CN109543892A - A kind of routing resource based on genetic algorithm - Google Patents
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Abstract
The invention discloses a kind of routing resources based on genetic algorithm, comprising the following steps: S1: inputting the parameter of working truck；S2: path is in optimized selection using genetic algorithm；S3: being added the working time of each website in path after optimization, and obtains the smallest path of expense as optimal path.A kind of routing resource based on genetic algorithm of the present invention, by the way that above-mentioned steps are arranged, the path optimization to job task is realized, job task is controlled by the way that the working time is added, effectively working path can be allocated, reduce the waste of operation resource.
Description
Technical field
The present invention relates to field of computer technology, and in particular to a kind of routing resource based on genetic algorithm.
Background technique
In recent years, as Chinese society rapid development of economy, water conservancy industry and sewage treatment industry develop also increasingly
Fastly, in order to constantly upgrade the efficiency and reduce entreprise cost, unattended website is more and more in industry.To ensure each website
It is stable and work normally.By traditional artificial operation and maintenance means, multiple websites of each region cannot be united
Planning is raised, Rational Maintenance gets up, and how will improve maintenance efficiency, reduces maintenance cost.Principle and implementation according to genetic algorithm
The design pattern of intelligent planning, it is by way of addition planning that genetic algorithm and intelligence is dynamic on the basis of optimized Genetic Algorithm
State planning combines, and goes to solve the problems, such as actual transport.The Path selection of vehicle is the important component in operation and maintenance.
Although in the prior art, being had existed to vehicle route by the optimization of genetic algorithm, these paths are excellent
Change often carries out in such a way that vehicle passes by path, and operation process needs vehicle to be stopped in website and completes to make in practice
Industry task, it is not accurate enough in field of operation optimization that this allows for existing path optimization's technology.
Summary of the invention
The technical problem to be solved by the present invention is to operation process in practice, and vehicle to be needed to be stopped and completed in website
Job task, it is not accurate enough, and it is an object of the present invention to provide a kind of base in field of operation optimization that this allows for existing path optimization's technology
In the routing resource of genetic algorithm, solve the above problems.
The present invention is achieved through the following technical solutions:
A kind of routing resource based on genetic algorithm, comprising the following steps: S1: the parameter of working truck is inputted；S2:
Path is in optimized selection using genetic algorithm；S3: the working time of each website is added in path after optimization, and obtains
The smallest path of expense is as optimal path out.
In the prior art, operation process needs vehicle to be stopped and the task that fulfils assignment in website in practice, this just makes
It is not accurate enough in field of operation optimization to obtain existing path optimization's technology.When considering the job task deadline, often occur
The job task of one vehicle is also carrying out, and when subsequent website does not all complete operation also, the operation of another vehicle whole is all
Through completing, this has resulted in the waste of operation resource.
The present invention is in application, first input the parameter of working truck；Path is in optimized selection using genetic algorithm again；Again
Then the working time of each website is added in path after optimization, and obtains the smallest path of expense as optimal path.
Genetic algorithm described here can use the genetic algorithm of existing belt restraining, and after carrying out path optimization, on selection road
Before diameter, the working time of each website is added in the paths, so that it may the actual time overhead of each website is obtained, with this
Actual time overhead can effectively reduce the operation wasting of resources as the judgment criteria in evaluation path.The present invention passes through setting
Above-mentioned steps realize the path optimization to job task, are controlled by the way that the working time is added job task, Ke Yiyou
Being allocated to working path for effect, reduces the waste of operation resource.
Further, the parameter of the working truck include the distance between each website and each website and starting central station it
Between distance, the type of job task, working truck quantity and working truck state.
Further, step S3 includes following sub-step: S31: selecting N paths in the path of optimization, is added every
The working time of a website is simultaneously iterated, and obtains the time overhead value of each path；S32: S31 is repeated to all paths
After being all selected, using the optimal path of time overhead value as global minima cost path, and by global minima cost path
As optimal path.
