CN110084463A - The method and system of the prewired goods of line haul based on big data - Google Patents
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Abstract
The invention discloses the method and system of the prewired goods of line haul based on big data, include at least step: S1: obtaining the gatehead that transport cargo, and be set as center；S2: the destination of each cargo is calculated at a distance from the center；S3: according to the distance of all cargos, class center is calculated；S4: it according to the class center, carries out with goods.By the above-mentioned means, the present invention efficiently calculates more outstanding vehicle and goods matching scheme, to improve the calculated performance of vehicle and goods matching algorithm, it is fair to promote, and improves efficiency.The configuration of the present invention is simple is novel, easy to operate, and the safety in utilization of grass trimmer can be improved.
Description
Technical field
The present invention relates to traffic freight traffic technique fields, more particularly, to a kind of prewired goods of the line haul based on big data
Method and system.
Background technique
Currently, vehicle and goods matching is mostly based on information of vehicles module and information of freight source module, these softwares are by information of vehicles
Classify with information of freight source and show, when the user clicks vehicle or when some classification of the source of goods, is presented to such letter of user
Ceasing the vehicle source of sequence, perhaps source of goods list to user recommends such vehicle source or information of freight source.
The sequence of traditional vehicle and goods matching software information, before the vehicle source for having handed over the driver user of annual fee is set；Popularization will have been given
Before the source of goods of the logistics company of expense is set, the source of goods and vehicle match (distance, loading require, freight volume maximizes) etc. are secondly just considered
Factor needs to take time to make a phone call to link up whether information matches this results in driver and logistics company user, not only time-consuming, but also
Missing justice, efficiency.
Summary of the invention
The present invention in order to overcome the deficiencies of the prior art, provide a kind of prewired goods of the line haul based on big data method and
System uses the dynamic datas such as vehicle source and the source of goods the characteristics of for vehicle and source of goods big data and is analyzed in real time, raising vehicle
The matched optimal case of goods, by vehicle and goods matching Combinatorial Optimization Model, using the Predatory search algorithm based on population to the mould
Type solves, and can efficiently calculate more outstanding vehicle and goods matching scheme, to improve the calculated performance of vehicle and goods matching algorithm, promotees
Into justice, and improve efficiency.
In order to solve the above technical problems, the present invention adopts the following technical scheme:
A kind of method of prewired goods of the line haul based on big data is provided, step is included at least:
S1: the gatehead that transport cargo is obtained, and is set as center；
S2: the destination of each cargo is calculated at a distance from the center；
S3: according to the distance of all cargos, class center is calculated；
S4: it according to the class center, carries out with goods.
In order to solve the above technical problems, the present invention also provides a kind of system of prewired goods of the line haul based on big data,
Include:
Center module for obtaining the gatehead that transport cargo, and is set as center；
Spacing module, for calculating the destination of each cargo at a distance from the center；
Class center module calculates class center for the distance according to all cargos；
With goods module, for according to the class center, carrying out with goods.
Be different from the prior art, using the present invention have it is following the utility model has the advantages that
In the present invention, after obtaining cargo gatehead, each cargo destination is calculated at a distance from gatehead, and in terms of this
Class center is calculated, is finally carried out with goods.By the above-mentioned means, the present invention efficiently calculates more outstanding vehicle and goods matching scheme, from
And the calculated performance of vehicle and goods matching algorithm is improved, it is fair to promote, and improves efficiency.
Detailed description of the invention
Fig. 1 is that the present invention is based on the overall procedure schematic diagrames of the method for the prewired goods of the line haul of big data；
Fig. 2 is that the present invention is based on the method flow diagrams of distance cluster；
Fig. 3 is that the present invention is based on the vehicle and goods matching algorithm flow charts of the Predatory search algorithm of population；
Fig. 4 is that the present invention is based on the algorithm flow charts that standard PSO is solved；
Fig. 5 is that the present invention is based on the particle swarm algorithm flow charts of predatory search strategy.
Specific embodiment
To explain the technical content, the achieved purpose and the effect of the present invention in detail, below in conjunction with embodiment and cooperate attached
Figure is explained.
As shown in Figure 1 to Figure 3, the present embodiment solves the side of the prewired goods of line haul by the Predatory search algorithm of population
Case.The algorithm utilizes the size of search space limitation, defines Local Search and the global search of algorithm, and empty by adjusting search
Between limitation, to realize the conversion between Local Search and global search, there is preferable concentration of local to search for and jump out office for it
The optimal ability in portion.
