CN109345166B - Method and apparatus for generating information - Google Patents
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Abstract
The embodiment of the application discloses a method and a device for generating information. One embodiment of the method comprises: receiving a target location information set and a subset upper limit value; in response to determining that the number of target location information in the set of target location information is greater than the subset upper limit, dividing the set of target location information into at least two subsets of target location information; for a target location information subset of the at least two target location information subsets, solving a vehicle scheduling scheme for the target location information subset based on a heuristic algorithm; merging at least two vehicle scheduling schemes obtained for at least two subsets of target location information; and taking the merged vehicle scheduling scheme as an initial vehicle scheduling scheme aiming at the target location information set, and optimizing the initial vehicle scheduling scheme based on a heuristic algorithm to obtain a final vehicle scheduling scheme aiming at the target location information set. The embodiment realizes the rapid generation of the vehicle scheduling scheme of the large-scale delivery problem.
Description
Technical Field
The embodiment of the application relates to the technical field of logistics, in particular to a method and a device for generating information.
Background
With the continuous development of Online To Offline (O2O) and new retail, the problem of daily distribution of goods from a central warehouse To various merchants scattered throughout the city has also increased dramatically. The new retail has the characteristics of short route, generally small goods, requirement of immediate delivery, large amount of gathering of delivery points and the like. How to carry out efficient distribution scheduling planning on large-scale distribution problems so as to save distribution cost is an urgent problem to be solved. At present, a distribution scheme can be solved based on a heuristic algorithm, and distribution scheduling planning can also be carried out in a manual mode. The heuristic algorithm can provide a better distribution scheduling scheme for small and medium-scale distribution scheduling tasks in reasonable time, but along with the scale enlargement of the distribution scheduling tasks, the solution space grows exponentially, and the solution time increases sharply. The delivery scheduling planning is carried out in a manual mode, the quality of the result depends on the experience accumulation and the quality of operators, and deep staff is seriously depended on. For a scene with a large order quantity, a scheduling scheme generated by manual experience is very uncontrollable, and the delivery cost cannot be effectively reduced.
Disclosure of Invention
The embodiment of the application provides a method and a device for generating information.
In a first aspect, an embodiment of the present application provides a method for generating information, where the method includes: receiving a target location information set and a subset upper limit value, wherein the target location information in the target location information set comprises an identifier of a target location; in response to determining that the number of target location information in the set of target location information is greater than the subset upper limit, dividing the set of target location information into at least two subsets of target location information; for a target location information subset of the at least two target location information subsets, solving a vehicle scheduling scheme aiming at the target location information subset based on a heuristic algorithm, wherein the vehicle scheduling scheme comprises a driving route of a vehicle and a target location identification sequence; combining at least two vehicle scheduling schemes obtained by aiming at least two target place information subsets to obtain a combined vehicle scheduling scheme; and taking the merged vehicle scheduling scheme as an initial vehicle scheduling scheme aiming at the target location information set, and optimizing the initial vehicle scheduling scheme based on a heuristic algorithm to obtain a final vehicle scheduling scheme aiming at the target location information set.
In some embodiments, the dividing the set of target location information into at least two subsets of target location information includes: determining the division number of the target location information subset according to the subset upper limit value; and dividing the target location information set based on the division number and a clustering algorithm to obtain target location information subsets with the division number.
In some embodiments, the determining the number of divisions of the target location information subset according to the subset upper limit value includes: determining the ratio of the number of the target location information in the target location information set to the upper limit value of the subset, and rounding the ratio downwards to obtain a first value; and taking the sum of the first value and a preset value as the dividing quantity of the target place information subset.
In some embodiments, the above method further comprises: receiving a subset lower limit value, wherein the subset lower limit value is less than or equal to the subset upper limit value; and after dividing the set of target location information into at least two subsets of target location information, the method further comprises: performing the following steps for the obtained target location information subsets of the at least two target location information subsets: comparing the number of the target location information in the target location information subset with the subset lower limit value and the subset upper limit value respectively; in response to determining that the number of target location information in the subset of target location information is greater than the subset upper limit, partitioning the subset of target location information; in response to determining that the number of destination location information in the subset of destination location information is less than the subset lower limit, merging the subset of destination location information to the other subset of destination location information.
In some embodiments, for a subset of destination point information of the at least two subsets of destination point information, solving a vehicle scheduling scheme for the subset of destination point information based on a heuristic algorithm includes: and based on a heuristic algorithm, solving the vehicle dispatching scheme of the target location information subset in the at least two target location information subsets in parallel.
In some embodiments, the solving of the vehicle scheduling plan for the subset of target location information based on a heuristic algorithm includes: receiving model setting parameters and algorithm parameters, wherein the model setting parameters comprise a cost function and constraint conditions, and the algorithm parameters comprise iteration times; establishing a mathematical model by using the cost function and the constraint condition; and solving the mathematical model by using the algorithm parameters and a heuristic algorithm to obtain a vehicle scheduling scheme, wherein the obtained vehicle scheduling scheme comprises a driving route and a target location identification sequence of the vehicle.
