CN109117993A - A kind of processing method of vehicle routing optimization - Google Patents
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Abstract
The invention discloses a kind of processing methods of vehicle routing optimization, comprising the following steps: a, acquisition quantity on order, dot number, site distance matrix and information of vehicles；B, whether setting time window and/or route template are opened；C, whether opened using quantity on order, dot number, site distance matrix, information of vehicles, time window, whether route template is opened as constraint condition, optimal path is calculated using ant colony genetic fusion algorithm iteration, and update site pheromones；The design is replicated and is converted after rationally utilizing constraint condition, ant colony genetic fusion algorithm iteration to calculate, and obtains shortest path, and result performance indicator is preferable.
Description
Technical field
The present invention relates to a kind of vehicle logistics fields, especially to the processing method of vehicle routing optimization.
Background technique
External research logistics route planning problem page about in 20th century earliest, double figures scholar first proposed about
Automobile dispenses the model of problem, i.e. VRP (Vehicle Routing Problem) model.Later since its logistics range is more next
Bigger, the practical application value of this problem is also higher and higher, so the people of research is also more and more, the scholar of every field is
It participates.Bodin et al. has summarized the seven big methods for solving these models based on these models.Based on G.B.Dantzig
Nineteen forty-seven propose Linear programming thought, B.Jackowski et al. 1985 propose implicit enumeration algorithms and
The exact algorithms such as the greedy algorithm of iteration that J.Culberson was proposed in 1992 successfully solve small-scale data site VRP, this
Class algorithm can accurately find out the optimal solution of the minimal path under conditions present in the case where number of outlet is little.And work as site
When larger, exact algorithm cannot be used again.Nineteen ninety-five is flutterred the collaboration in food by Eberhart and Kennedy according to flock of birds
Cooperation method has investigated particle swarm algorithm, finds out approximate solution in the way of RANDOM SOLUTION iteration.1999,
DonaldWaters and RonaldH.Ballou is from the network planning angle analysis in the path location problem of logistics distribution center.
EiichiT, MichihikoN, TadashiY, ToruL combine the factors such as traffic, the cost in reality, devise the double-deck number
Model is learned, the selection of home-delivery center is optimized.2002, Yang-JaJang combination Lagrangian Relaxation Algorithm and genetic algorithm were excellent
Change the path planning problem for solving logistics.2003, Jayaraman was in a multi-product, single production base, multi-logistics center
Under model, with the path planning problem of simulated annealing solving optimization logistics.2006, Marie-ClaudeBolduc etc.
People proposes under the vehicle route model that the multiple distribution points limited with having time window are mass produced, with heuristic mathematics
Algorithm carrys out Optimization Solution problem, and is also applied for the vehicle routing choice problem of multistage retailer.
Currently, the country is to the research under the multi-constraint condition of VRP still in theoretical research and experimental stage.These experiment at
Fruit has relatively better performance under given conditions, but can not carry out on a large scale using, it is possible to according to constraint
The variation of condition and performance gap itself that make algorithm is huge, cause the reusability of algorithm poor, and individually use ant colony
Algorithm is individually inaccurate using the optimal solution that genetic algorithm obtains, by experimental analysis, performance indicator is poor.
Summary of the invention
In order to solve the above technical problems, the object of the present invention is to provide it is a kind of according to quantity on order, dot number, site away from
From matrix, information of vehicles, whether opening time window and/or route template are as constraint condition, using ant colony genetic fusion algorithm
The processing method of iteration progress path optimization.
The technical solution adopted by the present invention is that:
A kind of processing method of vehicle routing optimization, comprising the following steps:
A, quantity on order, dot number, site distance matrix and information of vehicles are inputted；
B, whether setting time window and/or route template are opened；
C, whether opened with quantity on order, dot number, site distance matrix, information of vehicles, time window, route template is
No open is used as constraint condition, optimal path is calculated using ant colony genetic fusion algorithm iteration, and update site information
Element.
Further include step d, result will be calculated upload in the database of cloud server.