As shown in Figure 1, the method for the prewired goods of line haul based on big data includes at least following steps:
Selected center: the gatehead that transport cargo is obtained, and is set as center；
Distance calculates: calculating the destination of each cargo at a distance from the center；
Class center calculation: according to the distance of all cargos, class center is calculated；
Judge whether class center changes,
If changing, return step " class center calculation ", to redefine class center, until class center is constant；
Finally, according to the class center, being carried out with goods in the case where class center is remained unchanged.
In Fig. 2, first according to existing vehicle source and the source of goods, a certain number of vehicles and cargo are taken out at random；Then basis
Function calculates the matching degree of vehicle and the source of goods；According to the combination for choosing matching degree highest vehicle and the source of goods；Finally press
It according to the source of goods and vehicle of selection, goes to calculate according to algorithm, if being unsatisfactory for, records ungratified condition, calculate and then skim this next time
Combination, while being matched again；If met, then records the group and merge the optimal matching degree scheme of completion output.
In Fig. 3, system has recorded information (speed, position, vehicle, the vehicle of vehicle by the server of distributed queue
Long, load-carrying etc.) and the source of goods information (departure place, destination, cargo type, volume, weight)；Then, system passes through first
Cargo type screening, and by circuit analysis, final online is matched in road；It is finally reflected to user, that is, provides a user
User.
Algorithm principle is as follows: when Predatory search algorithm optimizing, first global search is carried out in entire search space, until finding
One more excellent solution；Then the region near more excellent solution carries out concentration search, if more excellent solution is not found in search many times yet,
Then abandon Local Search；Then global search is carried out in entire search space again, so recycled, until it is (or close to find optimal solution
Like optimal solution) until.
Optimization aim be meet driver demand most short vehicle running path with goods, vehicle is made with goods problem first
Basic assumption:
(1) known to driver position and the position of client；
(2) known to the demand of client；
(3) each client is only accessed by a vehicle；
(4) demand of each client must satisfy；
(5) demand on every route must not exceed the maximum mounted mass of vehicle；
(6) the total demand of client is not greater than the total mounted mass of vehicle；
Variable-definition is as follows:
K: distribution vehicle number
Q: the mounted mass of vehicle
L: the client's number for needing to service
gi: the cargo demand of i-th of client
cij: it indicates from client i to the transportation range of client j
In order to arrange freightways, first have to reasonably estimate the vehicle number of acknowledgement of consignment cargo task.In general,
The constraint of problem is more, and tissue route is just more difficult to, and the constrained task of satisfaction institute that a vehicle is completed is fewer.In actual conditions
Vehicle number is determined according to formula (4.1).
K=[∑ gi/aq] (4.1)
Wherein K is vehicle number, and [] indicates to be rounded downwards, 0 < a < 1, according to constraint condition number be adjusted.Constrain item
Part is more, then a is smaller, otherwise bigger, generally takes a=0.85.
To be conveniently constructed mathematical model, client's number consecutively is i (i=1,2 ... 3.., L).Constraint in model is mainly
The capacity-constrained of vehicle.Its mathematical model are as follows:
Objective function:
Constraint condition:
xijk=0 or 1, i, j=1,2,3 ... L；K=1,2,3 ... K (4.7)
yik=0 or 1, i=1,2,3 ... L；K=1,2,3 ... K (4.8)
In a model:
Formula (4.2) is objective function；
Formula (4.3) is the capacity-constrained of vehicle, i.e. vehicle cannot overload；
Formula (4.4) indicates that each client is accessed and is only accessed once；
Formula (4.5) indicates that the vehicle for reaching each client only has one；
Formula (4.6) indicates that the vehicle for leaving each client only has one；
Formula (4.7) and (4.8) are Integer constrained characteristic.
For the optimization aim of model while meeting client and needing, the operating range dispensed to vehicle is most short.
To the prewired goods routing problem of vehicle, genetic algorithm generally uses natural number coding, when with particle swarm algorithm,
The space for constructing L dimension is corresponding with L client's point task, the VRP routing problem of K vehicle, because sharing K vehicle, most
There are K paths more.In order in coding react vehicle match goods path, indicated using each particle with L+K-1 dimensional vector.
