CN113723804B - Vehicle-machine collaborative distribution method and system considering multiple unmanned aerial vehicle stations - Google Patents
Vehicle-machine collaborative distribution method and system considering multiple unmanned aerial vehicle stations Download PDFInfo
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Abstract
The application provides a vehicle-machine collaborative distribution method and system considering multiple unmanned aerial vehicle stations, and belongs to the technical field of logistics scheduling. The method comprises the following steps: randomly generating a vehicle-machine collaborative distribution scheme to form a scheme to be optimized; taking the scheme to be optimized as a historical optimal scheme and a current optimal scheme; judging whether the iteration times are larger than or equal to a preset value; under the condition that the iteration times are less than a preset value, executing a destruction operation and an insertion operation on the scheme to be optimized to form a corresponding scheme to be updated; determining whether the scheme to be updated is better than a historical optimal scheme; under the condition that the scheme to be updated is better than the historical optimal scheme, the historical optimal scheme and the current optimal scheme are replaced by the scheme to be updated; under the condition that the scheme to be updated is inferior to the historical optimal scheme, updating the historical optimal scheme and the current optimal scheme based on the simulated annealing criterion; and under the condition that the current iteration times are larger than or equal to a preset value, taking the historical optimal scheme as a final vehicle-machine collaborative distribution scheme.
Description
Technical Field
The application relates to the technical field of logistics scheduling, in particular to a vehicle-machine collaborative distribution method and system considering multiple unmanned aerial vehicle stations.
Background
With the development of unmanned aerial vehicle technology, a mode that vehicles and unmanned aerial vehicles cooperate to complete distribution is widely focused, and a plurality of logistics enterprises and technological companies have completed preliminary experiments of utilizing unmanned aerial vehicles to conduct terminal distribution. Currently, most unmanned aerial vehicles start from a warehouse to deliver and return to the warehouse. In practical situations, the warehouse cannot provide delivery service for customers beyond the delivery range of the unmanned aerial vehicle, so that the establishment of a plurality of unmanned aerial vehicle stations provides remote delivery service for the unmanned aerial vehicle, and the unmanned aerial vehicle station becomes an important mode for improving the utilization rate of the unmanned aerial vehicle and further playing the cooperative delivery of the vehicle and the machine. In an actual distribution scheme, how to distribute distribution tasks so that the total distribution cost is the lowest becomes a problem to be solved urgently, a reasonable path generation problem description model is built, and a find and solve algorithm is of great significance for the model.
Disclosure of Invention
The embodiment of the application aims to provide a vehicle-machine collaborative distribution method and a vehicle-machine collaborative distribution system considering multiple unmanned aerial vehicle stations, and the method and the system can generate an efficient scheduling scheme aiming at the logistics background of unmanned aerial vehicles matched with trucks.
In order to achieve the above object, an embodiment of the present application provides a vehicle-machine collaborative distribution method considering multiple unmanned aerial vehicle stations, including:
randomly generating a vehicle-machine collaborative distribution scheme to form a scheme to be optimized;
taking the scheme to be optimized as a historical optimal scheme and a current optimal scheme;
judging whether the current iteration times are larger than or equal to a preset value;
under the condition that the iteration times are smaller than the preset value, executing a destruction operation and an insertion operation on the scheme to be optimized to form a corresponding scheme to be updated;
determining whether the scheme to be updated is better than the history optimal scheme;
under the condition that the scheme to be updated is better than the historical optimal scheme, the historical optimal scheme and the current optimal scheme are replaced by the scheme to be updated;
updating the historical optimal scheme and the current optimal scheme based on a simulated annealing criterion under the condition that the scheme to be updated is inferior to the historical optimal scheme;
and under the condition that the current iteration times are larger than or equal to the preset value, taking the historical optimal scheme as a final vehicle-machine collaborative distribution scheme.