The present invention first selects N paths in the path of optimization, adds in application, in order to which all paths are compared
Enter the working time of each website and be iterated, obtain the time overhead value of each path, here firstly the need of passing through intelligence
Operating system proposes the type of job task and completes the time that each type required by task is wanted, and then again adds these data
Enter into path and be iterated, such as can first initialize 100 paths, and carry out 1000 iterative calculation, calculates 100
The overhead value of each path and an optimal paths are write down in path, by it if it is less than global optimum's path cost value
Global minima cost path is positioned, and as optimal path, when can will joined site works in this way
Between after path time expense handled and obtain optimal path.
Further, step S31 includes following sub-step: S311: M paths, the M < N are chosen in N paths；
S312: selecting the smallest path of time overhead in M paths, and after carrying out stochastic transformation to the smallest path of expense, chooses and become
The smallest path of time overhead value is changed in result as the optimal path in this M paths；S313: S311 and S312 are repeated
Each path into N paths is all selected.
The present invention is in application, when initialization N paths, this N necessarily relatively large value, such as 100, this just makes
When being handled, this paths itself can only often be handled for a paths, and can not be to path change when data
It is handled, the present invention selects M paths in this N paths, and M can be a lesser value, such as 4, at this moment calculates and opens
Pin value, and stochastic transformation is carried out, random rotation mentioned here refers to that the sequence of the inspection to the paths website is overturn, handed over
It changes or slides, a variety of situations of trends different in path can thus be extracted and be judged, to greatly improve most
The accuracy of shortest path selection.
Further, the N paths of selecting in the path of optimization are using following steps: path R, R is randomly generated
By all websites, website sum is g；Vehicle arrangement is carried out to the path of generation.
Further, the vehicle arrangement is the following steps are included: distribute to X adjacent in total path for the L vehicleLA station
Point, and XL≤ g, uses FLIndicate the L vehicle, first website to be gone, and XL+FL≤ g；The L vehicle is obtained according to the following formula
Total kilometrage:
S in formulaLFor the total kilometrage of the L vehicle；D is distance
Element in matrix；For in the R of pathIt arrivesDistance；For in the R of path
Initial point arrivesDistance；For in the R of pathTo the distance of initial point；
It is controlled using net cycle time of the following formula to the L vehicle:
TL+SL/ C <=WT；S in formulaLFor the total kilometrage of the L vehicle；C is the average speed of vehicle；WT is the preset time
Control amount；TLThe L vehicle is in the sum of all site works times.
The present invention in application, in order to entire path carry out time control, the time here include vehicle on the way when
Between and vehicle website carry out operation time, wherein the activity duration of website is known, this is just needed to vehicle on the way
Time calculated；According to formula TL+SLThe control to total time may be implemented in/C <=WT, wherein TLBe it is known, also
Be in a path the sum of working time, and obtain the time of road need to calculate vehicle operation mileage, by first by vehicle
It is assigned on the website in path, then follows the path of vehicle to run, that is, SLTotal kilometrage calculating is carried out, is established in calculating
Distance matrix, and using the element in distance matrix as seeking SLData, S is obtained by above formulaLAfterwards, T can be passed throughL+SL/ C <
=WT controls total time, can effectively incorporate operating time data and calculate by above-mentioned calculating process, thus
The path time expense after the site works time will be joined by, which realizing, is handled and obtains optimal path.
Further, the objective function of optimal route selection are as follows:
Need to meet Q minimum in formula；XHFor the website number for distributing to the H vehicle.
The present invention in application, necessarily multiple paths, which are combined, can just obtain final result in final Path selection,
When namely by vehicle allocation, the vehicle fleet size of distribution is fewer, more meets the requirement of time cost, so using above formula as mesh
Scalar functions are calculated, and the selection of optimal path may be implemented.
Compared with prior art, the present invention having the following advantages and benefits:
A kind of routing resource based on genetic algorithm of the present invention realizes and appoints to operation by the way that above-mentioned steps are arranged
The path optimization of business controls job task by the way that the working time is added, can effectively be allocated to working path,
Reduce the waste of operation resource.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention clearer, below with reference to embodiment, the present invention is made
Further to be described in detail, exemplary embodiment of the invention and its explanation for explaining only the invention, are not intended as to this
The restriction of invention.
