CN111144647B - General vehicle path planning method and system based on large-scale neighborhood search algorithm - Google Patents
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Abstract
The invention discloses a general vehicle path planning method and a system based on a large-scale neighborhood search algorithm, which comprises the following steps: generating a distance matrix; performing problem loading in a basic database based on user input data and a distance matrix to obtain a lane planning problem; inputting the lane planning problem into an initial solution generation algorithm, and carrying out grid search on parameters of the initial solution generation algorithm to obtain an initial solution; the large-scale neighborhood searching algorithm carries out Cross iteration on the initial solution through six operators, namely relocation, Or-opt2, Exchange, Cross _ Exchange, 2-opt and 2-opt to obtain a lane planning result; and performing multi-dimensional result presentation and online map display on the lane planning result. The invention comprehensively considers various real scenes and embeds different scenes and constraints into the same system so as to solve the problem of complex multi-scene in the practical application of enterprises; and (3) a large-scale neighborhood searching algorithm is adopted, up to six neighborhood operators are considered, and the optimal solution of the problem is fully searched.
Description
Technical Field
The invention relates to the technical field of vehicle path planning, in particular to a general vehicle path planning method and system based on a large-scale neighborhood search algorithm.
Background
The current situation of Chinese logistics is large and weak, and most of the times, people are pounded to achieve the aim, so that the operation efficiency is extremely low. The logistics distribution industry mainly relates to urban distribution links, and objects with requirements comprise chain enterprises, logistics companies and the like. It is often the case that a city will have one or more warehouse locations, and then a number of stores distributed throughout the city, with drivers and vehicles being scheduled to transport goods from the warehouse to the stores on a daily basis. Meanwhile, the requirements of each store are personalized, for example, the goods demand of each store is different, each store has a specified time window for accepting goods, some stores are in a shopping mall, and the type of delivery vehicles is limited. The reasonable route of arranging the vehicle distribution can not only improve the efficiency in management for the enterprise, but also save a large amount of logistics cost.
Currently, research on Vehicle Routing optimization (VRP) is algorithm design based on a single specific scene, and an algorithm and solution need to be redesigned for other scenes, for example, common VRPs with resource constraints, multi-Vehicle VRPs, semi-open VRPs, and the like. This is a significant disadvantage for the actual floor application of the enterprise, it reduces the flexibility of the enterprise's use, and slight changes in constraints require technicians to modify the model, resulting in a low willingness of the industry to use new technologies. Enterprises prefer to have a path optimization system supporting multiple scenarios and supporting user parameter input.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a general vehicle path planning method and a general vehicle path planning system based on a large-scale neighborhood search algorithm.
The invention discloses a general vehicle path planning method based on a large-scale neighborhood search algorithm, which comprises the following steps:
generating a distance matrix;
performing problem loading in a basic database based on user input data and the distance matrix to obtain a lane planning problem;
inputting the lane planning problem into an initial solution generation algorithm, and carrying out grid search on parameters of the initial solution generation algorithm to obtain an initial solution;
the large-scale neighborhood search algorithm adopts a simulated annealing framework, and the initial solution is subjected to Cross iteration through six operators, namely Relocate, Or-opt2, Exchange, Cross _ Exchange, 2-opt and 2-opt, so as to obtain a lane planning result;
and performing multi-dimensional result presentation and online map display on the lane planning result.
As a further improvement of the present invention, in the generating the distance matrix,
calling a map API to obtain the driving distance between any two points, and using the driving distance to act as data input of an optimization algorithm;
in an initial state, generating a full distance matrix through a full updating mode; when stores increase or positions change, generating a distance matrix through incremental updating;
the user may manually adjust the values in the distance matrix based on the output results.
As a further improvement of the invention, the initial solution generation algorithm is a Time-oriented near-neighbor algorithm proposed by Solomon, and grid search is performed on the algorithm parameters.
