CN111784260A - Transportation planning method and device, storage medium and processor - Google Patents

Transportation planning method and device, storage medium and processor Download PDF

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CN111784260A
CN111784260A CN202010676574.6A CN202010676574A CN111784260A CN 111784260 A CN111784260 A CN 111784260A CN 202010676574 A CN202010676574 A CN 202010676574A CN 111784260 A CN111784260 A CN 111784260A
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王铁铮
张宏宾
任博翰
蒋訢晔
潘焜
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State Grid Corp of China SGCC
State Grid Beijing Electric Power Co Ltd
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Abstract

The application discloses a transportation planning method, a transportation planning device, a storage medium and a processor. Wherein, the method comprises the following steps: determining a shortest path from a starting point to a terminal point according to a road network topological graph corresponding to an actual road network; constructing an objective function for representing the shortest path based on different objective demand factors, wherein the objective function is used for representing objective demand values of a terminal under different constraint conditions when the shortest path is used for transportation; calculating a decision attribute matrix of the target function by using a particle swarm optimization method, and determining a corresponding weight coefficient based on the decision attribute matrix of the target function; weighting the objective function using the weighting coefficients; and determining an optimal path from the starting point to the end point based on the weighted objective function. The technical problems that the material distribution efficiency is low and the cost is high in the process of distributing the electric materials are solved.

Description

Transportation planning method and device, storage medium and processor
Technical Field
The application relates to the field of power supply material distribution, in particular to a transportation planning method, a transportation planning device, a storage medium and a processor.
Background
Nowadays, electric power becomes an indispensable part in life and production of people, the connection among power grids is increasingly tight along with the continuous deep interconnection trend of the power grids, and the power grids rapidly cover new areas due to the increasing power consumption requirements, so that a scientific and efficient material distribution planning method needs to be constructed.
In order to construct a distribution planning system which is high in practicability and suitable for power grid enterprises by combining the material distribution status situation of the power industry, the problem to be solved is that how to determine the number and the positions of warehouses and transit points and the coal-to-electricity material transportation path under the conditions of various constraints such as the capacity of the warehouse and the transit point facilities, the capacity of transport vehicles, the distribution time, the special loading and unloading requirements and the like, so that the coal-to-electricity distribution system consumes the minimum total time, the minimum total cost, the highest path safety and the like, and the comprehensive optimal solution of multi-objective optimization is obtained. The most important is the Vehicle Routing Problem (VRP), which is described in scientific research as "to allocate and organize appropriate driving routes for a series of receiving and delivering points, orderly to allow vehicles to pass through them, and to reach a certain target (such as distance, cost, time, Vehicle usage, etc. the less the vehicles are, the better) under the condition that certain constraint conditions (such as cargo demand, delivery volume, delivery and delivery time, Vehicle load constraint, driving mileage constraint, end time constraint, etc.) are met. "need optimize the vehicle route when electric power material is dispatched and is delivered, and vehicle route optimization problem not only involves a plurality of constraint conditions but also needs to a plurality of objectives optimization, does not obtain effective optimization scheme yet at present, has the problem that the efficiency of material delivery is lower, and the delivery cost is higher.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the application provides a transportation planning method, a transportation planning device, a storage medium and a processor, and aims to at least solve the technical problems of low material distribution efficiency and high cost in an electric material distribution process.
According to an aspect of an embodiment of the present application, there is provided a transportation planning method, including: determining a shortest path from a starting point to a terminal point according to a road network topological graph corresponding to an actual road network; constructing an objective function for representing the shortest path based on different objective demand factors, wherein the objective function is used for representing objective demand values of a terminal under different constraint conditions when the shortest path is used for transportation; calculating a decision attribute matrix of the target function by using a particle swarm optimization method, and determining a corresponding weight coefficient based on the decision attribute matrix of the target function; weighting the objective function using the weighting coefficients; and determining an optimal path from the starting point to the end point based on the weighted objective function.
Optionally, determining a shortest path from a starting point to an end point according to a road network topological graph corresponding to an actual road network, including: constructing a corresponding road network topological graph according to an actual road network, wherein the road network topological graph comprises points and line segments, and the points at least comprise one of the following points: the system comprises a starting point, a transfer point and a terminal point, wherein the line segments are traffic roads; estimating the time expectation required by the transportation of each traffic road in the actual road network, and setting the weight value of the corresponding line segment according to the time expectation; determining an initial shortest path between any two points in the road network topological graph, and determining all paths from the initial point to the end point based on the initial shortest path; comparing the total weight values of all the paths to obtain a comparison result, wherein the total weight value is the total weight value of each sub-path in any one path of all the paths, and the sub-path is composed of any two points of the starting point, the transit point and the end point; and determining the shortest path from the starting point to the end point according to the comparison result.
