CN106845665A - For the method and system optimized to distribution route using genetic algorithm - Google Patents
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Abstract
The invention discloses a kind of method for being optimized to distribution route using genetic algorithm, including：First via line set is generated according to the geographical position corresponding with website, first route set includes N bar routes；Calculate the evaluation index corresponding with each bar route in the first route set；Determine whether to meet end condition, when the end condition is unsatisfactory for, the m bar routes of the evaluation index top ranked using genetic algorithm to being selected from the first route set are processed to produce N m bar routes, and with the m bar route combinations of the evaluation index top ranked it is new route set, wherein m ＜ N by the N m bars route；And by the new Route Set cooperation be the first route set repeat the calculation procedure and the combination step, until meeting end condition untill, and route corresponding with highest evaluation index when will meet end condition be defined as optimize distribution route.
Description
Technical field
The disclosure relates generally to route optimization, more particularly, to for being carried out to distribution route using genetic algorithm
The method and system of optimization.
Background technology
Route optimization is a key link in logistics distribution, arranges distribution route effectively to drop with holding water
Low cost of transportation, saves haulage time, and improve level of customer service.Therefore for the optimization problem of logistics distribution route,
Through there is very extensive research, but research method is confined to intelligent optimization algorithm or improved intelligent optimization algorithm.When adopting
, it is necessary to initially set up the mathematic(al) representation of optimization problem, i.e. object function when being solved with intelligent optimization algorithm, and set about
Beam condition, is solved by programming.Although the object function has had the expression formula of standard, it is in reality
System is a dynamical system for complexity, and mathematic(al) representation difficult to use enters description, that is, allow to use mathematic(al) representation to system
It is described, also contains some Utopian thoughts, such as experience, average value, it is difficult in accurately accounts for actual conditions
Randomness and complexity.Accordingly, it would be desirable to a kind of new method for optimizing distribution route.
The content of the invention
In consideration of it, in one aspect of the invention, it is proposed that one kind is excellent for being carried out to distribution route using genetic algorithm
The method of change, including：
First via line set is generated according to the geographical position corresponding with website, first route set includes N bars
Route；
Calculate the evaluation index corresponding with each bar route in the first route set；
Determine whether to meet end condition, when the end condition is unsatisfactory for, using genetic algorithm to from described first
The m bar routes of the evaluation index top ranked selected in route set are processed to produce N-m bar routes, and by the N-
M bars route is new route set, wherein m ＜ N with the m bar route combinations of the evaluation index top ranked；And
It is that the first route set repeats the calculation procedure and the combination step by the new Route Set cooperation, until
Untill meeting end condition, and route corresponding with highest evaluation index when will meet end condition is defined as matching somebody with somebody for optimization
Send route.
Preferably, the first route set is randomly generated.
Preferably, the evaluation index is to be dispensed the spent time according to respective routes.
Preferably, the end condition is to reach the iterations for pre-setting.
Preferably, each route in produced N-m bar routes is to from described first by using genetic algorithm
The m bar routes of the evaluation index top ranked selected in route set are intersected or are made a variation acquisition.
Preferably, as the m of the evaluation index top ranked using genetic algorithm to being selected from the first route set
When bar route is intersected, methods described includes：It is any from the m bars route to select two lines and to the two lines
Intersected to produce a variation route, and
When the m bar routes of the evaluation index top ranked using genetic algorithm to being selected from the first route set enter
During row variation, methods described includes：A route is arbitrarily selected from the m bars route and row variation is entered to a route
To produce a variation route.
Preferably, the route in the new route set is different.
In another aspect of the present invention, it is proposed that a kind of to be for what is optimized to distribution route using genetic algorithm
System, including：
Processor, be configured as execute instruction with：
First via line set is generated according to the geographical position corresponding with website, first route set includes N bars
Route；
Calculate the evaluation index corresponding with each bar route in the first route set；
Determine whether to meet end condition, when the end condition is unsatisfactory for, using genetic algorithm to from described first
The m bar routes of the evaluation index top ranked selected in route set are processed to produce N-m bar routes, and by the N-
M bars route is new route set, wherein m ＜ N with the m bar route combinations of the evaluation index top ranked；And
It is that the first route set repeats the calculation procedure and the combination step by the new Route Set cooperation, until
Untill meeting end condition, and route corresponding with highest evaluation index when will meet end condition is defined as matching somebody with somebody for optimization
Send route；And
Memory, is configured as storing the instruction.