Embodiment 1
A kind of routing resource based on genetic algorithm of the present invention, comprising the following steps: S1: input the ginseng of working truck
Number；S2: path is in optimized selection using genetic algorithm；S3: when the work of each website being added in path after optimization
Between, and obtain the smallest path of expense as optimal path.
When the present embodiment is implemented, the parameter of working truck is first inputted；Path is in optimized selection using genetic algorithm again；
The working time of each website subsequently is added in path after optimization, and obtains the smallest path of expense as optimal road
Diameter.Genetic algorithm described here can use the genetic algorithm of existing belt restraining, and after carrying out path optimization, it is selecting
Before path, the working time of each website is added in the paths, so that it may the actual time overhead of each website is obtained, with this
A actual time overhead can effectively reduce the operation wasting of resources as the judgment criteria in evaluation path.The present invention is by setting
Above-mentioned steps are set, the path optimization to job task is realized, job task is controlled by the way that the working time is added, it can be with
Effectively working path is allocated, reduces the waste of operation resource.
Embodiment 2
On the basis of embodiment 1, step S3 includes following sub-step: S31 to the present embodiment: being selected in the path of optimization
N paths out are added the working time of each website and are iterated, and obtain the time overhead value of each path；S32: it repeats
After execution S31 is selected to all paths, using the optimal path of time overhead value as global minima cost path, and will
Global minima cost path is as optimal path.
When the present embodiment is implemented, in order to which all paths are compared, N paths are first selected in the path of optimization,
The working time of each website is added and is iterated, obtains the time overhead value of each path, here firstly the need of passing through intelligence
Energy operating system proposes the type of job task and completes the time that each type required by task is wanted, then again by these data
It is added in path and is iterated, such as can first initialize 100 paths, and carry out 1000 iterative calculation, calculate 100
The overhead value of each path and an optimal paths are write down in paths, it will if it is less than global optimum's path cost value
It positions global minima cost path, and as optimal path, can will joined site works in this way
Path time expense after time is handled and obtains optimal path.
Embodiment 3
For the present embodiment on the basis of embodiment 2, step S31 includes following sub-step: S311: choosing M in N paths
Paths, the M < N；S312: selecting the smallest path of time overhead in M paths, and to the smallest path of expense carry out with
After machine transformation, the smallest path of time overhead value is as the optimal path in this M paths in selection transformation results；S313: weight
Each path of the S311 and S312 into N paths is executed again to be all selected.
When the present embodiment is implemented, when initializing N paths, this N necessarily relatively large value, such as 100, this is just
So that when being handled, this paths itself can only often be handled for a paths, and can not be to path change when number
According to being handled, the present invention selects M paths in this N paths, and M can be a lesser value, such as 4, at this moment calculates
Overhead value, and carry out stochastic transformation, random rotation mentioned here refer to the sequence of the inspection to the paths website overturn,
A variety of situations of trends different in path can thus be extracted and be judged, to greatly improve by exchange or sliding
The accuracy of optimal route selection.
Embodiment 4
The present embodiment on the basis of embodiment 2, the vehicle arrangement is the following steps are included: is distributed to always by the L vehicle
Adjacent X in pathLA website, and XL≤ g, uses FLIndicate the L vehicle, first website to be gone, and XL+FL≤ g；According to
Following formula obtains the total kilometrage of the L vehicle:
S in formulaLFor the total kilometrage of the L vehicle；D be away from
From the element in matrix；For in the R of pathIt arrivesDistance；For road
Initial point arrives in diameter RDistance；For in the R of pathTo the distance of initial point；
It is controlled using net cycle time of the following formula to the L vehicle:
TL+SL/ C <=WT；S in formulaLFor the total kilometrage of the L vehicle；C is the average speed of vehicle；WT is the preset time
Control amount；TLThe L vehicle is in the sum of all site works times.