As a further refinement of the invention, in the cross-iteration of the initial solution,
each iteration is carried out, one operator is randomly selected to carry out neighborhood search, and the probability of each operator being selected is determined according to the performance of each operator in the experiment;
the initial temperature of the simulated annealing is determined by the objective function value of the initial solution;
the cross iteration comprises two iteration stopping strategies of 'fast optimization' and 'deep optimization'.
As a further improvement of the invention, the result presentation comprises a result summary, a route summary and a route detail;
the online map is shown with different lanes and their delivery paths shown in different colors on the map.
The invention also discloses a general vehicle path planning system based on the large-scale neighborhood search algorithm, which comprises the following steps:
a matrix generation module to:
generating a distance matrix;
a problem loading module to:
performing problem loading in a basic database based on user input data and the distance matrix to obtain a lane planning problem;
an optimization module to:
inputting the lane planning problem into an initial solution generation algorithm, and carrying out grid search on parameters of the initial solution generation algorithm to obtain an initial solution;
the large-scale neighborhood search algorithm adopts a simulated annealing framework, and the initial solution is subjected to Cross iteration through six operators, namely Relocate, Or-opt2, Exchange, Cross _ Exchange, 2-opt and 2-opt, so as to obtain a lane planning result;
an output module to:
and performing multi-dimensional result presentation and online map display on the lane planning result.
As a further improvement of the present invention, in the matrix generation module,
calling a map API to obtain the driving distance between any two points, and using the driving distance to act as data input of an optimization algorithm;
in an initial state, generating a full distance matrix through a full updating mode; when stores increase or positions change, generating a distance matrix through incremental updating;
the user may manually adjust the values in the distance matrix based on the output results.
As a further development of the invention, in the optimization module,
the initial solution generation algorithm is a Time-oriented near-neighbor algorithm proposed by Solomon, and grid search is performed on the algorithm parameters so as to obtain a better initial feasible solution as much as possible.
As a further development of the invention, in the optimization module,
each iteration is carried out, one operator is randomly selected to carry out neighborhood search, and the probability of each operator being selected is determined according to the performance of each operator in the experiment;
the initial temperature of the simulated annealing is determined by the objective function value of the initial solution;
the cross iteration comprises two iteration stopping strategies of 'fast optimization' and 'deep optimization'.
As a further improvement of the present invention, in the output module,
the result presentation comprises result summarization and line detail;
the online map is shown with different lanes and their delivery paths shown in different colors on the map.
Compared with the prior art, the invention has the beneficial effects that:
the invention comprehensively considers various real scenes and embeds different scenes and constraints into the same system so as to solve the problem of complex multi-scene in the practical application of enterprises; for example, the user may specify whether the vehicle routing system is a pick-up problem or a delivery problem, whether it is an open problem or a closed problem, the special attributes of different vehicle models, the store delivery time window may be single or multiple, etc.;
the method adopts a large-scale neighborhood searching algorithm, considers up to six neighborhood operators and fully searches the optimal solution of the problem;
the invention is applied to the actual business of several clients, can save the logistics cost by about 20 percent compared with the prior traditional manual route planning, plans the delivery sequence of the vehicles in advance and plays a good role in supervision and management for the drivers.
Drawings
FIG. 1 is a flowchart of a general vehicle path planning method based on a large-scale neighborhood search algorithm according to an embodiment of the present invention;
fig. 2 is a frame diagram of a general vehicle path planning system based on a large-scale neighborhood search algorithm according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
The invention is described in further detail below with reference to the attached drawing figures:
as shown in fig. 1, the present invention provides a general vehicle path planning method based on a large-scale neighborhood search algorithm, including:
s1, generating a distance matrix; wherein the content of the first and second substances,
the actual driving distance between the store and the store is necessary basic data for optimizing the vehicle path, and the straight-line distance between two points adopted in the academic practice does not accord with the actual situation, so that the high-grade API call service is purchased, the driving distance between any two points is obtained, and the data input of the optimization algorithm is acted;
in addition, passThe generation mode of the overall distance matrix is to generate the map API in a full-scale mode by calling once, and for the problem of large scale, the method is very time-consuming and is not suitable for frequent use because the distance pair D needs to be crawledabA question of 500 stores, for example, would require 250000 distance pairs to be acquired, perhaps 24 hours.
