CN114139796A - Postal route planning method, apparatus, medium and equipment - Google Patents

Postal route planning method, apparatus, medium and equipment Download PDF

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CN114139796A
CN114139796A CN202111433588.6A CN202111433588A CN114139796A CN 114139796 A CN114139796 A CN 114139796A CN 202111433588 A CN202111433588 A CN 202111433588A CN 114139796 A CN114139796 A CN 114139796A
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胡晓菁
高岩
金霭汐
石英
冯媛
张月媛
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China Post Information Technology Beijing Co ltd
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Abstract

The embodiment of the application discloses a method, a device, a medium and equipment for route planning. Wherein, the method comprises the following steps: constructing a route planning problem of a target area; the route planning problem comprises a constraint condition and an optimization target; wherein the optimization objective comprises that the total transportation time of at least one single path in the objective area is shortest; the constraint conditions include: a transport carrier load constraint, a single path time constraint, a transport node number constraint, and a path number constraint; and solving the route planning problem by using a predetermined super-heuristic route planning model to obtain a route planning result. The technical scheme can solve the problem of route planning of a single starting point, multiple end points and a single starting point and multiple end points, has outstanding effects on the route planning problem of large data volume and complex constraint conditions, and effectively saves transportation cost, labor cost and time cost.

Description

Postal route planning method, apparatus, medium and equipment
Technical Field
The embodiment of the application relates to the technical field of data processing, in particular to a method, a device, a medium and equipment for route planning.
Background
With the rapid development of the internet, the express logistics industry becomes an important basis for smooth operation of an e-commerce system, and the scientific planning of a mail route is indispensable for realizing efficient express logistics operation.
In the prior art, a route planning problem is generally treated as a path planning problem with a single starting point and a single terminal point, and a constraint condition and an optimization target are established by using a linear distance between transportation nodes. And solving by adopting a traditional path planning model, such as an breadth-first algorithm, a Dijkstra algorithm and the like.
In fact, the distribution task is usually a path planning problem of a single starting point and multiple end points or a single starting point and multiple end points, and the transportation path is not straight in general. In addition, the prior art cannot solve the problem of route planning with large data volume and complex constraint conditions.
Disclosure of Invention
The embodiment of the application provides a route planning method, a route planning device, a route planning medium and route planning equipment, which can solve the route planning problem of single starting point, multiple destination points and/or multiple starting points and single destination points by constructing complex constraint conditions, thereby effectively saving the transportation cost, the labor cost and the time cost.
In a first aspect, an embodiment of the present application provides a route planning method, where the method includes:
constructing a route planning problem of a target area; the route planning problem comprises a constraint condition and an optimization target; wherein the optimization objective comprises that the total transportation time of at least one single path in the target area is shortest; the constraint conditions include: a transport carrier load constraint, a single path time constraint, a transport node number constraint, and a path number constraint;
and solving the route planning problem by using a predetermined super-heuristic route planning model to obtain a route planning result.
In a second aspect, an embodiment of the present application provides a route planning apparatus, including:
the route planning problem construction module is used for constructing a route planning problem of a target area; the route planning problem comprises a constraint condition and an optimization target; wherein the optimization objective comprises that the total transportation time of at least one single path in the target area is shortest; the constraint conditions include: a transport carrier load constraint, a single path time constraint, a transport node number constraint, and a path number constraint;
and the post route planning result determining module is used for solving the post route planning problem by utilizing a predetermined super-heuristic post route planning model to obtain a post route planning result.
In a third aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a route planning method according to an embodiment of the present application.
In a fourth aspect, an embodiment of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable by the processor, where the processor executes the computer program to implement the route planning method according to the embodiment of the present application.
