CN116934205B - Public-iron hollow shaft spoke type logistics network optimization method - Google Patents
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Abstract
The invention discloses a public-iron-air shaft spoke type logistics network optimization method, which relates to the technical field of path planning, and can enable a user to quickly select destination nodes by establishing a public-iron-air shaft spoke type logistics network, determine the connection relation among all nodes, and determine the optimal path containing all destination nodes on the premise of considering various transportation modes such as highway transportation, railway transportation, air transportation and the like on the basis of all nodes and the connection relation, thereby effectively saving freight cost.
Description
Technical Field
The invention relates to the technical field of path planning, in particular to a public iron space shaft spoke type logistics network optimization method.
Background
Logistics (English name: logistics) initially means "goods delivery". Which is part of the supply chain activity. It is an efficient, low cost process of planning, implementing and controlling commodity and service consumption and related information flow and storage from origin to consumption to meet customer needs. The logistics takes storage as a center, and the production and market synchronization are promoted. The logistics refers to the whole process of planning, implementing and managing raw materials, semi-finished products, finished products and related information from a commodity origin to a commodity consumption place by means of transportation, storage, distribution and the like at the lowest cost so as to meet the demands of customers.
In existing shipping processes, after shipment of a lot, it is often necessary to ship to multiple destinations, and shipping routes are often made by staff to ship the shipment to the multiple destinations. However, the method of customizing the freight route is not only free from regulations to draw the shortest route, but also is not considered fully for the transportation mode, resulting in an increase in freight cost.
Disclosure of Invention
The invention aims to provide a public iron hollow shaft spoke type logistics network optimization method which solves the problems in the prior art.
The invention is realized by the following technical scheme:
A public iron space spoke type logistics network optimization method comprises the following steps:
obtaining logistics route data, and establishing a public-iron-air shaft spoke logistics network according to the logistics route data, wherein the public represents road transportation, the iron represents railway transportation, and the air represents air transportation;
based on the public-iron-hollow spoke type logistics network, determining the transportation cost and the transportation time between any two nodes, and carrying out weighted summation on the transportation cost and the transportation time to obtain a comprehensive transportation factor between any two nodes;
Obtaining logistics data to be planned, and coding by adopting a binary coding mode based on all nodes in the public-iron-hollow radial logistics network to generate a plurality of initial coding individuals corresponding to the logistics data to be planned;
according to the initial coding individuals, iteration is carried out by adopting an SAGA algorithm which is improved by the population fitness score, the optimal coding individuals meeting the set conditions are obtained according to the comprehensive transportation factors, and the optimal coding individuals are used as node selection results;
And acquiring a logistics network optimization result corresponding to the logistics data to be planned based on the node selection result.
In one possible embodiment, obtaining logistics route data and establishing a public-iron-hollow axis radial logistics network according to the logistics route data comprises the following steps:
obtaining logistics route data, wherein the logistics route data comprises a plurality of logistics nodes and paths among the logistics nodes;
determining a plurality of coverage areas, wherein the coverage areas comprise a hub node and a plurality of non-hub nodes, paths for connecting the hub node with all the non-hub nodes in the coverage areas exist, and each non-hub node is connected to only one hub node; a connecting path exists between any two hub nodes;
When two non-hub nodes in the same coverage area have connection paths, direct transportation is allowed; when two non-hub nodes in the same coverage area do not have connection paths, transferring transportation through the hub nodes in the coverage area; when two non-hub nodes in two different coverage areas have connection paths, direct transportation is allowed, and only air transportation is allowed; when the two non-junction nodes in the two different coverage areas do not have connection paths, the two non-junction nodes without the connection paths are transported in a transferring way through the junction nodes in the two different coverage areas;
And forming the connection relation among the nodes in the coverage areas into a public-iron-hollow spoke type logistics network.
In one possible implementation manner, based on the public-iron-hollow spoke type logistics network, determining the transportation cost and the transportation time between any two nodes, and weighting and summing the transportation cost and the transportation time to obtain a comprehensive transportation factor between any two nodes, wherein the comprehensive transportation factor comprises the following steps:
Based on the public-iron-hollow spoke type logistics network, determining a transportation mode and a path length between any two nodes with connecting paths; the transportation mode comprises road transportation, railway transportation and/or air transportation;
Determining a first unit time and a first unit cost required for transporting a unit volume of goods by a unit distance in a transportation mode according to the transportation mode between any two nodes with connection paths;
Determining a first comprehensive transportation factor between any two nodes with connecting paths according to the first unit time, the first unit cost and the path length between any two nodes with connecting paths; wherein, different transportation modes correspond to different first comprehensive transportation factors;
Determining a second unit time and a second unit cost required for transporting the unit weight of goods by unit distance in the transportation mode according to the transportation mode between any two nodes with connection paths;
Determining a second comprehensive transportation factor between any two nodes with connecting paths according to the second unit time, the second unit cost and the path length between any two nodes with connecting paths; wherein the different transportation modes correspond to different second comprehensive transportation factors.