Optionally, determining an initial shortest path between any two points in the road network topological graph, and determining all paths from the initial point to the end point based on the initial shortest path, includes: setting a node set, wherein the type of the node set at least comprises one of the following types; the node comprises a first class node, a second class node and a third class node, wherein the first class node is a permanently marked node, the second class node is a node in a modification state, and the third class node is an unmarked node; taking each starting point as a first type node, and searching a second type node adjacent to each starting point; in the searching process, if the weight value of the sub-path formed by each starting point and the adjacent second type node is smaller than a preset threshold value, taking the weight value as a new weight value of the sub-path; marking the second class node corresponding to the new weight value as a first class node; and repeating the searching process until all the nodes in the node set are marked as the first-class nodes, and obtaining all the paths from the starting point to the end point.
Optionally, constructing an objective function for representing the shortest path based on different objective demand factors includes: determining an objective function based on the constraint target and the relevant parameters of the constraint target; wherein the constraint objective comprises at least one of: the end point satisfaction rate represents the proportion of the received goods and materials at the end point to the actually required goods and materials; the objective function comprises a plurality of single objective functions as: an end point satisfaction rate objective function, a delivery time objective function and a delivery cost objective function; adding constraint conditions to the objective function to obtain the objective function containing the constraint conditions, wherein the constraint conditions at least comprise one of the following conditions: the method comprises the following steps of balance constraint of material flow inflow and outflow of a transfer point, quantity constraint of the transfer point, quantity constraint of material distribution, load constraint of a material distribution vehicle, material supply constraint, material flow constraint and fund flow constraint.
Optionally, determining a corresponding weight coefficient based on the decision attribute matrix of the objective function includes: preprocessing a decision attribute matrix of the objective function, and determining a weight coefficient of the objective function based on a preprocessing result, wherein the objective function comprises: a cost-type objective function and a benefit-type objective function.
Optionally, preprocessing the decision attribute matrix of the objective function includes: when the objective function is a cost-type objective function, the following processing is carried out on the decision attribute matrix of the objective function:
Figure BDA0002584267390000031
obtaining a decision attribute matrix of the preprocessed objective function; and when the objective function is a benefit objective function, performing the following processing on a decision attribute matrix of the objective function:
Figure BDA0002584267390000032
and obtaining another preprocessed decision attribute matrix of the objective function.
Optionally, determining the weight coefficients of the objective function based on the preprocessing result includes: determining the entropy of the objective decision attribute matrix based on the preprocessed objective decision attribute matrix; and determining the weight coefficient of the objective function based on the entropy of the objective function decision attribute matrix.
Optionally, weighting the objective function by using the weighting coefficient, and determining an optimal path from the starting point to the end point based on the weighted objective function, includes: weighting the objective function by using the weight coefficient to obtain a mixed weighted objective function; obtaining a first function and a second function in the mixed weighted target function, wherein the first function and the second function are respectively: the cost type objective function and the benefit type objective function after being processed; processing the first function by using a first processing function, and processing the second function by using a second processing function to obtain a membership function of the objective function, wherein the first processing function and the second processing function are respectively as follows: decreasing the semigradient membership function and increasing the semilinear membership function; taking a solution set when the membership function of the target function has the maximum membership as an overall optimal solution set, wherein when the membership function of the target function has the maximum membership, the overall satisfaction of the target function is maximum, the total distribution time is minimum and the total transportation cost is minimum; and determining an optimal path from the starting point to the end point based on the overall optimal solution set.
According to another aspect of the embodiments of the present application, there is also provided a transportation planning apparatus, including: the first determining device is used for determining the shortest path from the starting point to the end point according to the road network topological graph corresponding to the actual road network; the construction device is used for constructing an objective function for representing the shortest path based on different objective demand factors, wherein the objective function is used for representing objective demand values of the end point under different constraint conditions when the shortest path is used for transportation; the calculating device is used for calculating a decision attribute matrix of the target function by utilizing a particle swarm method and determining a corresponding weight coefficient based on the decision attribute matrix of the target function; processing means for weighting the objective function using the weighting coefficients; and second determining means for determining an optimal path from the start point to the end point based on the weighted objective function.
According to another aspect of the embodiments of the present application, there is also provided a non-volatile storage medium, where the non-volatile storage medium includes a stored program, and when the program runs, a device in which the non-volatile storage medium is located is controlled to execute the transportation planning method described above.
According to yet another aspect of the embodiments of the present application, there is also provided a processor for executing a program stored in a memory, wherein the program when executed performs the transportation planning method described above.