Preferably, the first route set is randomly generated.
Preferably, the evaluation index is to be dispensed the spent time according to respective routes.
Preferably, the end condition is to reach the iterations for pre-setting.
Preferably, each route in produced N-m bar routes is to from described first by using genetic algorithm
The m bar routes of the evaluation index top ranked selected in route set are intersected or are made a variation acquisition.
Preferably, the processor be further configured to execute instruction with：
When the m bar routes of the evaluation index top ranked using genetic algorithm to being selected from the first route set enter
It is any from the m bars route to select two lines and the two lines are intersected to produce one newly when row intersects
Route, and
When the m bar routes of the evaluation index top ranked using genetic algorithm to being selected from the first route set enter
During row variation, a route is arbitrarily selected from the m bars route and enters row variation to a route to produce one newly
Route.
Preferably, the route in the new route set is different.
In another aspect of the present invention, it is proposed that a kind of computer-readable medium, held when being included in by computing device
Row includes the instruction of the operation of the following：
First via line set is generated according to the geographical position corresponding with website, first route set includes N bars
Route；
Calculate the evaluation index corresponding with each bar route in the first route set；
Determine whether to meet end condition, when the end condition is unsatisfactory for, using genetic algorithm to from described first
The m bar routes of the evaluation index top ranked selected in route set are processed to produce N-m bar routes, and by the N-
M bars route is new route set, wherein m ＜ N with the m bar route combinations of the evaluation index top ranked；And
It is that the first route set repeats the calculation procedure and the combination step by the new Route Set cooperation, until
Untill meeting end condition, and route corresponding with highest evaluation index when will meet end condition is defined as matching somebody with somebody for optimization
Send route.
Preferably, the first route set is randomly generated.
Preferably, the evaluation index is to be dispensed the spent time according to respective routes.
Preferably, the end condition is to reach the iterations for pre-setting.
Preferably, each route in produced N-m bar routes is to from described first by using genetic algorithm
The m bar routes of the evaluation index top ranked selected in route set are intersected or are made a variation acquisition.
Preferably, the processor be further configured to execute instruction with：
When the m bar routes of the evaluation index top ranked using genetic algorithm to being selected from the first route set enter
It is any from the m bars route to select two lines and the two lines are intersected to produce one newly when row intersects
Route, and
When the m bar routes of the evaluation index top ranked using genetic algorithm to being selected from the first route set enter
During row variation, a route is arbitrarily selected from the m bars route and enters row variation to a route to produce one newly
Route.
Preferably, the route in the new route set is different.
Be combined together for genetic algorithm in system simulation technology and intelligent optimization algorithm by the present invention, solves logistics and matches somebody with somebody
Route optimization problem in sending.System simulation technology can effectively in simulating reality dynamical system, for time plus space
Plus the complication system of stochastic variable is a kind of good solution.Genetic algorithm is the nature for simulating Darwinian evolutionism
The computation model of the biological evolution process of selection and genetic mechanisms is one kind by simulating natural evolution process searches optimal solution
Method.System simulation technology and intelligent optimization algorithm are combined, using simulation model is instead of mathematic(al) representation and makes
Solved with optimized algorithm, so as to avoid limitation of the static thinking for Algorithm for Solving.
Brief description of the drawings
According to combining following description of the exemplary drawings to exemplary embodiment, the other details of the disclosure, aspect and excellent
Point will become obvious, in the accompanying drawings：
Fig. 1 schematically shows excellent for being carried out to distribution route using genetic algorithm according to an embodiment of the invention
The flow chart of the method for change；
Fig. 2 schematically show distribution route is carried out by Softwares of System Simulation according to an embodiment of the invention it is excellent
The flow chart of the method for change；
Fig. 3 schematically shows the schematic diagram that distribution route according to an embodiment of the invention optimizes system；
Fig. 4 schematically shows the schematic diagram for dispensing model according to an embodiment of the invention；And
Fig. 5 schematically shows excellent for being carried out to distribution route using genetic algorithm according to an embodiment of the invention
The block diagram of the system of change.
Accompanying drawing does not show to all circuits or structure of embodiment.Through all accompanying drawing identical reference tables
Show same or analogous part or feature.