Therefore, the invention provides two distance matrix generation modes of 'total update' and 'incremental update', and supposing that the number of the existing stores is n, the number of the newly added stores is m, and the 'total update' needs to be acquired (n + m)2The distance is determined, and the incremental update can automatically detect the difference between a newly introduced store and an existing store, and only crawl the newly added distance brought by the newly added store, namely m2+2mn, generally m is much smaller than n, so the acquisition time of the distance matrix can be greatly saved. In addition, the system of the invention also supports the manual adjustment of the values in the distance matrix by the user so as to make the distance matrix more consistent with the actual situation.
When a user uses the system for the first time, as initialization, a full distance matrix needs to be generated through a 'full update' mode; and when the positions of the customer stores are irregularly adjusted along with the planning of the enterprise, and the road information also changes along with the time, under the condition, the distance matrix data required by the model can be quickly generated by the user only by using an incremental updating mode, so that the time is greatly saved.
S2, problem loading is carried out in a basic database based on user input data and the distance matrix, and a lane planning problem is obtained; wherein the content of the first and second substances,
user input data including order data, selected vehicle, window time to receive cargo, etc.; because the requirements of each order are different, the lane planning problem corresponding to each order is established by matching the data in the basic data with each order.
S3, inputting the route planning problem into an initial solution generation algorithm, and carrying out grid search on the parameters of the initial solution generation algorithm (Time-oriented near-neighbor algorithm proposed by Solomon) to find out the best initial solution, which is beneficial to improving the subsequent iteration effect;
the large-scale neighborhood search algorithm adopts a simulated annealing framework, and Cross iteration is carried out on the initial solution through six operators, namely Relocate, Or-opt2, Exchange, Cross _ Exchange, 2-opt and 2-opt, so as to obtain a lane planning result;
wherein the content of the first and second substances,
each iteration is carried out, one operator is randomly selected to carry out neighborhood search, and the probability of each operator being selected is determined according to the performance of each operator in the experiment; compared with the traditional large-scale neighborhood search, the method can greatly improve the iteration efficiency and has small influence on the quality of the solution. The whole algorithm adopts a simulated annealing frame, the initial temperature of the simulated annealing is determined by the objective function value of the initial solution, a certain probability is ensured to accept the differential solution, and the situation that the simulated annealing falls into local optimum is avoided;
according to the general requirements of users, the invention provides two iteration stopping strategies, one is less in iteration times, the other is more in iteration times, and the iteration times are positively correlated with small problem scale, and the iteration stopping strategies correspond to the user side and are respectively 'fast optimization' and 'deep optimization'.
S4, performing multi-dimensional result presentation and online map display on the lane planning result; wherein the content of the first and second substances,
the invention designs three-dimensional result presentation and an online map display, which comprises the following steps:
a) the result is summarized, the optimization result is globally summarized, and a user can visually see the overall situation of the optimization scheme, including the total number of used vehicles, the total form mileage of a vehicle fleet, the total distribution time, the total cost of the scheme and the like, and the most visual scheme is displayed for the user;
b) the method comprises the steps of line summarization, wherein the planning result of each lane (vehicle) is summarized, and the line comprises departure and return time, used vehicle type, driving distance and the like of the lane;
c) and (4) displaying the details of each lane by opening the folding strip of each lane in the lane summary, wherein the details comprise the sequence of visiting each store, the time of arriving at each store, the weight of loaded and unloaded goods, the accumulated travel distance and the like, and a user can conveniently track the condition of each lane.
d) Finally, the invention displays different lines and distribution paths thereof on the map in different colors, and the user can visually see the effect of the whole planning.
Further, the user can manually adjust the values in the distance matrix according to the output result, so that the output result required by the user is obtained after iterative optimization.