In the embodiment of the application, the shortest path from a starting point to a terminal point is determined according to a road network topological graph corresponding to an actual road network; constructing an objective function for representing the shortest path based on different objective demand factors, wherein the objective function is used for representing objective demand values of a terminal under different constraint conditions when the shortest path is used for transportation; calculating a decision attribute matrix of the target function by using a particle swarm optimization method, and determining a corresponding weight coefficient based on the decision attribute matrix of the target function; weighting the objective function using the weighting coefficients; and determining an optimal path from a starting point to an end point based on the weighted objective function, adding a weight coefficient to the objective function of the constructed shortest path, and determining the optimal path from the starting point to the end point based on the weighted objective function, thereby simplifying the complexity of solution, obtaining an optimal distribution scheme, achieving the purposes of improving the material distribution efficiency and saving the cost, and further solving the technical problems of low material distribution efficiency and high cost in the process of electric material distribution.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic flow chart diagram of a transportation planning method according to an embodiment of the present application;
FIG. 2 is a topology diagram after an optional assignment of edge weights according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a transportation planning apparatus according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In accordance with an embodiment of the present application, there is provided a method embodiment of transportation planning, it should be noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system, such as a set of computer-executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
Fig. 1 is a transportation planning method according to an embodiment of the present application, as shown in fig. 1, the method includes the following steps:
step S102, determining a shortest path from a starting point to a terminal point according to a road network topological graph corresponding to an actual road network;
step S104, constructing an objective function for representing the shortest path based on different objective demand factors, wherein the objective function is used for representing objective demand values of a terminal under different constraint conditions when the shortest path is used for transportation;
step S106, calculating a decision attribute matrix of the objective function by using a particle swarm optimization method, and determining a corresponding weight coefficient based on the decision attribute matrix of the objective function;
step S108, weighting the target function by using the weighting coefficient; and determining an optimal path from the starting point to the end point based on the weighted objective function.
In some optional embodiments, in order to obtain an accurate shortest path, when determining the shortest path from the starting point to the end point according to the road network topological graph corresponding to the actual road network, the following process may be performed: constructing a corresponding road network topological graph according to an actual road network, wherein the road network topological graph comprises points and line segments, and the points at least comprise one of the following points: the system comprises a starting point, a transfer point and a terminal point, wherein the line segments are traffic roads; estimating the time expectation required by transportation of each traffic road in an actual road network, and setting a weight value of a segment corresponding to the time expectation according to the time expectation, wherein during estimation, a three-point estimation method (such as optimistic estimation time, pessimistic estimation time and most probable estimation time) can be used for estimating the time expectation required by transportation of the road under different conditions, and fig. 2 shows a road network topological graph and the side rights of an assignment topological graph; determining an initial shortest path between any two points in the road network topological graph, and determining all paths from the initial point to the end point based on the initial shortest path; comparing the total weight values of all the paths to obtain a comparison result, wherein the total weight value is the total weight value of each sub-path in any one path of all the paths, and the sub-path is composed of any two points of the starting point, the transit point and the end point; and determining the shortest path from the starting point to the end point according to the comparison result. For example, if five paths from the same starting point to the same end point are provided and the total path weights are 5,8,10,13, and 14, respectively, then when the total path weight is less than or equal to 5, the corresponding path is the shortest path.
When determining an initial shortest path between any two points in the road network topological graph, setting a node set by using a Dijkstra (Dijkstra) algorithm, wherein the category of the node set at least comprises one of the following categories; the node comprises a first class node, a second class node and a third class node, wherein the first class node is a permanently marked node, the second class node is a node in a modification state, and the third class node is an unmarked node; taking each starting point as a first type node, and searching unmarked nodes adjacent to each starting point; in the searching process, if the weight value of a sub-path formed by each starting point and the adjacent unmarked nodes is smaller than a preset threshold value, taking the weight value as a new weight value of the sub-path; and marking the marked nodes corresponding to the new weight values as permanent marked nodes. After the initial shortest path is determined, in order to determine all paths from the initial point to the end point based on the initial shortest path, the above search process is repeated until each node in the node set is markedAnd obtaining all paths from the starting point to the end point for permanently marking the nodes. Wherein, the calculation formula of the total weight is as follows:
Figure BDA0002584267390000061
wherein u is0Is a starting point PiEnd point PjThe shortest path between them, w (e) is the weight of each edge.
After all paths from the starting point to the end point are determined, an objective function for representing the shortest path needs to be constructed based on different objective demand factors, and the construction of the objective function can be realized through the following processes: determining an objective function based on the constraint target and the relevant parameters of the constraint target; wherein the constraint objective comprises at least one of: the system comprises an end point satisfaction rate, distribution time and distribution cost, wherein the end point satisfaction rate represents the proportion of received materials at the end point to actually required materials, and the objective function comprises a plurality of single objective functions: a satisfaction rate objective function, a delivery time objective function and a delivery cost objective function; adding constraint conditions to the objective function to obtain the objective function containing the constraint conditions, wherein the constraint conditions at least comprise one of the following conditions: the method comprises the following steps of balance constraint of material flow inflow and outflow of a transfer point, quantity constraint of the transfer point, quantity constraint of material distribution, load constraint of a material distribution vehicle, material supply constraint, material flow constraint and fund flow constraint.