Wherein, the size order of each element value indicates dispatching order of each client's point in total path, this like-particles can with it is final
The corresponding number of tasks for for example setting client's point in a VRP of solution be 8, the vehicle number of home-delivery center is 3, the position vector of certain particle
X is as shown in table 1,
Table 1
| Client's point | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 0 | 0 |
| X | 5.1 | 7.2 | 3.7 | 1.3 | 1.5 | 8.2 | 4.4 | 8.9 | 2.1 | 6.8 |
In order to facilitate calculating, first have to carry out integer specification to X again by the size order of element value, as a result such as table 2
It is shown,
Table 2
Wherein, O is vehicle location.Then the path of vehicle homographic solution is
Vehicle 1:c1-4-5
Vehicle 2:c2-3-7-1
Vehicle 3:c3-2-6-8
Prewired goods functional value
It evaluates whether that matched standard is the size with value of goods, determines that with value of goods be to adapt to value function.For convenient for writing
Program, can be directly using objective function as with value of goods function.For given Vehicle Routing Problems, due to there is the appearance of vehicle
Amount constraint must add a part of capacity-constrained on objective function.It can locate on formula (4.2) plus a penalty
Manage this constraint:
The latter half of formula (4.9) is the penalty value applied to the infeasible solution beyond capacity-constrained, and M is infinity, real
M can be taken to a sufficiently large number in the processing of border.In this way, infeasible solution can assign great adaptive value, it can quilt in iteration
It eliminates.
For the ease of following description, the mathematical model of PSO algorithm is provided again here: wherein used variable
With it is preceding consistent.
I: particle (corresponding vehicle)
N: the dimension (vehicle of corresponding vehicle, vehicle commander, vehicle-mounted heavy etc.) of particle
T: the algebra (matching degree of corresponding vehicle and cargo) of evolution
X: the position (position of corresponding vehicle) of particle
V: the speed (speed of corresponding vehicle) of particle
P: the desired positions (desired positions that corresponding vehicle corresponds to the source of goods) where particle
C1, C2: accelerated factor (priority of corresponding vehicle)
R1, R2: two random factors (load-carrying that corresponding vehicle occurs at random)
L≤i≤d, 1≤i≤N:
As shown in figure 4, the specific implementation steps are as follows for the algorithm of standard PSO solution:
Step 1、
1.1: initialization population.The position vector x of each particle takes the integer between 1~(L+K-1), each grain at random
Son velocity vector y take at random-(L+K-1)~(L+K-1) between integer；Set constant w, c1, c2, M；
1.2: the position vector of each particle is converted to the form of total path.
1.3: calculating the adaptive value of each particle by formula (4.9), using initial adaptive value as individual history optimal solution, and seek
Look for optimal solution in total group；
Step 2, x is calculated by formula (4.12) to each particle, calculates V according to formula (4.10)；Work as X, V is more than its range
When press boundary value；
Step 3, the form that X is converted to total path；
If the current adaptive value of Step 4, some particle is better than its history adaptive optimal control value, remember that current adaptive value is to be somebody's turn to do
History adaptive optimal control value, while remembering that current location is the particle history optimal location；
Step 5, optimal solution in total group is found, is updated if being better than history optimal solution.For individuals all in subgroup
To have the case where multiple bodies are all optimal value in infeasible solution or subgroup, then it is current optimal in subgroup for taking one of them at random
Solution；
Step 6, it is such as not up to termination condition, then returns to Step 2.
As shown in figure 5, the particle swarm algorithm based on predatory search strategy is as follows,
For ease of description, definition is made to variable first:
LN: the sum limited for setting,
CM: to limit lower maximum cycle
CR: for the number of circulation
LL: to limit rank
In population, a limitation range is set.This is set to limit the maximum speed as particle, in search range
Circulation searching optimal solution is carried out, whether the adaptive value after judging search becomes smaller.Such as become smaller, then recalculates and limit and continue
Circulation, until finding optimal solution.
Specific implementation process:
Step 1 randomly chooses an initial point x in initialization space, enables xmin=x, CR=0, LL=0；
If step 2, LL < LN, in the current limitation RN (LL) of x, n particle is initialized.Current limitation is used as particle
Maximum speed.Several times according to standard PSO formula iteration, and its history optimal solution x is takenN-min, then go to step 3；Otherwise it ties
Beam.
