CN108038576A - Based on the logistics distribution routing resource and system for improving dijkstra's algorithm - Google Patents
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Abstract
The invention discloses a kind of based on the logistics distribution routing resource and system that improve dijkstra's algorithm, first obtain the path model between goods delivery initial position and final position, the path model includes the weights of each selection factor between each node, selection factor includes object dispatching urgency level, road crowding and path distance, then the synthesis weights between each node of above-mentioned path model are calculated, again the computing of dijkstra's algorithm is improved to integrate the weights of corresponding path distance in weights replacement dijkstra's algorithm, select the path of a total synthesis weights minimum.The present invention considers the selection factor of multinomial influence path planning, can obtain being more in line with the comprehensive optimization route of the individual demand of actual logistics distribution.
Description
Technical Field
The invention relates to the field of navigation, in particular to the aspect of path selection in the navigation process, and more particularly relates to a logistics distribution path selection method and system based on an improved Dijkstra algorithm.
Background
The Dijkstra algorithm has stable performance and can be well adapted to network topology change, and is a classical algorithm for path planning. The idea of the Dijkstra algorithm is as follows: and G-E is a weighted directed graph, the set V of all vertexes in the graph is divided into two groups, the first group is a vertex set (represented by S, only one source point in S is initially obtained, every shortest path is obtained, the vertex set is added into the set S until all vertexes are added into S, the algorithm is finished), the second group is a vertex set (represented by U) of the rest undetermined shortest paths, and the vertexes of the second group are added into S in sequence according to the ascending order of the lengths of the shortest paths. In the joining process, the shortest path length from the source point v to each vertex in S is always kept no longer than the shortest path length from the source point v to any vertex in U. In addition, each vertex corresponds to a distance, the distance of the vertex in S is the shortest path length from v to the vertex, and the distance of the vertex in U is the current shortest path length from v to the vertex, only including the vertex in S as the middle vertex.
Dijkstra algorithm steps:
a. initially, S only contains the source point, i.e., S ═ v, where v is 0 in distance. U includes vertices other than v, i.e., U ═ rest vertices, where v has an edge with vertex U in U, then < U, v > is normally weighted, and where U is not an edge-out adjacency point for v, then the < U, v > is weighted to ∞.
b. And selecting a vertex k with the minimum distance v from the U, and adding k into S (the selected distance is the length of the shortest path from v to k).
c. Modifying the distance of each vertex in the U by taking k as a newly considered middle point; if the distance from the source point v to the vertex u (passing through the vertex k) is shorter than the original distance (not passing through the vertex k), the distance value of the vertex u is modified, and the weight of the distance of the vertex k of the modified distance value is added to the upper side.
d. Repeating steps b and c until all vertices are contained in S.
The Dijkstra algorithm can obtain the shortest path from the starting point to the end point, but in the actual logistics distribution navigation path planning problem, due to the complexity and diversity of logistics distribution, the actual logistics distribution path selection also presents personalization and diversification, for example, the shortest path cost is not necessarily the lowest, so that the selection of the logistics distribution path not only takes the path distance as the unique standard, but also needs to consider other factors influencing the path selection, and only considers the path distance and cannot meet the path selection requirement of logistics distribution.
Disclosure of Invention
The invention aims to solve the technical problem that a scheme for selecting a path by adopting a Dijkstra algorithm in the prior art cannot well meet the requirement of path selection in logistics distribution, and provides a logistics distribution path selection method and a logistics distribution path selection system based on an improved Dijkstra algorithm.