The objective function constructed above is as follows:
Figure BDA0002584267390000062
Figure BDA0002584267390000063
Figure BDA0002584267390000064
wherein z is1For demand point satisfaction rate objective function, z2As an objective function of delivery time, z3For distribution costObjective function, SgiThe number of G types of materials available from the supply point i is G ∈ G, i ∈ U, DgkThe number of G types of materials needed by the demand point k is shown as G ∈ G, k ∈ W, LAijJ ∈ V, i ∈ U, LB, representing the distance from the supply point i to the transit point jjkRepresents the distance from the transit point j to the demand point k, j ∈ V, k ∈ W;
Figure BDA0002584267390000065
represents the time taken between the supply point i and the transit point j by the transport mode M, i ∈ U, j ∈ V, M ∈ M;
Figure BDA0002584267390000066
represents the time spent between the transit point j and the demand point k by the transportation means M, k ∈ W, j ∈ V, M ∈ M;
Figure BDA0002584267390000071
unit cost between the supply point i and the transit point j through the transportation means M, i ∈ U, j ∈ V, M ∈ M;
Figure BDA0002584267390000072
unit cost, k ∈ W, j ∈ V, M ∈ M, spent between the transit point j and the demand point k by the transportation means M;
Figure BDA0002584267390000073
the maximum transportation flow of m types of transportation modes between the supply point i and the transit point j is represented;
Figure BDA0002584267390000074
representing the maximum transport flow of m types of transport modes between the transit point j and the demand point k; RU (RU)gk: representing the relative tension of the material g required by the demand point k, the sum of the tension of all the demand points being 1, i.e.
Figure BDA0002584267390000075
Wg: represents the unit weight of the g materials; capm: represents the unit maximum load of the m types of transportation tools; q: represents a sufficiently large integer；ωk: the weight of the satisfaction rate of each demand point is represented and is determined by a decision maker according to the actual situation balance of each point; rho1，ρ2: representing a fairness coefficient p between demand points1Coefficient of difference rho2The sum of the two is 1;
Figure BDA0002584267390000076
represents the amount of materials of g types from a supply point i to a transit point j in M transportation modes, i ∈ U, j ∈ V, M ∈ M;
Figure BDA0002584267390000077
representing the quantity of g-type materials from a transfer point j to a demand point k in a transmission mode, k ∈ W, j ∈ V, M ∈ M;
Figure BDA0002584267390000078
representing the maximum number of classes m of vehicles from the supply point i to the transit point j;
Figure BDA0002584267390000079
representing the maximum number of m types of transport vehicles from the i transit point to the k demand point;
Figure BDA00025842673900000710
representing a traffic flow from a supply point i to a transit point j, 1 if any, and 0 if not;
Figure BDA00025842673900000711
representing a traffic flow from transit point j to demand point k, 1 if any, and 0 if not, α representing minimum satisfaction for each demand point, LocjIndicating whether the endpoint j is established, is set to 1, otherwise is 0.
After different objective functions are constructed, introducing corresponding constraint conditions to the objective functions, wherein the constraint conditions are as follows: balance constraint of material flow in and out of a transfer point:
Figure BDA00025842673900000712
∑ constraint of the number of transit pointsjLocjLess than or equal to n j ∈ V, material distribution quantity and material distribution vehicleVehicle load restraint:
Figure BDA00025842673900000713
Figure BDA00025842673900000714
and (3) material supply restraint:
Figure BDA00025842673900000715
Figure BDA00025842673900000716
material flow restraint:
Figure BDA00025842673900000717
Figure BDA00025842673900000718
Figure BDA0002584267390000081
and (3) restricting fund flow:
Figure BDA0002584267390000082
in some embodiments, a decision attribute matrix of the objective function may be calculated by using a particle swarm method, and the process may be implemented by: and taking the target function as an optimization target, and solving the target function by using the particle swarm optimization to obtain a decision attribute matrix of the target function. After the decision attribute macroseism of the objective function is obtained, the corresponding weight coefficient can be determined based on the decision attribute matrix of the objective function, and the process can be realized by the following steps: preprocessing a decision attribute matrix of the objective function, and determining a weight coefficient of the objective function based on a preprocessing result, wherein the objective function comprises: a cost-type objective function and a benefit-type objective function.
The particle swarm algorithm is briefly introduced as follows: the particle swarm algorithm is an animal bionic colony solving algorithm established according to collective behaviors occurring in the process of foraging and migrating birds, a batch of initial particles are randomly generated, the particles dynamically learn according to individual optimal values and global optimal values, the search strategy of the particles is changed, the search direction is adjusted to search for the optimal particles from the positions, and the optimal particles are followed in a solution space for searching. In the searching process, each particle has a memory function and can remember the searched optimal position; each particle also has a velocity that determines the distance and direction of flight. The speed is dynamically adjusted based on its own flight experience and the flight experience of the fellow. The particles adjust the speed and the position of the particles according to the individual and global optimal values in the current memory, and the searching effect is achieved by continuous updating.
