CN107292460B - Effective vehicle path optimization system - Google Patents

Effective vehicle path optimization system Download PDF

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CN107292460B
CN107292460B CN201710687033.1A CN201710687033A CN107292460B CN 107292460 B CN107292460 B CN 107292460B CN 201710687033 A CN201710687033 A CN 201710687033A CN 107292460 B CN107292460 B CN 107292460B
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潘荣兰
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Chongqing bonish Technology Co., Ltd
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Abstract

An effective vehicle path optimization system comprises a data acquisition module, an algorithm execution module and a path output module, wherein the data acquisition module is used for acquiring relevant information required by genetic operation, the algorithm execution module adopts an improved genetic algorithm to optimize a vehicle path, and the path output module is used for outputting an optimal vehicle path obtained after the genetic algorithm is terminated. The invention has the beneficial effects that: the invention takes the shortest path distance and the punctual delivery time as optimization targets, constructs an effective vehicle path optimization system, adopts the self-adaptive genetic algorithm to select the most appropriate transport path, can effectively reduce the transport cost, saves the punishment cost of arriving early or late, and improves the economic benefit of logistics companies.

Description

Effective vehicle path optimization system
Technical Field
The invention relates to the field of vehicle path optimization, in particular to an effective vehicle path optimization system.
Background
With the continuous development of urban traffic, higher requirements are put on logistics distribution centers, and how to realize the optimization of distribution routes is in front of logistics enterprises. The logistics distribution route is optimized, the distribution time can be shortened, the goods can be sent to the destination more quickly, the operation efficiency of an enterprise is improved, and meanwhile, the satisfaction degree of customers can also be improved by sending the goods to the destination within the set time quickly.
The invention provides an effective vehicle path optimization system, which is constructed by taking shortest path distance and punctual delivery time as optimization targets, adopts a self-adaptive genetic algorithm to select the most appropriate transport path, can effectively reduce the transport cost, saves the punishment cost of arriving early or late, and improves the economic benefit of logistics companies.
Disclosure of Invention
In view of the above problems, the present invention aims to provide an effective vehicle path optimization system.
The purpose of the invention is realized by the following technical scheme:
an effective vehicle path optimization system comprises a data acquisition module, an algorithm execution module and a path output module, wherein the data acquisition module is used for acquiring relevant information required by vehicle path optimization, the algorithm execution module adopts an improved genetic algorithm to optimize a vehicle path, and the path output module is used for outputting an optimal vehicle path obtained after the genetic algorithm is terminated.
The beneficial effects created by the invention are as follows: the invention takes the shortest path distance and the punctual delivery time as optimization targets, constructs an effective vehicle path optimization system, adopts the self-adaptive genetic algorithm to select the most appropriate transport path, can effectively reduce the transport cost, saves the punishment cost of arriving early or late, and improves the economic benefit of logistics companies.
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The invention is further described with the aid of the accompanying drawings, in which, however, the embodiments do not constitute any limitation to the invention, and for a person skilled in the art, without inventive effort, further drawings may be derived from the following figures.
FIG. 1 is a schematic structural view of the present invention;
reference numerals:
a data acquisition module 1; an algorithm execution module 2; a path output module 3.
Detailed Description
The invention is further described with reference to the following examples.
Referring to fig. 1, the effective vehicle path optimization system of the embodiment includes a data obtaining module 1, an algorithm executing module 2 and a path output module 3, where the data obtaining module 1 is configured to obtain relevant information required for vehicle path optimization, the algorithm executing module 2 performs vehicle path optimization by using an improved genetic algorithm, and the path output module 3 is configured to output an optimal vehicle path obtained after the genetic algorithm is terminated.
Preferably, the related information acquired by the data acquiring module 1 includes: the number of customer points, the distribution center, and the location coordinates of the customer points.
The optimal embodiment takes the shortest path distance and the on-time delivery time as optimization targets, an effective vehicle path optimization system is constructed, the most appropriate transport path is selected by adopting an improved adaptive genetic algorithm, the transport cost can be effectively reduced, the punishment cost of arriving early or late is saved, and the economic benefit of a logistics company is improved.
Preferably, the algorithm executing module 2 adopts an improved genetic algorithm to optimize the vehicle path, and specifically includes:
step 1, initializing to generate a chromosome population by adopting a natural number coding mode based on full arrangement of customers, and ensuring the feasibility of the chromosome population;
step 2, calculating the fitness value of each individual in the initial population according to the fitness function, and storing the fitness value and the chromosome of the individual with the maximum fitness value;
step 3, performing genetic operation in a designated genetic algebra, and generating a group of individuals more suitable for the environment through random selection, intersection and mutation operations;
and 4, executing condition judgment for setting a termination condition, and stopping the operation of the algorithm when the genetic algorithm meets the termination condition.
Preferably, step 2 in the algorithm execution module 2 calculates the fitness value of each individual in the initial population according to the fitness function, and an improved fitness function is adopted, and k delivery points are defined, so that the fitness function is:
Figure GDA0002611768990000021
Figure GDA0002611768990000022
Figure GDA0002611768990000023
in the formula, d (r)m,rn) The distance between the m-th and n-th distribution points, dmaxMaximum delivery distance, r, of the delivery path0Denotes a logistics distribution center, rkRepresenting the last delivery point, d the total distance of the delivery path, and μ the weights of the delivery distance and time-effect cost, respectively, T the time-effect cost of the delivery, LiSpecified delivery time, t, for the ith delivery pointiIs the actual delivery time of the ith point, P is the allowable time error, c1Is a time penalty coefficient.
In the design of the fitness function, the optimal goals of the length of the vehicle path and the delivery arrival time are taken, so that the purpose of completing the delivery of the materials by using the shortest delivery distance and the most appropriate time is achieved.
Preferably, step 3 in the algorithm execution module 2 performs the following genetic operations within a formulated genetic algebra, specifically:
a. generating a new generation of individuals through a selection operation;
b. calculating the cross rate p of the remaining individuals by adopting an improved cross probability algorithmcAnd for these remaining individuals the calculated crossover rate p is calculatedc is intoPerforming line crossing operation;
Figure GDA0002611768990000031
in the formula (f)maxRepresents the maximum fitness value of the population, favgRepresents the population mean fitness value, f′Representing the individual fitness value, k, to be crossed1And k3Is a constant value, and 0<k1,k3<1，fminRepresenting a population minimum fitness value;
c. calculating the mutation rate p of the remaining individuals by using an improved mutation probability algorithmmAnd for these individuals with a probability pmCarrying out mutation operation;
Figure GDA0002611768990000032
in the formula (f)minRepresenting the minimum fitness value of the population, fmaxRepresents the maximum fitness value of the population, favgRepresenting the population mean fitness value, f' representing the fitness value of the individual to be mutated, k2And k4Is a constant value, and 0<k2,k4<1。
The preferred embodiment adopts a self-adaptive cross probability algorithm, adopts different cross probabilities at different stages of evolution, and improves the searching capability and the convergence capability of the algorithm to a great extent; the improved variation probability algorithm is adopted, the diversity degree of the population in the evolution process is taken as the basis, and the variation probability of the individuals in different evolution stages is adjusted in a self-adaptive mode, so that the diversity and the global convergence of the population are effectively maintained, and the convergence speed of the algorithm is effectively improved.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims (3)