In one possible implementation manner, the obtaining the logistics data to be planned, based on all nodes in the public-iron-hollow spoke logistics network, adopts a binary coding mode to perform coding, and generates a plurality of initial coding units corresponding to the logistics data to be planned, including:
Obtaining logistics data to be planned, wherein the logistics data to be planned comprises the weight/volume of goods to be transported, a charging mode of the goods to be transported, an initial node of the goods to be transported and at least one destination node of the goods to be transported, and the charging mode of the goods to be transported comprises charging according to the weight or the volume;
outputting all nodes in the public-iron-hollow spoke type logistics network into a list form to obtain a node list;
According to the sequence of each node in the node list, a binary code is randomly allocated to each node to obtain an initial coding individual, and each element in the initial coding individual is 1 or 0; when the element is 1, the node corresponding to the element is selected; when the element is 0, the node corresponding to the element is not selected;
And repeatedly acquiring a plurality of initial coding individuals, wherein each initial coding individual comprises all destination nodes of goods to be transported.
In one possible implementation manner, according to the initial coding individual, iteration is performed by adopting an SAGA algorithm improved by population fitness score, an optimal coding individual meeting a set condition is obtained according to a comprehensive transportation factor, and the optimal coding individual is used as a node selection result, which comprises the following steps:
a1, constructing an optimized population according to the initial coding individuals;
a2, acquiring an adaptability value corresponding to each coding individual in the optimized population according to the comprehensive transportation factors, and determining the optimal coding individual according to the adaptability values of all the coding individuals;
A3, determining an initial annealing temperature T 0 according to the fitness value corresponding to the optimal coding individual, and enabling the current temperature T to be the initial temperature T 0;
A4, determining an inner loop iteration counter g=1 and a maximum iteration number G max;
A5, determining a variation probability value and a cross probability value according to the population fitness degree, executing a selection operation, and determining an iteration operation corresponding to each coding individual in the optimized population, wherein the iteration operation is a variation operation or a cross operation;
a6, determining a child population corresponding to the optimized population according to iterative operation, variation probability values and cross probability values corresponding to each coding individual in the optimized population, and updating the optimized population according to the child population to obtain an updated optimized population;
A7, judging whether the count value of the inner loop iteration counter G is greater than or equal to the maximum iteration number G max, if so, entering a step A8, otherwise, reacquiring the fitness value corresponding to each coding individual in the optimized population, adding one to the count value of the inner loop iteration counter G, and returning to the step A5;
A8, judging whether the current temperature T is smaller than a preset termination temperature, if so, taking the optimal coding individual as a node selection result, otherwise, carrying out annealing operation, and returning to the step A4.
In one possible implementation manner, obtaining the fitness value corresponding to each coding individual in the optimized population according to the comprehensive transportation factor, and determining the optimal coding individual according to the fitness values of all the coding individuals, including:
determining selected nodes corresponding to the coding individuals aiming at each coding individual in the optimized population to obtain nodes to be passed;
Determining a charging mode corresponding to an encoded individual according to a charging mode of goods to be transported, wherein the charging mode comprises charging according to weight or volume;
when the charging mode corresponding to the coding individual is volume charging, planning and traversing all first paths which are to be passed through the nodes and have the smallest sum of the first comprehensive transportation factors on the basis of the nodes to be passed through, and taking a negative value corresponding to the sum of all the first comprehensive transportation factors on the first paths as an adaptability value of the coding individual; the starting node of the first path is the starting node of the goods to be transported;
when the charging mode corresponding to the coding individual is time-consuming by weight, planning and traversing all second paths which are to be passed through the nodes and have the smallest sum of the second comprehensive transportation factors on the basis of the nodes to be passed through, and taking a negative value corresponding to the sum of all the second comprehensive transportation factors on the second paths as an adaptability value of the coding individual; the starting node of the second path is the starting node of the goods to be transported;
and determining the coding individual with the largest fitness value as the optimal coding individual.
In one possible implementation manner, according to the fitness value corresponding to the optimally encoded individual, the initial temperature T 0 of annealing is determined as follows:
T0=-fbest/ln0.2
Wherein f best represents the fitness value corresponding to the optimally encoded individual.
In one possible implementation, determining a variation probability value and a cross probability value according to a population fitness score, performing a selection operation, and determining an iteration operation corresponding to each coding individual in the optimized population, including:
Determining a variation probability value and a cross probability value according to the population fitness degree set score, wherein the variation probability value and the cross probability value are as follows: ；；
Wherein p c represents a cross probability value, p m represents a variation probability value, k 1 represents a first coefficient, k 2 represents a second coefficient, f a represents an average fitness value of the encoded individual, f best represents a fitness value of the optimally encoded individual, and pi represents a circumference ratio;
Determining a selection probability value between (0, 1) as p 1;
For each coding individual in the optimized population, randomly generating a random number r 1 between (0 and 1), judging whether the random number r 1 is larger than or equal to a selection probability value p 1, if so, determining that the iteration operation of the coding individual is a mutation operation, otherwise, determining that the iteration operation of the coding individual is a crossover operation; wherein the mutation operation is performed depending on the mutation probability value, and the crossover operation is performed depending on the crossover probability value.