In some embodiments, the cost function is a total cost, wherein the total cost is calculated from a total distance traveled on the route, a mileage cost of the route, and a depreciation cost of the vehicles on the route.
In a second aspect, an embodiment of the present application provides an apparatus for generating information, where the apparatus includes: a first receiving unit configured to receive a set of destination point information and a subset upper limit value, wherein the destination point information in the set of destination point information includes an identification of a destination point; a dividing unit configured to divide the set of destination location information into at least two subsets of destination location information in response to determining that the number of destination location information in the set of destination location information is greater than the subset upper limit; a solving unit configured to solve a vehicle scheduling scheme for at least two subsets of target location information based on a heuristic algorithm for the subset of target location information, wherein the vehicle scheduling scheme comprises a driving route of a vehicle and a sequence of target location identifications; a merging unit configured to merge at least two vehicle scheduling schemes obtained for at least two subsets of target location information to obtain a merged vehicle scheduling scheme; and the optimization unit is configured to use the merged vehicle scheduling scheme as an initial vehicle scheduling scheme for the target location information set, and optimize the initial vehicle scheduling scheme based on a heuristic algorithm to obtain a final vehicle scheduling scheme for the target location information set.
In some embodiments, the dividing unit includes: a determination subunit configured to determine the number of divisions of the target location information subset according to the subset upper limit value; and a dividing unit configured to divide the target location information set based on the division number and a clustering algorithm to obtain the division number of target location information subsets.
In some embodiments, the determining subunit is further configured to: determining the ratio of the number of the target location information in the target location information set to the upper limit value of the subset, and rounding the ratio downwards to obtain a first value; and taking the sum of the first value and a preset value as the dividing quantity of the target place information subset.
In some embodiments, the above apparatus further comprises: a second receiving unit configured to receive a subset lower limit value, wherein the subset lower limit value is less than or equal to the subset upper limit value; an execution unit configured to execute the following steps for the obtained target location information subsets of the at least two target location information subsets: comparing the number of the target location information in the target location information subset with the subset lower limit value and the subset upper limit value respectively; in response to determining that the number of target location information in the subset of target location information is greater than the subset upper limit, partitioning the subset of target location information; in response to determining that the number of destination location information in the subset of destination location information is less than the subset lower limit, merging the subset of destination location information to the other subset of destination location information.
In some embodiments, the above solving unit is further configured to: and based on a heuristic algorithm, solving the vehicle dispatching scheme of the target location information subset in the at least two target location information subsets in parallel.
In some embodiments, the above solving unit is further configured to: receiving model setting parameters and algorithm parameters, wherein the model setting parameters comprise a cost function and constraint conditions, and the algorithm parameters comprise iteration times; establishing a mathematical model by using the cost function and the constraint condition; and solving the mathematical model by using the algorithm parameters and a heuristic algorithm to obtain a vehicle scheduling scheme, wherein the obtained vehicle scheduling scheme comprises a driving route and a target location identification sequence of the vehicle.
In some embodiments, the cost function is a total cost, wherein the total cost is calculated from a total distance traveled on the route, a mileage cost of the route, and a depreciation cost of the vehicles on the route.
In a third aspect, an embodiment of the present application provides an apparatus, including: one or more processors; a storage device, on which one or more programs are stored, which, when executed by the one or more processors, cause the one or more processors to implement the method as described in any implementation manner of the first aspect.
In a fourth aspect, the present application provides a computer-readable medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method as described in any implementation manner of the first aspect.
According to the method and the device for generating information, the target location information set and the subset upper limit value are received, and the target location information set is divided into at least two target location information subsets in response to the fact that the number of the target location information in the target location information set is larger than the subset upper limit value. Then, for each of the at least two subsets of destination location information, a vehicle scheduling scheme for the subset of destination location information is solved based on a heuristic algorithm. And then, combining at least two vehicle dispatching schemes obtained aiming at the at least two target place information subsets to obtain a combined vehicle dispatching scheme. And finally, taking the merged vehicle scheduling scheme as an initial vehicle scheduling scheme for the target location information set, and optimizing the initial vehicle scheduling scheme based on a heuristic algorithm to obtain a final vehicle scheduling scheme for the target location information set. Therefore, the vehicle scheduling scheme for large-scale delivery problems is generated quickly.
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Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture diagram in which one embodiment of the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of a method for generating information according to the present application;
FIG. 3 is a schematic illustration of an application scenario of a method for generating information according to the present application;
FIG. 4 is a flow diagram of yet another embodiment of a method for generating information according to the present application;
FIG. 5 is a schematic block diagram illustrating one embodiment of an apparatus for generating information according to the present application;
FIG. 6 is a block diagram of a computer system suitable for use in implementing the apparatus of an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 illustrates an exemplary system architecture 100 to which a method for generating information or an apparatus for generating information of embodiments of the present application may be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. Various communication client applications, such as vehicle scheduling-type software, web browser applications, shopping-type applications, search-type applications, instant messaging tools, mailbox clients, social platform software, and the like, may be installed on the terminal devices 101, 102, 103.