According to the technical scheme provided by the embodiment of the application, the route planning problem of the target area is constructed, and the route planning problem is solved by utilizing a predetermined super-heuristic route planning model to obtain a route planning result. The route planning problem comprises a constraint condition and an optimization target; wherein the optimization objective comprises that the total transportation time of at least one single path in the objective area is shortest; the constraint conditions include: a transport carrier load constraint, a single path time constraint, a transport node number constraint, and a path number constraint. The technical scheme can solve the problem of route planning of a single starting point, multiple end points and a single starting point and multiple end points, has outstanding effects on the route planning problem of large data volume and complex constraint conditions, and effectively saves transportation cost, labor cost and time cost.
Drawings
Fig. 1A is a flowchart of a route planning method according to an embodiment of the present application;
FIG. 1B is a schematic diagram of a route planning provided by an embodiment of the present application;
FIG. 2 is a flowchart of a route planning method according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a route planning apparatus according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to a fifth 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 application and are not limiting of the application. It should be further noted that, for the convenience of description, only some of the structures related to the present application are shown in the drawings, not all of the structures.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the steps may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Example one
Fig. 1A is a flowchart of a route planning method provided in an embodiment of the present application, where the present embodiment is applicable to any route planning scenario, and the method may be executed by a route planning apparatus provided in the embodiment of the present application, and the apparatus may be implemented by software and/or hardware, and may be integrated in an electronic device.
As shown in fig. 1A, the route planning method includes:
s110, constructing a route planning problem of a target area; the route planning problem comprises a constraint condition and an optimization target; wherein the optimization objective comprises that the total transportation time of at least one single path in the target area is shortest; the constraint conditions include: a transport carrier load constraint, a single path time constraint, a transport node number constraint, and a path number constraint.
The method can be executed by electronic equipment such as a computer, and the electronic equipment can construct a route planning problem according to the actual scene of the target area. Before constructing the route planning problem of the target area, the electronic equipment can determine the position information of each node in the route transportation network and the actual distance information of the route according to the target area map. Depending on the speed of the transport carrier, the electronic device may calculate transport time information. The electronic equipment can also acquire information such as the position of each node, the running distance of the transport carrier, the transport time, the loading time and the like from the map navigation software in a one-stop mode. The electronic equipment can construct a topological graph of the mail transport network according to the information.
Fig. 1B is a schematic diagram of route planning provided in this embodiment of the present application. As shown in fig. 1B, it is assumed that there are 17 nodes in the target area, where one central node represents an express processing center, i.e., node No. 0, and the remaining nodes are transportation nodes, i.e., nodes No. 1-16. The transport carrier can be started from the central node and transports the express mail to each transport node. One or more transport paths may be included in a target area, and one or more transport nodes may be included on each transport path. The route planning problem of the target area may be a route planning form for determining the optimal route of the target area according to information such as node positions, transportation distances between nodes, transportation time and the like of the target area.
The route planning problem may include a constraint and an optimization objective. The optimization objective may be an objective that is specifically desired for the target area, e.g., for transportation cost considerations, the optimization objective may be a shortest travel distance; for labor cost considerations, the optimization objective may be a minimum transportation path; the optimization goal may be to minimize transit time for time cost considerations. The optimization objective may include one or more. Specifically, the optimization objective may include that the total transit time of at least one single route in the objective area is shortest.
The constraints may include: a transport carrier load constraint, a single path time constraint, a transport node number constraint, and a path number constraint. The shipping carrier load constraint may be to define a load of each shipping carrier not to exceed a maximum load. The time constraint of the single path may be that the transportation time of the single path does not exceed a preset working time. The constraint on the number of the transport nodes may be that the number of the transport nodes on a single path does not exceed a preset maximum number of the transport nodes. The number of paths constraint may be that the number of transport paths within the target area does not exceed the number of transport carriers.