Optionally, the method comprises:
calculating the total energy consumption value of the vehicle-machine cooperative distribution scheme according to a formula (1),
wherein minZ is the minimum value, V is the set of points where the clients to be distributed are located, f ij For the energy consumption value, x of the truck from the point i of the customer to the point j of the customer ijt An indicator variable for whether truck T is traveling from customer point i to customer point j, T is the collection of trucks, D is the collection of drones,for unmanned plane d from station S d The energy consumption value, y, to the point j where the customer is located ijd For the unmanned plane d from the customer' S point i to the site S d Then to the indicating variable F of the point j of the customer T For the base energy consumption of a single truck, +.>For whether the truck t is from the distribution centre V 0 Indicating variable, F, driving to the point j of the customer D Basic energy consumption for a single unmanned aerial vehicle, +.>For the unmanned plane d whether from the affiliated station S d An indicator variable that flies to the point j where the customer is located;
and comparing the energy consumption values of any two of the vehicle-machine cooperative distribution schemes to determine the advantages and disadvantages of the two vehicle-machine cooperative distribution schemes.
Optionally, the randomly generating the vehicle-machine collaborative distribution scheme to form the scheme to be optimized includes:
acquiring a point set of a customer to be distributed;
judging whether the point set of the client is provided with the point of the unselected client;
under the condition that the point set of the client is judged to have the point of the unselected client, randomly selecting a point of the unselected client from the point set of the client;
calculating the first energy consumption increased by the truck after the selected point of the customer joins the path of the truck;
judging whether the unmanned aerial vehicle can complete the selected distribution task of the point where the client is located;
under the condition that the unmanned aerial vehicle can finish the distribution task of the selected point of the client, calculating the second energy consumption increased by the unmanned aerial vehicle after the selected point of the client joins the path of the unmanned aerial vehicle;
judging whether the first energy consumption is smaller than the second energy consumption or not;
under the condition that the first energy consumption is larger than or equal to the second energy consumption, adding the selected node where the client is located into a path of the unmanned aerial vehicle, judging whether the point set where the client is located has the point where the client which is not selected, and executing the corresponding steps of the method;
under the condition that the first energy consumption is larger than or equal to the second energy consumption, adding the selected node where the customer is located into a path of a truck, judging whether the point set where the customer is located has the point where the unselected customer is located or not again, and executing the corresponding steps of the method;
under the condition that the unmanned aerial vehicle cannot finish the distribution task of the selected point of the client, adding the selected node of the client into the path of the unmanned aerial vehicle, and executing the corresponding steps of the method;
and outputting the formed scheme to be optimized under the condition that the point set where the customer is located does not exist in the unselected point set where the customer is located.
Optionally, the adding the selected node of the customer to the path of the truck includes:
judging whether the current truck can complete the selected distribution task of the point where the customer is located;
and under the condition that the current truck can complete the distribution task of the selected point of the customer, selecting a new truck to add the distribution task of the selected point of the customer to the path of the new truck.
Optionally, the destroying operation includes:
judging whether the current damage times are larger than or equal to a preset damage operator or not;
under the condition that the current destruction times are less than the destruction operators, respectively calculating and deleting the point of any client, and then, obtaining the total energy consumption value of the scheme to be optimized;
comparing each total energy consumption value to screen out the scheme to be optimized with the minimum total energy consumption value, and judging whether the current damage times are larger than or equal to a preset damage operator again;
and outputting the scheme to be optimized after the destruction operation is finished under the condition that the current destruction times are judged to be greater than or equal to the destruction operator.
Optionally, the inserting operation includes:
randomly selecting a point of the unselected client from the point set of the client deleted from the destruction operation;
traversing each pluggable node in the scheme to be optimized, inserting the point of the client into the pluggable node and calculating the total energy consumption value of the scheme to be optimized after insertion;
the remorse operator is calculated according to formula (2),
wherein ,to insert the remorse operator after the i-th insertable point, Δf i 2 For the total energy consumption value of the scheme to be optimized after inserting the ith pluggable point, Δf i 1 The total energy consumption value of the scheme to be optimized before the ith pluggable point is inserted;
selecting the scheme to be optimized with the smallest remorse operator;
judging whether points of unselected clients exist in the point set of the client deleted by the destruction operation;
under the condition that the point set of the client deleted by the destroy operation still has the point of the unselected client, randomly selecting the point of the unselected client from the point set of the client deleted by the destroy operation again, and executing the corresponding steps of the method;
and outputting the scheme to be optimized with the operation completed under the condition that the point set of the deleted client in the destruction operation is judged to have no point of the unselected client.