Calculating is iterated in not up to iteration upper limit value in the step c, comprising the following steps:
C1, initialization with Ant colony and ant；
C2, ant are according to determining sex exploration strategy or the lower site of random sex exploration policy selection；
C3, judge whether ant goes through all over all sites, if so, c4 is entered step, if it is not, then return step c2；
C4, acquisition go through the optimal ant of circulation gone out behind all sites；
C5, it the global optimum ant obtained in optimal ant and iterative process will be recycled carries out duplication and intersect；
C6, variation is swapped to global optimum ant and is inverted variation；
C7, judge global optimum ant whether iterative evolution, if so, c8 is entered step, if otherwise entering step c9；
C8, the optimal solution for obtaining iterative evolution replace the site information of former global optimum ant, enter step c9；
C9, site pheromones are updated；
C10, judge whether the number of iterations is more than iteration upper limit value, if so, output is as a result, if it is not, then return step c2.
Bicycle multiple path routing model or more vehicle multipath systems are established according to quantity on order, dot number in the step c
Model or the mixed path model for combining bicycle multiple path routing model and Duo Che multipath system model.
The information of vehicles includes the volume of vehicle fleet size and/or vehicle and/or the maximum volume and/or vehicle of vehicle vanning
Vanning dead weight and/or vehicle operation maximum range and/or vehicle operation maximum time.
Mixed path model, the mathematical model established in mixed path model are used in the step c are as follows:
Wherein, defining each number node is { 1,2 ..., L }；
Transportation range cost from site i to site j is cij,
Each participation distribution vehicle number is k { k=1,2 ..., K }；
Iteration upper limit value is Nm；
Defined variable are as follows:
If the task of site i is completed by distribution vehicle k, yik=1, otherwise, yik=0；
If vehicle k drives to site j, x by site iijk=1, otherwise, xijk=0；
In the mixed path model include vehicle vanning maximum volume and/or vehicle vanning dead weight and/
Or the maximum range of vehicle operation and/or the maximum time of vehicle operation are as constraint condition, restricted model are as follows:
Wherein, the weight of site i task cargo is gi；
The volume of site i task cargo is qi；
The dead weight of distribution vehicle k is Gk；
The maximum range of distribution vehicle k is Sk；
The maximum volume of distribution vehicle k is Qk；
The net cycle time of distribution vehicle k is Tk；
Distribution vehicle k dispenses the site i task cargo work time as tki。
If time window is opened, the time window of mixed path model includes first time window section and the second time window area
Between, it is divided from site i to site j by the working time, first time window section is DBEi1—DBSi1, the second time window area
Between be DBEi2—DBSi2, and DBEi1< DBSi1< DBEi2< DBSi2T when if distribution vehicle reacheski≤DBEi1, then tki=
DBEi1；If distribution vehicle reaches, DBSi1≤tki≤DBEi2, then tki=DBEi2；Other times section reaches, tki=tki。
Beneficial effects of the present invention:
The processing method of vehicle routing optimization of the present invention may choose whether the opening time according to quantity on order, dot number
Window and/or route template, time window, route template, information of vehicles constraint under, using ant colony genetic fusion algorithm, defeated
The path of vehicle is optimized after the quantity on order that enters, dot number, obtains the optimal solution of lower cost, the design is rationally sharp
With constraint condition, ant colony genetic fusion algorithm iteration is replicated and is converted after calculating, and obtains shortest path, and result performance
Index is preferable, and is shown in capable of uploading onto the server.
Detailed description of the invention
A specific embodiment of the invention is described further with reference to the accompanying drawing.
Fig. 1 is the flow chart of processing method of the present invention.
Fig. 2 is the time window or route template schematic diagram of bicycle multiple path routing model of the present invention.
Fig. 3 is the time window or route template schematic diagram of the more vehicle multipath system models of the present invention.
Fig. 4 is the time window or route template schematic diagram of mixed path model of the present invention.
Specific embodiment
As Figure 1-Figure 4, the processing method of the design vehicle routing optimization can be applied to mixed mud vehicle, civilian vehicle
Etc. navigation, comprising the following steps:
A, quantity on order, dot number, site distance matrix and information of vehicles are inputted；
B, whether setting time window and/or route template are opened；
C, whether opened with quantity on order, dot number, site distance matrix, information of vehicles, time window, route template is
No open is used as constraint condition, optimal path is calculated using ant colony genetic fusion algorithm iteration, and update site information
Element.