Specifically, the optimization objective that the total transportation time of at least one single path in the objective area is shortest may be represented as:
Figure BDA0003381159560000051
Figure BDA0003381159560000052
p∈{0,1}；
wherein T is the total transportation time of the target area path, k represents the number of paths, njRepresenting the number of nodes in the jth path,
Figure BDA0003381159560000053
representing the transportation time of the transportation carrier in the jth path from the (i-1) th node to the ith node,
Figure BDA0003381159560000054
represents the nth path of the jth pathjNode to origin transit time, sign (n)j) Representing whether the transport carrier returns to the starting point;
and, the transport carrier loading constraint is:
Figure BDA0003381159560000061
wherein q isiRepresenting the load of the i-th node, QjRepresenting the maximum loading of the transport carrier on the jth path;
and, the time constraint of the single path is:
Figure BDA0003381159560000062
wherein, TjRepresenting the maximum transportation time of the jth route;
and, the number of transit nodes constraint is:
nj≤Nj；
wherein N isjRepresenting the maximum node number of the jth path;
and, the number of paths constraint is:
k≤V；
wherein V represents the number of transport carriers.
The constraint conditions in the scheme can comprehensively cover various conditions in the actual scene of express transportation, and the balanced distribution of transportation resources is favorably realized, so that the transportation cost, the labor cost and the time cost are effectively saved.
In one possible implementation, optionally, the optimization objective further includes that the total transportation route of at least one single route in the objective area is shortest; the constraints also include a single-path constraint.
As will be readily appreciated, the electronic device may also set optimization goals and constraints from a distance perspective to achieve optimization of the route planned for the route. In particular, the optimization objective may be that the total transport distance of at least one single route within the target area is shortest. The constraints may include a single-path constraint, i.e. the path of the single path does not exceed the maximum transport path of the transport carrier.
Specifically, the optimization objective with the shortest total route of at least one single route in the target area route may be represented as:
Figure BDA0003381159560000071
Figure BDA0003381159560000072
p∈{0,1}；
wherein D is the total transportation distance of the target area path, k represents the number of paths, njRepresenting the number of nodes in the jth path,
Figure BDA0003381159560000073
representing the transport route of the transport carrier from the (i-1) th node to the ith node in the jth path,
Figure BDA0003381159560000074
representing a unidirectional transport path, sign (n), of the jth pathj) Representing whether the transport carrier returns to the starting point;
and the path constraint of the single path is as follows:
Figure BDA0003381159560000075
wherein D isjRepresenting the maximum transportation route of the jth route.
The solution is used for solving the postal route planning problem from the distance angle, the actual scene of express delivery transportation can be finely depicted, and the balanced distribution of transportation resources is facilitated, so that the transportation cost, the labor cost and the time cost are effectively saved.
And S120, solving the route planning problem by using a predetermined super-heuristic route planning model to obtain a route planning result.
For the constructed route planning problem of the target area, the electronic equipment can solve the route planning problem by using a predetermined route planning model, and then a route planning result is obtained. The route planning model may be a path planning method based on graph search, such as a depth-first algorithm, a breadth-first algorithm, and Dijkstra algorithm. The route planning model may also be a sampling-based path planning method, such as a fast-spanning stochastic tree algorithm. The route planning model may also be a path planning method based on superheuristic.
The path planning method based on the super heuristic aims at the problem to be solved, and in the whole optimization process, an appropriate algorithm is used for completing optimization search in an appropriate time according to specific conditions, so that the path planning method is combined optimization of various heuristic algorithms. The path planning method based on the super heuristic has two mechanisms, namely a feedback mechanism and a selection mechanism. The path planning method based on the super heuristic can control various underlying heuristic algorithms, each underlying heuristic algorithm has the same priority level initially, the path planning method based on the super heuristic selects one underlying heuristic algorithm in each iteration through a selection mechanism, and calculates the increment of fitness before and after the iteration to score the underlying heuristic algorithms, thereby realizing the selection of the underlying heuristic algorithms. After multiple iterations, the priority of each underlying heuristic algorithm changes, and the probability that the underlying heuristic algorithm with the higher priority is selected is higher.
The path planning method based on the super heuristic can sort the lower-layer heuristic algorithms through the priority queue, select one lower-layer heuristic algorithm from the queue to solve the path planning problem each time, and finally feed back the solving result according to the obtained solution. And updating the queue of the corresponding bottom-layer heuristic algorithm based on the solving result of the path planning method of the super heuristic until the termination condition is met.