As shown in fig. 2, the present invention provides a general vehicle path planning system based on a large-scale neighborhood search algorithm, which includes: the system comprises a matrix generation module, a problem loading module, an optimization module and an output module; wherein the content of the first and second substances,
the matrix generation module is used for generating a distance matrix; wherein the content of the first and second substances,
the actual driving distance between the store and the store is necessary basic data for optimizing the vehicle path, and the straight-line distance between two points adopted in the academic practice does not accord with the actual situation, so that the high-grade API call service is purchased, the driving distance between any two points is obtained, and the data input of the optimization algorithm is acted;
in addition, the traditional distance matrix generation mode is to generate the map API in a full amount by calling once, and for the problem of large scale, the method is very time-consuming and is not suitable for frequent use because the distance pair D which needs to be crawledabA question of 500 stores, for example, would require 250000 distance pairs to be acquired, perhaps 24 hours.
Therefore, the invention provides two distance matrix generation modes of 'total update' and 'incremental update', and supposing that the number of the existing stores is n, the number of the newly added stores is m, and the 'total update' needs to be acquired (n + m)2The distance is determined, and the incremental update can automatically detect the difference between a newly introduced store and an existing store, and only crawl the newly added distance brought by the newly added store, namely m2+2mn, generally m is much smaller than n, so the acquisition time of the distance matrix can be greatly saved. In addition, the system of the invention also supports the manual adjustment of the values in the distance matrix by the user so as to make the distance matrix more consistent with the actual situation.
When a user uses the system for the first time, as initialization, a full distance matrix needs to be generated through a 'full update' mode; and when the positions of the customer stores are irregularly adjusted along with the planning of the enterprise, and the road information also changes along with the time, under the condition, the distance matrix data required by the model can be quickly generated by the user only by using an incremental updating mode, so that the time is greatly saved.
The problem loading module is used for loading problems in the basic database based on the user input data and the distance matrix to obtain the lane planning problem; wherein the content of the first and second substances,
user input data including order data, selected vehicle, window time to receive cargo, etc.; because the requirements of each order are different, the lane planning problem corresponding to each order is established by matching the data in the basic data with each order.
The optimization module is used for inputting the route planning problem into an initial solution generation algorithm, carrying out grid search on parameters of the initial solution generation algorithm (a Time-oriented near-neighbor algorithm proposed by Solomon), finding out the best initial solution, and being beneficial to improving the subsequent iteration effect;
the large-scale neighborhood search algorithm adopts a simulated annealing framework, and Cross iteration is carried out on the initial solution through six operators, namely Relocate, Or-opt2, Exchange, Cross _ Exchange, 2-opt and 2-opt, so as to obtain a lane planning result;
wherein the content of the first and second substances,
each iteration is carried out, one operator is randomly selected to carry out neighborhood search, and the probability of each operator being selected is determined according to the performance of each operator in the experiment; compared with the traditional large-scale neighborhood search, the method can greatly improve the iteration efficiency and has small influence on the quality of the solution. The whole algorithm adopts a simulated annealing frame, the initial temperature of the simulated annealing is determined by the objective function value of the initial solution, a certain probability is ensured to accept the differential solution, and the situation that the simulated annealing falls into local optimum is avoided;
according to the general requirements of users, the invention provides two iteration stopping strategies, one is less in iteration times, the other is more in iteration times, and the iteration times are positively correlated with small problem scale, and the iteration stopping strategies correspond to the user side and are respectively 'fast optimization' and 'deep optimization'.
The output module is used for carrying out multi-dimensional result presentation and online map display on the lane planning result; wherein the content of the first and second substances,
the invention designs three-dimensional result presentation and an online map display, which comprises the following steps:
a) the result is summarized, the optimization result is globally summarized, and a user can visually see the overall situation of the optimization scheme, including the total number of used vehicles, the total form mileage of a vehicle fleet, the total distribution time, the total cost of the scheme and the like, and the most visual scheme is displayed for the user;
b) the method comprises the steps of line summarization, wherein the planning result of each lane (vehicle) is summarized, and the line comprises departure and return time, used vehicle type, driving distance and the like of the lane;
c) and (4) displaying the details of each lane by opening the folding strip of each lane in the lane summary, wherein the details comprise the sequence of visiting each store, the time of arriving at each store, the weight of loaded and unloaded goods, the accumulated travel distance and the like, and a user can conveniently track the condition of each lane.
d) Finally, the invention displays different lines and distribution paths thereof on the map in different colors, and the user can visually see the effect of the whole planning.