The terminal apparatuses 101, 102, and 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like. When the terminal apparatuses 101, 102, 103 are software, they can be installed in the electronic apparatuses listed above. It may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services) or as a single piece of software or software module. And is not particularly limited herein.
The server 105 may be a server providing various services, such as a background server providing support for information displayed on the terminal devices 101, 102, 103. The background server may analyze and other processes the received data of the target location information set, the subset upper limit value, and the like, and feed back a processing result (e.g., a final vehicle scheduling scheme) to the terminal device.
The server may be hardware or software. When the server is hardware, it may be implemented as a distributed server cluster formed by multiple servers, or may be implemented as a single server. When the server is software, it may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services), or as a single piece of software or software module. And is not particularly limited herein.
It should be noted that the method for generating information provided in the embodiment of the present application may be executed by the terminal devices 101, 102, and 103, or may be executed by the server 105. Accordingly, the device for generating information may be provided in the terminal apparatuses 101, 102, and 103, or may be provided in the server 105, which is not limited herein.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to FIG. 2, a flow 200 of one embodiment of a method for generating information in accordance with the present application is shown. The method for generating information comprises the following steps:
In the present embodiment, the execution subject of the method for generating information (e.g., the terminal device 101, 102, 103 or the server 105 shown in fig. 1) may receive the target location information set and the subset upper limit value. As an example, when the execution subject is the terminal device 101, 102, 103 shown in fig. 1, the execution subject may directly receive the target location information set and the subset upper limit value input by the user. When the execution subject is the server 105 shown in fig. 1, the execution subject may receive the target location information set and the subset upper limit value from a terminal with which the user inputs information by a wired connection manner or a wireless connection manner.
Here, the destination point corresponding to each destination point information in the destination point information set may be a customer point that needs to deliver goods. For example, in a logistics distribution scenario, the location information of the target location may be a target address of the item order distribution, coordinates of the target location, and the like, for example, a residential building, a commercial building, a container, and the like. For convenience of scheduling, a corresponding unique identifier may be set for each target location. The identification of the target location may include, but is not limited to, the following: numbers, letters, words, etc. Here, each destination point information in the destination point information set may include, but is not limited to, location information of the destination point, an identification of the destination point, an order identification and an item weight of an item order corresponding to the destination point, and the like. The order identification may be used to uniquely identify an order, such as an order number. Here, the subset upper limit value may be a value representing a maximum value of the number of pieces of destination point information that can be included in each subset after the set of destination point information is divided into at least two subsets.
In response to determining that the number of target location information in the set of target location information is greater than the subset upper limit, the set of target location information is divided into at least two subsets of target location information, step 202.
In this embodiment, the execution subject may compare the number of pieces of destination information in the destination information set with the subset upper limit value. In response to determining that the number of target location information in the set of target location information is greater than the subset upper limit, the execution subject may divide the set of target location information into at least two subsets of target location information. As an example, the execution subject may divide the target location information corresponding to the target location information in the target location information set according to the position information of the target location corresponding to the target location information, for example, divide the target location information corresponding to several target locations having a distance smaller than a certain threshold into one target location information subset.
In general, in response to determining that the number of destination location information in the set of destination location information is less than or equal to the subset upper limit value, the executing agent may directly use a heuristic algorithm to solve for the vehicle scheduling plan for the set of destination location information. It should be noted that, solving the vehicle dispatching scheme by using the heuristic algorithm is a technology widely researched and applied at present, and is not described herein again.
In some optional implementation manners of this embodiment, dividing the target location information set into at least two target location information subsets may be implemented by:
first, the number of divisions of the target location information subset is determined based on the subset upper limit value. As an example, the execution body may determine the number of divisions of the target location information subset in various ways according to the number of target location information in the target location information set and the subset upper limit value. It will be appreciated that the determined number of divisions needs to satisfy the following formula: the division number x the subset upper limit value is greater than or equal to the number of the target location information in the target location information set.
And then, dividing the target location information set based on the division number and a clustering algorithm to obtain a division number of target location information subsets. Here, the execution body may cluster the target location information in the target location information set using a clustering algorithm with the number of divisions as the number of target clusters K. As an example, clustering may be performed according to the position information of the target location in the destination location information, so as to obtain K clusters of the number of target clusters, where a cluster is a target location information subset. Here, the above-mentioned clustering algorithm may be various clustering algorithms including, but not limited to: K-Means (K mean) algorithm, K-Means + + algorithm, K-center clustering algorithm (K-Medoide), ISODATA algorithm, etc. In addition, the distance calculation method used in the clustering process may include, but is not limited to: manhattan distance, euclidean distance, spherical linear distance, navigation distance matrix, and the like.
In some optional implementations, the determining the number of divisions of the target location information subset according to the subset upper limit value may specifically include the following: first, the execution subject may determine a ratio of the number of target location information in the target location information set to the subset upper limit, and rounded down the ratio to obtain a first value. Then, the execution subject may use a sum of the first value and a preset value as the division number of the target location information subset.