In some embodiments, the pretreatment process may employ the following methods: when the objective function is a cost-type objective function, performing the following processing on a decision attribute matrix of the objective function:
Figure BDA0002584267390000083
obtaining a decision attribute matrix of the preprocessed objective function; when the objective function is a benefit-type objective function, performing the following processing on a decision attribute matrix of the objective function:
Figure BDA0002584267390000084
and obtaining another preprocessed decision attribute matrix of the objective function. After the preprocessing result is obtained, the weight coefficient of the objective function can be determined according to the preprocessing result, and the specific process can be realized through the following steps: determining the entropy of the objective decision attribute matrix based on the preprocessed objective decision attribute matrix; and determining the weight coefficient of the objective function based on the entropy of the objective function decision attribute matrix.
In the embodiment of calculating the weight coefficient, firstly, the multi-objective optimization is converted into the single-objective optimization based on the particle swarm optimization, the weight coefficient of the objective function is obtained by using the comprehensive evaluation method and taking the objective function as the index and the objective function value as the index value, as shown in fig. 3, the method is a flow chart based on the particle swarm optimization, and the objective function is respectively taken as the flow chart based on the particle swarm optimization
Figure BDA0002584267390000085
As an optimization target (spatial dimension of the particle), model solution is performed, and the process is as follows: let k dimension space, have m particles; particle i position: x is the number ofi＝(xi1，xi2，...，xik) (ii) a Velocity of particle i: v. ofi＝(vi1，vi2，...，vik) (ii) a Historical best position for particle i experienced: p is a radical ofi＝(pi1，pi2，...，pik) (ii) a Best positions experienced by all particles within a population (or within a domain): p is a radical ofg＝(pg1，pg2，...，pgk) (ii) a In the m iteration, each particle velocity n-dimensional component and position are updated:
Figure BDA0002584267390000091
Figure BDA0002584267390000092
wherein, c1、c2Is a learning factor used for adjusting the maximum step length of learning; w is the inertial weight used to adjust the search capability to the solution space. The decision attribute of the objective function is obtained through the above process, the decision attribute table of the objective function is shown in table 1, and a decision attribute matrix [ r ] of the objective function is constructed from table 1ij]k×k(i, j ═ 1, 2.. times, k), preprocessing decision attributes of the cost type objective function and the benefit objective function respectively to obtain a preprocessed objective function decision matrix [ r'ij]k×k，rijExpressed as a function fjTo optimize the objective, fiThe decision attribute value of (1).
Table 1:
Figure BDA0002584267390000093
the specific treatment method comprises the following steps: with respect to the cost-type objective function,
Figure BDA0002584267390000094
to effectThe target function of the beneficial type is obtained,
Figure BDA0002584267390000095
to simplify the evaluation process, an entropy weight method may be applied to solve the objective function weights, as follows: computing an attribute objective function
Figure BDA0002584267390000096
Entropy E ofi：
Figure BDA0002584267390000097
As a function of the number of samples, the purpose being toi∈[0，1]；r′ijSatisfy 0 < r'ij＜1，
Figure BDA0002584267390000098
And when r isijWhen equal to 0, rijln(rij) 0. And calculating the weight of the objective function: di＝1-Ei，i＝1，2，...，k；
Figure BDA0002584267390000099
Wherein d isiRepresenting an objective function
Figure BDA00025842673900000910
Degree of information deviation of (D), λiRepresenting an objective function
Figure BDA00025842673900000911
The weight of (c).
In determining the weight coefficient lambdaiThen, for the optimal solution set, the objective function may be weighted by using the weighting factor, and an optimal path from the starting point to the end point is determined based on the weighted objective function, which may be implemented by: weighting the objective function by using the weight coefficient to obtain a mixed weighted objective function; obtaining a first function and a second function in the mixed weighted target function, wherein the first function and the second function are respectively: the cost type objective function and the benefit type objective function after being processed; by using a firstProcessing the first function by a processing function, and processing the second function by a second processing function to obtain a membership function of the objective function, wherein the first processing function and the second processing function are respectively: decreasing the semigradient membership function and increasing the semilinear membership function; taking a solution set when the membership function of the target function has the maximum membership as an overall optimal solution set, wherein when the membership function of the target function has the maximum membership, the overall satisfaction of the target function is maximum, the total distribution time is minimum and the total transportation cost is minimum; and determining an optimal path from the starting point to the end point based on the overall optimal solution set.