Further, the user can manually adjust the values in the distance matrix according to the output result, so that the output result required by the user is obtained after iterative optimization.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. A general vehicle path planning method based on a large-scale neighborhood search algorithm is characterized by comprising the following steps:
generating a distance matrix;
performing problem loading in a basic database based on user input data and the distance matrix to obtain a lane planning problem;
inputting the lane planning problem into an initial solution generation algorithm, and carrying out grid search on parameters of the initial solution generation algorithm to obtain an initial solution;
the large-scale neighborhood search algorithm adopts a simulated annealing framework, and the initial solution is subjected to Cross iteration through six operators, namely Relocate, Or-opt2, Exchange, Cross _ Exchange, 2-opt and 2-opt, so as to obtain a lane planning result;
performing multi-dimensional result presentation and online map display on the lane planning result;
in the generating of the distance matrix, the distance matrix is generated,
calling a map API to obtain the driving distance between any two points, and using the driving distance to act as data input of an optimization algorithm;
in an initial state, generating a full distance matrix through a full updating mode; when stores increase or positions change, generating a distance matrix through incremental updating;
the user can manually adjust the values in the distance matrix according to the output result;
the initial solution generation algorithm is a Time-oriented near-neighbor algorithm proposed by Solomon, and grid search is carried out on the algorithm parameters;
in the cross-iteration of the initial solution,
each iteration is carried out, one operator is randomly selected to carry out neighborhood search, and the probability of each operator being selected is determined according to the performance of each operator in the experiment;
the initial temperature of the simulated annealing is determined by the objective function value of the initial solution;
the cross iteration comprises two iteration stopping strategies of 'fast optimization' and 'deep optimization'.
2. The universal vehicle path planning method of claim 1 wherein the result presentation comprises a result summary, a route summary and a route detail;
the online map is shown with different lanes and their delivery paths shown in different colors on the map.
3. A general vehicle path planning system based on a large-scale neighborhood search algorithm is characterized by comprising the following components:
a matrix generation module to:
generating a distance matrix;
a problem loading module to:
performing problem loading in a basic database based on user input data and the distance matrix to obtain a lane planning problem;
an optimization module to:
inputting the lane planning problem into an initial solution generation algorithm, and carrying out grid search on parameters of the initial solution generation algorithm to obtain an initial solution;
the large-scale neighborhood search algorithm adopts a simulated annealing framework, and the initial solution is subjected to Cross iteration through six operators, namely Relocate, Or-opt2, Exchange, Cross _ Exchange, 2-opt and 2-opt, so as to obtain a lane planning result;
an output module to:
performing multi-dimensional result presentation and online map display on the lane planning result;
in the matrix generation module, the matrix generation module is configured to generate a matrix,
calling a map API to obtain the driving distance between any two points, and using the driving distance to act as data input of an optimization algorithm;
in an initial state, generating a full distance matrix through a full updating mode; when stores increase or positions change, generating a distance matrix through incremental updating;
the user can manually adjust the values in the distance matrix according to the output result;
in the optimization module, the optimization module is used for optimizing the optimization module,
the initial solution generation algorithm is a Time-oriented near-neighbor algorithm proposed by Solomon, and grid search is carried out on the algorithm parameters;
in the optimization module, the optimization module is used for optimizing the optimization module,
each iteration is carried out, one operator is randomly selected to carry out neighborhood search, and the probability of each operator being selected is determined according to the performance of each operator in the experiment;
the initial temperature of the simulated annealing is determined by the objective function value of the initial solution;
the cross iteration comprises two iteration stopping strategies of 'fast optimization' and 'deep optimization'.
4. The universal vehicle path planning system of claim 3 wherein, in said output module,
the result presentation comprises result summarization and line detail;
the online map is shown with different lanes and their delivery paths shown in different colors on the map.
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