Here, the preset value is a positive integer, and the size of the preset value can be set according to actual needs, which is not limited herein. As an example, the size of the preset value may be 1, and at this time, the division number K of the target location information subset may be calculated by the following formula (1):
wherein N istotalRepresenting the number of target location information in a set of target location information, NclusterMaxThe subset upper limit value is indicated.
In some optional implementations of this embodiment, the method for generating information may further include the following steps not shown in fig. 2:
the execution body may receive a subset lower limit value, where the subset lower limit value may be less than or equal to the subset upper limit value. Here, the subset lower limit value may be a value representing a minimum value of the number of pieces of destination information that can be included in each subset after dividing the set of destination information into at least two subsets. And
after step 202, the above-described method for generating information may further include the following not shown in fig. 2:
for each of the at least two subsets of destination point information obtained in step 202, the executing entity may perform the following steps:
step S1: comparing the number of target location information in the target location information subset with the subset lower limit value and the subset upper limit value respectively.
Step S2: the execution principal may divide the subset of target location information in response to determining that the number of target location information in the subset of target location information is greater than the subset upper limit. As an example, the execution body may divide the pieces of destination point information in the destination point information subset into two or more destination point information subsets in various ways. Such as random division. For another example, the target location information is divided according to the distance between the target locations corresponding to the target location information in the target location information subset. As another example, the subset of target location information is partitioned using a clustering algorithm.
Step S3: in response to determining that the number of destination location information in the subset of destination location information is less than the subset lower limit, merging the subset of destination location information to the other subset of destination location information. As an example, the execution subject may employ various parties to merge the subset of target location information into other subsets of target location information. For example, the executing entity may first calculate the distance between the cluster center of the target location information subset and the cluster centers of the other target location information subsets, and use the target location information subset corresponding to the minimum distance as the target location information subset to be merged. And then merging the target location information subset and the target location information subset to be merged.
It should be noted that the executing entity may repeatedly execute step S1, step S2, and step S3 until the number of the target location information included in each of the at least two target location information subsets is between the subset lower limit and the subset upper limit. By the implementation mode, the number of the target location information contained in each of the at least two finally obtained target location information subsets can be ensured to be approximately the same. Therefore, when the heuristic algorithm is used for solving the vehicle dispatching scheme for each target location information subset, the problem that time consumption for solving the vehicle dispatching scheme is too long due to the fact that the number of the target location information is too large can be solved.
And step 203, for the target location information subset in the at least two target location information subsets, solving a vehicle scheduling scheme aiming at the target location information subset based on a heuristic algorithm.
In this embodiment, for each of the at least two subsets of destination point information obtained by dividing in step 202, the executing entity may solve the vehicle scheduling scheme for the subset of destination point information based on a heuristic algorithm. The solved vehicle scheduling scheme may include a driving route of a vehicle of at least one vehicle and a target location identification sequence. The target location identification sequence can be used here to characterize the sequence of the individual target locations that the vehicle needs to visit during its travel along the travel route. Here, the heuristic algorithm may include, but is not limited to, a genetic algorithm, a simulated annealing algorithm, an ant colony algorithm, a tabu search algorithm, and the like. It should be noted that the technology for solving the vehicle dispatching scheme based on the heuristic algorithm is a well-known technology which is widely researched and applied at present, and therefore, the detailed description is omitted here.
In some optional implementations of the embodiment, the solving of the vehicle scheduling scheme for the target location information subset based on the heuristic algorithm may specifically include the following:
first, the execution body may receive a model setting parameter and an algorithm parameter, where the model setting parameter may include a cost function and a constraint condition, and the algorithm parameter may include an iteration number. The execution agent may then build a mathematical model using the cost function and the constraints. Finally, the executing body can solve the mathematical model by using the algorithm parameters and a heuristic algorithm to obtain a vehicle dispatching scheme, wherein the obtained vehicle dispatching scheme comprises a driving route and a target location identification sequence of the vehicle.
Here, the cost function may be used to represent how excellent the vehicle scheduling scheme is. For example, a smaller value of the cost function indicates a higher degree of excellence of the vehicle scheduling scheme. In practice, the cost function can be set according to actual needs. For example, one of the following may be set as the cost function: 1) the total vehicle driving distance represents the sum of the driving distances of all vehicles participating in scheduling; 2) using the vehicle number to represent the total number of vehicles participating in the dispatch; 3) the vehicle distance is extremely poor and represents the mileage difference between the vehicles with the longest driving distance and the shortest driving distance in the vehicle scheduling scheme, and the smaller the value is, the vehicle scheduling scheme can better take the load balance in the fleet into account. In addition, the quality evaluation index of a certain vehicle scheduling scheme may include, but is not limited to, the following: average weight loading rate, average volume loading rate. The average weight loading rate is the ratio of the sum of the weights of the goods of the delivery order to the weight limit of the vehicle, the average is the average value of the weight loading rate of each vehicle, and the higher the index is, the better the scheme is. The average volume loading rate is the ratio of the sum of the cargo volumes of the delivery orders of the single vehicles to the rated volume of the cargo box of the vehicle, and the volume loading rates of all the vehicles are averaged, wherein the higher the index is, the better the scheme is.