In another aspect, the present application also provides a co-vehicle delivery system that contemplates multiple unmanned aerial vehicle stations, the system comprising a processor configured to perform a method as described in any of the above.
In yet another aspect, the present application also provides a computer readable storage medium storing instructions for reading by a machine to cause the machine to perform a method as described in any one of the above.
According to the vehicle-machine collaborative distribution method and system considering multiple unmanned aerial vehicle stations, the completed distribution scheme is formed according to different distribution efficiencies and modes of unmanned aerial vehicles and vehicles at the same time when the distribution scheme is generated, so that the technical defect that the generation algorithm in the prior art is difficult to meet the requirement of the generation of the distribution scheme under the vehicle-machine collaborative condition is overcome, and the efficiency of generating the distribution scheme is improved.
Additional features and advantages of embodiments of the application will be set forth in the detailed description which follows.
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The accompanying drawings are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain, without limitation, the embodiments of the application. In the drawings:
FIG. 1 is a flow chart of a vehicle-to-machine collaborative distribution method that considers multiple unmanned aerial vehicle sites according to one embodiment of the application;
FIG. 2 is a partial flow chart of a vehicle-to-machine collaborative distribution method that considers multiple unmanned aerial vehicle sites in accordance with one embodiment of the present application;
FIG. 3 is a partial flow chart of a vehicle-to-machine collaborative distribution method that accounts for multiple unmanned sites according to one embodiment of the application;
fig. 4 is a partial flow chart of a vehicle-to-machine collaborative distribution method that considers multiple unmanned aerial vehicle sites according to one embodiment of the application.
Detailed Description
The following describes the detailed implementation of the embodiments of the present application with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the application, are not intended to limit the application.
Fig. 1 is a flowchart illustrating a vehicle-to-machine collaborative distribution method considering a plurality of unmanned aerial vehicle stations according to an embodiment of the present application. In this fig. 1, the method may include:
in step S10, a vehicle-machine collaborative distribution scheme is randomly generated to form a scheme to be optimized;
in step S11, the scheme to be optimized is used as a historical optimal scheme and a current optimal scheme;
in step S12, it is determined whether the current iteration number is greater than or equal to a preset value;
in step S13, under the condition that the iteration number is less than the preset value, performing a destruction operation and an insertion operation on the scheme to be optimized to form a corresponding scheme to be updated;
in step S14, it is determined whether the scheme to be updated is better than the history optimal scheme;
in step S15, in the case where the scheme to be updated is better than the history optimal scheme, the history optimal scheme and the current optimal scheme are replaced with the scheme to be updated;
in step S16, in the case where the scheme to be updated is inferior to the history optimal scheme, updating the history optimal scheme and the current optimal scheme based on the simulated annealing criterion;
in step S17, when the current iteration number is greater than or equal to the preset value, the history optimal solution is used as the final collaborative distribution solution of the vehicle and the machine.