The invention solves the technical problem, adopts the technical scheme that a logistics distribution path selection method based on an improved Dijkstra algorithm is constructed, and comprises the following steps:
s1, obtaining a path model between the goods distribution starting position and the goods distribution end position, wherein the path model comprises the weight of each selection factor between nodes taking the fork as a node, and the selection factors comprise the object distribution emergency degree, the road congestion degree and the path distance;
s2, calculating the comprehensive weight value between each node of the path model, wherein the comprehensive weight value between any node p and q is calculated by the following formula:
f＝ω1×f1+ω2×f2+…+ωn×fn，
wherein f represents the integrated weight, n represents the total number of the selection factors, and f1、f2… and fnRespectively, the weight, omega, of each factor between the node p and the node q1、ω2… and ωnAre respectively f1、f2… and fnA corresponding weight;
s3, replacing the weight of the corresponding path distance in the Dijkstra algorithm with the comprehensive weight to carry out the operation of improving the Dijkstra algorithm, and selecting a path with the minimum total comprehensive weight.
In the logistics distribution path selection method of the present invention, the normalized weight of the kth selection factor is obtained by the following method:
obtaining the standard degree S of a 1-9 level judgment matrix of each selection factork，k＝1、2、…、n；
The following treatment is respectively carried out on each selection factor: respectively solving the matrix standard degree values of the kth selection factor relative to all the selection factors, and then multiplying the matrix standard degree values of all the selection factors to obtain the n-th square root;
then, the n-th-order square root of each selection factor is normalized, and the normalized result is the normalized weight.
In the method for selecting a distribution route according to the present invention, the method further includes the steps of:
before processing by using the 1-9 level judgment matrix standard degree, updating the priority of each selection factor in response to the setting of a user.
In the method for selecting logistics distribution route of the invention, the weight of the emergency degree of distribution of the objects is omega10.6370, and the weight of the degree of road congestion is ω20.2583, the path distance is weighted by ω3＝0.1047。
In the method for selecting a logistics distribution route of the present invention, in the process of obtaining the weight of each selection factor, the method further comprises the steps of: checking whether the normalized weight obtained by calculation accords with the actual importance among weight types or not by carrying out consistency on the normalized weight, if so, taking the weight calculated this time as the final value of the weight, and otherwise, obtaining the value of the matrix standard degree to be reselected to calculate the weight;
the method for consistency judgment is as follows:
judgment ofIf the value of (a) is less than 0.1, the consistency is met if the value of (b) is less than 0.1, otherwise the consistency is not met;
wherein,R.I. is equal to the value of the n-th order matrix in the average random consistency table, lambdamaxThe element of the j-th column of the i-th row of the judgment matrix is the value of the matrix standard degree of the i-th selection factor relative to the j-th selection factor, i is 1, 2, … and n, and j is 1, 2, … and n.
According to another aspect of the present invention, to solve the technical problem, there is provided a logistics distribution path selection system based on the improved Dijkstra algorithm, including the following modules:
the system comprises a model acquisition module, a route model acquisition module and a route model management module, wherein the model acquisition module is used for acquiring a route model between a goods distribution starting position and a goods distribution end position, the route model comprises weights of all selection factors between nodes with a fork as a node, and the selection factors comprise an object distribution emergency degree, a road congestion degree and a route distance;
a weight calculation module, configured to calculate a comprehensive weight between nodes of the path model, where the comprehensive weight between any node p and q is calculated by the following formula:
f＝ω1×f1+ω2×f2+…+ωn×fn，
wherein f represents the integrated weight, n represents the total number of the selection factors, and f1、f2… and fnRespectively, the weight, omega, of each factor between the node p and the node q1、ω2… and ωnAre respectively f1、f2… and fnA corresponding weight;
and the path selection module is used for replacing the weight of the corresponding path distance in the Dijkstra algorithm with the comprehensive weight to carry out the operation of improving the Dijkstra algorithm and selecting a path with the minimum total comprehensive weight.
In the logistics distribution path selection system of the invention, the normalized weight of the kth selection factor is obtained by the following method:
obtaining the standard degree S of a 1-9 level judgment matrix of each selection factork，k＝1、2、…、n；
The following treatment is respectively carried out on each selection factor: respectively solving the matrix standard degree values of the kth selection factor relative to all the selection factors, and then multiplying the matrix standard degree values of all the selection factors to obtain the n-th square root; then, the n-th-order square root of each selection factor is normalized, and the normalized result is the normalized weight.