Constraints can be used to express constraints for solving a vehicle dispatch plan, often in the form of inequalities or equations. In practice, the constraint conditions may be set according to actual needs, for example, one or more of the following may be set as the constraint conditions: the vehicles participating in the dispatch do not exceed the total number of vehicles, each destination point can only be visited once by one vehicle, time window constraints on cargo, load capacity constraints on vehicles, priority constraints on cargo delivery, and the like.
In practical applications, after the executing entity establishes the mathematical model using the cost function and the constraint condition, the mathematical model may be solved by using the algorithm parameters and the heuristic algorithm. In the solving process, whether the iteration times reach the iteration times included by the algorithm parameters or whether the execution results meet the cut-off conditions can be judged, and if the iteration times reach the iteration times included by the algorithm parameters or the execution results meet the cut-off conditions, the vehicle dispatching scheme is output. The cutoff condition of the execution result may be flexibly set according to the requirement of the actual application scenario, for example, the cutoff condition of the execution result may include that the value of the cost function of the vehicle scheduling scheme is smaller than a certain value.
In some alternative implementations, the cost function may be a total cost, wherein the total cost is calculated from a total distance traveled by the route, a mileage cost of the route, and a depreciation cost of the vehicle on the route. As an example, assuming that the total cost is cost, the calculation formula of cost may be as shown in formula (2);
wherein route _ distanceiRepresents the total travel distance, unit _ cost, of the ith linkiRepresents the mileage cost, fixed _ cost, of the vehicle on the ith lineiRepresents the fixed depreciation cost of the vehicle on the ith route, and n represents the total number of routes.
And 204, combining at least two vehicle scheduling schemes obtained by aiming at least two target place information subsets to obtain a combined vehicle scheduling scheme.
In this embodiment, the executing entity may combine at least two vehicle dispatching schemes obtained for the at least two target location information subsets to obtain a combined vehicle dispatching scheme. As an example, for each target location information subset, the executing entity may obtain a set of solutions, where the set of solutions includes a travel route of at least one vehicle and a target location identification sequence, and merge the sets of solutions of the target location information subsets to obtain a merged vehicle scheduling scheme.
And step 205, taking the merged vehicle scheduling scheme as an initial vehicle scheduling scheme for the target location information set, and optimizing the initial vehicle scheduling scheme based on a heuristic algorithm to obtain a final vehicle scheduling scheme for the target location information set.
In this embodiment, the executing body may combine the step 204 to obtain a vehicle scheduling scheme as an initial vehicle scheduling scheme for the target location information set, and optimize the initial vehicle scheduling scheme based on a heuristic algorithm, so as to obtain a final vehicle scheduling scheme for the target location information set. As an example, the executing entity may take the initial vehicle scheduling scheme as an initial solution for the set of destination point information, and from the initial solution, iteratively optimize using a heuristic algorithm, thereby obtaining a final vehicle scheduling scheme for the set of destination point information. It should be noted that the heuristic algorithm used herein may be the same as or different from the heuristic algorithm used in step 203, and is not limited herein.
With continued reference to fig. 3, fig. 3 is a schematic diagram of an application scenario of the method for generating information according to the present embodiment. In the application scenario of fig. 3, a user first inputs a target location information set and a subset upper limit value to the terminal device 301, where the target location information set includes an identifier of a target location: s1, S2, S3, S4, S5, S6, S7, S8, S9 and S10. Second, in response to determining that the number of target location information in the set of target location information is greater than the subset upper limit value, the terminal device 301 may divide the set of target location information into two target location information subsets { S1, S2, S4, S5, S9} and { S3, S6, S7, S8, S10 }. Then, for each of the two subsets of destination point information, the terminal device 301 may solve the vehicle scheduling scheme for the subset of destination point information based on a heuristic algorithm. Obtaining a vehicle scheduling scheme for the target location information subsets { S1, S2, S4, S5, S9 }: the route L1 and the destination point identification sequences S1, S4, S5 of the vehicle a, and the route L2 and the destination point identification sequences S2, S9 of the vehicle B. Obtaining a vehicle scheduling scheme for the target location information subsets { S3, S6, S7, S8, S10 }: the route L3 and the target place identification sequence S3, S6, S8, S10, S7 of the vehicle C. Then, combining two vehicle dispatching schemes obtained according to the two target location information subsets to obtain a combined vehicle dispatching scheme: the route L1 and the destination location identification sequences S1, S4, S5 of the vehicle a, the route L2 and the destination location identification sequences S2, S9 of the vehicle B, the route L3 and the destination location identification sequences S3, S6, S8, S10, S7 of the vehicle C. Finally, the terminal device 301 uses the merged vehicle scheduling scheme as an initial vehicle scheduling scheme for the target location information set, and optimizes the initial vehicle scheduling scheme based on a heuristic algorithm to obtain a final vehicle scheduling scheme for the target location information set.