The present embodiment implement when, in order to entire path carry out time control, the time here include vehicle on the way
Time and vehicle carry out the time of operation in website, and wherein the activity duration of website is known, this is just needed to vehicle on road
On time calculated；According to formula TL+SLThe control to total time may be implemented in/C <=WT, wherein TLBe it is known,
Be exactly in a path the sum of working time, and obtain the time of road need to calculate vehicle operation mileage, pass through first will
In vehicle allocation to the website in path, the path of vehicle to run, that is, S then followLTotal kilometrage calculating is carried out, is built in calculating
Vertical distance matrix, and using the element in distance matrix as seeking SLData, S is obtained by above formulaLAfterwards, T can be passed throughL+SL/C
<=WT controls total time, can effectively incorporate operating time data and calculate by above-mentioned calculating process, from
And the path time expense after the site works time will be joined and is handled and obtain optimal path by realizing.
Embodiment 5
The present embodiment is on the basis of embodiment 4, the objective function of optimal route selection are as follows:
Need to meet Q minimum in formula；XHFor the website number for distributing to the H vehicle.
When the present embodiment is implemented, in final Path selection, necessarily multiple paths, which are combined, just can most be terminated
Fruit, that is, when by vehicle allocation, the vehicle fleet size of distribution is fewer, more meets the requirement of time cost, so making using above formula
It is calculated for objective function, the selection of optimal path may be implemented.
Above-described specific embodiment has carried out further the purpose of the present invention, technical scheme and beneficial effects
It is described in detail, it should be understood that being not intended to limit the present invention the foregoing is merely a specific embodiment of the invention
Protection scope, all within the spirits and principles of the present invention, any modification, equivalent substitution, improvement and etc. done should all include
Within protection scope of the present invention.
Claims (7)
1. a kind of routing resource based on genetic algorithm, which comprises the following steps:
S1: the parameter of working truck is inputted；
S2: path is in optimized selection using genetic algorithm；
S3: being added the working time of each website in path after optimization, and obtains the smallest path of expense as optimal road
Diameter.
2. a kind of routing resource based on genetic algorithm according to claim 1, which is characterized in that the Operation Van
Parameter include the distance between the distance between each website and each website and starting central station, job task type, make
Industry vehicle fleet size and working truck state.
3. a kind of routing resource based on genetic algorithm according to claim 1, which is characterized in that step S3 includes
Following sub-step:
S31: selecting N paths in the path of optimization, and the working time of each website is added and is iterated, obtains every
The time overhead value in path；
S32: it repeats after S31 is selected to all paths, using the optimal path of time overhead value as global minima
Cost path, and using global minima cost path as optimal path.
4. a kind of routing resource based on genetic algorithm according to claim 3, which is characterized in that step S31 packet
Include following sub-step:
S311: M paths, the M < N are chosen in N paths；
S312: selecting the smallest path of time overhead in M paths, and after carrying out stochastic transformation to the smallest path of expense, choosing
Take in transformation results the smallest path of time overhead value as the optimal path in this M paths；
S313: it repeats each path of S311 and S312 into N paths and is all selected.
5. a kind of routing resource based on genetic algorithm according to claim 3, which is characterized in that described to optimize
N paths are selected in path out using following steps:
Path R is randomly generated, R passes through all websites, and website sum is g；
Vehicle arrangement is carried out to the path of generation.
6. a kind of routing resource based on genetic algorithm according to claim 5, which is characterized in that the vehicle peace
Row the following steps are included:
The L vehicle is distributed into X adjacent in total pathLA website, and XL≤ g, uses FLIndicate first that the L vehicle to be gone
Website, and XL+FL≤ g；
The total kilometrage of the L vehicle is obtained according to the following formula:
S in formulaLFor the total kilometrage of the L vehicle；D is distance
Element in matrix；For in the R of pathIt arrivesDistance；For in the R of path
Initial point arrivesDistance；For in the R of pathTo the distance of initial point；
It is controlled using net cycle time of the following formula to the L vehicle:
TL+SL/ C <=WT；S in formulaLFor the total kilometrage of the L vehicle；C is the average speed of vehicle；WT is the control of preset time
Amount；TLThe L vehicle is in the sum of all site works times.
7. a kind of routing resource based on genetic algorithm according to claim 6, which is characterized in that optimal route choosing
The objective function selected are as follows:
Need to meet Q minimum in formula；XHFor the website number for distributing to the H vehicle.
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