In the logistics distribution routing system of the invention, the following modules are also included:
and the priority updating module is used for responding to the setting of a user and updating the priority of each selection factor before processing by utilizing the 1-9 level judgment matrix standard degree.
In the logistics distribution routing system of the invention, the weight of the emergency degree of the distribution of the objects is omega10.6370, and the weight of the degree of road congestion is ω20.2583, the path distance is weighted by ω3＝0.1047。
In the logistics distribution path selection system, the weight of each selection factor is obtained by the following modules:
the consistency processing module is used for checking whether the normalized weight obtained by calculation meets the actual importance among weight types or not by carrying out consistency on the normalized weight, if so, the weight calculated this time is taken as the final value of the weight, and otherwise, the value of the matrix standard degree is obtained again to calculate the weight;
the method for consistency judgment is as follows:
judgment ofIf the value of (a) is less than 0.1, the consistency is met if the value of (b) is less than 0.1, otherwise the consistency is not met;
wherein,R.I. is equal to the value of the n-th order matrix in the average random consistency table, lambdamaxThe element of the j-th column of the i-th row of the judgment matrix is the value of the matrix standard degree of the i-th selection factor relative to the j-th selection factor, i is 1, 2, … and n, and j is 1, 2, … and n.
The invention discloses a logistics distribution route selection method and a logistics distribution route selection system based on an improved Dijkstra algorithm. The invention comprehensively considers a plurality of selection factors influencing path planning, and can obtain a comprehensive optimal path which better meets the individual requirements of actual logistics distribution.
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The invention will be further described with reference to the accompanying drawings and examples, in which:
fig. 1 is a flowchart of an embodiment of a logistics distribution path selection method based on the improved Dijkstra algorithm according to the invention;
FIG. 2 is a hierarchical structure diagram of integrated weights;
fig. 3 is a network path topology diagram.
Detailed Description
For a more clear understanding of the technical features, objects and effects of the present invention, embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
As three weight indexes affecting logistics distribution path planning: the following embodiments select only these three factors as all selection factors, and in other embodiments, there may be other selection factors in addition to the above three selection factors. When the user actually selects the path, the priorities of the three weight indexes may be different according to different requirements, and thus the planned paths are different. The priorities of the five weights can be set by the user, and then the optimal path meeting the requirements of different users is planned, so that the user can be prompted to carry out priority sequencing on various influencing factors in advance, and the method can update the previous sequencing after the user is sequenced (if the priority is not stored before), the sequencing is directly stored.
The invention researches logistics distribution navigation path planning, takes a fork road between a goods distribution starting position and a goods distribution end position as a node, aims to find an optimal path integrating multiple weights, and provides an integrated optimal distribution path planning service for users. The main weight types influencing the logistics distribution path planning are determined by combining the characteristics of logistics distribution, then the weight types are required to be subjected to weight fusion to obtain a comprehensive weight, and then the comprehensive weight is used for performing path planning to obtain a comprehensive optimal path. How to perform multi-weight fusion to determine the comprehensive weight is the key research content of the invention.
The objective linear weighting method is a main method for solving the optimization problem of the multi-objective method by converting an objective problem into a plurality of objective functions, establishing a linear combination of the objective functions to change the plurality of objective optimization problems into a total objective optimization problem, and unifying the objective functions by using a coefficient weighting method. Assigning appropriate weight coefficients omega to P targets in the multi-target planning problem according to the importance degreei，ωi>0, i is 1, 2, …, P, andwill be provided withAs a new objective function. The invention utilizes a target linear weighting method to perform multi-weight fusion to determine a comprehensive weight, and further establishes an improved logistics distribution path planning model.
Referring to fig. 1, the logistics distribution path selection method based on the improved Dijkstra algorithm of the embodiment includes the following steps:
and S1, acquiring a path model between the goods distribution starting position and the goods distribution end position, wherein the path model comprises the weight of each selection factor between nodes taking the fork as a node, and the selection factors comprise the object distribution emergency degree, the road congestion degree and the path distance.