Specific embodiment
Specific embodiment of the invention is described more fully below, it should be noted that the embodiments described herein is served only for citing
Illustrate, be not intended to limit the invention.In the following description, in order to provide thorough understanding of the present invention, a large amount of spies are elaborated
Determine details.It will be apparent, however, to one skilled in the art that：This hair need not be carried out using these specific details
It is bright.In other instances, in order to avoid obscuring the present invention, known circuit, material or method are not specifically described.
Throughout the specification, meaning is referred to " one embodiment ", " embodiment ", " example " or " example "
：Special characteristic, structure or the characteristic described with reference to the embodiment or example are comprised at least one embodiment of the invention.
Therefore, phrase " in one embodiment ", " in embodiment ", " example " for occurring in each place of entire disclosure
Or " example " is not necessarily all referring to same embodiment or example.Furthermore, it is possible to will be specific with any appropriate combination and/or sub-portfolio
Feature, structure or property combination in one or more embodiments or example.Additionally, those of ordinary skill in the art should manage
Solution, accompanying drawing is provided to descriptive purpose provided herein, and accompanying drawing is not necessarily drawn to scale.Art used herein
Language "and/or" includes any and all combination of the project that one or more correlations are listed.
Fig. 1 shows the method for being used to optimize distribution route using genetic algorithm according to an embodiment of the invention
100 flow chart.
In step S101, first via line set, the first route set are generated according to the geographical position corresponding with website
Including N bar routes.First route set can be randomly generated.
In step S102, the evaluation index corresponding with each bar route in the first route set is calculated.Evaluation index can
Being to be dispensed the spent time according to respective routes.
In step S103, it is determined whether meet end condition, when end condition is unsatisfactory for, using genetic algorithm to from
The m bar routes of the evaluation index top ranked selected in one route set are processed to produce N-m bar routes, and by N-m
Bar route is new route set, wherein m ＜ N with the m bar route combinations of the evaluation index top ranked.End condition can be with
It is to reach the iterations for pre-setting.Each route in produced N-m bar routes is by using genetic algorithm pair
The m bar routes of the evaluation index top ranked selected from the first route set are intersected or are made a variation what is obtained.
When the m bar routes of the evaluation index top ranked using genetic algorithm to being selected from the first route set are handed over
During fork, method 100 includes：It is any from the m bars route to select two lines and the two lines are intersected to produce
A raw variation route, and work as using genetic algorithm to the evaluation index top ranked of selection from the first route set
When m bar routes enter row variation, method 100 includes：A route is arbitrarily selected from the m bars route and to a route
Enter row variation to produce a variation route.
It should be noted that being processed the m bar routes of evaluation index top ranked with generation when using genetic algorithm
During N-m bar routes, the route of repetition is there may be in produced N-m bar routes.For the road for producing N-m bars different
Line, the route selected in the m bar routes to evaluation index top ranked is intersected or is made a variation to produce a route
When, the new route for producing and the route for being produced using genetic algorithm are compared, if the comparison show that profit
With the route identical route existed in the route of genetic algorithm generation with new generation, then the route of the new generation is abandoned, and
And route is regenerated using genetic algorithm, untill in the absence of overlapping route.
It is that the first route set repeats the calculation procedure and combination step by new Route Set cooperation in step S104
Suddenly, untill until meeting end condition, and route corresponding with highest evaluation index when will meet end condition is defined as
The distribution route of optimization.
Describe to be used to utilize according to an embodiment of the invention with reference to specific Softwares of System Simulation below with reference to Fig. 2
The method that genetic algorithm is optimized to distribution route.
In step S201, using Logistics Simulation System, such as flexsim, Automod etc. set up distribution route optimization
System, as shown in figure 3, including simulation model library, write genetic algorithm required for tree node, model cootrol interface figure use
Family interface (GUI).
When simulation model library is built, required graphics is drawn first, for example, divide province to draw map of China, then lead
Enter in simulation software；Entity needed for then setting up simulation model, such as produce entity, the reality of dispatching website of distribution vehicle
Body, and these entities are preserved into part library.
Genetic algorithm node is added in part library, as shown in figure 3, the node of file clip-type is used to control hereditary calculation in figure
The relevant parameter of method, the node of S types is used to write genetic algorithm.
Model cootrol interface GUI is the One function of simulation software, is carried out in need to only pulling existing control into gui interface
Layout, is then linked with model library, genetic algorithm parameter node.