Further include step d, result will be calculated upload in the database of cloud server.
In the design preferred embodiment, the logistics route planning problem of single starting point is first only considered, i.e. distribution vehicle is from starting point
It sets out and returns again to starting point behind N number of site.This problem can be regarded as the queuing combinatorial problem of N number of site, and solution space is N's
Factorial, and optimal solution is exactly the minimum value of the distance sum of this N number of site arrangement.
Four kinds of situations can be divided into according to whether time window and route template are opened: 1, time window is opened and route template is opened
The path model opened, i.e. LT-CVRPTW (Line Template-Vehicle Routing Problem with Time
Windows)；2, time window is opened and path model that route template is not turned on, i.e. CVRPTW (Vehicle Routing
Problem with Time Windows)；3, time window is not turned on and the path model of route template unlatching, i.e. LT-CVRP
(Line Template-Vehicle Routing Problem)；4, the path that time window is not turned on and route template is not turned on
Model, i.e. CVRP (Vehicle Routing Problem).
Constraint condition has the following: the more time windows in path open or close；Route template opens or closes；And vehicle is believed
Breath may include the volume of vehicle fleet size and/or vehicle and/or the maximum volume of vehicle vanning and/or the maximum load of vehicle vanning
The maximum time of the maximum range and/or vehicle operation of amount and/or vehicle operation.
Following restriction can be carried out from functional requirement: vehicle completes delivery stroke from unique home-delivery center
After must return to starting point；Date of arrival, one day nearest order must arrange to dispense at the latest for order setting；Each vehicle is necessary
Route template numbers are set, and be not turned on route template is set as -1；Vehicle can only dispense in the region made.
Calculating is iterated in not up to iteration upper limit value in the step c, comprising the following steps:
C1, initialization with Ant colony and ant；
C2, ant are according to determining sex exploration strategy or the lower site of random sex exploration policy selection；
C3, judge whether ant goes through all over all sites, if so, c4 is entered step, if it is not, then return step c2；
C4, acquisition go through the optimal ant of circulation gone out behind all sites；
C5, it the global optimum ant obtained in optimal ant and iterative process will be recycled carries out duplication and intersect；
C6, variation is swapped to global optimum ant and is inverted variation；
C7, judge global optimum ant whether iterative evolution, if so, c8 is entered step, if otherwise entering step c9；
C8, the optimal solution for obtaining iterative evolution replace the site information of former global optimum ant, enter step c9；
C9, site pheromones are updated；
C10, judge whether the number of iterations is more than iteration upper limit value, if so, output is as a result, if it is not, then return step c2.
Further, as shown in figs 2-4, bicycle multiple path routing model or is established according to quantity on order, dot number more
Vehicle multipath system model or the mixed path model for combining bicycle multiple path routing model and Duo Che multipath system model.
The situation that uses of bicycle multiple path routing model is a, vehicle between known site in the case where distance costs, by
The sequence for arriving at site to one comes so that the distance costs of this scheme is minimum, this model is similar to traveling salesman problem, i.e.,
TSP.Need to consider the dead weight of vehicle, the working time of maximum range, vehicle in bicycle multiple path routing model, and
Consider that vehicle itself is safeguarded and has holidays by turns, bicycle multiple path routing model is optional to use whether time window and/or route template are opened
Four kinds of path models complete all dispatching tasks in the case where only distributing a vehicle, and meet client to the phase of cargo
Answer demand.Vehicle must satisfy in the case that route template is not turned under bicycle multiple path routing model, can complete independently quantity it is few
Order taking responsibility and the constraint demand of system is able to satisfy in certain penalty mechanism, and under the premise of line profile is opened,
The line profile number of these orders be it is identical, with these orders binding same line template number vehicle meeting above-mentioned constraint
Under the premise of condition, the dispatching task of these orders can be individually completed, bicycle multiple path routing model is specifically established can be in conventional skill
It is selected in art.