S2, let the weight of emergency degree of distribution be f1The road congestion degree weight is recorded as f2The path distance weight is recorded as f3The comprehensive weight is marked as F, and because the influence degrees of all the weight types on the comprehensive weight are different, the influence degrees of all the weight types on the F are respectively marked as omega1、ω2、ω3Then, the calculation formula of the integrated weight is as follows:
F＝ω1*f1+ω2*f2+ω3*f3(1)
wherein, ω is1+ω2+ω3Then, the influence of each weight type on the comprehensive weight is determined as 1.
In the problem of logistics distribution path planning, an Analytic Hierarchy Process (AHP) is used, the importance degree between every two weights is firstly determined qualitatively, and then qualitative analysis is converted into quantitative data calculation to further obtain the influence of each weight type on the comprehensive weight. The specific steps of solving the influence degree of each weight type of multi-target linear weighting on the comprehensive weight in the path planning problem of logistics distribution by using the AHP are as follows:
firstly, a hierarchical structure diagram of the comprehensive weight is established by combining with the target problem, as shown in fig. 2:
then, every two of the weights are compared to each other to construct a judgment matrix of each weight type. The judgment matrix standard degree is introduced, and the judgment matrix standard degree of 1-9 levels proposed by T.L.Saaty et al is adopted, and is shown in the following table:
table 11-9 level judgment matrix standard degree
According to the judgment matrix standard table, constructing judgment matrices of various weight types (one of the cases is selected, because the priority of each set weight is different, the expression of the final comprehensive weight is different, and therefore the planned path is different), as shown in table 2 below:
TABLE 2 judgment matrix for each weight type
As can be seen from the table, considering the practical situation, the embodiment specifies the relative importance degree between each two weights, such as: the influence degree of the path distance weight on the comprehensive weight is higher than the influence degree of the road congestion weight and the object emergency weight, and the influence degree of the road congestion weight is slightly higher than the influence degree of the object emergency weight.
Then, the influence degree of each weight type, i.e. the combined weight coefficient, is calculated to obtain ω1、ω2、ω3The value of (c). Generally, the combining weight coefficient can be calculated by using a geometric mean method (root method) and a canonical column mean method (sum method), and the method is calculated by using the geometric mean method. The calculation steps for solving the combination weight coefficient are as follows:
firstly, the product of each element of each row of the judgment matrix is calculated to obtain a matrix B with 3 rows and one column, which is shown in the following table 3:
TABLE 3 product of each row element of the matrix
Calculating the root of 3 th power of each element of the B matrix to obtain a matrix C, as shown in the following Table 4:
table 4 column normalization
Then, normalizing the matrix C to obtain a combination weight coefficient, namely, each influence degree value:
ω1＝0.6370，ω2＝0.2583，ω3＝0.1047。
and finally, checking whether the calculated relative weight coefficient accords with the actual importance among the weight types through consistency judgment, thereby checking the correctness of the multi-weight fusion. A unified average random consistency Table is introduced, as shown in Table 5 below
TABLE 5 average random consistency Table
The check consistency formula is as follows:
wherein:
n is the number of parameters, i.e. the number of types of each weight, R.I. represents the value of n-order matrix in the average random consistency table, lambdamaxIs the largest feature root of the decision matrix. If C.R.<0.1, the decision matrix is considered consistent in this example. Is represented by the formula
Can obtain lambdamax3.0385, will λmaxSubstituting n into equation 3 may yield c.i. (3.0385-3)/(3-1) ═ 0.0192, substituting c.i. into equation 2 may yield c.r. ═ 0.0192/0.52 ═ 0.03696<0.1. Therefore, the judgment matrix obtained by the calculation satisfies consistency and is consistent with the actual importance of each weightAnd if the result is consistent, the correctness is verified. In summary, the obtained combined weight coefficient is taken into formula 1, and the comprehensive weight of the logistics distribution path planning model is obtained as follows:
F＝0.637*f1+0.2583*f2+0.1047*f3(5)
s3, replacing the weight of the corresponding path distance in the Dijkstra algorithm with the comprehensive weight to carry out the operation of improving the Dijkstra algorithm, and selecting a path with the minimum total comprehensive weight.