In one possible implementation manner, determining a child population corresponding to the optimized population according to the iteration operation, the variation probability value and the cross probability value corresponding to each coding individual in the optimized population, and updating the optimized population according to the child population to obtain an updated optimized population, including:
When the iterative operation corresponding to the coded individuals in the optimized population is a mutation operation, aiming at each element in the coded individuals, the element is mutated by adopting a roulette selection mechanism according to the mutation probability value, so as to obtain mutated coded individuals;
When the iterative operation corresponding to the coded individuals in the optimized population is the cross operation, crossing the elements according to the cross probability value by adopting a roulette selection mechanism aiming at each element in the coded individuals to obtain crossed coded individuals;
The mutated coded individuals and the crossed coded individuals form a child population corresponding to the optimized population together, and each coded individual corresponds to one child generation individual in the child population;
Judging whether the fitness value of the child individual is larger than that of the original coding individual, if so, replacing the original coding individual by the child individual to obtain an updated optimized population, otherwise, updating the original coding individual by a probability selection method to obtain the updated optimized population.
In one possible implementation, updating the original coded individuals by using a probability selection method to obtain an updated optimized population includes:
The selection probability is determined as follows: ；
Wherein p 2 represents a selection probability, e represents a natural constant, k 3 represents a third coefficient, T represents a current temperature, f n represents an fitness value of a child individual, and f o represents an fitness value of an original coding individual;
And replacing the original coding individuals by child individuals according to the selection probability and the roulette selection mechanism to obtain an updated optimized population.
According to the public-iron-air spoke type logistics network optimization method provided by the invention, the public-iron-air spoke type logistics network is established, so that a user can quickly select the destination nodes, the connection relation among the nodes is determined, and on the premise of considering various transportation modes such as highway transportation, railway transportation and air transportation, the optimal path containing the destination nodes is determined on the basis of all the nodes and the connection relation, and the freight cost can be effectively saved.
Drawings
In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings that are needed in the examples will be briefly described below, it being understood that the following drawings only illustrate some examples of the present invention and therefore should not be considered as limiting the scope, and that other related drawings may be obtained from these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a public iron hollow shaft spoke type logistics network optimization method provided by the embodiment of the invention.
Description of the embodiments
For the purpose of making apparent the objects, technical solutions and advantages of the present invention, the present invention will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present invention and the descriptions thereof are for illustrating the present invention only and are not to be construed as limiting the present invention.
As shown in fig. 1, this embodiment provides a method for optimizing a public space spoke type logistics network, which includes:
s101, acquiring logistics route data, and establishing a public-iron-air shaft spoke type logistics network according to the logistics route data, wherein the public represents road transportation, the iron represents railway transportation, and the air represents air transportation.
The logistics route data can comprise logistics nodes, a transportation mode between any two logistics nodes and a transportation distance, so that a public-iron-hollow spoke logistics network can be established according to the logistics route data.
A straight-through line may exist between any two nodes, and may be connected through other nodes or may exist in the connection relationship between any two nodes, so that road transportation, railway transportation and air transportation are integrated, and the path is conveniently planned.
S102, determining the transportation cost and the transportation time between any two nodes based on the public-iron-hollow spoke type logistics network, and carrying out weighted summation on the transportation cost and the transportation time to obtain the comprehensive transportation factor between any two nodes.
In order to simplify the calculation model and the calculation amount, the embodiment can calculate different transportation modes to determine the transportation factors in the unit distance, so that the comprehensive transportation cost of unit cargoes when being transported from one node to another node can be calculated, and the optimal path can be determined based on the comprehensive transportation cost.
And S103, acquiring logistics data to be planned, and encoding by adopting a binary encoding mode based on all nodes in the public-iron-hollow spoke logistics network to generate a plurality of initial encoding individuals corresponding to the logistics data to be planned.
There may be multiple direct lines between any two nodes, or may be connected through other nodes, or there may be no direct line, so it is necessary to determine the optimal path of travel of the nodes. For example, there is a direct path between two nodes, but the aggregate transport factor of the route that may traverse another node is smaller, so that it is necessary to determine that the other node is also the node that needs to traverse.
In conventional path planning schemes, it is often not considered whether there is a connection line between two nodes, nor whether the line passing through other nodes is better.
S104, according to the initial coding individuals, adopting SAGA (Simulated annealing genetic Algorithm) Algorithm improved by population fitness degree, iterating, obtaining optimal coding individuals meeting set conditions according to comprehensive transportation factors, and taking the optimal coding individuals as node selection results
The crossover probability determines the diversity and the mutation probability determines the ability to jump out of local optimum. However, general genetic algorithms generally set both to be constant, and it is difficult to debug to an optimal value. Therefore, the embodiment adopts the SAGA algorithm improved by the population fitness score degree for iteration, so that more diversified individuals are generated in the crossing process, and excellent genetic inheritance is reserved at the same time, and the global searching capability is enhanced.
And continuously iterating through an algorithm to obtain an optimal coding individual, wherein the optimal coding individual is used for representing an optimal node set, so that an optimal path can be planned according to the optimal node set.
