific implementation manner and the application scope may be changed, and in summary, the content of the present specification should not be construed as a limitation to the present application.

Claims

1. A capacity distribution method, comprising:

2. The method according to claim 1, wherein the allocating the target tasks in the task set to the available capacity in the capacity set according to the distance between the target tasks in the task set and the available capacity in the capacity set and the distance between the target tasks in the task set to obtain a target allocation relationship comprises:

distributing the target tasks in the task set to the available transport capacity in the transport capacity set according to the distance between the target tasks in the task set and the available transport capacity in the transport capacity set, the distance between the target tasks in the task set and a transport capacity distribution strategy to obtain a target distribution relation;

wherein the capacity allocation policy comprises: the distance between the target task assigned to the single available capacity and the single available capacity is smaller than the distance between the target task assigned to the single available capacity and other available capacities; and the distance between the target tasks allocated by the single available capacity is smaller than the distance between the target tasks allocated by the single available capacity and the target tasks allocated by other available capacities.

3. The method according to claim 2, wherein the allocating the target tasks in the task set to the available capacity in the capacity set according to the distance between the target task in the task set and the available capacity in the capacity set, the distance between the target tasks in the task set, and a capacity allocation policy, and obtaining a target allocation relationship, comprises:

for each available transport capacity in the transport capacity set, allocating target tasks for the available transport capacity one by one according to the distance between the target tasks in the task set and the available transport capacity and the principle of from near to far until the allocation of all the target tasks in the task set is completed, and obtaining an initial allocation relation;

redistributing the target tasks meeting the redistribution condition and distributed to each available transport capacity in the initial distribution relation among other available transport capacities until the target tasks meeting the redistribution condition do not exist in the target tasks distributed to each available transport capacity, and obtaining a target distribution relation;

4. The method according to claim 3, wherein the step of redistributing the available capacity of each of the initial allocation relations among the available capacities until the target capacity of each of the available capacities of the target tasks does not exist, comprises:

taking the initial distribution relation as an initial solution, and initializing a tabu table based on the initial solution;

performing iterative processing based on the initial solution, the tabu table and the iterative operator until the iterative cost of the iterative processing for M times is kept unchanged to obtain a target solution;

5. The method of claim 4, wherein each iteration process comprises the steps of:

selecting the iterative operator;

6. The method according to claim 4 or 5, wherein the iterative operator comprises at least one of:

7. The method of claim 6, wherein when the iterative operator is a first function, the cost increment resulting from the exchange of the execution order of the two target tasks by the iterative operator is:

the cost reduction is as follows:

the cost reduction is as follows:

wherein the content of the first and second substances,

and

8. The method according to claim 3, wherein for each available capacity in the capacity set, allocating target tasks for the available capacity one by one according to a distance between a target task in the task set and the available capacity on a near-to-far basis until allocation of all target tasks in the task set is completed, and obtaining an initial allocation relationship comprises:

calculating the distance between each available transport capacity in the transport capacity set and each target task in the task set through a routing algorithm to obtain a distance set;

starting from the shortest distance in the distance set, allocating the target task corresponding to the shortest distance to the available transport capacity corresponding to the shortest distance, after allocation is completed, allocating the target task corresponding to the next short distance in the distance set to the available transport capacity corresponding to the next short distance, and repeating the allocation process until allocation of all target tasks in the task set is completed;

and randomly setting the execution front-back order for the target task allocated to each available capacity to obtain an initial allocation relation.

9. The method of any of claims 1-5, wherein the target assignment relationship comprises: the target sequence of the assigned target tasks is executed by each available capacity.

10. The method of any of claims 1-5, wherein obtaining the capacity set and the task set comprises:

acquiring a transport capacity set;

calculating the total available capacity N of the transport capacity corresponding to the transport capacity set;

when the number of the tasks to be carried is larger than N, sequencing all the tasks to be carried according to the sequence of the priorities from high to low;

and acquiring the tasks arranged at the top N positions to obtain a task set corresponding to the transport capacity set, wherein the target task in the task set is the task arranged at the top N positions.

11. An electronic device comprising a memory, a processor and a computer program stored on the memory, characterized in that the processor executes the computer program to implement the method of any of claims 1-10.

12. A computer-readable storage medium, on which a computer program/instructions is stored, characterized in that the computer program/instructions, when executed by a processor, implements the method of any of claims 1-10.

13. A computer program product comprising computer programs/instructions, characterized in that the computer programs/instructions, when executed by a processor, implement the method of any of claims 1-10.