Specifically, the solving of the route planning problem by using a predetermined super-heuristic route planning model to obtain a route planning result includes:
constructing a bottom heuristic algorithm set based on the route planning problem of the target area;
selecting a bottom heuristic algorithm from the bottom heuristic algorithm set according to a preset selection mechanism;
evolving the pre-acquired input population by using an heuristic operator of the selected bottom heuristic algorithm to obtain an output population, and calculating the total transportation duration and/or the total transportation distance increment of the input population and the output population on at least one single path in the target area;
determining an input population of the next iteration according to the fitness increment;
and if the iteration times reach the preset iteration times, terminating the iteration, and determining the route planning result of the target area by using the finally obtained output population.
In the scheme, the electronic equipment can use a genetic algorithm as a high-level strategy to construct the super-heuristic route planning model. Specifically, the electronic device may select a bottom heuristic algorithm suitable for solving the problem according to the route planning problem of the target area, and group the bottom heuristic algorithms into a set. The electronic device may then select one of the set of underlying heuristic algorithms according to a preset selection mechanism, such as a random selection mechanism. When the super-heuristic route planning model is constructed, the electronic equipment can preset parameters such as the size of the population, the iteration times and the like, and can also initialize the input population. And evolving the pre-acquired input population by using an heuristic operator of the selected bottom heuristic algorithm to obtain an output population, and calculating the total transportation time and/or the total transportation distance increment of the input population and the output population on at least one single path in the target area to optimize the fitness increment on the target. According to the fitness increment, the electronic device can judge whether the optimization target has improvement or whether the improvement exceeds a preset condition, and further determine whether the input population of the next iteration is replaced. Through continuous iteration, the input population is evolved towards the optimization goal. And when the iteration times reach the preset iteration times, terminating the iteration. The electronic equipment can determine the route planning result of the target area according to the finally obtained output population. The electronic equipment can select the individual with the lowest price in the output population as the optimal delivery sequence, and then the route planning result of the target area is obtained.
According to the method, the superheuristic route planning model is adopted, the local optimization characteristic of a bottom heuristic algorithm and the global optimization characteristic of a high-level strategy are integrated, the route planning problem can be accurately and efficiently solved, and the optimal route planning scheme can be determined.
According to the technical scheme provided by the embodiment of the application, the route planning problem of the target area is constructed, and the route planning problem is solved by utilizing a predetermined super-heuristic route planning model to obtain a route planning result. The route planning problem comprises a constraint condition and an optimization target; wherein the optimization objective comprises that the total transportation time of at least one single path in the objective area is shortest; the constraint conditions include: a transport carrier load constraint, a single path time constraint, a transport node number constraint, and a path number constraint. The technical scheme can solve the problem of route planning of a single starting point, multiple end points and a single starting point and multiple end points, has outstanding effects on the route planning problem of large data volume and complex constraint conditions, and effectively saves transportation cost, labor cost and time cost.
Example two
Fig. 2 is a flowchart of a route planning method according to a second embodiment of the present invention, which is optimized based on the above-described embodiment.
As shown in fig. 2, the method of this embodiment specifically includes the following steps:
s210, constructing a route planning problem of the target area.
S220, constructing a bottom heuristic algorithm set based on the route planning problem of the target area.
Wherein the set of underlying heuristic algorithms may include simulated annealing algorithms and 3-opt algorithms. The steps for solving the path planning problem using the simulated annealing algorithm may be as follows:
(1) initialization: initial temperature T, initial solution S, and iteration number L of each T value;
(2) setting k to 1, resulting in a new solution S';
(3) calculating an increment Δ T ═ C (S') -C (S), where C (S) is a cost function;
(4) if Δ T<0 then accepts S' as the new current solution, otherwise with probability
Figure BDA0003381159560000101
Accepting S' as a new current solution;
(5) and if the termination condition is met, outputting the current solution as the optimal solution, and ending the program.
(6) And T is gradually reduced and tends to 0, k is equal to k +1, and the step (2) is skipped. Until the number of iterations is met.