S105, based on the node selection result, obtaining a logistics network optimization result corresponding to the logistics data to be planned.
According to the public-iron-air spoke type logistics network optimization method provided by the invention, the public-iron-air spoke type logistics network is established, so that a user can quickly select the destination nodes, the connection relation among the nodes is determined, and on the premise of considering various transportation modes such as highway transportation, railway transportation and air transportation, the optimal path containing the destination nodes is determined on the basis of all the nodes and the connection relation, and the freight cost can be effectively saved.
Optionally, the public-iron-hollow spoke type logistics network belongs to a spoke type network with a plurality of hub nodes distributed randomly, a plurality of nodes need to be selected in the whole network to be expanded into hub nodes, each hub has a specific coverage area, the traffic in the coverage area realizes centralized transfer in the hub nodes, and the transportation requirement of the whole area can be met only by simultaneously having a plurality of hubs.
The public-iron-hollow spoke type logistics network is of a single distribution network structure, one non-hub transportation node can only be connected with one hub node at most, and one hub node can be connected with a plurality of non-hub nodes at the same time.
The public-iron-hollow spoke type logistics network is a local mixed-axis spoke type network, namely, when non-hub transportation nodes are connected with hub nodes, the non-hub nodes in the same hub coverage area can be allowed to be connected with each other, and the non-hub nodes in different hub coverage areas can be allowed to be directly connected, but the connection mode can only be air transportation.
In one possible embodiment, obtaining logistics route data and establishing a public-iron-hollow axis radial logistics network according to the logistics route data comprises the following steps:
Logistics route data is acquired, wherein the logistics route data comprises a plurality of logistics nodes and paths among the logistics nodes.
A plurality of coverage areas are determined, the coverage areas including a hub node and a plurality of non-hub nodes, the hub nodes having paths of connection with all non-hub nodes in the coverage areas, each non-hub node being connected to only one hub node. There is a path of connection between any two hub nodes.
The coverage area can be divided into sections, each section is used as a coverage area, and then the provincial city is necessarily a hub node.
Optionally, the method may further include further subdividing, when a connection relationship exists between a city corresponding to one logistics node and cities corresponding to other logistics nodes, if the number of connection relationships exceeds a preset threshold, the logistics node may be used as a hub node. After the hub node is determined, the other nodes are all non-hub nodes.
When there is a connection path between two non-hub nodes in the same coverage area, then direct transport is allowed. When there is no connection path between two non-hub nodes in the same coverage area, the hub nodes in the coverage area are used for transferring transportation. When there are connection paths for two non-hub nodes in two different coverage areas, then direct transport is allowed and only air transport is allowed. When there are no connection paths for two non-junction nodes in two different coverage areas, then the two non-junction nodes without connection paths are transported by junction nodes in two different coverage areas.
And forming the connection relation among the nodes in the coverage areas into a public-iron-hollow spoke type logistics network.
In one possible implementation manner, based on the public-iron-hollow spoke type logistics network, determining the transportation cost and the transportation time between any two nodes, and weighting and summing the transportation cost and the transportation time to obtain a comprehensive transportation factor between any two nodes, wherein the comprehensive transportation factor comprises the following steps:
and determining the transportation mode and the path length between any two nodes with connecting paths based on the public-iron-hollow spoke type logistics network. The transportation means include road transportation, rail transportation and/or air transportation.
According to the transportation mode between any two nodes with connection paths, a first unit time and a first unit cost required for transporting the unit volume of goods for unit distance under the transportation mode are determined. That is, when the transportation mode is road transportation, the first unit time and the first unit cost required for transporting the unit volume of the cargo by the unit distance in the road transportation mode are determined, and other transportation modes are similar and are not described herein.
And determining a first comprehensive transportation factor between any two nodes with connection paths according to the first unit time, the first unit cost and the path length between any two nodes with connection paths. Wherein, different transportation modes correspond to different first comprehensive transportation factors.
Alternatively, the first unit time multiplied by the path length may obtain the transit time between any two nodes having connection paths. The first unit cost multiplied by the path length can obtain the cost of transportation between any two nodes that have a connection path. And adding the first product of the transportation time multiplied by the first weight factor and the second product of the transportation cost multiplied by the second weight factor to obtain a first comprehensive transportation factor between any two nodes with connection paths.
According to the transportation mode between any two nodes with connecting paths, a second unit time and a second unit cost required for transporting the cargoes of unit weight by unit distance under the transportation mode are determined.
And determining a second comprehensive transportation factor between any two nodes with connection paths according to the second unit time, the second unit cost and the path length between any two nodes with connection paths. Wherein the different transportation modes correspond to different second comprehensive transportation factors.
Alternatively, the second unit time multiplied by the path length can obtain the transit time between any two nodes having connection paths. The second unit cost multiplied by the path length can obtain the cost of transportation between any two nodes that have a connection path. And adding the first product of the transportation time multiplied by the first weight factor and the second product of the transportation cost multiplied by the second weight factor to obtain a second comprehensive transportation factor between any two nodes with connection paths.