In step S202, website is set.The quantity of dispatching website is input into layout options' card of gui interface, for example,
10, output dispatching site table, and every a line in table be input into one dispense website where city name.
In step S203, the distance between website is set.In layout options' card of gui interface, click on and set apart from table
Lattice, ejection form in be input into website two-by-two between distance.
In step S204, the specific dispatching circuit model of generation.The parameter set by step S202 and S203, using imitative
Entity in true mode storehouse, generates specific simulation model, as shown in figure 4, the five-pointed star of a red is exactly one and matches somebody with somebody in figure
See off a little, have a circuit between any two dispatching website, 2 points of line is connected in such as Fig. 4, vehicle can be from certain point
Set out, according to the circuit for assuming by all dispatching websites once after, output dispatching spent time is false as this is evaluated
Surely the index of circuit quality is dispensed.
In step S205, the relevant parameter of genetic algorithm is set.Ginseng is input into the genetic algorithm tab of gui interface
Number, including route set sizes, iterations, selection, intersection, variation.Here route set is exactly that distribution route optimization is asked
One set of feasible solution of topic, the number solved in disaggregation is that the number of dispatching circuit is exactly route set sizes, a solution or
Bar distribution route is referred to as an individual.The condition that iterations terminates as genetic algorithm.Selection, intersection, variation are hereditary calculations
Basic operator in method, for producing new route set in an iterative process.Selection be choose from current route set it is excellent
Victory is individual and remains into the operation of variation route set, intersects and variation is to be recombinated the individuality in current route set, from
And the operation of new individual is obtained, the new individual that the winning individual and restructuring chosen is obtained just constitutes a new route set.
In step S206, route set is initialized.Use the Initialize_ in genetic algorithm tree node
Population child nodes, randomly generate route set.The process is as follows：Website to being input into is numbered.For example, such as preceding institute
State, 10 websites have input in step S202, then website is numbered with the integer between 1 to 10, by site number with
Machine is upset and obtains a Serial No., and this sequence just represents a dispatching circuit, it is also possible to a referred to as individual, randomly generates
Route set sizes are individual, just constitute original route set.
In step S207, the dispatching circuit model generated in step S204 is operated in, draw evaluation index.Using it is individual as
The input of simulation model, runs simulation model, and using the distribution time for drawing as the index for evaluating individual quality.
In step S208, judge whether current route set runs completion, if it is not complete, then continuing executing with step
S207, if completed, performs step S209.
In step S209, judge whether to meet end condition.Here end condition is the mesh that iterations reaches setting
Scale value, if being unsatisfactory for end condition, performs step S210, if meeting end condition, performs step S211.
In step S210, new route set is drawn by genetic algorithm iteration, and jump toward step S207.Specifically, make
New route set, the process bag are generated with the generate_next_population child nodes in genetic algorithm tree node
Include：A certain amount of winning individuality is retained by selection opertor, then some new individualities is obtained by intersection, mutation operator restructuring,
So as to constitute new route set.
In step S211, simulation model out of service draws distribution route.The distribution route is commented in current route set
Valency index highest route.
In whole optimization process, the number of times of simulation model operation is multiplied by iterations equal to route set sizes, finally
The route for once running is exactly the distribution route after optimization.
It should be noted that different Logistics Simulation System and intelligent optimization algorithm can be selected to come to distribution route
Optimize.
Technical scheme is avoided and solves dynamic problem with static thinking, and its is simple to operate, is only needed
City name where by being input into dispensing station point quantity, website and two-by-two the distance between website, it is possible to by emulation
An optimization dispatching circuit is determined in experiment.
Fig. 5 schematically shows embodiments in accordance with the present invention for recognizing thing using multi-power spectrum x-ray imaging system
The schematic diagram of the system 500 of product.System 500 includes processor 510, for example, digital signal processor (DSP).Processor 510 can
To be performed for the single assembly or multiple devices of the different actions of approach described herein 100.Specifically, processor
510 be configured as execute instruction with：First via line set, the first via are generated according to the geographical position corresponding with website
Line set includes N bar routes；Calculate the evaluation index corresponding with each bar route in the first route set；Determine whether
End condition is met, when the end condition is unsatisfactory for, using genetic algorithm to the selection from the first route set
The m bar routes of evaluation index top ranked are processed to produce N-m bar routes, and by the N-m bars route and institute's commentary
The m bar route combinations of valency index top ranked are new route set, wherein m ＜ N；And be by the new Route Set cooperation
First route set repeat the calculation procedure and the combination step, until meeting end condition untill, and will meet eventually
Only route corresponding with highest evaluation index during condition is defined as the distribution route of optimization.The processor 510 can be entered
One step be configured to execute instruction with：When using genetic algorithm to from the first route set select evaluation index ranking most
It is any from the m bars route to select two lines and the two lines are intersected when m bar routes high are intersected
To produce a variation route, and when utilize genetic algorithm to the evaluation index ranking that is selected from the first route set most
When m bar routes high enter row variation, a route is arbitrarily selected from the m bars route and row variation is entered to a route
To produce a variation route.