And more vehicle multipath system models establish extension on the basis of bicycle multipath, and when quantity on order is more, one
Vehicle is difficult to need to arrange more vehicles then at this time effectively to complete order dispatching task to carry out path planning.More vehicle multichannels
Diameter model generally requires route template to close, and to avoid there is the chaotic accident of distribution, these assigned vehicles must be simultaneously
Meet and be no more than the maximum load of vehicle, the maximum range of vehicle and vehicle highest working time daily, then exists again
Path planning is carried out under the constraint condition of time window and route template, more vehicle multiple path routing models are specifically established can be in routine techniques
In selected.
And the preferred path model of the design is mixed path model, line profile must be turned in mixed path model,
And the quantity of order is more, meets certain quantitative requirement.Since the quantity on order of dispatching is more, and line profile has been opened
It opens, then participates in dispatching and the matched site of line profile number with the consistent vehicle of line profile, all line profile dispatchings
Model is bicycle multiple path routing model；And part of site is not matched to vehicle, these do not match the site of vehicle by not having
The vehicle for binding line profile number completes dispatching task, and since such dot number is more, a vehicle is difficult to complete current
Dispatching task, it is therefore desirable to which more vehicles participate in carrying out order dispatching task, and it is more that the dispatching of logicalnot circuit template site forms more vehicles
Path model, bicycle multiple path routing model and more vehicle multiple path routing models collectively form mixed path model at this time.
Wherein, the site in mixed path model can be divided into two large divisions, and a part is the site in line profile, separately
A part is the site outside line profile, and vehicle is completed the site dispatching in line profile according to the line profile number of binding and appointed
Then business, the site of unbound line profile dispensed outside line profile find out total path of all vehicles for participating in dispatching
With the optimal solution of route, bicycle multiple path routing model collectively forms mixed path model with more vehicle multiple path routing models.
The mathematical model established in mixed path model are as follows:
Wherein, defining each number node is { 1,2 ..., L }；
Transportation range cost from site i to site j is cij, transportation range cost is primary concern is that distance, false herein
If every section is a fixed value apart from required cost.
Each participation distribution vehicle number is k { k=1,2 ..., K }；
Iteration upper limit value is Nm；
Defined variable are as follows:
If the task of site i is completed by distribution vehicle k, yik=1, otherwise, yik=0；
If vehicle k drives to site j, x by site iijk=1, otherwise, xijk=0；
In the mixed path model include vehicle vanning maximum volume and/or vehicle vanning dead weight and/
Or the max mileage of vehicle operation and/or the maximum time of vehicle operation are as constraint condition, restricted model are as follows:
Wherein, the weight of site i task cargo is gi；
The volume of site i task cargo is qi；
The dead weight of distribution vehicle k is Gk；
The maximum mileage of exercising of distribution vehicle k is Sk；
The maximum volume of distribution vehicle k is Qk；
The net cycle time of distribution vehicle k is Tk；
Distribution vehicle k dispenses the site i task cargo work time as tki。
Herein, above-mentioned formula (1) indicates the airlift of each car all no more than the maximum load G of the vehicle；Formula
(2) indicate the measurement of cargo of the delivery of each car all no more than the maximum volume Q of the vehicle；Formula (3) indicates in the design
Assuming that TkIt is set as 8 hours, then it represents that the maximum operating time of vehicle can divide according to the actual situation no more than 8 hours
Lunch break with vehicle is come the peak period for the dispatching that is staggered；Formula (4) indicates the maximum operating range of each car all no more than
The maximum operating range S of the vehicle；Meanwhile formula (5) expression will also guarantee that each car has been involved in dispatching task；And herein
Route template must be turned on, i.e. LT=1；Time window, which can be opened, to close, TW=1 or 0；
If time window is opened, the time window of mixed path model includes first time window section and the second time window area
Between, it is divided from site i to site j by the working time, first time window section is DBEi1—DBSi1, the second time window area
Between be DBEi2—DBSi2, and DBEi1< DBSi1< DBEi2< DBSi2T when if distribution vehicle reacheski≤DBEi1, then tki=
DBEi1；If distribution vehicle reaches, DBSi1≤tki≤DBEi2, then tki=DBEi2；Other times section reaches, tki=tki。
DBE hereini1、DBSi1、DBEi2、DBSi2For specific works time value, such as two time window sections are respectively 8:
00-10:00,12:00-14:00；It is divided herein according to operating time value, as 0-2,4-6；It is calculated convenient for machine.