Wherein a weighting factor lambda is appliediWeighting the single objective function to form a mixed weighted objective function, wherein the process is as follows:
Figure BDA0002584267390000101
wherein { 1' } ∪ { i "} ═ i }, zi' and zi *Respectively processing cost type and benefit type target functions, and taking the target function in the formula as an optimization target to obtain a multi-target model global optimal solution set; then, introducing a fuzzy satisfaction degree theory, and constructing an optimal solution method of the model, wherein the method comprises the following specific steps: defining a membership function of an objective function i in a pareto optimization scheme r as
Figure BDA0002584267390000102
Respectively introducing a decreasing half gradient membership function to process a cost-type target and an increasing half linear membership function to process a benefit-type target function:
Figure BDA0002584267390000103
Figure BDA0002584267390000104
Figure BDA0002584267390000105
wherein m and n represent the objective function respectively, i ═ m + n, R represent the number of the first pareto solution sets, R represents the number of the first pareto solution sets,
Figure BDA0002584267390000106
and
Figure BDA0002584267390000107
respectively representing objective functions in the pareto optimization scheme r
Figure BDA0002584267390000108
The solution with the highest degree of membership is the globally optimal solution.
The present application is further illustrated by the following specific examples:
the method is characterized in that material distribution in the process of building a regional power grid newly added in a certain province is used as an evidence analysis object, wherein 4 material demand points are provided, 3 material warehouse nodes away from the demand points are provided, in the material distribution, transfer points are built to reduce the time spent, 5 transfer points are used as alternatives and are limited by the building capacity, and A, B two materials are needed when 3 demand points are started. Table 2 shows material information that 3 supply points can provide, table 3 shows material information that 4 demand points need, and tables 2 and 3 show the following:
table 2:
supply point Material supply Point 1 Material supply point 2 Material supply point 3
A 100 80 90
B 80 100 90
Table 3:
demand point Material demand point 1 Material demand point 2 Points of material demand 3 Material demand point 4
A 80 70 90 80
B 79 90 80 85
Assuming transport conditions: the distances from the supply point to the transit point and from the transit point to the demand point are respectively shown in tables 4 and 5; the transport stream information is shown in table 6 and table 7, which are the unit delivery time between the supply point, the transit point and the demand point; assuming that the delivery time is proportional to the distance, the unit delivery cost is related to the delivery distance; the unit transportation time is assumed to be 2 by using the same model train, and the unit transportation time in different road sections is shown in table 8 due to different speeds of the trucks in the road sections; the unit transportation cost is shown in table 9, and the weight of the train is different from that of the truck, the train is 10, the truck is 3, the unit weight of the material A is 1, and the unit weight of the material B is 2. The materials are sorted according to the emergency situation, from the demand point 1 to the demand point 4, the weight distribution is respectively assumed to be 0.4, 0.3,0.2 and 0.1, the demand coefficient of each demand point to the materials A, B is shown in the table 10, and the fairness coefficient rho are taken into consideration1Value of 0.3, coefficient of variation rho2The value is 0.3. Tables 4-10 are shown below:
table 4:
material supply Point 1 Material supply point 2 Material supply point 3
Transit point 1 10 15 12
Transfer point2 12 11 14
Transit point 3 13 13 15
Transit point 4 14 12 11
Transit point 5 15 10 13
Table 5:
transit point 1 Transit point 2 Transit point 3 Transit point 4 Transit point 5
Material demand point 1 4 3 5 3 4
Material demand point 2 5 4 3 5 5
Material demand point 3 3 2 4 4 5
Material demand point 4 5 5 4 3 4
Table 6:
Figure BDA0002584267390000111
Figure BDA0002584267390000121
table 7:
material demand point 1 Material demand point 2 Points of material demand 3 Material demand point 4
Transit point 1 30 28 30 30
Transit point 2 30 30 27 30
Transit point 3 29 30 30 30
Transit point 4 27 29 30 28
Transit point 5 28 24 25 28
Table 8:
material demand point 1 Material demand point 2 Points of material demand 3 Material demand point 4
Transit point 1 8 4 3 4
Transit point 2 7 4 3 4
Transit point 3 4 2 3 4
Transit point 4 4 5 3 5
Transit point 5 7 4 2 5
Table 9:
material demand point 1 Material demand point 2 Points of material demand 3 Material demand point 4
Transit point 1 6 4 5 5
Transit point 2 2 4 7 3
Transit point 3 6 3 6 8
Transit point 4 6 8 7 6
Transit point 5 3 5 4 6
Table 10:
demand point A B
Material demand point 1 0.7 0.3
Material demand point 2 0.5 0.5
Material demand point 3 0.6 0.4
Material demand point 4 0.4 0.6
Step one, planning a shortest path: the actual traffic network is abstracted and simplified and converted into a traffic topological graph, the edges are traffic roads, the nodes are traffic hubs, transit points and demand points, the duration expectation of each road section is determined by a three-point method, and the duration expectation is assigned as the weight of each edge of the topological graph. And (3) obtaining the shortest path from each warehouse to a demand point by applying a Dijkstra algorithm, and balancing the selection of the warehouses and the determination of the material transportation quantity by using the actually required analysis priority in order to improve the efficiency.