The 3-opt algorithm is a local search algorithm aiming at the problem of the traveling salesman, and is characterized in that connection deletion among three non-adjacent nodes in a path is selected, other 7 different connection modes are tried, the path length after the different connection modes is calculated, and the connection mode with the shortest path length is selected as a new connection mode. The process is repeated for different three connections in the path until all three connections have no new connection means. That is, starting from an arbitrary feasible solution, two sides outside the solution are repeatedly used to replace two sides in the solution. This replacement operation is performed until a local optimum is reached, as long as a better solution is obtained.
The two bottom-layer heuristic algorithms can achieve a good local optimization effect in a super-heuristic route planning model, the 3-opt algorithm can perform internal optimization on each route through three kinds of arc exchange, and the simulated annealing algorithm has probability leap performance and is beneficial to searching for a local optimal solution.
And S230, determining target data of the heuristic operator of each bottom heuristic algorithm in the bottom heuristic algorithm set on the target area on the optimization target of the total transportation duration and/or the total transportation distance of at least one single path.
It is easy to understand that the electronic device may determine, by using the heuristic operator of each of the bottom heuristic sets, corresponding target data on the total transit duration and/or total transit distance optimization target for at least one single path in the target region.
And S240, calculating the normalized target data of each heuristic operator according to the target data and a performance evaluation mechanism of a preset heuristic operator.
In order to facilitate the performance comparison of each bottom heuristic in the bottom heuristic set, the electronic device may calculate the normalized target data of each heuristic according to the target data and a preset heuristic performance evaluation mechanism. The performance evaluation mechanism of the preset heuristic operator can include the calculation amount of the obtained solution set, the size of the target space covered by the approximate solution set, the distribution condition of the approximate solution set in the target space and the like.
And S250, determining an optimal bottom heuristic algorithm according to the normalized target data.
Based on the normalized target data, the electronic device can compare which underlying heuristic or set of underlying heuristics is optimal. When the plurality of underlying heuristic algorithms are optimal, the electronic device may randomly select one of the plurality of underlying heuristic algorithms.
And S260, evolving the pre-acquired input population by using an heuristic operator of the optimal bottom heuristic algorithm to obtain an output population, and calculating the total transportation duration and/or the fitness increment of the input population and the output population on at least one single path in the target area on the optimized target of the total transportation path.
The electronic equipment utilizes an heuristic operator of an optimal bottom heuristic algorithm to evolve the pre-acquired input population, and an output population can be obtained. And determining the input population of the next iteration by calculating the total transportation time length and/or the total transportation distance increment of at least one single path of the input population and the output population in the target area.
And S270, determining the input population of the next iteration according to the fitness increment.
And S280, if the iteration times reach the preset iteration times, terminating the iteration, and determining the route planning result of the target area by using the finally obtained output population.
According to the technical scheme provided by the embodiment of the application, the route planning problem of the target area is constructed, and the bottom heuristic algorithm set is constructed based on the route planning problem. And determining target data of the heuristic operator of each bottom heuristic algorithm in the bottom heuristic algorithm set on the target area of at least one single-path total transportation duration and/or total transportation path optimization target. And calculating the normalized target data of each heuristic operator according to the target data and a performance evaluation mechanism of the preset heuristic operator, and further determining an optimal bottom heuristic algorithm. And calculating the total transportation time length of at least one single path of the input population and the output population in the target area and/or the fitness increment on the total transportation path optimization target by using a genetic algorithm. And through iteration, determining the input population of the next iteration continuously according to the fitness increment. And when the iteration times reach the preset iteration times, terminating the iteration, and determining a route planning result of the target area by using the finally obtained output population. The technical scheme can solve the problem of route planning of a single starting point, multiple end points and a single starting point and multiple end points, has outstanding effects on the route planning problem of large data volume and complex constraint conditions, and effectively saves transportation cost, labor cost and time cost.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a route planning device according to a third embodiment of the present invention, which is capable of executing the route planning method according to any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.