The design is in the identical situation of route average unit cost, shortest path of the purpose under single distribution point situation.First
Each constraint condition is inputted, including whether information of vehicles, time window are opened, and whether line profile is opened etc., it is calculated using ant colony
The exploration strategy of method can be random search and be also possible to determining sex exploration, and ant records road after having traversed whole cities site
Diameter, and update pheromones.Traditional ant group algorithm is that pheromones are constantly superimposed to find out the optimal solution under iteration, this algorithm melts
Hybrid genetic algorithm, records path and the pheromones of global optimum ant, and does heredity with the optimal ant path of iteration and pheromones
Algorithm is intersected, mutation operation.The ability of searching optimum of genetic algorithm is taken full advantage of in this way, suitably change genetic algorithm
Crossover probability, search range can be significantly improved, have better adaptability to discrete-variable problem, in the not up to iteration upper limit
When, it repeats the above steps, the ant path finally tended towards stability is exactly the path optimizing exported.
The design is replicated and is converted after rationally utilizing constraint condition, ant colony genetic fusion algorithm iteration to calculate, and is obtained
Shortest path, and result performance indicator is preferable, and shown in capable of uploading onto the server.
The following are prove the better experimental result of the design result performance indicator:
Whether firstly, being opened according to time window and route template, there are four types of situations for tool: time window is not turned on and route mould
Plate is not turned on, time window is opened and line profile is not turned on, and time window is not turned on and line profile unlatching, time window unlatching and line
Road template is opened, and three kinds of path models are proved according to these four experimental conditions.Analyze order_25, order_ respectively simultaneously
50, the relationship of the shortest distance and the number of iterations of these three different number orders of order_100, order_25, order_50,
The particular content of order_100 omits herein.It is in conjunction with generally accepted to ant group algorithm parameter setting standard, its information is heuristic
The factor, three parameter values of expected heuristic value and the pheromones rate of decay:
| Parameter | Meaning | Value |
| α | Information heuristic greedy method | 1.0 |
| β | Expected heuristic value | 4.0 |
| ρ | The pheromones rate of decay | 0.75 |
(1) bicycle multiple path routing model:
This experiment is carried out using the data in order_25 file, and this group of data share 25 including home-delivery center
A site information, since site is few, so being that bicycle is dispensed, distribution vehicle returns in dispatching after a wheel dispatching
The heart, path are a closed circuits, indicate that one site of the vehicle only reaches once.
(2) more vehicle multiple path routing models:
This experiment using in order_50 file data carry out, and this group of data including home-delivery center including be total to
There are 50 site information, and there are more cars to be dispensed, has the circuit in multiple paths in path, using the algorithmic rule
Afterwards, the vehicle of identical template numbers can only arrive corresponding site dispatching order.
(3) mixed path model:
The data of this experiment are that data in order_100 file carry out, including home-delivery center totally 100 sites.
Finally from the shortest distance and two big constraint conditions of these three orders of order_25, order_50, order_100
Relationship compares.
It can be seen that most short dispatching distance under CVRP and LT-CVRP model is shorter when quantity on order is identical, and
Most short dispatching distance under corresponding CVRPTW and LT-CVRPTW model is longer, illustrates that the unlatching of time window makes the shortest distance
Calculated result is elongated；Most short dispatching distance under same CVRP and CVRPTW model is shorter, and corresponding LT-CVRP with
Most short dispatching distance under LT-CVRPTW model is longer, illustrates that the unlatching of route template also becomes the calculated result of the shortest distance
Greatly；And in the case where model is the same, order numbers are more, and the shortest distance being calculated is also longer.In same experimental conditions
Under the premise of, the increase of quantity on order, the unlatching of route template, the unlatching of time window is all that the calculated result of shortest path is elongated.
And the design ant colony genetic fusion algorithm makes herein compared with conventional ant group algorithm, Ant-cycle model etc.
With order_25 file provide data to before improvement ant group algorithm, genetic algorithm and improved fusion ant colony and heredity
Algorithm respectively carries out 10 experiments, and the parameters of algorithm are arranged according to the universal standard.Can according to optimal performance index Eo, when
Between performance indicator ET, these three indexs of robust performance index ER compare the comprehensive performance of algorithm, these three refer to that target value is smaller,
Indicate that the comprehensive performance of algorithm is better.