In the method provided by the embodiment of the application, the target location information set is divided, the vehicle scheduling scheme of each target location information subset obtained by dividing is solved based on a heuristic algorithm, the solved vehicle scheduling schemes are combined, the combined vehicle scheduling scheme is used as an initial vehicle scheduling scheme for the target location information set, and the heuristic algorithm is used again to optimize the initial vehicle scheduling scheme, so that a final vehicle scheduling scheme for the target location information set is obtained. Therefore, the large-scale distribution problem is decomposed into a plurality of sub-problems, the solutions of the sub-problems are combined to serve as the initial solution of the large-scale distribution problem, the initial solution serves as a better solution, the speed of solving the large-scale distribution problem by a heuristic algorithm can be greatly increased, and the generated final vehicle scheduling scheme can be guaranteed to be more excellent.
With further reference to fig. 4, a flow 400 of yet another embodiment of a method for generating information is shown. The flow 400 of the method for generating information comprises the steps of:
In this embodiment, the principle of step 401 is similar to that of step 201 in the embodiment shown in fig. 2, and is not described here again.
In this embodiment, the principle of step 402 is similar to that of step 202 in the embodiment shown in fig. 2, and is not described here again.
And 403, based on a heuristic algorithm, solving the vehicle dispatching scheme of the target location information subset in the at least two target location information subsets in parallel.
In this embodiment, at least two subsets of target location information obtained in step 402 are selected. The executing agent may solve the vehicle dispatching plan for at least two subsets of destination point information in parallel based on a heuristic algorithm. As an example, the executing body may first create a thread pool of K threads, then create algorithm instances of K heuristic algorithms, then take each target location information subset as an input of each heuristic algorithm instance, and execute the K heuristic algorithm instances in parallel on the K threads, thereby obtaining a vehicle scheduling scheme of the K target location information subsets.
And step 404, combining at least two vehicle dispatching schemes obtained by aiming at least two target place information subsets to obtain a combined vehicle dispatching scheme.
In this embodiment, step 404 is similar to step 204 in the embodiment shown in fig. 2, and is not described here again.
And 405, taking the merged vehicle scheduling scheme as an initial vehicle scheduling scheme for the target location information set, and optimizing the initial vehicle scheduling scheme based on a heuristic algorithm to obtain a final vehicle scheduling scheme for the target location information set.
In this embodiment, step 405 is similar to step 205 in the embodiment shown in fig. 2, and is not described here again.
As can be seen from fig. 4, the flow 400 of the method for generating information in the present embodiment highlights the step of solving a vehicle dispatch plan for at least two subsets of destination point information in parallel, as compared to the corresponding embodiment of fig. 2. Therefore, the scheme described in the embodiment can reduce the time for solving the vehicle scheduling scheme of at least two target place information subsets, so that the speed for solving the final vehicle scheduling scheme is further increased.
With further reference to fig. 5, as an implementation of the method shown in the above figures, the present application provides an embodiment of an apparatus for generating information, which corresponds to the method embodiment shown in fig. 2, and which is particularly applicable to various electronic devices.
As shown in fig. 5, the apparatus 500 for generating information of the present embodiment includes: a first receiving unit 501, a dividing unit 502, a solving unit 503, a combining unit 504 and an optimizing unit 505. The first receiving unit 501 is configured to receive a set of destination point information and a subset upper limit value, wherein the destination point information in the set of destination point information includes an identifier of a destination point; the dividing unit 502 is configured to divide the set of target location information into at least two subsets of target location information in response to determining that the number of target location information in the set of target location information is greater than the subset upper limit; the solving unit 503 is configured to, for a subset of target location information of the at least two subsets of target location information, solve a vehicle scheduling scheme for the subset of target location information based on a heuristic algorithm, wherein the vehicle scheduling scheme includes a driving route of the vehicle and a sequence of target location identifications; the merging unit 504 is configured to merge at least two vehicle scheduling schemes obtained for at least two subsets of target location information, resulting in a merged vehicle scheduling scheme; the optimization unit 505 is configured to use the merged vehicle scheduling scheme as an initial vehicle scheduling scheme for the target location information set, and optimize the initial vehicle scheduling scheme based on a heuristic algorithm to obtain a final vehicle scheduling scheme for the target location information set.
In this embodiment, specific processes of the first receiving unit 501, the dividing unit 502, the solving unit 503, the combining unit 504 and the optimizing unit 505 of the apparatus 500 for generating information and technical effects brought by the specific processes can refer to the related descriptions of step 201, step 202, step 203, step 204 and step 205 in the corresponding embodiment of fig. 2, which are not described herein again.
In some optional implementations of this embodiment, the dividing unit 502 includes: a determination subunit (not shown in the figure) configured to determine the number of divisions of the target location information subset according to the subset upper limit value; and a dividing subunit (not shown in the figure) configured to divide the target location information set based on the number of divisions and a clustering algorithm to obtain the number of target location information subsets.