In the method shown in fig. 1, step S10 may be used to randomly generate an initial collaborative distribution scheme for vehicles through a plurality of preset methods. For this preset variety of methods, then, various means known to the person skilled in the art may be used, such as methods based on genetic algorithm coding, etc. In a preferred example of the present application, the method of generating an initial co-vehicle delivery profile may include the steps as shown in FIG. 2. In this fig. 2, the method may include:
in step S20, a set of points where the customer to be distributed is located is acquired;
in step S21, it is determined whether there are any points in the point set where the client is located that are not selected;
in step S22, under the condition that it is determined that the point set of the client has the unselected point of the client, randomly selecting a point of the unselected client from the point set of the client;
in step S23, calculating a first energy consumption increased by the truck after the selected customer' S point joins the path of the truck;
in step S24, it is determined whether the unmanned aerial vehicle can complete the distribution task of the point where the selected customer is located;
in step S25, under the condition that it is determined that the unmanned aerial vehicle can complete the distribution task of the point where the selected client is located, calculating a second energy consumption increased by the unmanned aerial vehicle after the point where the selected client is located joins the path of the unmanned aerial vehicle;
in step S26, it is determined whether the first energy consumption is smaller than the second energy consumption;
in step S27, under the condition that the first energy consumption is greater than or equal to the second energy consumption, adding the selected node where the customer is located into the path of the unmanned aerial vehicle, judging again whether the point set where the customer is located has the point where the customer is not selected, and executing the corresponding steps of the method;
in step S28, under the condition that the first energy consumption is greater than or equal to the second energy consumption, adding the selected node of the customer into the path of the truck, judging again whether the point set of the customer has the point of the unselected customer, and executing the corresponding steps of the method;
under the condition that the unmanned aerial vehicle cannot finish the distribution task of the point where the selected client is located, adding the node where the selected client is located into the path of the unmanned aerial vehicle, and executing the corresponding steps of the method;
in step S29, if it is determined that there is no point in the point set where the client is located, which is not selected, the formed solution to be optimized is output.
In this fig. 2, step S20 may be used to obtain a set of points where the customer to be distributed is located. In a conventional distribution mission, the customer's points, the unmanned plane's stops, and the trucks ' departure points are randomly distributed within the area to be distributed. The stop point of the unmanned aerial vehicle and the departure point of the truck are preset and known, and the positions of the points of the clients are often different in different delivery tasks. Therefore, step S20 is required to acquire the point set of the customer to be distributed. Specifically, the step S20 may be to obtain the mass and the type of the goods to be delivered at the point of each customer and the position coordinates of the point of the customer, so as to facilitate the adjustment of the collaborative planning scheme of the vehicle and the machine for the obtained information.
Steps S21 to S28 may be used to generate the vehicle-machine collaborative planning scheme (scheme to be optimized). Specifically, step S22 randomly selects a point of the client that is not selected from the set of points of the client to allocate, thereby avoiding repeated selection; step S24 judges whether the selected point of the client can be added to the path of the unmanned plane. If so, steps S25 and S26 may continue to be performed at this point to determine the path to join the customer 'S point to the truck or the path to the drone by comparing the first energy consumption of the selected customer' S point to the path to the truck with the second energy consumption of the drone path. The calculation manner of the first energy consumption and the second energy consumption may be various manners known to those skilled in the art, for example, calculating the mileage increased by calculating the point where the customer is located by the truck or the unmanned aerial vehicle, combining the unit energy consumption of the truck or the unmanned aerial vehicle, and finally calculating the first energy consumption or the second energy consumption. In step S21, if it is determined that there are no points in the set of points where the customer is located, it is indicated that all the points where the customer is located have been allocated, and then the complete vehicle-machine collaborative planning scheme may be directly output.
In the method shown in fig. 2, step S24 is added to determine whether the unmanned aerial vehicle can complete the delivery task of the point where the customer is located, because in the prior art, the delivery process of the unmanned aerial vehicle is directly flown from the stop point to the point where the customer is located. Due to the problem of the endurance of the unmanned aerial vehicle, the unmanned aerial vehicle only completes the distribution task of the point where one customer is located in one distribution process. Under the condition that the selected point of the client is far away from the stop point of the unmanned aerial vehicle, the second energy consumption of the unmanned aerial vehicle does not need to be calculated at the moment, and the second energy consumption is directly distributed to the path of the truck.
In addition, the delivery modes of trucks and unmanned aerial vehicles are different. In order to complete the task of multiple delivery points, multiple trucks are often required to complete the delivery task of the truck. Then, to accommodate this condition, step S28 may be to determine whether the current truck is capable of completing the delivery task at the point of the selected customer; and under the condition that the current truck can finish the distribution task of the point of the selected customer, selecting a new truck to add the distribution task of the point of the selected customer to the path of the new truck.