In addition to calculating the first and second integrated transport factors, other parameters may be employed as integrated transport factors. For example, the transit time can be used as an integrated transit factor, and the path with the shortest transit time can be used as a logistics network optimization result.
When the transport path passes through a plurality of nodes, the first integrated transport factor/second integrated transport factor between the nodes is added, and the integrated transport cost of the transport can be obtained.
In one possible implementation manner, the obtaining the logistics data to be planned, based on all nodes in the public-iron-hollow spoke logistics network, adopts a binary coding mode to perform coding, and generates a plurality of initial coding units corresponding to the logistics data to be planned, including:
the method comprises the steps of obtaining logistics data to be planned, wherein the logistics data to be planned comprises the weight/volume of goods to be transported, a charging mode of the goods to be transported, an initial node of the goods to be transported and at least one destination node of the goods to be transported, and the charging mode of the goods to be transported comprises charging according to the weight or the volume.
And outputting all the nodes in the public-iron-hollow spoke type logistics network into a list form to obtain a node list.
And according to the sequence of each node in the node list, randomly distributing a binary code for each node to obtain an initial coding individual, wherein each element in the initial coding individual is 1 or 0. When the element is 1, a node corresponding to the element is selected. When the element is 0, it indicates that the node corresponding to the element is not selected.
And repeatedly acquiring a plurality of initial coding individuals, wherein each initial coding individual comprises all destination nodes of goods to be transported.
Optionally, the initial node of the item to be transported and at least one destination node of the item to be transported in the initial encoding unit are both encoded with 1 to indicate that the item needs to pass through these nodes.
In one possible implementation manner, according to the initial coding individual, iteration is performed by adopting an SAGA algorithm improved by population fitness score, an optimal coding individual meeting a set condition is obtained according to a comprehensive transportation factor, and the optimal coding individual is used as a node selection result, which comprises the following steps:
A1, constructing an optimized population according to the initial coding individuals.
A2, acquiring the fitness value corresponding to each coding individual in the optimized population according to the comprehensive transportation factors, and determining the optimal coding individual according to the fitness value of all the coding individuals.
The essence of the comprehensive transportation factor is the comprehensive transportation cost of unit cargoes in unit distance, so that the comprehensive transportation cost can be determined when knowing the weight of cargoes, the volume of cargoes and the transportation distance between nodes, and the smaller the comprehensive transportation cost is, the better the adaptation value is, and therefore the negative value of the comprehensive transportation cost is taken as the adaptation value corresponding to the coding individual.
A3, determining an initial annealing temperature T 0 according to the fitness value corresponding to the optimal coding individual, and enabling the current temperature T to be the initial temperature T 0.
A4, determining an inner loop iteration counter g=1 and a maximum iteration number G max.
A5, determining a variation probability value and a cross probability value according to the population fitness degree, executing a selection operation, and determining an iteration operation corresponding to each coding individual in the optimized population, wherein the iteration operation is a variation operation or a cross operation.
A6, determining a child population corresponding to the optimized population according to the iteration operation, the variation probability value and the cross probability value corresponding to each coding individual in the optimized population, and updating the optimized population according to the child population to obtain the updated optimized population.
And A7, judging whether the count value of the inner loop iteration counter G is greater than or equal to the maximum iteration number G max, if so, entering a step A8, otherwise, reacquiring the fitness value corresponding to each coding individual in the optimized population, adding one to the count value of the inner loop iteration counter G, and returning to the step A5.
A8, judging whether the current temperature T is smaller than a preset termination temperature, if so, taking the optimal coding individual as a node selection result, otherwise, carrying out annealing operation, and returning to the step A4.
Optionally, after each iteration of the encoded individual, an out-of-range process is required, where the out-of-range process refers to: the corresponding elements of the initial node of the goods to be transported and at least one destination node of the goods to be transported in the coding individual are 1, and when the elements are not 1, the elements are corrected to be 1.
In one possible implementation manner, obtaining the fitness value corresponding to each coding individual in the optimized population according to the comprehensive transportation factor, and determining the optimal coding individual according to the fitness values of all the coding individuals, including:
and determining selected nodes corresponding to the coding individuals aiming at each coding individual in the optimized population to obtain nodes to be passed.
According to the charging mode of goods to be transported, determining the charging mode corresponding to the coding individual, wherein the charging mode comprises charging according to weight or charging according to volume.
When the charging mode corresponding to the coding individual is volume charging, planning and traversing all first paths which are to be passed through the nodes and have the smallest sum of the first comprehensive transportation factors on the basis of the nodes to be passed through, and taking a negative value corresponding to the sum of all the first comprehensive transportation factors on the first paths as the fitness value of the coding individual. The starting node of the first path is the starting node of the goods to be transported.
Optionally, based on the nodes to be traversed, planning to traverse all the first paths to be traversed with the minimum sum of the first comprehensive transportation factors may include: and carrying out path optimization by adopting a path optimization algorithm (such as an ant colony optimization algorithm) based on the first comprehensive transportation factors to be passed through the nodes and among all the nodes until the addition of all the first comprehensive transportation factors corresponding to the searched paths is minimum, thereby obtaining a first path.