In some optional implementations of this embodiment, the determining subunit is further configured to: determining the ratio of the number of the target location information in the target location information set to the upper limit value of the subset, and rounding the ratio downwards to obtain a first value; and taking the sum of the first value and a preset value as the dividing quantity of the target place information subset.
In some optional implementations of this embodiment, the apparatus 500 further includes: a second receiving unit (not shown in the figures) configured to receive a subset lower limit value, wherein the subset lower limit value is less than or equal to the subset upper limit value; an execution unit (not shown in the figures) configured to perform the following steps for the obtained target location information subsets of the at least two target location information subsets: comparing the number of the target location information in the target location information subset with the subset lower limit value and the subset upper limit value respectively; in response to determining that the number of target location information in the subset of target location information is greater than the subset upper limit, partitioning the subset of target location information; in response to determining that the number of destination location information in the subset of destination location information is less than the subset lower limit, merging the subset of destination location information to the other subset of destination location information.
In some optional implementations of this embodiment, the solving unit 503 is further configured to: and based on a heuristic algorithm, solving the vehicle dispatching scheme of the target location information subset in the at least two target location information subsets in parallel.
In some optional implementations of this embodiment, the solving unit 503 is further configured to: receiving model setting parameters and algorithm parameters, wherein the model setting parameters comprise a cost function and constraint conditions, and the algorithm parameters comprise iteration times; establishing a mathematical model by using the cost function and the constraint condition; and solving the mathematical model by using the algorithm parameters and a heuristic algorithm to obtain a vehicle scheduling scheme, wherein the obtained vehicle scheduling scheme comprises a driving route and a target location identification sequence of the vehicle.
In some alternative implementations of the present embodiment, the cost function is a total cost, wherein the total cost is calculated from a total travel distance of the route, a unit mileage cost of the route, and a depreciation cost of the vehicle on the route.
Referring now to FIG. 6, shown is a block diagram of a computer system 600 suitable for use in implementing the apparatus of an embodiment of the present application. The apparatus shown in fig. 6 is only an example, and should not bring any limitation to the function and use range of the embodiments of the present application.
As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program performs the above-described functions defined in the method of the present application when executed by a Central Processing Unit (CPU) 601.
It should be noted that the computer readable medium described herein can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present application may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a first receiving unit, a dividing unit, a solving unit, a merging unit, and an optimizing unit. Where the names of these units do not in some cases constitute a limitation on the unit itself, for example, the first receiving unit may also be described as a "unit that receives the target location information set and the subset upper limit value".
As another aspect, the present application also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be present separately and not assembled into the device. The computer readable medium carries one or more programs which, when executed by the apparatus, cause the apparatus to: receiving a target location information set and a subset upper limit value, wherein the target location information in the target location information set comprises an identifier of a target location; in response to determining that the number of target location information in the set of target location information is greater than the subset upper limit, dividing the set of target location information into at least two subsets of target location information; for a target location information subset of the at least two target location information subsets, solving a vehicle scheduling scheme aiming at the target location information subset based on a heuristic algorithm, wherein the vehicle scheduling scheme comprises a driving route of a vehicle and a target location identification sequence; combining at least two vehicle scheduling schemes obtained by aiming at least two target place information subsets to obtain a combined vehicle scheduling scheme; and taking the merged vehicle scheduling scheme as an initial vehicle scheduling scheme aiming at the target location information set, and optimizing the initial vehicle scheduling scheme based on a heuristic algorithm to obtain a final vehicle scheduling scheme aiming at the target location information set.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (14)
1. A method for generating information, comprising:
receiving a target location information set and a subset upper limit value, wherein the target location information in the target location information set comprises an identification of a target location;
in response to determining that the number of target location information in the set of target location information is greater than the subset upper limit, dividing the set of target location information into at least two subsets of target location information;
for each of at least two subsets of destination point information, solving a vehicle dispatch plan for the subset of destination point information based on a heuristic algorithm, comprising: receiving model setting parameters and algorithm parameters, wherein the model setting parameters comprise a cost function and constraint conditions, and the algorithm parameters comprise iteration times; establishing a mathematical model using the cost function and the constraint condition; solving the mathematical model by using the algorithm parameters and a heuristic algorithm to obtain a vehicle scheduling scheme, wherein the cost function comprises: the vehicle distance range extreme difference represents the mileage difference between the vehicles with the longest driving distance and the shortest driving distance in the vehicle scheduling scheme, wherein the vehicle scheduling scheme comprises the driving route and the target location identification sequence of the vehicles;
combining at least two vehicle scheduling schemes obtained by aiming at least two target place information subsets to obtain a combined vehicle scheduling scheme;
and taking the merged vehicle scheduling scheme as an initial vehicle scheduling scheme aiming at the target location information set, and optimizing the initial vehicle scheduling scheme based on a heuristic algorithm to obtain a final vehicle scheduling scheme aiming at the target location information set.
2. The method of claim 1, wherein the dividing the set of destination location information into at least two subsets of destination location information comprises:
determining the division number of the target location information subset according to the subset upper limit value;
and dividing the target location information set based on the division number and a clustering algorithm to obtain target location information subsets with the division number.