System 500 can also include input/output (I/O) device 530, for from other entities receive signal or to its
His entity sending signal.
Additionally, system 500 includes memory 520, the memory 520 can have following form：It is non-volatile or volatile
Property memory, for example, Electrically Erasable Read Only Memory (EEPROM), flash memory etc..Memory 520 stores computer-readable
Instruction, when processor 510 performs the computer-readable instruction, the computer-readable instruction makes computing device as herein described
Action.
Shown in the drawings of some block diagrams and/or flow chart.It should be understood that some sides in block diagram and/or flow chart
Frame or its combination can be realized by computer program instructions.These computer program instructions can be supplied to all-purpose computer,
The processor of special-purpose computer or other programmable data processing units, so that these instructions can be with when by the computing device
Create the device for realizing illustrated function/operation in these block diagrams and/or flow chart.
Therefore, the technology of the disclosure can be realized in the form of hardware and/or software (including firmware, microcode etc.).Separately
Outward, the technology of the disclosure can take the form of the computer program product on the computer-readable medium of the instruction that is stored with, should
Computer program product is available for instruction execution system (for example, one or more processors) to use or combined command execution system
Use.In the context of the disclosure, computer-readable medium can include, store, transmit, propagate or transmit instruction
Arbitrary medium.For example, computer-readable medium can include but is not limited to electricity, magnetic, optical, electromagnetic, infrared or semiconductor system,
Device, device or propagation medium.The specific example of computer-readable medium includes：Magnetic memory apparatus, such as tape or hard disk
(HDD)；Light storage device, such as CD (CD-ROM)；Memory, such as random access memory (RAM) or flash memory；And/or wired/
Wireless communication link.
Detailed description above has been elaborated for being calculated using heredity by using schematic diagram, flow chart and/or example
Method and numerous embodiments of the system optimized to distribution route using genetic algorithm that method is optimized to distribution route.
In the case where this schematic diagram, flow chart and/or example are comprising one or more functions and/or operation, people in the art
Member is it should be understood that each function and/or operation in this schematic diagram, flow chart or example can be by various structures, hardware, soft
Part, firmware or substantial their any combination to realize individually and/or jointly.In one embodiment, the implementation of the disclosure
If the stem portion of the example theme can be by application specific integrated circuit (ASIC), field programmable gate array (FPGA), numeral letter
Number processor (DSP) or other integrated forms are realized.However, those skilled in the art will appreciate that reality disclosed herein
Applying some aspects of example can equally realize in integrated circuits, being embodied as being counted at one or more on the whole or partly
One or more computer programs run on calculation machine are (for example, be embodied as run in one or more computer system
Individual or multiple programs), one or more programs run on the one or more processors are embodied as (for example, being embodied as one
One or more programs run on individual or multi-microprocessor), it is embodied as firmware, or be substantially embodied as aforesaid way
Any combination, and those skilled in the art are according to the disclosure, will be provided with design circuit and/or write-in software and/or firmware generation
The ability of code.Additionally, it would be recognized by those skilled in the art that the mechanism of theme described in the disclosure can be used as the journey of diversified forms
Sequence product is distributed, and regardless of the particular type of the actual signal bearing medium for being used for and performing distribution, disclosure institute
The exemplary embodiment for stating theme is applicable.The example of signal bearing medium is included but is not limited to：Recordable-type media, it is such as soft
Disk, hard disk drive, compact-disc (CD), digital universal disc (DVD), digital magnetic tape, computer storage etc.；And mode transmission is situated between
Matter, such as numeral and/or analogue communication medium (for example, optical fiber cable, waveguide, wired communications links, wireless communication link).