Step 3 enables x=xN-minSong, if f (x) < f (xmin), it enables and preferably solves x so farmin=x, cycle-index CR=0,
Rank LL=0 is limited, limitation is then recalculated, goes to step 2；Otherwise 4 are gone to step；
Step 4 enables CR=CR+1, if CR > CM, enables CR=0, LL=LL+l, then goes to step 5；Otherwise 2 are gone to step；
If step 5, LN=[LN/5], LL=LN- [LN/5] is enabled (by limiting the jump of rank youngster, to realize from local
Search the conversion of global search), and go to step 2；Otherwise 2 are directly gone to step.
In above-mentioned steps, the concrete operations of limitation are recalculated are as follows: a particle is initially deleted at random in initial space, this LN
A value, according to arranging from small to large, forms LN limitation at a distance from the x preferably solved so far.
In order to test the performance of the particle swarm algorithm based on predatory search strategy, it is examined to ask large-scale vehicle with goods
Whether topic can provide preferable result.The present invention is tested using the E.n51.k5 that Christofides and Eilont561 is proposed
Problem is verified.The test problem are as follows: 50 customer demand point tasks.The capacity of vehicle is 160, vehicle number 5.Each
The coordinate and customer demand of business point are shown in Table 3.It is required that the distribution route of reasonable arrangement vehicle, keeps dispatching mileage most short.
Table 3
Parameter setting are as follows: population d=50, inertia weight=0.75, accelerated factor cl=c2=1.49, the dimension of particle
N takes L+K-1=50+5-1=54 to tie up, and penalty coefficient takes M=1010, cycle-index CM=250 under limiting, the total ditch between fields of limitation
=50.Random walk 18 times on computers of particle swarm algorithm based on predatory search strategy are used to the example.It acquires optimal
Shortest driving total distance is 524.629.
Corresponding 5 vehicle routes are as follows:
Vehicle 1:27-48-23-7-43-24-25-14-6
Vehicle 2:12-37-44-15-45-33-39-10-9-5-46
Vehicle 3:8-26-31-28-3-36-35-20-22-1-32
Vehicle 4:47-4-17-42-19-40-41-13-18
Vehicle 5:11-16-2-29-21-50-34-30-9-38
Shown in random walk 18 times result tables 4:
Table 4
| Number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| Optimal value | 563.566 | 542.132 | 524.629 | 538.979 | 524.629 | 534.458 | 528.564 | 524.629 | 524.629 |
| Number | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 |
| Optimal value | 561.939 | 524.629 | 524.629 | 538.979 | 524.629 | 544.402 | 527.217 | 524.629 | 539.069 |
From table 4, it can be seen that running 18 times, the average value of the optimal result of the PSO algorithm based on predatory search strategy is
534.233.Searching for success rate is 44%.It solves and differs smaller between gained optimal result, it is as a result relatively stable within 8%.
Its response time is 2s, and convergence rate is very fast.It is solved with this algorithm, the result is that satisfactorily.This explanation, is being planted
In the case that group is larger, the solving result of the particle swarm algorithm based on predatory search strategy is preferable, the success rate of search
It is higher.Not only fast convergence rate but also result stabilization.Due to the introducing of predatory search strategy, its exploring ability is improved, is made
The convergence rate of particle swarm algorithm based on predatory search strategy is significantly improved；Its development ability, energy are improved simultaneously
It enough finds and more preferably solves, compensate for the defect that particle swarm algorithm is easily trapped into local optimum.
To sum up, the present invention uses the recommendation of above-mentioned big data, can calculate vehicle and the source of goods according to driver's user information
Matching value, and information of freight source is recommended to driver user according to matching value；To intelligent, a according to the information realization of driver user
Property the source of goods recommend so that can be shortened transport mileage to driver user, more efficient matching.
The above description is only an embodiment of the present invention, is not intended to limit the scope of the invention, all to utilize this hair
Equivalents made by bright specification and accompanying drawing content are applied directly or indirectly in relevant technical field, similarly include
In scope of patent protection of the invention.
Claims (10)
1. a kind of method of the prewired goods of line haul based on big data, which is characterized in that include at least step:
S1: the gatehead that transport cargo is obtained, and is set as center；
S2: the destination of each cargo is calculated at a distance from the center；
S3: according to the distance of all cargos, class center is calculated；
S4: it according to the class center, carries out with goods.
2. the method for the prewired goods of the line haul according to claim 1 based on big data, which is characterized in that step S4 it
Before, further includes:
S41: judging whether the class center changes,
If so, thening follow the steps S42: return step S3 recalculates class center；
Conversely, thening follow the steps S4.