As shown in fig. 3, the apparatus may include:
a route planning problem construction module 310, configured to construct a route planning problem for the target area; the route planning problem comprises a constraint condition and an optimization target; wherein the optimization objective comprises that the total transportation time of at least one single path in the target area is shortest; the constraint conditions include: a transport carrier load constraint, a single path time constraint, a transport node number constraint, and a path number constraint;
and the route planning result determining module 320 is configured to solve the route planning problem by using a predetermined super-heuristic route planning model to obtain a route planning result.
In this scheme, optionally, the optimization objective that the total transportation time of at least one single path in the target area is shortest is as follows:
Figure BDA0003381159560000131
Figure BDA0003381159560000132
p∈{0,1}；
wherein T is the total transportation time of the target area path, k represents the number of paths, njRepresenting the number of nodes in the jth path,
Figure BDA0003381159560000133
representing the transportation time of the transportation carrier in the jth path from the (i-1) th node to the ith node,
Figure BDA0003381159560000134
represents the nth path of the jth pathjNode to origin transit time, sign (n)j) Representing whether the transport carrier returns to the starting point;
and, the transport carrier loading constraint is:
Figure BDA0003381159560000135
wherein q isiRepresenting the load of the i-th node, QjRepresenting the maximum loading of the transport carrier on the jth path;
and, the time constraint of the single path is:
Figure BDA0003381159560000141
wherein, TjRepresenting the maximum transportation time of the jth route;
and, the number of transit nodes constraint is:
nj≤Nj；
wherein N isjRepresenting the maximum node number of the jth path;
and, the number of paths constraint is:
k≤V；
wherein V represents the number of transport carriers.
In a possible embodiment, optionally, the optimization objective further includes that the total transportation route of at least one single route in the objective area is shortest; the constraints also include a single-path constraint.
On the basis of the foregoing embodiment, optionally, the optimization goal that the total path of at least one single path in the target area path is shortest is as follows:
Figure BDA0003381159560000142
Figure BDA0003381159560000143
p∈{0,1}；
wherein D is the total transportation distance of the target area path, k represents the number of paths, njRepresenting the number of nodes in the jth path,
Figure BDA0003381159560000144
representing the transport route of the transport carrier from the (i-1) th node to the ith node in the jth path,
Figure BDA0003381159560000145
representing a unidirectional transport path, sign (n), of the jth pathj) Representing whether the transport carrier returns to the starting point;
and the path constraint of the single path is as follows:
Figure BDA0003381159560000151
wherein D isjRepresenting the maximum transportation route of the jth route.
On the basis of the above solution, optionally, the route planning result determining module 320 includes:
the bottom heuristic algorithm set constructing unit is used for constructing a bottom heuristic algorithm set based on the route planning problem of the target area;
the bottom heuristic algorithm determining unit is used for selecting a bottom heuristic algorithm from the bottom heuristic algorithm set according to a preset selection mechanism;
the fitness increment determining unit is used for evolving the pre-acquired input population by utilizing the selected heuristic operator of the bottom heuristic algorithm to obtain an output population, and calculating the total transportation duration and/or the fitness increment of the total transportation path optimization target of the input population and the output population in at least one single path in the target area;
the next input population determining unit is used for determining the input population of the next iteration according to the fitness increment;
and the route planning result determining unit is used for terminating the iteration if the iteration times reach the preset iteration times and determining the route planning result of the target area according to the finally obtained output population.
Optionally, the set of bottom heuristic algorithms includes a simulated annealing algorithm and a 3-opt algorithm.
In a preferred embodiment, optionally, the bottom-layer heuristic algorithm determining unit is specifically configured to:
determining target data of heuristic operators of each bottom heuristic algorithm in the bottom heuristic algorithm set on a target area on at least one single-path total transportation duration and/or total transportation path optimization target;
calculating the normalized target data of each heuristic operator according to the target data and a performance evaluation mechanism of a preset heuristic operator;
and determining an optimal bottom heuristic algorithm according to the normalized target data.