The historical optimal scheme is an optimal scheme (a vehicle-machine cooperative distribution scheme) in a plurality of iterative processes, and the current optimal scheme is an optimal scheme in each iterative process. For the initial collaborative distribution scheme of the vehicle and the machine, since only one scheme is generated, step S11 may directly use the initial collaborative distribution scheme of the vehicle and the machine as the historical optimal scheme and the current optimal scheme.
Steps S12 through S16 may be used to update and adjust the formed co-delivery scheme of the vehicle and machine. Specifically, step S12 may be used to count the number of updates (iterates), so that if the number of updates reaches a preset requirement (preset value), the loop is skipped in time, and a final co-delivery scheme of the vehicle and the machine is obtained (step S17). Step S13 may be used to perform the destruction and insertion operations on the solution to be optimized, thereby forming a corresponding solution to be updated.
For this disruption and insertion operation, although it may take a variety of forms known to those skilled in the art. However, in a preferred example of the present application, the destruction operation may include the steps as shown in fig. 3. Specifically, in this fig. 3, the destruction operation may include:
in step S30, it is determined whether the current number of destructions is greater than or equal to a preset destruction operator;
in step S31, under the condition that the current number of destruction is less than the destruction operator, respectively calculating the total energy consumption value of the scheme to be optimized after deleting the point where any customer is located;
in step S32, each total energy value is compared to screen out a scheme to be optimized with the minimum total energy value, and whether the current destruction times are greater than or equal to a preset destruction operator is judged again;
in step S33, in the case where it is determined that the current number of destructions is greater than or equal to the destruction operator, the scheme to be optimized after the destruction operation is completed is output.
In the method shown in fig. 3, step S30 may be used to count the number of damage, so as to avoid excessive damage to the collaborative distribution scheme of the vehicle and the machine, so that the convergence speed of the algorithm is reduced, and finally, the preset value of the preset iteration number cannot meet the requirement of generating the optimal scheme. As for the specific determination manner of the specific value of the destruction operator, in one example of the present application, the value level of the total energy consumption value of the initial scheme may be determined, for example, different energy consumption value levels are set for the total energy consumption value, and the value of one destruction operator is set for each energy consumption level, so as to realize dynamic adjustment of the destruction operator; in another example of the application, the calculation may also be for the number of points where the customer is in the set of points where the customer is. Since the allocation is made for point-by-point in the method shown in fig. 2, the greater the number of points a customer is located at, the greater the number of non-optimal allocations that may occur during the allocation process. Thus, the destruction operator may be calculated by determining the number of points of the client in the set of points of the client based on the number of points of the client. For example, taking one hundredth, one thousandth, etc. of the number of points of the client in the point set of the client, in order to adapt to the numerical requirement, an upward evidence obtaining operation can be performed, so as to meet the integer requirement of the destruction operator.
Step S31 and step S32 may be used to delete the customer delivery point with the largest energy consumption of the single delivery task in the current solution, and the step S31 to step S32 are repeatedly performed in combination with step S30, so that the destruction operation can delete a plurality of points with larger energy consumption, thereby facilitating the subsequent insertion operation. Wherein the total energy consumption value is calculated in a number of ways, which are known to the person skilled in the art. Considering, however, that the calculated total energy consumption value needs to reflect the merits of the current scheme, in a preferred example of the present application, the total energy consumption value may be calculated according to the formula (1),
wherein minZ is the minimum value, V is the set of points where the customers to be distributed are located, f ij For truck slave clientsThe energy consumption value x of the driving at point i to the point j of the customer ijt An indicator variable for whether truck T is traveling from customer point i to customer point j, T is the collection of trucks, D is the collection of drones,for unmanned plane d from station S d The energy consumption value, y, to the point j where the customer is located ijd For the unmanned plane d from the customer' S point i to the site S d Then to the indicating variable F of the point j of the customer T For the base energy consumption of a single truck, +.>For whether the truck t is from the distribution centre V 0 Indicating variable, F, driving to the point j of the customer D Basic energy consumption for a single unmanned aerial vehicle, +.>For the unmanned plane d whether from the affiliated station S d The variables indicated to point j where the customer is flying. Based on the total energy consumption calculated by the formula (1), when comparing the advantages and disadvantages of the two schemes, the calculated total energy consumption can be directly compared, and obviously, the advantages and disadvantages of the two schemes can be obtained.