And secondly, multi-objective optimization calculation: and carrying out empirical analysis on the extracted model based on the obtained data, and solving the material multi-target delivery scheduling model by using the extracted algorithm.
Thirdly, solving is carried out by taking each target as a unique target of the model, and the solving result is shown in table 11; then, a fuzzy multi-objective decision model is adopted to obtain the optimal value and the worst value under each objective function, and the solving result is shown in table 12:
table 11:
total delivery time minimum Maximum overall satisfaction Total distribution cost is lowest
Demand Point
1 satisfaction 0.71429 1 0.96982
Demand Point 2 satisfaction 0.64048 0.97222 0.5881
Demand point 3 satisfaction 1 0.75 0.98167
Demand point 4 satisfaction 1 0.75 0.755
Total delivery time 280 426 442
Total delivery cost 15958 27228 13466
Overall satisfaction 0.5445 0.8667 0.58533
Transfer point addressing situation (1,0,1,1,0) (1,1,0,1,0) (1,1,0,0,1)
Table 12:
overall satisfaction Total delivery time minimum Total distribution cost is lowest
Optimum value 0.8667 280 13466
Worst value 0.84321 443 29270
Wherein, each object membership is constructedThe degree function is as follows:
Figure BDA0002584267390000135
respectively, the membership function with the maximum overall satisfaction degree, the minimum total distribution time and the minimum total transportation cost.
Figure BDA0002584267390000131
Figure BDA0002584267390000132
Figure BDA0002584267390000133
At minimum satisfaction of the objective function
Figure BDA0002584267390000134
Constructing a linear model, calculating by an entropy weight method and analyzing actual demands, distributing the total satisfaction, the total distribution time and the total distribution cost to be 0.5,0.3 and 0.2, taking the bias weight to be 0.4, and converting the multi-target decision problem into a single-target decision problem:
Figure BDA0002584267390000142
Figure BDA0002584267390000141
the data are solved, the total satisfaction rate of the three objective function values of the optimal solution is 0.86455 (membership degree 0.90963), the total distribution time is 302 (membership degree 0.86455), and the total transportation cost is 14861 (membership degree 0.92312).
Fig. 3 is a transportation planning apparatus according to an embodiment of the present application, and as shown in fig. 3, the apparatus includes the following modules:
the first determining module 32 is configured to determine a shortest path from a starting point to an end point according to a road network topological graph corresponding to an actual road network;
the building module 34 is configured to build different objective functions for representing the shortest path based on different objective demand factors, where the different objective functions are used to represent demand situations of each objective when transporting using the shortest path;
the calculating module 36 is configured to calculate a decision attribute of the objective function and a weight coefficient corresponding to the decision attribute by using a particle swarm optimization;
the processing module 38 is used for weighting different objective functions by adopting weighting coefficients and converting the weighted objective functions into a single objective function;
and a second determining module 40, which determines the optimal path from the starting point to the end point based on the single objective function.
It should be noted that, reference may be made to the description related to the embodiment shown in fig. 1 for a preferred implementation of the embodiment shown in fig. 3, and details are not described here again.
According to another aspect of the embodiments of the present application, there is also provided a non-volatile storage medium, where the non-volatile storage medium includes a stored program, and when the program runs, a device in which the non-volatile storage medium is controlled to execute the transportation planning method described above.
According to another aspect of the embodiments of the present application, there is also provided a processor for executing a program stored in a memory, wherein the program executes the transportation planning method described above.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.

Claims (10)

1. A transportation planning method, comprising:
determining a shortest path from a starting point to a terminal point according to a road network topological graph corresponding to an actual road network;
constructing an objective function for representing the shortest path based on different objective demand factors, wherein the objective function is used for representing objective demand values of the destination under different constraint conditions when shortest path transportation is used;
calculating a decision attribute matrix of the target function by using a particle swarm optimization method, and determining a corresponding weight coefficient based on the decision attribute matrix of the target function;
weighting the objective function using the weighting coefficients; and determining an optimal path from the starting point to the end point based on the weighted objective function.
2. The method of claim 1, wherein determining the shortest path from the starting point to the end point according to the road network topology map corresponding to the actual road network comprises:
according to an actual road network, constructing a corresponding road network topological graph, wherein the road network topological graph comprises points and line segments, and the points at least comprise one of the following points: the starting point, the transit point and the end point, and the line segment is a traffic road;
estimating the time expectation required by the transportation of each traffic road in the actual road network, and setting the weight value of the corresponding line segment according to the time expectation;
determining an initial shortest path between any two points in the road network topological graph, and determining all paths from the initial point to the end point based on the initial shortest path;
comparing the total weight values of all the paths to obtain a comparison result, wherein the total weight value is the total weight value of each sub-path in any one path of all the paths, and the sub-path is composed of any two points of the starting point, the transit point and the end point;
and determining the shortest path from the starting point to the end point according to the comparison result.