The product can execute the route planning method provided by the embodiment of the application, and has corresponding functional modules and beneficial effects of the execution method.
Example four
A fourth embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a route planning method according to any of the embodiments of the present invention:
constructing a route planning problem of a target area; the route planning problem comprises a constraint condition and an optimization target; wherein the optimization objective comprises that the total transportation time of at least one single path in the target area is shortest; the constraint conditions include: a transport carrier load constraint, a single path time constraint, a transport node number constraint, and a path number constraint;
and solving the route planning problem by using a predetermined super-heuristic route planning model to obtain a route planning result.
Any combination of one or more computer-readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. 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 (a non-exhaustive list) of the computer readable storage medium would include the following: 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 context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
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 any of a variety of 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, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention 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).
EXAMPLE five
The fifth embodiment of the application provides electronic equipment. Fig. 4 is a schematic structural diagram of an electronic device according to a fifth embodiment of the present application. As shown in fig. 4, the present embodiment provides an electronic device 400, which includes: one or more processors 420; the storage device 410 is configured to store one or more programs, and when the one or more programs are executed by the one or more processors 420, the one or more processors 420 implement the route planning method provided in this embodiment of the present application, the method includes:
constructing a route planning problem of a target area; the route planning problem comprises a constraint condition and an optimization target; wherein the optimization objective comprises that the total transportation time of at least one single path in the target area is shortest; the constraint conditions include: a transport carrier load constraint, a single path time constraint, a transport node number constraint, and a path number constraint;
and solving the route planning problem by using a predetermined super-heuristic route planning model to obtain a route planning result.
Of course, those skilled in the art will understand that the processor 420 also implements the technical solution of the route planning method provided in any embodiment of the present application.
The electronic device 400 shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 4, the electronic device 400 includes a processor 420, a storage device 410, an input device 430, and an output device 440; the number of the processors 420 in the electronic device may be one or more, and one processor 420 is taken as an example in fig. 4; the processor 420, the storage device 410, the input device 430, and the output device 440 in the electronic apparatus may be connected by a bus or other means, and are exemplified by a bus 450 in fig. 4.
The storage device 410 is a computer-readable storage medium, and can be used to store software programs, computer-executable programs, and module units, such as program instructions corresponding to the route planning method in the embodiment of the present application.
The storage device 410 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the storage 410 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, storage 410 may further include memory located remotely from processor 420, which may be connected via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input means 430 may be used to receive input numbers, character information, or voice information, and to generate key signal inputs related to user settings and function control of the electronic device. The output device 440 may include a display screen, speakers, or other electronic equipment.
The electronic equipment provided by the embodiment of the application can solve the problem of route planning of a single starting point, multiple destination points and/or a single destination point with multiple starting points by constructing a complex constraint condition, thereby effectively saving the transportation cost, the labor cost and the time cost.
The route planning device, the medium and the electronic equipment provided in the above embodiments can execute the route planning method provided in any embodiment of the present application, and have corresponding functional modules and beneficial effects for executing the method. For details of the route planning method provided in any of the embodiments of the present application, reference may be made to the above-described embodiments.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims (10)

1. A method of route planning, the method comprising:
constructing a route planning problem of a target area; the route planning problem comprises a constraint condition and an optimization target; wherein the optimization objective comprises that the total transportation time of at least one single path in the target area is shortest; the constraint conditions include: a transport carrier load constraint, a single path time constraint, a transport node number constraint, and a path number constraint;
and solving the route planning problem by using a predetermined super-heuristic route planning model to obtain a route planning result.