For this insertion operation, in a preferred example of the application, the steps shown in fig. 4 may be included. In this fig. 4, the inserting operation may include:
in step S40, a point of the client is selected randomly from the point set of the client deleted from the destruction operation;
in step S41, each pluggable node in the scheme to be optimized is traversed, the point where the customer is located is inserted into the pluggable node and the total energy consumption value of the scheme to be optimized after the insertion is calculated;
in step S42, the remorse is calculated according to formula (2),
wherein ,to insert the remorse operator after the i-th insertable point, Δf i 2 For the total energy consumption value of the scheme to be optimized after inserting the ith pluggable point, Δf i 1 The total energy consumption value of the scheme to be optimized before the ith pluggable point is inserted;
in step S43, selecting the scheme to be optimized with the smallest remorse operator;
in step S44, it is determined whether or not there are any points in the point set where the client deleted by the destruction operation is located;
under the condition that the point set of the client deleted by the destroy operation still has the point of the unselected client, randomly selecting the point of the unselected client from the point set of the client deleted by the destroy operation again, and executing the corresponding steps of the method;
in step S45, in the case where it is determined that there is no point in the point set where the client deleted in the destruction operation is located, the to-be-optimized solution in which the operation is completed is output.
The point where the client deleted in the foregoing destruction operation is the point where the total energy consumption value of the single delivery task is maximum, and to complete the entire delivery task, the delivery task at each point needs to be completed. Therefore, in this insertion operation, it is necessary to perform reassignment insertion for each customer site deleted in the destruction operation. In the method shown in fig. 4, the steps S40 and S44 are just used to ensure that the points where all the deleted clients are located in the destruction operation are reinserted into the solution to be optimized in steps S41 to S43, so as to obtain the solution to be optimized, i.e. the solution to be updated, after the operation is completed. Step S41 may be used to traverse each pluggable point in the solution to be optimized (i.e., all possible nodes in the truck or drone path that are capable of accomplishing the delivery task) for the point where the currently selected customer is located. Step S42 may then calculate the amount of increase in the total energy consumption value caused by the formed remorse operator of each scheme to be optimized, i.e. after inserting the point where the customer is located. Finally, the scheme to be optimized with the smallest increment is selected through the step S43 until the step S44 judges that all the points where the clients deleted by the destroyed operation are located are plugged back, and finally the scheme to be optimized, namely the scheme to be updated, which is completed by the operation is output through the step S45.
Steps S14 to S16 may then be to determine how to update the historical optimal solution and the current optimal solution for the solution to be updated formed by the destroy operation and the insert operation. Specifically, in the case that the scheme to be updated is better than the historical optimal scheme, it is explained that the currently formed scheme to be updated is better than all previous schemes, so that the historical optimal scheme and the current optimal scheme can be directly replaced by the scheme to be updated, and a new round of iteration is started. Otherwise, if the scheme to be updated is inferior to the history optimal scheme. At this time, it is explained that the scheme to be updated is not optimal, but in order to avoid the problem of the local optimal solution being trapped in the iteration process, step S16 may be adopted, that is, updating the historical optimal scheme and the current optimal scheme based on the simulated annealing criterion is adopted, and a new round of iteration is started.
In the case that the number of iterations is greater than or equal to the preset value, in step S12, it is indicated that the optimal solution can be obtained at present, so that the historical optimal solution can be directly output as the final collaborative distribution solution of the vehicle and the machine.
In another aspect, the present application also provides a co-vehicle delivery system that contemplates multiple unmanned aerial vehicle stations, the system comprising a processor configured to perform a method as described in any of the above.
In yet another aspect, the present application also provides a computer readable storage medium storing instructions for reading by a machine to cause the machine to perform a method as described in any one of the above.