Optimal performance index Eo, formula indicate are as follows:
cbThe optimal value of the shortest distance is calculated after expression algorithm optimization, c* indicates that the theory of algorithm Solve problems is optimal
Value.
Time performance index ET, formula indicate are as follows:
IaAfter indicating that algorithm is run multiple times, the average value of iteration, T when meeting termination condition0Indicate that algorithm iteration is once spent
The average calculation times taken, ImaxIndicate maximum number of iterations initially set.
Robust performance index ER, formula indicate are as follows:
Ca indicates that the average value of acquired shortest path is run multiple times in algorithm, and c* indicates that the theory of algorithm Solve problems is optimal
Value.
The number of iterations Imax=500 being arranged herein, using individual ant group algorithm to the VRP model solution of this paper, letter
The strategy that breath element updates sets c1=0 and c2=1 using traditional current iteration advantest method and global optimum's method strategy.
The system of Ant-cycle model also known as ant week model, the model updates pheromones using the strategy of global optimization,
The mode of Pheromone update is only with the mutation operation of genetic algorithm only with cross and variation in the model.
Ant-Q system model algorithm pheromones are updated using traditional global optimum more new strategy, and set c1
=0 and c2=1.
Improved algorithm information element more new strategy carries out the update of pheromones using mixed strategy, and order_25 is called to order
Single data carry out continuous 10 tests to algorithm, and the value that c1 and c2 is arranged all is 0.5, have become the improved overall situation at this time most
Excellent more new strategy.
According to the calculating to the performance indicator for improving front and back, after the ant-genetic algorithm and improvement before ant group algorithm, improvement
Ant colony and heredity three index results of blending algorithm:
It can be seen that under the premise of the number of iterations Imax is identical according to the indices that table is shown, individually use ant colony
Algorithm is to the VRP model solution of this paper, and without the duplication of genetic algorithm, intersection, mutation operation, ant group algorithm is in time performance
It takes advantage in index.But ant group algorithm does not find optimal solution in this experiment, only has found opposite optimal solution, and
Ant group algorithm is not got the upper hand in optimal performance index and robust performance index, therefore using ant-genetic algorithm than using
Ant colony optimization for solving will be got well.
Three performance index values of the ant-genetic algorithm of Ant-cycle model and the three of improved ant-genetic algorithm
A performance indicator compares, it can be seen that and three performance index values of improved algorithm are smaller, therefore the algorithm after changing
Comprehensive performance will be got well.
Simultaneously as can be seen that the ant-genetic algorithm and improved ant-genetic algorithm of Ant-Q system model are at this
Optimal solution can be found in secondary experiment, therefore optimal performance index is identical, but improve after algorithm time performance index than changing
Want small into preceding, robust performance is also improved result than small before improving, therefore after improving algorithm it is comprehensive
It can be got well than the comprehensive performance of the ant-genetic algorithm of Ant-Q system model.
Further, the design output output can for example, by the clouds such as Baidu map API backstage web services and
Path planning after scheduling is shown to Baidu map by LBS cloud service, and the concrete vehicle between a large amount of site is driven
Navigation, how to be passed to these data is also an important aspect in Baidu map API code, the related clothes of LBS cloud
Business can help to solve this method, and the mass data of user can be uploaded in the cloud database of oneself by LBS cloud, and
It can freely support the access of big flow per second, data can be shown on map in the form of point and be passed through a little by Baidu map
Hit the relevant information that can check point.But also it can be by constantly requesting to refresh newest site dynamic.
The above description is only a preferred embodiment of the present invention, and the present invention is not limited to above embodiment, as long as with
Essentially identical means realize that the technical solution of the object of the invention belongs within protection scope of the present invention.
Claims (8)
1. a kind of processing method of vehicle routing optimization, which comprises the following steps:
A, quantity on order, dot number, site distance matrix and information of vehicles are inputted；
B, whether setting time window and/or route template are opened；
C, whether opened with quantity on order, dot number, site distance matrix, information of vehicles, time window, whether route template is opened
It opens as constraint condition, optimal path is calculated using ant colony genetic fusion algorithm iteration, and update site pheromones.