Claims (21)
1. a kind of method for being optimized to distribution route using genetic algorithm, including：
First via line set is generated according to the geographical position corresponding with website, first route set includes N bar routes；
Calculate the evaluation index corresponding with each bar route in the first route set；
Determine whether to meet end condition, when the end condition is unsatisfactory for, using genetic algorithm to from first route
The m bar routes of the evaluation index top ranked selected in set are processed to produce N-m bar routes, and by the N-m bars
Route is new route set, wherein m ＜ N with the m bar route combinations of the evaluation index top ranked；And
It is that the first route set repeats the calculation procedure and the combination step by the new Route Set cooperation, until meeting
Untill end condition, and route corresponding with highest evaluation index when will meet end condition is defined as the dispatching road of optimization
Line.
2. method according to claim 1, wherein, the first route set is randomly generated.
3. method according to claim 1, wherein, the evaluation index is dispensed according to respective routes to be spent
Time.
4. method according to claim 1, wherein, the end condition is to reach the iterations for pre-setting.
5. method according to claim 1, wherein, each route in produced N-m bar routes be by using
The m bar routes of evaluation index top ranked of the genetic algorithm to being selected from the first route set are intersected or made a variation to be obtained
.
6. method according to claim 5, wherein,
When the m bar routes of the evaluation index top ranked using genetic algorithm to being selected from the first route set are handed over
During fork, methods described includes：It is any from the m bars route to select two lines and the two lines are intersected to produce
A raw variation route, and
When the m bar routes of the evaluation index top ranked using genetic algorithm to being selected from the first route set become
Different time, methods described includes：A route is arbitrarily selected from the m bars route and a route is entered row variation to produce
A raw variation route.
7. method according to claim 1, wherein, the route in the new route set is different.
8. a kind of system for being optimized to distribution route using genetic algorithm, including：
Processor, be configured as execute instruction with：
First via line set is generated according to the geographical position corresponding with website, first route set includes N bar routes；
Calculate the evaluation index corresponding with each bar route in the first route set；
Determine whether to meet end condition, when the end condition is unsatisfactory for, using genetic algorithm to from first route
The m bar routes of the evaluation index top ranked selected in set are processed to produce N-m bar routes, and by the N-m bars
Route is new route set, wherein m ＜ N with the m bar route combinations of the evaluation index top ranked；And
It is that the first route set repeats the calculation procedure and the combination step by the new Route Set cooperation, until meeting
Untill end condition, and route corresponding with highest evaluation index when will meet end condition is defined as the dispatching road of optimization
Line；And
Memory, is configured as storing the instruction.
9. system according to claim 8, wherein, the first route set is randomly generated.
10. system according to claim 8, wherein, the evaluation index is to be dispensed to be spent according to respective routes
Time.
11. systems according to claim 8, wherein, the end condition is to reach the iterations for pre-setting.
12. systems according to claim 8, wherein, each route in produced N-m bar routes be by using
The m bar routes of evaluation index top ranked of the genetic algorithm to being selected from the first route set are intersected or made a variation to be obtained
.
13. systems according to claim 12, wherein, the processor be further configured to execute instruction with：
When the m bar routes of the evaluation index top ranked using genetic algorithm to being selected from the first route set are handed over
It is any from the m bars route to select two lines and the two lines are intersected to produce a variation route during fork,
And
When the m bar routes of the evaluation index top ranked using genetic algorithm to being selected from the first route set become
In the different time, a route is arbitrarily selected from the m bars route and a route is entered row variation to produce a variation route.
14. systems according to claim 8, wherein, the route in the new route set is different.
A kind of 15. computer-readable mediums, being included in be performed during by computing device includes the instruction of the operation of the following：
First via line set is generated according to the geographical position corresponding with website, first route set includes N bar routes；
Calculate the evaluation index corresponding with each bar route in the first route set；
Determine whether to meet end condition, when the end condition is unsatisfactory for, using genetic algorithm to from first route
The m bar routes of the evaluation index top ranked selected in set are processed to produce N-m bar routes, and by the N-m bars
Route is new route set, wherein m ＜ N with the m bar route combinations of the evaluation index top ranked；And
It is that the first route set repeats the calculation procedure and the combination step by the new Route Set cooperation, until meeting
Untill end condition, and route corresponding with highest evaluation index when will meet end condition is defined as the dispatching road of optimization
Line.
16. computer-readable mediums according to claim 15, wherein, the first route set is randomly generated.