According to the vehicle-machine collaborative distribution method and system considering multiple unmanned aerial vehicle stations, the completed distribution scheme is formed according to different distribution efficiencies and modes of unmanned aerial vehicles and vehicles at the same time when the distribution scheme is generated, so that the technical defect that the generation algorithm in the prior art is difficult to meet the requirement of the generation of the distribution scheme under the vehicle-machine collaborative condition is overcome, and the efficiency of generating the distribution scheme is improved.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. 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-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products 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.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus 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.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
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.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (5)
1. A vehicle-machine cooperative distribution method considering multiple unmanned aerial vehicle stations, characterized in that the method comprises the following steps:
randomly generating a vehicle-machine collaborative distribution scheme to form a scheme to be optimized;
taking the scheme to be optimized as a historical optimal scheme and a current optimal scheme;
judging whether the current iteration times are larger than or equal to a preset value;
under the condition that the iteration times are smaller than the preset value, executing a destruction operation and an insertion operation on the scheme to be optimized to form a corresponding scheme to be updated;
determining whether the scheme to be updated is better than the history optimal scheme;
under the condition that the scheme to be updated is better than the historical optimal scheme, the historical optimal scheme and the current optimal scheme are replaced by the scheme to be updated;
updating the historical optimal scheme and the current optimal scheme based on a simulated annealing criterion under the condition that the scheme to be updated is inferior to the historical optimal scheme;
under the condition that the current iteration times are larger than or equal to the preset value, taking the historical optimal scheme as a final vehicle-machine collaborative distribution scheme;
the method comprises the following steps:
calculating the total energy consumption value of the vehicle-machine cooperative distribution scheme according to a formula (1),
wherein minZ is the minimum value, V is the set of points where the customers to be distributed are located, f ij For the energy consumption value, x of the truck from the point i of the customer to the point j of the customer ijt An indicator variable for whether truck T is traveling from customer point i to customer point j, T is the collection of trucks, D is the collection of drones,for unmanned plane d from station S d The energy consumption value, y, to the point j where the customer is located ijd For the unmanned plane d from the customer' S point i to the site S d Then to the indicating variable F of the point j of the customer T For the base energy consumption of a single truck, +.>For whether the truck t is from the distribution centre V 0 Indicating variable, F, driving to the point j of the customer D Basic energy consumption for a single unmanned aerial vehicle, +.>For the unmanned plane d whether from the affiliated station S d An indicator variable that flies to the point j where the customer is located;
comparing the energy consumption values of any two vehicle-machine cooperative distribution schemes to determine the advantages and disadvantages of the two vehicle-machine cooperative distribution schemes;
the randomly generating the car-machine collaborative distribution scheme to form the scheme to be optimized comprises the following steps:
acquiring a point set of a customer to be distributed;
judging whether the point set of the client is provided with the point of the unselected client;
under the condition that the point set of the client is judged to have the point of the unselected client, randomly selecting a point of the unselected client from the point set of the client;
calculating the first energy consumption increased by the truck after the selected point of the customer joins the path of the truck;
judging whether the unmanned aerial vehicle can complete the selected distribution task of the point where the client is located;
under the condition that the unmanned aerial vehicle can finish the distribution task of the selected point of the client, calculating the second energy consumption increased by the unmanned aerial vehicle after the selected point of the client joins the path of the unmanned aerial vehicle;
judging whether the first energy consumption is smaller than the second energy consumption or not;
under the condition that the first energy consumption is larger than or equal to the second energy consumption, adding the selected node where the client is located into a path of the unmanned aerial vehicle, judging whether the point set where the client is located has the point where the client which is not selected, and executing the corresponding steps of the method;
under the condition that the first energy consumption is larger than or equal to the second energy consumption, adding the selected node where the customer is located into a path of a truck, judging whether the point set where the customer is located has the point where the unselected customer is located or not again, and executing the corresponding steps of the method;
under the condition that the unmanned aerial vehicle cannot finish the distribution task of the selected point of the client, adding the selected node of the client into the path of the unmanned aerial vehicle, and executing the corresponding steps of the method;
outputting the formed scheme to be optimized under the condition that the point set of the client does not have the unselected point of the client;
the step of adding the selected node of the customer to the path of the truck comprises the following steps:
judging whether the current truck can complete the selected distribution task of the point where the customer is located;
and under the condition that the current truck can complete the distribution task of the selected point of the customer, selecting a new truck to add the distribution task of the selected point of the customer to the path of the new truck.