When the charging mode corresponding to the coding individual is time-consuming by weight, planning and traversing all second paths which are to be passed through the nodes and have the smallest sum of the second comprehensive transportation factors on the basis of the nodes to be passed through, and taking a negative value corresponding to the sum of all the second comprehensive transportation factors on the second paths as the fitness value of the coding individual. The starting node of the second path is the starting node of the goods to be transported.
Optionally, planning to traverse all the second paths to be passed through the nodes based on the nodes to be passed through, where the sum of the second comprehensive transport factors is the smallest, may include: and carrying out path optimization by adopting a path optimization algorithm based on the nodes to be passed and the second comprehensive transportation factors among all the nodes until the addition of all the second comprehensive transportation factors corresponding to the searched paths is minimum, thereby obtaining the first path.
And determining the coding individual with the largest fitness value as the optimal coding individual.
In one possible implementation manner, according to the fitness value corresponding to the optimally encoded individual, the initial temperature T 0 of annealing is determined as follows:
T0=-fbest/ln0.2
Wherein f best represents the fitness value corresponding to the optimally encoded individual.
In one possible implementation, determining a variation probability value and a cross probability value according to a population fitness score, performing a selection operation, and determining an iteration operation corresponding to each coding individual in the optimized population, including:
Determining a variation probability value and a cross probability value according to the population fitness degree set score, wherein the variation probability value and the cross probability value are as follows:
Wherein p c represents a cross probability value, p m represents a variation probability value, k 1 represents a first coefficient, k 2 represents a second coefficient, f a represents an average fitness value of the encoded individual, f best represents a fitness value of the optimally encoded individual, and pi represents a circumference ratio.
The embodiment adoptsNonlinear transformation if/>< Pi/6, the current optimal individual fitness value is far from the average value, and smaller values indicate more scattered population characteristics. Similarly, ifAnd if the fitness value is more than or equal to pi/6, the optimal individual fitness value is close to the average value, and the larger the fitness value is, the more concentrated the population characteristics are. The method ensures that more diversified individuals are generated in the crossing process, maintains excellent genetic inheritance, and strengthens global searching capability. The adaptive variable probability is increased at the beginning and the end of the algorithm, the convergence speed of the algorithm is increased, and the algorithm is enabled to jump out of the local optimum capacity more strongly.
The selection probability value between (0, 1) is determined to be p 1.
For each coding individual in the optimized population, randomly generating a random number r 1 between (0 and 1), judging whether the random number r 1 is larger than or equal to a selection probability value p 1, if so, determining that the iteration operation of the coding individual is a mutation operation, otherwise, determining that the iteration operation of the coding individual is a crossover operation. Wherein the mutation operation is performed depending on the mutation probability value, and the crossover operation is performed depending on the crossover probability value. It should be noted that, the crossover operation and the mutation operation belong to basic operations of the genetic algorithm, and the description of this embodiment is omitted.
In one possible implementation manner, determining a child population corresponding to the optimized population according to the iteration operation, the variation probability value and the cross probability value corresponding to each coding individual in the optimized population, and updating the optimized population according to the child population to obtain an updated optimized population, including:
When the iterative operation corresponding to the coded individuals in the optimized population is a mutation operation, the elements are mutated according to the mutation probability value aiming at each element in the coded individuals, and a roulette selection mechanism is adopted to obtain mutated coded individuals. Mutation refers to the transformation of 1 to 0 or 1 to 0 in the encoded individual. In addition to the selection of whether to mutate by adopting the roulette selection mechanism, random numbers between (0, 1) can be randomly generated, when the random numbers are smaller than the mutation probability value, mutation operation is performed, otherwise, mutation operation is not performed, and the original coded individual is directly used as the mutated coded individual.
When the iterative operation corresponding to the coded individuals in the optimized population is the cross operation, according to the cross probability value, the elements are crossed by adopting a roulette selection mechanism for each element in the coded individuals, so that the crossed coded individuals are obtained. It should be noted that, the cross operation is similar to the mutation operation, and the description of this embodiment is omitted.
The mutated coded individuals and the crossed coded individuals form a child population corresponding to the optimized population together, and each coded individual corresponds to one child generation individual in the child population.
Judging whether the fitness value of the child individual is larger than that of the original coding individual, if so, replacing the original coding individual by the child individual to obtain an updated optimized population, otherwise, updating the original coding individual by a probability selection method to obtain the updated optimized population.
In one possible implementation, updating the original coded individuals by using a probability selection method to obtain an updated optimized population includes:
The selection probability is determined as follows:
Wherein p 2 represents the selection probability, e represents the natural constant, k 3 represents the third coefficient, T represents the current temperature, f n represents the fitness value of the child individual, and f o represents the fitness value of the original encoded individual.
And replacing the original coding individuals by child individuals according to the selection probability and the roulette selection mechanism to obtain an updated optimized population.