3. The method of claim 2, wherein the determining the number of divisions of the subset of target location information in accordance with the subset upper limit value comprises:
determining the ratio of the number of the target location information in the target location information set to the subset upper limit value, and rounding the ratio downwards to obtain a first value;
and taking the sum of the first value and a preset value as the dividing quantity of the target location information subset.
4. The method of claim 2, wherein the method further comprises:
receiving a subset lower limit value, wherein the subset lower limit value is less than or equal to the subset upper limit value; and
after dividing the set of target location information into at least two subsets of target location information, the method further comprises:
performing the following steps for the obtained target location information subsets of the at least two target location information subsets: comparing the number of the target location information in the target location information subset with the subset lower limit value and the subset upper limit value respectively; in response to determining that the number of target location information in the subset of target location information is greater than the subset upper limit, partitioning the subset of target location information; in response to determining that the number of target location information in the subset of target location information is less than the subset lower limit, merging the subset of target location information to other subsets of target location information.
5. The method of claim 1, wherein solving, for a subset of destination location information of the at least two subsets of destination location information, a vehicle scheduling plan for the subset of destination location information based on a heuristic algorithm comprises:
and based on a heuristic algorithm, solving the vehicle dispatching scheme of the target location information subset in the at least two target location information subsets in parallel.
6. The method of claim 1, wherein the cost function is a total cost, wherein the total cost is calculated from a total distance traveled of the route, a mileage cost of the route, and a depreciation cost of the vehicle on the route.
7. An apparatus for generating information, comprising:
a first receiving unit configured to receive a set of destination point information and a subset upper limit value, wherein the destination point information in the set of destination point information includes an identification of a destination point;
a dividing unit configured to divide the set of target location information into at least two subsets of target location information in response to determining that the number of target location information in the set of target location information is greater than the subset upper limit value;
a solving unit configured to, for each of at least two subsets of destination point information, solve a vehicle dispatching plan for the subset of destination point information based on a heuristic algorithm, wherein the vehicle dispatching plan comprises a driving route of the vehicle and a sequence of destination point identifications, the solving unit being further configured to: receiving model setting parameters and algorithm parameters, wherein the model setting parameters comprise a cost function and constraint conditions, and the algorithm parameters comprise iteration times; establishing a mathematical model using the cost function and the constraint condition; solving the mathematical model by using the algorithm parameters and a heuristic algorithm to obtain a vehicle scheduling scheme, wherein the cost function comprises: the vehicle distance range is extremely poor and represents the mileage difference between the vehicles with the longest driving distance and the shortest driving distance in the vehicle scheduling scheme;
a merging unit configured to merge at least two vehicle scheduling schemes obtained for at least two subsets of target location information to obtain a merged vehicle scheduling scheme;
and the optimization unit is configured to use the merged vehicle scheduling scheme as an initial vehicle scheduling scheme for the target location information set, and optimize the initial vehicle scheduling scheme based on a heuristic algorithm to obtain a final vehicle scheduling scheme for the target location information set.
8. The apparatus of claim 7, wherein the dividing unit comprises:
a determination subunit configured to determine the number of divisions of the target location information subset according to the subset upper limit value;
and the dividing unit is configured to divide the target location information set based on the division number and a clustering algorithm to obtain the division number of target location information subsets.
9. The apparatus of claim 8, wherein the determining subunit is further configured to:
determining the ratio of the number of the target location information in the target location information set to the subset upper limit value, and rounding the ratio downwards to obtain a first value;
and taking the sum of the first value and a preset value as the dividing quantity of the target location information subset.
10. The apparatus of claim 8, wherein the apparatus further comprises:
a second receiving unit configured to receive a subset lower limit value, wherein the subset lower limit value is less than or equal to the subset upper limit value;
an execution unit configured to execute the following steps for the obtained target location information subsets of the at least two target location information subsets: comparing the number of the target location information in the target location information subset with the subset lower limit value and the subset upper limit value respectively; in response to determining that the number of target location information in the subset of target location information is greater than the subset upper limit, partitioning the subset of target location information; in response to determining that the number of target location information in the subset of target location information is less than the subset lower limit, merging the subset of target location information to other subsets of target location information.
11. The apparatus of claim 7, wherein the solving unit is further configured to:
and based on a heuristic algorithm, solving the vehicle dispatching scheme of the target location information subset in the at least two target location information subsets in parallel.
12. The apparatus of claim 7, wherein the cost function is a total cost, wherein the total cost is calculated from a total distance traveled of the route, a mileage cost of the route, and a depreciation cost of the vehicle on the route.
13. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-6.
14. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-6.
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| CN111461430A (en) * | 2020-03-31 | 2020-07-28 | 北京百度网讯科技有限公司 | Method and device for generating route information |
| CN111784045B (en) * | 2020-06-29 | 2023-09-22 | 北京百度网讯科技有限公司 | Optimization method, device, equipment and storage medium for delivery vehicle |
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