2. The method of claim 1, wherein the destroying operation comprises:
judging whether the current damage times are larger than or equal to a preset damage operator or not;
under the condition that the current destruction times are less than the destruction operators, respectively calculating and deleting the point of any client, and then, obtaining the total energy consumption value of the scheme to be optimized;
comparing each total energy consumption value to screen out the scheme to be optimized with the minimum total energy consumption value, and judging whether the current damage times are larger than or equal to a preset damage operator again;
and outputting the scheme to be optimized after the destruction operation is finished under the condition that the current destruction times are judged to be greater than or equal to the destruction operator.
3. The method of claim 1, wherein the inserting operation comprises:
randomly selecting a point of the unselected client from the point set of the client deleted from the destruction operation;
traversing each pluggable node in the scheme to be optimized, inserting the point of the client into the pluggable node and calculating the total energy consumption value of the scheme to be optimized after insertion;
the remorse operator is calculated according to formula (2),
wherein ,to insert the remorse operator after the i-th insertable point, Δf i 2 For the total energy consumption value of the scheme to be optimized after inserting the ith pluggable point, Δf i 1 The total energy consumption value of the scheme to be optimized before the ith pluggable point is inserted;
selecting the scheme to be optimized with the smallest remorse operator;
judging whether points of unselected clients exist in the point set of the client deleted by the destruction operation;
under the condition that the point set of the client deleted by the destroy operation still has the point of the unselected client, randomly selecting the point of the unselected client from the point set of the client deleted by the destroy operation again, and executing the corresponding steps of the method;
and outputting the scheme to be optimized with the operation completed under the condition that the point set of the deleted client in the destruction operation is judged to have no point of the unselected client.
4. A co-vehicle delivery system that contemplates multiple unmanned stations, wherein the system comprises a processor configured to perform the method of any of claims 1 to 3.
5. A computer readable storage medium storing instructions for reading by a machine to cause the machine to perform the method of any one of claims 1 to 3.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010111452A (en) * | 2008-11-04 | 2010-05-20 | Internatl Business Mach Corp <Ibm> | Method, device, and program for obtaining delivery route of a plurality of vehicles |
| CN101739812A (en) * | 2009-12-10 | 2010-06-16 | 浙江师范大学 | Route optimizing method for logistic distribution vehicle |
| CN110937293A (en) * | 2018-09-21 | 2020-03-31 | 北京京东尚科信息技术有限公司 | Distribution station and distribution method |
| CN113177762A (en) * | 2021-05-14 | 2021-07-27 | 大连海事大学 | Multi-center electric vehicle-unmanned aerial vehicle distribution path optimization method |
-
2021
- 2021-08-30 CN CN202111002179.0A patent/CN113723804B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010111452A (en) * | 2008-11-04 | 2010-05-20 | Internatl Business Mach Corp <Ibm> | Method, device, and program for obtaining delivery route of a plurality of vehicles |
| CN101739812A (en) * | 2009-12-10 | 2010-06-16 | 浙江师范大学 | Route optimizing method for logistic distribution vehicle |
| CN110937293A (en) * | 2018-09-21 | 2020-03-31 | 北京京东尚科信息技术有限公司 | Distribution station and distribution method |
| CN113177762A (en) * | 2021-05-14 | 2021-07-27 | 大连海事大学 | Multi-center electric vehicle-unmanned aerial vehicle distribution path optimization method |
Non-Patent Citations (1)
| Title |
|---|
| Efficient optimization technique for multiple DG allocation in distribution networks.《Applied Soft Computing》.2020,第86卷全文. * |
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