1. An effective vehicle path optimization system is characterized by comprising a data acquisition module, an algorithm execution module and a path output module, wherein the data acquisition module is used for acquiring relevant information required by vehicle path optimization, and the algorithm execution module adopts an improved genetic algorithm to optimize a vehicle path, and specifically comprises the following steps:
step 1, initializing to generate a chromosome population by adopting a natural number coding mode based on full arrangement of customers, and ensuring the feasibility of the chromosome population;
step 2, calculating the fitness value of each individual in the initial population according to the fitness function, and storing the fitness value and the chromosome of the individual with the maximum fitness value;
step 3, performing genetic operation in a designated genetic algebra, and generating a group of individuals more suitable for the environment through random selection, intersection and mutation operations;
step 4, executing condition judgment for setting a termination condition, and stopping the operation of the algorithm when the genetic algorithm meets the termination condition; step 2 of the algorithm execution module calculates the fitness value of each individual in the initial population according to the fitness function, an improved fitness function is adopted, k distribution points are defined, and then the fitness function is as follows:
Figure FDA0002611768980000011
Figure FDA0002611768980000012
Figure FDA0002611768980000013
in the formula, d (r)m,rn) The distance between the m-th and n-th distribution points, dmaxMaximum delivery distance, r, for delivery of the path0Denotes a logistics distribution center, rkRepresenting the last delivery point, d the total distance of the delivery path, and μ the weights of the delivery distance and time-effect cost, respectively, T the time-effect cost of the delivery, LiSpecified delivery time, t, for the ith delivery pointiIs the actual delivery time of the ith point, P is the allowable time error, c1Is a time penalty coefficient; and the path output module is used for outputting the optimal vehicle path obtained after the genetic algorithm is terminated.
2. The system of claim 1, wherein the relevant information obtained by the data obtaining module comprises: the number of customer points, the distribution center, and the location coordinates of the customer points.
3. The system of claim 2, wherein the algorithm execution module performs the following genetic operations in step 3 within a predetermined genetic algebra, specifically:
a. generating a new generation of individuals through a selection operation;
b. calculating the cross rate p of the remaining individuals by adopting an improved cross probability algorithmcAnd for these remaining individuals the calculated crossover rate p is calculatedcPerforming cross operation;
Figure FDA0002611768980000021
in the formula (f)maxRepresents the maximum fitness value of the population, favgRepresenting the population mean fitness value, f' tableIndicating the individual fitness value, k, of the intersection1And k3Is a constant value, and 0<k1,k3<1,fminRepresenting a population minimum fitness value;
c. calculating the mutation rate p of the remaining individuals by using an improved mutation probability algorithmmAnd for these individuals with a probability pmCarrying out mutation operation;
Figure FDA0002611768980000022
in the formula (f)minRepresenting the minimum fitness value of the population, fmaxRepresents the maximum fitness value of the population, favgRepresenting the population mean fitness value, f' representing the fitness value of the individual to be mutated, k2And k4Is a constant value, and 0<k2,k4<1。
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