3. The method of claim 2, wherein determining an initial shortest path between any two points in said road network topology graph, and determining all paths from said initial point to said end point based on said initial shortest path comprises:
setting a node set, wherein the type of the node set at least comprises one of the following types; the node comprises a first type node, a second type node and a third type node, wherein the first type node is a permanently marked node, the second type node is a node in a modification state, and the third type node is an unmarked node;
taking the starting points as first-class nodes, and searching second-class nodes adjacent to the starting points;
in the searching process, if the weight value of the sub-path formed by the starting point and the adjacent second-class node is smaller than a preset threshold value, taking the weight value as a new weight value of the sub-path; marking the second class node corresponding to the new weight value as the first class node;
and repeating the searching process until all the nodes in the node set are marked as the first type of nodes, and obtaining all the paths from the starting point to the end point.
4. The method of claim 1, wherein constructing an objective function for representing the shortest path based on different objective demand factors comprises:
determining the objective function based on a constraint target and relevant parameters of the constraint target; wherein the constraint objective comprises at least one of: the end point satisfaction rate represents the proportion of the received goods and materials at the end point to the actually required goods and materials; the objective function comprises a plurality of single objective functions: an end point satisfaction rate objective function, a delivery time objective function and a delivery cost objective function;
adding constraint conditions to the objective function to obtain an objective function containing constraint conditions, wherein the constraint conditions at least comprise one of the following conditions: the method comprises the following steps of balance constraint of material flow inflow and outflow of a transfer point, quantity constraint of the transfer point, quantity constraint of material distribution, load constraint of a material distribution vehicle, material supply constraint, material flow constraint and fund flow constraint.
5. The method of claim 1,
calculating a decision attribute matrix of the objective function by using a particle swarm optimization method, wherein the decision attribute matrix comprises the following steps: taking the target function as an optimization target, and solving the target function by using the particle swarm optimization to obtain a decision attribute matrix of the target function;
determining a corresponding weight coefficient based on the decision attribute matrix of the objective function, including: preprocessing a decision attribute matrix of the objective function, and determining a weight coefficient of the objective function based on a preprocessing result, wherein the objective function comprises: a cost-type objective function and a benefit-type objective function.
6. The method of claim 5,
preprocessing the decision attribute matrix of the objective function, including: when the objective function is a cost-type objective function, the following processing is carried out on the decision attribute matrix of the objective function:
Figure FDA0002584267380000031
obtaining a decision attribute matrix of the preprocessed objective function; and when the objective function is a benefit objective function, performing the following processing on a decision attribute matrix of the objective function:
Figure FDA0002584267380000032
obtaining a decision attribute matrix of the other preprocessed objective function;
determining weight coefficients for the objective function based on the pre-processing results, including: determining the entropy of the objective decision attribute matrix based on the preprocessed objective decision attribute matrix; and determining the weight coefficient of the objective function based on the entropy of the objective function decision attribute matrix.
7. The method of claim 1, wherein weighting the objective function using the weighting coefficients and determining an optimal path from the starting point to the end point based on the weighted objective function comprises:
weighting the objective function by using the weight coefficient to obtain a mixed weighted objective function;
obtaining a first function and a second function in the mixed weighted target function, wherein the first function and the second function are respectively: the cost type objective function and the benefit type objective function after being processed;
processing the first function by using a first processing function, and processing the second function by using a second processing function to obtain a membership function of the objective function, wherein the first processing function and the second processing function are respectively as follows: decreasing the semigradient membership function and increasing the semilinear membership function;
taking a solution set when the membership function of the target function has the maximum membership as an overall optimal solution set, wherein when the membership function of the target function has the maximum membership, the overall satisfaction of the target function is maximum, the total distribution time is minimum and the total transportation cost is minimum;
and determining an optimal path from the starting point to the end point based on the overall optimal solution set.
8. A transportation planning apparatus, comprising:
the first determining device is used for determining the shortest path from the starting point to the end point according to the road network topological graph corresponding to the actual road network;
the construction device is used for constructing an objective function for representing the shortest path based on different objective demand factors, wherein the objective function is used for representing objective demand values of the end point under different constraint conditions when the shortest path is used for transportation;
the calculating device is used for calculating a decision attribute matrix of the target function by utilizing a particle swarm method and determining a corresponding weight coefficient based on the decision attribute matrix of the target function;
processing means for weighting the objective function using the weighting coefficients;
second determining means for determining an optimal path from the start point to the end point based on the weighted objective function.
9. A non-volatile storage medium, comprising a stored program, wherein the program, when executed, controls a device in which the non-volatile storage medium is located to perform a transportation planning method according to any one of claims 1 to 7.
10. A processor for executing a program stored in a memory, wherein the program when executed performs the transportation planning method of any of claims 1 to 7.
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