2. a kind of processing method of vehicle routing optimization according to claim 1, it is characterised in that: further include step d, incite somebody to action
Result is calculated to upload in the database of cloud server.
3. a kind of processing method of vehicle routing optimization according to claim 1, which is characterized in that in the step c
Not up to calculating is iterated in iteration upper limit value, comprising the following steps:
C1, initialization with Ant colony and ant；
C2, ant are according to determining sex exploration strategy or the lower site of random sex exploration policy selection；
C3, judge whether ant goes through all over all sites, if so, c4 is entered step, if it is not, then return step c2；
C4, acquisition go through the optimal ant of circulation gone out behind all sites；
C5, it the global optimum ant obtained in optimal ant and iterative process will be recycled carries out duplication and intersect；
C6, variation is swapped to global optimum ant and is inverted variation；
C7, judge global optimum ant whether iterative evolution, if so, c8 is entered step, if otherwise entering step c9；
C8, the optimal solution for obtaining iterative evolution replace the site information of former global optimum ant, enter step c9；
C9, site pheromones are updated；
C10, judge whether the number of iterations is more than iteration upper limit value, if so, output is as a result, if it is not, then return step c2.
4. a kind of processing method of vehicle routing optimization according to claim 1, it is characterised in that: root in the step c
Bicycle multiple path routing model or more vehicle multipath system models are established according to quantity on order, dot number or combine bicycle multipath
The mixed path model of model and Duo Che multipath system model.
5. a kind of processing method of vehicle routing optimization according to claim 4, it is characterised in that: the information of vehicles packet
Include vehicle fleet size and/or vehicle volume and/or vehicle vanning maximum volume and/or vehicle vanning dead weight and/
Or vehicle operation maximum range and/or vehicle operation maximum time.
6. a kind of processing method of vehicle routing optimization according to claim 5, which is characterized in that adopted in the step c
With mixed path model, the mathematical model established in mixed path model are as follows:
Wherein, defining each number node is { 1,2 ..., L }；
Transportation range cost from site i to site j is cij,
Each participation distribution vehicle number is k { k=1,2 ..., K }；
Iteration upper limit value is Nm；
Defined variable are as follows:
If the task of site i is completed by distribution vehicle k, yik=1, otherwise, yik=0；
If vehicle k drives to site j, x by site iijk=1, otherwise, xijk=0.
7. a kind of processing method of vehicle routing optimization according to claim 6, which is characterized in that the mixed path mould
It include in the maximum volume of vehicle vanning and/or the dead weight of vehicle vanning and/or the maximum traveling of vehicle operation in type
Journey and/or the maximum time of vehicle operation are as constraint condition, restricted model are as follows:
Wherein, the weight of site i task cargo is gi；
The volume of site i task cargo is qi；
The dead weight of distribution vehicle k is Gk；
The maximum range of distribution vehicle k is Sk；
The maximum volume of distribution vehicle k is Qk；
The net cycle time of distribution vehicle k is Tk；
Distribution vehicle k dispenses the site i task cargo work time as tki。
8. a kind of processing method of vehicle routing optimization according to claim 7, which is characterized in that if time window is opened,
The time window of mixed path model includes first time window section and the second time window section, and work is pressed from site i to site j
Time is divided, and first time window section is DBEi1—DBSi1, the second time window section is DBEi2—DBSi2, and DBEi1
< DBSi1< DBEi2< DBSi2T when if distribution vehicle reacheski≤DBEi1, then tki=DBEi1；If distribution vehicle reaches,
DBSi1≤tki≤DBEi2, then tki=DBEi2；Other times section reaches, tki=tki。