Referring to fig. 3, it can be seen from the path network topology map that ten nodes 0 to 9 in the road network are marked in the tested path network topology map, the node 0 in the road network is set as the starting node of the path, and three values marked on the road segment between two nodes sequentially represent three weights of the path distance weight, the road congestion weight, and the object distribution urgency weight, respectively.
Firstly, a multilayer data dictionary is used for extracting and managing corresponding information of the path network topology, wherein the multilayer data dictionary comprises three items of weight information, namely start node information, destination node information, a path distance weight, a road congestion degree weight and an object distribution emergency degree weight, and the following table 6 shows the information.
Combining the obtained road network information, according to a logistics distribution path planning model established in a path planning document, calculating by utilizing a multi-objective linear weighting method through multi-term weight fusion to obtain a comprehensive weight, and then combining Dijkstra algorithm to search paths, thereby obtaining a comprehensive optimized path considering three weight types of a path distance weight, a road congestion weight and an object distribution urgency.
TABLE 6 Multi-layer data dictionary
Firstly, preprocessing the road network information data to obtain multidimensional arrays of three weights of path distance, road congestion degree and distribution urgency degree, which are respectively shown in formulas 6, 7 and 8, wherein if two nodes are not adjacent, the numerical value is defined as infinite.
Substituting each weight of each road section of two adjacent nodes into a comprehensive weight expression of the established logistics distribution path planning model: f is 0.637F1+0.2583*f2+0.1047*f3(formula 5), the comprehensive weight of each segment of the two adjacent nodes can be obtained, and the matrix form of the comprehensive weight of the path network topology graph can be obtained, as shown in the following formula 9:
the numerical value in matrix 9 represents the comprehensive weight of the route between two nodes of the logistics distribution path planning model, and the comprehensive weight of the non-adjacent nodes is infinite. By the matrix and the Dijkstra algorithm, a path with the minimum comprehensive weight is obtained, namely a comprehensive optimal path which is more in line with the individual requirements of users and is provided with multiple comprehensive weight types from the starting node to the target node is planned. Set 0 as the start node, the simulation test results are shown in table 7:
table 7 improved model test results
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A logistics distribution path selection method based on an improved Dijkstra algorithm is characterized by comprising the following steps:
s1, obtaining a path model between the goods distribution starting position and the goods distribution end position, wherein the path model comprises the weight of each selection factor between nodes taking the fork as a node, and the selection factors comprise the object distribution emergency degree, the road congestion degree and the path distance;
s2, calculating the comprehensive weight value between each node of the path model, wherein the comprehensive weight value between any node p and q is calculated by the following formula:
f＝ω1×f1+ω2×f2+…+ωn×fn，
wherein f represents the integrated weight, n represents the total number of the selection factors, and f1、f2… and fnRespectively, the weight, omega, of each factor between the node p and the node q1、ω2… and ωnAre respectively f1、f2… and fnA corresponding weight;
s3, replacing the weight of the corresponding path distance in the Dijkstra algorithm with the comprehensive weight to carry out the operation of improving the Dijkstra algorithm, and selecting a path with the minimum total comprehensive weight.
2. The logistics distribution path selection method of claim 1, wherein the normalized weight of the k-th selection factor is obtained by:
obtaining the standard degree S of a 1-9 level judgment matrix of each selection factork，k＝1、2、…、n；
The following treatment is respectively carried out on each selection factor: respectively solving the matrix standard degree values of the kth selection factor relative to all the selection factors, and then multiplying the matrix standard degree values of all the selection factors to obtain the n-th square root;
then, the n-th-order square root of each selection factor is normalized, and the normalized result is the normalized weight.