Optionally, based on the node selection result, obtaining a logistic network optimization result corresponding to logistic data to be planned, including: and determining the node to be passed based on the node selection result, and determining a first path or a second path by adopting a path optimizing algorithm. It should be noted that, when only one destination node exists, if a direct path exists between the start node and the destination node, the direct path is directly used as a result of optimizing the logistics network.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (2)
1. A public iron space spoke type logistics network optimization method is characterized by comprising the following steps:
obtaining logistics route data, and establishing a public-iron-air shaft spoke logistics network according to the logistics route data, wherein the public represents road transportation, the iron represents railway transportation, and the air represents air transportation;
based on the public-iron-hollow spoke type logistics network, determining the transportation cost and the transportation time between any two nodes, and carrying out weighted summation on the transportation cost and the transportation time to obtain a comprehensive transportation factor between any two nodes;
Obtaining logistics data to be planned, and coding by adopting a binary coding mode based on all nodes in the public-iron-hollow radial logistics network to generate a plurality of initial coding individuals corresponding to the logistics data to be planned;
According to the initial coding individual, iteration is carried out on the initial coding individual by adopting an SAGA algorithm which is improved in population fitness score degree, an optimal coding individual meeting set conditions is obtained according to comprehensive transportation factors, and the optimal coding individual is used as a node selection result;
based on the node selection result, obtaining a logistics network optimization result corresponding to logistics data to be planned;
Obtaining logistics route data, and establishing a public-iron-hollow spoke logistics network according to the logistics route data, wherein the method comprises the following steps of:
obtaining logistics route data, wherein the logistics route data comprises a plurality of logistics nodes and paths among the logistics nodes;
determining a plurality of coverage areas, wherein the coverage areas comprise a hub node and a plurality of non-hub nodes, paths for connecting the hub node with all the non-hub nodes in the coverage areas exist, and each non-hub node is connected to only one hub node; a connecting path exists between any two hub nodes;
When two non-hub nodes in the same coverage area have connection paths, direct transportation is allowed; when two non-hub nodes in the same coverage area do not have connection paths, transferring transportation through the hub nodes in the coverage area; when two non-hub nodes in two different coverage areas have connection paths, direct transportation is allowed, and only air transportation is allowed; when the two non-junction nodes in the two different coverage areas do not have connection paths, the two non-junction nodes without the connection paths are transported in a transferring way through the junction nodes in the two different coverage areas;
Forming a public-iron-hollow spoke type logistics network by connecting the nodes in the coverage areas with each other;
Based on the public-iron-hollow spoke type logistics network, determining the transportation cost and the transportation time between any two nodes, and carrying out weighted summation on the transportation cost and the transportation time to obtain a comprehensive transportation factor between any two nodes, wherein the method comprises the following steps:
Based on the public-iron-hollow spoke type logistics network, determining a transportation mode and a path length between any two nodes with connecting paths; the transportation mode comprises road transportation, railway transportation and/or air transportation;
Determining a first unit time and a first unit cost required for transporting a unit volume of goods by a unit distance in a transportation mode according to the transportation mode between any two nodes with connection paths;
Determining a first comprehensive transportation factor between any two nodes with connecting paths according to the first unit time, the first unit cost and the path length between any two nodes with connecting paths; wherein, different transportation modes correspond to different first comprehensive transportation factors;
Determining a second unit time and a second unit cost required for transporting the unit weight of goods by unit distance in the transportation mode according to the transportation mode between any two nodes with connection paths;
Determining a second comprehensive transportation factor between any two nodes with connecting paths according to the second unit time, the second unit cost and the path length between any two nodes with connecting paths; wherein, different transportation modes correspond to different second comprehensive transportation factors;
Obtaining logistics data to be planned, coding by adopting a binary coding mode based on all nodes in the public-iron-hollow radial logistics network, and generating a plurality of initial coding individuals corresponding to the logistics data to be planned, wherein the method comprises the following steps of:
Obtaining logistics data to be planned, wherein the logistics data to be planned comprises the weight/volume of goods to be transported, a charging mode of the goods to be transported, an initial node of the goods to be transported and at least one destination node of the goods to be transported, and the charging mode of the goods to be transported comprises charging according to the weight or the volume;
outputting all nodes in the public-iron-hollow spoke type logistics network into a list form to obtain a node list;
According to the sequence of each node in the node list, a binary code is randomly allocated to each node to obtain an initial coding individual, and each element in the initial coding individual is 1 or 0; when the element is 1, the node corresponding to the element is selected; when the element is 0, the node corresponding to the element is not selected;
Repeatedly obtaining a plurality of initial coding individuals, wherein each initial coding individual comprises all destination nodes of goods to be transported;
According to the initial coding individual, iteration is carried out on the initial coding individual by adopting an SAGA algorithm which is improved by the population fitness score, the optimal coding individual meeting the set condition is obtained according to the comprehensive transportation factor, and the optimal coding individual is used as a node selection result, and the method comprises the following steps:
a1, constructing an optimized population according to the initial coding individuals;
a2, acquiring an adaptability value corresponding to each coding individual in the optimized population according to the comprehensive transportation factors, and determining the optimal coding individual according to the adaptability values of all the coding individuals;
A3, determining an initial annealing temperature T 0 according to the fitness value corresponding to the optimal coding individual, and enabling the current temperature T to be the initial temperature T 0;