2. The method according to claim 1, wherein the optimization goal for the shortest total length of transportation of the at least one single route within the target area is:
Figure FDA0003381159550000011
Figure FDA0003381159550000012
p∈{0,1}；
wherein T is the total transportation time of the target area path, k represents the number of paths, njRepresenting the number of nodes in the jth path,
Figure FDA0003381159550000013
representing the transportation time of the transportation carrier in the jth path from the (i-1) th node to the ith node,
Figure FDA0003381159550000014
represents the nth path of the jth pathjNode to origin transit time, sign (n)j) Representing whether the transport carrier returns to the starting point;
and, the transport carrier loading constraint is:
Figure FDA0003381159550000015
wherein q isiRepresenting the load of the i-th node, QjRepresenting the maximum loading of the transport carrier on the jth path;
and, the time constraint of the single path is:
Figure FDA0003381159550000016
wherein, TjRepresenting the maximum transportation time of the jth route;
and, the number of transit nodes constraint is:
nj≤Nj；
wherein N isjRepresenting the maximum node number of the jth path;
and, the number of paths constraint is:
k≤V；
wherein V represents the number of transport carriers.
3. The method of claim 1, wherein the optimization objective further comprises minimizing a total haul route for at least one single route within the target area; the constraints also include a single-path constraint.
4. The method according to claim 3, wherein the shortest total route of at least one single route in the target area route is optimized by:
Figure FDA0003381159550000021
Figure FDA0003381159550000022
p∈{0,1}；
wherein D is the total transportation distance of the target area path, k represents the number of paths, njRepresenting the number of nodes in the jth path,
Figure FDA0003381159550000023
representing the transport route of the transport carrier from the (i-1) th node to the ith node in the jth path,
Figure FDA0003381159550000024
representing a unidirectional transport path, sign (n), of the jth pathj) Representing whether the transport carrier returns to the starting point;
and the path constraint of the single path is as follows:
Figure FDA0003381159550000025
wherein D isjRepresenting the maximum transportation route of the jth route.
5. The method of claim 3, wherein solving the route planning problem using a predetermined super-heuristic route planning model to obtain route planning results comprises:
constructing a bottom heuristic algorithm set based on the route planning problem of the target area;
selecting a bottom heuristic algorithm from the bottom heuristic algorithm set according to a preset selection mechanism;
evolving the pre-acquired input population by using an heuristic operator of the selected bottom heuristic algorithm to obtain an output population, and calculating the total transportation duration and/or the total transportation distance increment of the input population and the output population on at least one single path in the target area;
determining an input population of the next iteration according to the fitness increment;
and if the iteration times reach the preset iteration times, terminating the iteration, and determining the route planning result of the target area by using the finally obtained output population.
6. The method of claim 5, wherein the set of underlying heuristics includes a simulated annealing algorithm and a 3-opt algorithm.
7. The method according to claim 5, wherein said selecting one of the set of underlying heuristics according to a predetermined selection mechanism comprises:
determining target data of heuristic operators of each bottom heuristic algorithm in the bottom heuristic algorithm set on a target area on at least one single-path total transportation duration and/or total transportation path optimization target;
calculating the normalized target data of each heuristic operator according to the target data and a performance evaluation mechanism of a preset heuristic operator;
and determining an optimal bottom heuristic algorithm according to the normalized target data.
8. A route planning apparatus, the apparatus comprising:
the route planning problem construction module is used for constructing a route planning problem of a target area; the route planning problem comprises a constraint condition and an optimization target; wherein the optimization objective comprises that the total transportation time of at least one single path in the target area is shortest; the constraint conditions include: a transport carrier load constraint, a single path time constraint, a transport node number constraint, and a path number constraint;
and the post route planning result determining module is used for solving the post route planning problem by utilizing a predetermined super-heuristic post route planning model to obtain a post route planning result.
9. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out a route planning method according to any one of claims 1-7.
10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the computer program, implements a route planning method according to any one of claims 1-7.
CN202111433588.6A 2021-11-29 2021-11-29 Postal route planning method, apparatus, medium and equipment Pending CN114139796A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115994635A (en) * 2023-03-23 2023-04-21 广东鉴面智能科技有限公司 Belt optimal discharging transportation path detection method, system and medium

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115994635A (en) * 2023-03-23 2023-04-21 广东鉴面智能科技有限公司 Belt optimal discharging transportation path detection method, system and medium

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