3. The logistics distribution routing method of claim 2, further comprising the steps of:
before processing by using the 1-9 level judgment matrix standard degree, updating the priority of each selection factor in response to the setting of a user.
4. The method of claim 2, wherein the emergency degree of distribution of the objects is weighted by ω10.6370, and the weight of the degree of road congestion is ω20.2583, the path distance is weighted by ω3＝0.1047。
5. The logistics distribution route selection method of claim 2, wherein the obtaining of the weight of each selection factor further comprises the steps of: checking whether the normalized weight obtained by calculation accords with the actual importance among weight types or not by carrying out consistency on the normalized weight, if so, taking the weight calculated this time as the final value of the weight, and otherwise, obtaining the value of the matrix standard degree to be reselected to calculate the weight;
the method for consistency judgment is as follows:
judgment ofIf the value of (a) is less than 0.1, the consistency is met if the value of (b) is less than 0.1, otherwise the consistency is not met;
wherein,R.I. is equal to the value of the n-th order matrix in the average random consistency table, lambdamaxThe element of the j-th column of the i-th row of the judgment matrix is the value of the matrix standard degree of the i-th selection factor relative to the j-th selection factor, i is 1, 2, … and n, and j is 1, 2, … and n.
6. A logistics distribution path selection system based on an improved Dijkstra algorithm is characterized by comprising the following modules:
the system comprises a model acquisition module, a route model acquisition module and a route model management module, wherein the model acquisition module is used for acquiring a route model between a goods distribution starting position and a goods distribution end position, the route model comprises weights of all selection factors between nodes with a fork as a node, and the selection factors comprise an object distribution emergency degree, a road congestion degree and a route distance;
a weight calculation module, configured to calculate a comprehensive weight between nodes of the path model, where the comprehensive weight between any node p and q is calculated by the following formula:
f＝ω1×f1+ω2×f2+…+ωn×fn，
wherein f represents the integrated weight, n represents the total number of the selection factors, and f1、f2… and fnRespectively, the weight, omega, of each factor between the node p and the node q1、ω2… and ωnAre respectively f1、f2… and fnA corresponding weight;
and the path selection module is used for replacing the weight of the corresponding path distance in the Dijkstra algorithm with the comprehensive weight to carry out the operation of improving the Dijkstra algorithm and selecting a path with the minimum total comprehensive weight.
7. The logistics distribution routing system of claim 6, wherein the normalized weight of the k-th selection factor is obtained by:
obtaining the standard degree S of a 1-9 level judgment matrix of each selection factork，k＝1、2、…、n；
The following treatment is respectively carried out on each selection factor: respectively solving the matrix standard degree values of the kth selection factor relative to all the selection factors, and then multiplying the matrix standard degree values of all the selection factors to obtain the n-th square root;
then, the n-th-order square root of each selection factor is normalized, and the normalized result is the normalized weight.
8. The logistics distribution routing system of claim 7, further comprising the following modules:
and the priority updating module is used for responding to the setting of a user and updating the priority of each selection factor before processing by utilizing the 1-9 level judgment matrix standard degree.