A4, determining an inner loop iteration counter g=1 and a maximum iteration number G max;
A5, determining a variation probability value and a cross probability value according to the population fitness degree, executing a selection operation, and determining an iteration operation corresponding to each coding individual in the optimized population, wherein the iteration operation is a variation operation or a cross operation;
a6, determining a child population corresponding to the optimized population according to iterative operation, variation probability values and cross probability values corresponding to each coding individual in the optimized population, and updating the optimized population according to the child population to obtain an updated optimized population;
A7, judging whether the count value of the inner loop iteration counter G is greater than or equal to the maximum iteration number G max, if so, entering a step A8, otherwise, reacquiring the fitness value corresponding to each coding individual in the optimized population, adding one to the count value of the inner loop iteration counter G, and returning to the step A5;
a8, judging whether the current temperature T is smaller than a preset termination temperature, if so, taking the optimal coding individual as a node selection result, otherwise, carrying out annealing operation, and returning to the step A4;
Acquiring the fitness value corresponding to each coding individual in the optimized population according to the comprehensive transportation factors, and determining the optimal coding individual according to the fitness value of all the coding individuals, wherein the method comprises the following steps:
determining selected nodes corresponding to the coding individuals aiming at each coding individual in the optimized population to obtain nodes to be passed;
Determining a charging mode corresponding to an encoded individual according to a charging mode of goods to be transported, wherein the charging mode comprises charging according to weight or volume;
when the charging mode corresponding to the coding individual is volume charging, planning and traversing all first paths which are to be passed through the nodes and have the smallest sum of the first comprehensive transportation factors on the basis of the nodes to be passed through, and taking a negative value corresponding to the sum of all the first comprehensive transportation factors on the first paths as an adaptability value of the coding individual; the starting node of the first path is the starting node of the goods to be transported;
when the charging mode corresponding to the coding individual is time-consuming by weight, planning and traversing all second paths which are to be passed through the nodes and have the smallest sum of the second comprehensive transportation factors on the basis of the nodes to be passed through, and taking a negative value corresponding to the sum of all the second comprehensive transportation factors on the second paths as an adaptability value of the coding individual; the starting node of the second path is the starting node of the goods to be transported;
Determining the coding individual with the largest fitness value as the optimal coding individual;
according to the fitness value corresponding to the optimal coding individual, determining the initial annealing temperature T 0 as follows:
T0=-fbest/ln0.2
wherein f best represents the fitness value corresponding to the optimal coding individual;
determining a variation probability value and a cross probability value according to the population fitness degree, executing a selection operation, and determining iterative operation corresponding to each coding individual in the optimized population, wherein the iterative operation comprises the following steps:
Determining a variation probability value and a cross probability value according to the population fitness degree set score, wherein the variation probability value and the cross probability value are as follows:
Wherein p c represents a cross probability value, p m represents a variation probability value, k 1 represents a first coefficient, k 2 represents a second coefficient, f a represents an average fitness value of the encoded individual, f best represents a fitness value of the optimally encoded individual, and pi represents a circumference ratio;
Determining a selection probability value between (0, 1) as p 1;
For each coding individual in the optimized population, randomly generating a random number r 1 between (0 and 1), judging whether the random number r 1 is larger than or equal to a selection probability value p 1, if so, determining that the iteration operation of the coding individual is a mutation operation, otherwise, determining that the iteration operation of the coding individual is a crossover operation; wherein the mutation operation is performed depending on the mutation probability value, and the crossover operation is performed depending on the crossover probability value;
Determining a child population corresponding to the optimized population according to iterative operation, variation probability value and cross probability value corresponding to each coding individual in the optimized population, and updating the optimized population according to the child population to obtain an updated optimized population, wherein the method comprises the following steps:
When the iterative operation corresponding to the coded individuals in the optimized population is a mutation operation, aiming at each element in the coded individuals, the element is mutated by adopting a roulette selection mechanism according to the mutation probability value, so as to obtain mutated coded individuals;
When the iterative operation corresponding to the coded individuals in the optimized population is the cross operation, crossing the elements according to the cross probability value by adopting a roulette selection mechanism aiming at each element in the coded individuals to obtain crossed coded individuals;
The mutated coded individuals and the crossed coded individuals form a child population corresponding to the optimized population together, and each coded individual corresponds to one child generation individual in the child population;
Judging whether the fitness value of the child individual is larger than that of the original coding individual, if so, replacing the original coding individual by the child individual to obtain an updated optimized population, otherwise, updating the original coding individual by a probability selection method to obtain the updated optimized population.
2. The method for optimizing a public space-iron spoke type logistics network according to claim 1, wherein updating original coded individuals by a probability selection method to obtain an updated optimized population comprises:
The selection probability is determined as follows:
Wherein p 2 represents a selection probability, e represents a natural constant, k 3 represents a third coefficient, T represents a current temperature, f n represents an fitness value of a child individual, and f o represents an fitness value of an original coding individual;
And replacing the original coding individuals by child individuals according to the selection probability and the roulette selection mechanism to obtain an updated optimized population.
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