3. the method for the prewired goods of the line haul according to claim 1 based on big data, which is characterized in that the step
Before S1, further includes:
S01: the position of each vehicle, loading capacity, customer quantity, the cargo demand of each client, each cargo are obtained not
With the transportation range between client；
S02: determining the quantity of vehicle according to formula (4.1),
K=[∑ gi/ aq] ... ... (4.1),
Wherein, K indicates the quantity of vehicle, and q indicates the loading capacity of vehicle, giIndicate the cargo demand of i-th of client, [] indicates
It is rounded downwards, a indicates adjustable constraint condition, and 0 < a < 1.
4. the method for the prewired goods of the line haul according to claim 3 based on big data, which is characterized in that obtaining visitor
Further include step S03 after the quantity at family: according to the loading capacity of following formula control vehicle:
And constraint condition are as follows:
xijk=0 or 1, i, j=1,2,3 ... L；K=1,2,3 ... K ... ... (4.7)
yik=0 or 1, i=1,2,3 ... L；K=1,2,3 ... K ... ... (4.8)
Wherein:
Formula (4.2) is objective function；
Formula (4.3) is that the loading capacity of vehicle constrains, i.e., vehicle cannot overload；
Formula (4.4) indicates that each client is accessed and is only accessed once；
Formula (4.5) indicates that the vehicle for reaching each client only has one；
Formula (4.6) indicates that the vehicle for leaving each client only has one；
Formula (4.7) and (4.8) are Integer constrained characteristic；
Wherein, L indicates customer quantity, cijIt indicates from client i to the transportation range of client j,
5. the method for the prewired goods of the line haul according to claim 4 based on big data, which is characterized in that in step
Further include step S04 after S03: carrying out quadratic constraints according to loading capacity of the formula (4.9) to control vehicle,
Wherein, M is penalty value, and M is infinity.
6. a kind of system of the prewired goods of line haul based on big data characterized by comprising
Center module for obtaining the gatehead that transport cargo, and is set as center；
Spacing module, for calculating the destination of each cargo at a distance from the center；
Class center module calculates class center for the distance according to all cargos；
With goods module, for according to the class center, carrying out with goods.
7. the system of the prewired goods of the line haul according to claim 6 based on big data, which is characterized in that described to match goods
Module is also used to:
Judge whether the class center changes.
8. the system of the prewired goods of the line haul according to claim 6 based on big data, which is characterized in that further include:
Initial information module, for obtain the position of each vehicle, loading capacity, customer quantity, each client cargo demand,
The transportation range of each cargo between the different customers；
Function module, for determining the quantity of vehicle according to formula (4.1),
K=[∑ gi/ aq] ... ... (4.1),
Wherein, K indicates the quantity of vehicle, and q indicates the loading capacity of vehicle, giIndicate the cargo demand of i-th of client, [] indicates
It is rounded downwards, a indicates adjustable constraint condition, and 0 < a < 1.
9. the system of the prewired goods of the line haul according to claim 8 based on big data, which is characterized in that the function
Module is also used to control the loading capacity of vehicle according to following formula:
And constraint condition are as follows:
xijk=0 or 1, i, j=1,2,3 ... L；K=1,2,3 ... K ... ... (4.7)
yik=0 or 1, i=1,2,3 ... L；K=1,2,3 ... K ... ... (4.8)
Wherein:
Formula (4.2) is objective function；
Formula (4.3) is that the loading capacity of vehicle constrains, i.e., vehicle cannot overload；
Formula (4.4) indicates that each client is accessed and is only accessed once；
Formula (4.5) indicates that the vehicle for reaching each client only has one；
Formula (4.6) indicates that the vehicle for leaving each client only has one；
Formula (4.7) and (4.8) are Integer constrained characteristic；
Wherein, L indicates customer quantity, cijIt indicates from client i to the transportation range of client j,
10. the method for the prewired goods of the line haul according to claim 9 based on big data, which is characterized in that further include
Punish module, for carrying out quadratic constraints according to loading capacity of the formula (4.9) to control vehicle,
Wherein, M is penalty value, and M is infinity.
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| CN109359771A (en) * | 2018-10-11 | 2019-02-19 | 福建龙易配信息科技有限公司 | A kind of line haul vehicle and goods matching algorithm based on big data |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111127896A (en) * | 2019-12-16 | 2020-05-08 | 苏州水易数据科技有限公司 | Intelligent transportation control method and system based on big data processing and analysis |
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