17. computer-readable mediums according to claim 15, wherein, the evaluation index is carried out according to respective routes
Dispatching the spent time.
18. computer-readable mediums according to claim 15, wherein, the end condition be reach pre-set repeatedly
Generation number.
19. computer-readable mediums according to claim 15, wherein, each road in produced N-m bar routes
Line is that the m bars route of the evaluation index top ranked to being selected from the first route set by using genetic algorithm is carried out
Intersect or make a variation what is obtained.
20. computer-readable mediums according to claim 19, wherein, the processor is further configured to execution and refers to
Order with：
When the m bar routes of the evaluation index top ranked using genetic algorithm to being selected from the first route set are handed over
It is any from the m bars route to select two lines and the two lines are intersected to produce a variation route during fork,
And
When the m bar routes of the evaluation index top ranked using genetic algorithm to being selected from the first route set become
In the different time, a route is arbitrarily selected from the m bars route and a route is entered row variation to produce a variation route.
21. computer-readable mediums according to claim 15, wherein, each not phase of route in the new route set
Together.
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| WO2018090778A1 (en) * | 2016-11-17 | 2018-05-24 | 北京京东尚科信息技术有限公司 | Method and system for optimizing a distribution route by using genetic algorithm |
| CN108647810A (en) * | 2018-04-19 | 2018-10-12 | 安吉汽车物流股份有限公司 | The distribution method and device of order shipment, computer-readable medium |
| CN114024848A (en) * | 2020-11-23 | 2022-02-08 | 北京八分量信息科技有限公司 | Method for improving system robustness by optimizing node communication |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109886490B (en) * | 2019-02-22 | 2022-11-04 | 广西大学 | Matching optimization method for combined vehicle combined transportation |
| CN109992899B (en) * | 2019-04-08 | 2023-04-14 | 中国人民解放军陆军防化学院 | Decontamination station equipment facility layout method based on improved SLP and genetic algorithm |
| CN111489194B (en) * | 2020-04-01 | 2023-12-08 | 拉扎斯网络科技（上海）有限公司 | Map information processing method, apparatus, readable storage medium and electronic device |
| CN112149921B (en) * | 2020-10-20 | 2024-04-19 | 国网重庆市电力公司营销服务中心 | Large-scale electric logistics vehicle path planning method and system and charging planning method |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103440522A (en) * | 2013-08-31 | 2013-12-11 | 福州大学 | Vehicle dispatching method with genetic algorithm and MapReduce combined |
| CN103761588A (en) * | 2014-02-18 | 2014-04-30 | 张家港美核电子科技有限公司 | Hazardous chemical substance transport scheduling method based on multi-target modeling optimization |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105260787A (en) * | 2015-09-15 | 2016-01-20 | 浪潮软件股份有限公司 | Sorting operation balanced scheduling method based on genetic algorithm |
| CN105868843A (en) * | 2016-03-22 | 2016-08-17 | 南京邮电大学 | Route planning method oriented to goods delivery |
| CN106845665A (en) * | 2016-11-17 | 2017-06-13 | 北京京东尚科信息技术有限公司 | For the method and system optimized to distribution route using genetic algorithm |
-
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- 2016-11-17 CN CN201611011907.3A patent/CN106845665A/en active Pending
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103440522A (en) * | 2013-08-31 | 2013-12-11 | 福州大学 | Vehicle dispatching method with genetic algorithm and MapReduce combined |
| CN103761588A (en) * | 2014-02-18 | 2014-04-30 | 张家港美核电子科技有限公司 | Hazardous chemical substance transport scheduling method based on multi-target modeling optimization |
Non-Patent Citations (2)
| Title |
|---|
| 周婷 等: "基于时间成本的地下物流配送路线优化模型", 《物流工程与管理》 * |
| 罗庆 等: "基于混合遗传算法的物流配送路径优化分析", 《中央民族大学学报（自然科学版）》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018090778A1 (en) * | 2016-11-17 | 2018-05-24 | 北京京东尚科信息技术有限公司 | Method and system for optimizing a distribution route by using genetic algorithm |
| CN108647810A (en) * | 2018-04-19 | 2018-10-12 | 安吉汽车物流股份有限公司 | The distribution method and device of order shipment, computer-readable medium |
| CN114024848A (en) * | 2020-11-23 | 2022-02-08 | 北京八分量信息科技有限公司 | Method for improving system robustness by optimizing node communication |
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