9. The method of claim 7The logistics distribution routing system of (1), wherein the weight of the urgency of distribution of the objects is ω10.6370, and the weight of the degree of road congestion is ω20.2583, the path distance is weighted by ω3＝0.1047。
10. The logistics distribution routing system of claim 7, wherein the obtaining of the weight of each selection factor is implemented by:
the consistency processing module is used for checking whether the normalized weight obtained by calculation meets the actual importance among weight types or not by carrying out consistency on the normalized weight, if so, the weight calculated this time is taken as the final value of the weight, and otherwise, the value of the matrix standard degree is obtained again to calculate the weight;
the method for consistency judgment is as follows:
judgment ofIf the value of (a) is less than 0.1, the consistency is met if the value of (b) is less than 0.1, otherwise the consistency is not met;
wherein,R.I. is equal to the value of the n-th order matrix in the average random consistency table, lambdamaxThe element of the j-th column of the i-th row of the judgment matrix is the value of the matrix standard degree of the i-th selection factor relative to the j-th selection factor, i is 1, 2, … and n, and j is 1, 2, … and n.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN108985692A (en) * | 2018-07-13 | 2018-12-11 | 南京邮电大学 | A kind of postal transportation networks method of agrochemical product |
| CN109472391A (en) * | 2018-09-20 | 2019-03-15 | 重庆满惠网络科技有限公司 | A kind of logistics information monitoring management system based on big data |
| CN109471444A (en) * | 2018-12-12 | 2019-03-15 | 南京理工大学 | Based on the parking AGV paths planning method for improving dijkstra's algorithm |
| CN109886502A (en) * | 2019-03-06 | 2019-06-14 | 北京工业大学 | A kind of routing method reducing night dispatching noise effect |
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| CN111573126A (en) * | 2020-05-11 | 2020-08-25 | 盐城工学院 | Modular intelligent logistics system material distribution path planning method based on omnidirectional wheel |
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|---|---|---|---|---|
| CN108921326A (en) * | 2018-06-06 | 2018-11-30 | 广东工业大学 | A kind of intelligent cultural gene Logistics Distribution Method based on similarity study |
| CN108985692A (en) * | 2018-07-13 | 2018-12-11 | 南京邮电大学 | A kind of postal transportation networks method of agrochemical product |
| CN109472391A (en) * | 2018-09-20 | 2019-03-15 | 重庆满惠网络科技有限公司 | A kind of logistics information monitoring management system based on big data |
| CN109471444B (en) * | 2018-12-12 | 2022-03-01 | 南京理工大学 | Parking AGV path planning method based on improved Dijkstra algorithm |
| CN109471444A (en) * | 2018-12-12 | 2019-03-15 | 南京理工大学 | Based on the parking AGV paths planning method for improving dijkstra's algorithm |
| CN109886502A (en) * | 2019-03-06 | 2019-06-14 | 北京工业大学 | A kind of routing method reducing night dispatching noise effect |
| CN110472789A (en) * | 2019-08-15 | 2019-11-19 | 国网甘肃省电力公司信息通信公司 | A kind of logistics distribution shortest path first solved based on Grobner bases |
| CN111367275A (en) * | 2020-02-18 | 2020-07-03 | 吉利汽车研究院（宁波）有限公司 | Intelligent driving control method, device and system and storage medium |
| CN111486861A (en) * | 2020-04-21 | 2020-08-04 | 百度在线网络技术（北京）有限公司 | Path planning method, device, equipment and medium |
| CN111573126A (en) * | 2020-05-11 | 2020-08-25 | 盐城工学院 | Modular intelligent logistics system material distribution path planning method based on omnidirectional wheel |
| CN111573126B (en) * | 2020-05-11 | 2022-04-05 | 盐城工学院 | Modular intelligent logistics system material distribution path planning method based on omnidirectional wheel |
| CN111784260A (en) * | 2020-07-14 | 2020-10-16 | 国网北京市电力公司 | Transportation planning method and device, storage medium and processor |
| CN112511230A (en) * | 2020-11-20 | 2021-03-16 | 国网浙江省电力有限公司宁波供电公司 | Optimal optical fiber path selection method and device |
| CN112511230B (en) * | 2020-11-20 | 2022-07-12 | 国网浙江省电力有限公司宁波供电公司 | Optimal optical fiber path selection method and device |
| CN112561451A (en) * | 2020-12-25 | 2021-03-26 | 生活半径(北京)信息技术有限公司 | Logistics distribution sorting method, device and system |
| CN112862625A (en) * | 2021-01-07 | 2021-05-28 | 国网浙江杭州市余杭区供电有限公司 | Power supply path acquisition method based on Dijkstra algorithm |
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