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109858703A (en) * | 2019-02-15 | 2019-06-07 | 中国环境监测总站 | A kind of method and system for the shortest path obtaining sampling |
| CN110852524A (en) * | 2019-11-19 | 2020-02-28 | 腾讯科技（深圳）有限公司 | Method, device and equipment for determining time window and storage medium |
| CN111148110A (en) * | 2020-03-30 | 2020-05-12 | 汉腾汽车有限公司 | Vehicle networking road side unit deployment method based on genetic ant colony hybrid algorithm |
| CN111461624A (en) * | 2020-04-20 | 2020-07-28 | 上海东普信息科技有限公司 | Logistics line planning method, device, equipment and storage medium |
| CN111461402A (en) * | 2020-03-06 | 2020-07-28 | 上海汽车集团股份有限公司 | Logistics schedule optimization method and device, computer readable storage medium and terminal |
| CN111860957A (en) * | 2020-06-18 | 2020-10-30 | 浙江工业大学 | Multi-vehicle type vehicle path planning method considering secondary distribution and balance time |
| CN111915078A (en) * | 2020-07-29 | 2020-11-10 | 中国烟草总公司湖北省公司 | Data-driven flexible cigarette distribution line planning method and system |
| CN112465180A (en) * | 2019-09-09 | 2021-03-09 | 北京京东振世信息技术有限公司 | Vehicle path planning method and device |
| CN113177662A (en) * | 2021-05-08 | 2021-07-27 | 深圳小狮快送科技有限公司 | Method for optimizing distribution vehicle route with fixed distribution scheme constraint |
| CN113723675A (en) * | 2021-08-20 | 2021-11-30 | 深圳依时货拉拉科技有限公司 | Automatic dispatching method for part collecting pieces and computer equipment |
| CN116795108A (en) * | 2023-06-09 | 2023-09-22 | 西南交通大学 | Intelligent unmanned vehicle distribution method based on multi-source sensing signals |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102508935A (en) * | 2011-09-22 | 2012-06-20 | 南京大学 | On-chip network mapping method based on ant-colony chaos genetic algorithm |
| CN104992242A (en) * | 2015-07-01 | 2015-10-21 | 广东工业大学 | Method for solving logistic transport vehicle routing problem with soft time windows |
| CN106251009A (en) * | 2016-07-27 | 2016-12-21 | 清华大学 | A kind of optimized algorithm of the Vehicle Routing Problems solving time window |
| US20170017880A1 (en) * | 2014-06-23 | 2017-01-19 | International Business Machines Corporation | Solving vehicle routing problems using evolutionary computing techniques |
-
2018
- 2018-07-27 CN CN201810845753.0A patent/CN109117993B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102508935A (en) * | 2011-09-22 | 2012-06-20 | 南京大学 | On-chip network mapping method based on ant-colony chaos genetic algorithm |
| US20170017880A1 (en) * | 2014-06-23 | 2017-01-19 | International Business Machines Corporation | Solving vehicle routing problems using evolutionary computing techniques |
| CN104992242A (en) * | 2015-07-01 | 2015-10-21 | 广东工业大学 | Method for solving logistic transport vehicle routing problem with soft time windows |
| CN106251009A (en) * | 2016-07-27 | 2016-12-21 | 清华大学 | A kind of optimized algorithm of the Vehicle Routing Problems solving time window |
Non-Patent Citations (1)
| Title |
|---|
| 唐菁敏 等: "《基于改进型遗传蚁群算法的TDOA多点定位研究》", 《通信技术》 * |
Cited By (18)
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|---|---|---|---|---|
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| CN112465180A (en) * | 2019-09-09 | 2021-03-09 | 北京京东振世信息技术有限公司 | Vehicle path planning method and device |
| CN112465180B (en) * | 2019-09-09 | 2023-09-26 | 北京京东振世信息技术有限公司 | Vehicle path planning method and device |
| CN110852524A (en) * | 2019-11-19 | 2020-02-28 | 腾讯科技（深圳）有限公司 | Method, device and equipment for determining time window and storage medium |
| CN110852524B (en) * | 2019-11-19 | 2023-04-18 | 腾讯科技（深圳）有限公司 | Method, device and equipment for determining time window and storage medium |
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| CN111860957A (en) * | 2020-06-18 | 2020-10-30 | 浙江工业大学 | Multi-vehicle type vehicle path planning method considering secondary distribution and balance time |
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| CN111915078A (en) * | 2020-07-29 | 2020-11-10 | 中国烟草总公司湖北省公司 | Data-driven flexible cigarette distribution line planning method and system |
| CN111915078B (en) * | 2020-07-29 | 2023-09-26 | 中国烟草总公司湖北省公司 | Flexible cigarette distribution line planning method and system based on data driving |
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