CN108846623A - Based on the complete vehicle logistics dispatching method and device of multiple target ant group algorithm, storage medium, terminal - Google Patents
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Abstract
It is a kind of based on the complete vehicle logistics dispatching method and device of multiple target ant group algorithm, storage medium, terminal, the method includes：Complete vehicle logistics data are obtained, the complete vehicle logistics data include order data and transport power data；Based on M candidate allocation scheme of the complete vehicle logistics data acquisition, wherein M >=1；The candidate allocation scheme is denoted as ant, the set that M ant is constituted is denoted as ant colony, in the ant colony during each ant transfer, give up the ant that all object vectors are dominated in the ant colony, to obtain Noninferior Solution Set, projection of the ant in each target is denoted as the object vector corresponding to the target；When the transfering state of the ant colony meets preset termination condition, optimal scheduling scheme is chosen from the Noninferior Solution Set of acquisition according to business scenario.The scheme provided through the invention can be realized the automatic dispatching of complete vehicle logistics, and be conducive to realize optimal scheduling, reduce the dynamic dispatching cost of freight on the whole.
Description
Technical field
The present invention relates to automobile logistics technical fields, more particularly to a kind of complete vehicle logistics based on multiple target ant group algorithm
Dispatching method and device, storage medium, terminal.
Background technique
Complete vehicle logistics refer to that vehicle dispenses a series of activities that website, dealer are transported to End-Customer from main engine plants, respectively
And process, complete vehicle logistics dispatch a series of problems, such as needing to solve logistics route planning, prestowage and vehicle scheduling.
The factor that the scheduling of existing complete vehicle logistics is related to is complex, and constraint condition is numerous, and target is polynary and mutual restriction, example
It such as include that main engine plants and its warehouse, logistics company and its transfer storage facility, common carrier and its contract driver, dealer and its warehouse are more
A aspect is a multi-objective optimization question for conclusion.
And most of logistics company is that scheduling transportation scheme is customized according to artificial experience, prestowage process mostly uses hand
Work operation, prestowage scheme depend entirely on the experience of dispatcher.Such complete vehicle logistics scheduling mode, which exists, to be considered to become
The shortcomings such as amount factor is few, scheduling scheme is non-optimal, transport capacity resource utilization rate is not high, order reaction speed is slow, are unable to reach
The expection of automobile vendor and client.
Summary of the invention
Present invention solves the technical problem that being how to realize the automatic dispatching of complete vehicle logistics, more rationally, comprehensively to adjust
Degree logic reduces the dynamic dispatching cost of freight on the whole.
In order to solve the above technical problems, the embodiment of the present invention provides a kind of complete vehicle logistics tune based on multiple target ant group algorithm
Degree method, including：Complete vehicle logistics data are obtained, the complete vehicle logistics data include order data and transport power data；Based on described
M candidate allocation scheme of complete vehicle logistics data acquisition, wherein M >=1；The candidate allocation scheme is denoted as ant, by M ant
The set that ant is constituted is denoted as ant colony, in the ant colony during each ant transfer, gives up all object vectors in the ant colony
The ant dominated, to obtain Noninferior Solution Set, projection of the ant in each target is denoted as the mesh corresponding to the target
Mark vector；When the transfering state of the ant colony meets preset termination condition, according to business scenario from the noninferior solution of acquisition
It concentrates and chooses optimal scheduling scheme.
Optionally, described to include based on M candidate allocation scheme of the complete vehicle logistics data acquisition：Loop iteration it is random
The order data and transport power data are matched, for each iteration, when Order splitting finishes and matched transport power is minimum, or
Person, when capacity deployment finishes, candidate allocation that the matching result of allocated order and transport power is obtained as current iteration
Scheme；The candidate allocation scheme that all previous iteration obtains is screened based on default constraint condition, to obtain the M candidate allocation side
Case.
Optionally, the loop iteration order data and transport power data described in random fit include：From the transport power number
A transport power is randomly selected in and starts inner iteration, and the process of the inner iteration includes：Traversing the order data includes
Order meets all orders for loading constraint with the transport power to be screened out from it；Judge whether the transport power is fully loaded with；When described
It when transport power underload, empties the matching result of the transport power and re-executes the inner iteration, until the result of this inner iteration
For the transport power full load, judge whether the order that the order data includes is assigned；When the order data includes
Order splitting does not finish, and the transport power data transport power that includes is unallocated when finishing, and continues random from the transport power data
It extracts a transport power and executes the inner iteration, until the capacity deployment that the transport power data include finishes or the order data packet
The Order splitting included finishes, to complete one cycle iteration.
Optionally, the candidate allocation side that the matching result of allocated order and transport power is obtained as current iteration
Case includes：The quantity for the transport power that the quantity and last loop iteration for comparing the transport power that this loop iteration determines determine；If this
The quantity for the transport power that secondary loop iteration determines is less than the quantity for the transport power that last loop iteration determines, this loop iteration is true
The candidate allocation scheme that the matching result of fixed transport power and order is obtained as current iteration.
Optionally, the order that the traversal order data includes, is loaded with being screened out from it to meet with the transport power
Constraint all orders include：An order is randomly selected from the order data；Judge the transport power and the order is
It is no to meet the loading constraint；When the transport power and the order are unsatisfactory for loading constraint, again from the order numbers
An order is randomly selected in, until when this order randomly selected meets loading constraint with the transport power, judgement
Whether the order that the order data includes, which traverses, finishes；When the order that the order data includes, which does not traverse, to be finished, continue
An order is randomly selected from the order data and judges whether the transport power and this order randomly selected meet institute
Loading constraint is stated, until the order that the order data includes all is traversed and finished.
Optionally, the default constraint condition is selected from：Prestowage constraint；The constraint of intention direction；City numbers constraint can be spelled.
Optionally, every ant in the ant colony is assigned with i initialization information prime matrix and heuristic information matrix,
I is number of targets, and the initialization information prime matrix and heuristic information matrix and target correspond, wherein for every ant
Each target, the heuristic information matrix is used to describe the initial matching of order and transport power of the ant under the target
As a result, the initialization information prime matrix is initial between each transport power in order each under the target for describing the ant
Transition probability.
Optionally, for every ant, the heuristic information matrix is indicated based on following formula：Bx=(buv)；Wherein, BxFor the heuristic information matrix of x-th of target of the ant, 1≤x≤i, buvFor the inspiration
The element that u row v is arranged in information matrix, u are u-th of order in the order data, and 1≤u≤U, U are the order numbers
According to the total orders for including, v be the transport power data in v-th of transport power, 1≤v≤V, V be the transport power data include it is total
Transport power number, works as buvIt indicates that u-th of order matches with v-th of transport power in the ant when=1, works as buvIt indicates when=0 in institute
U-th of order in ant is stated not match that with v-th of transport power.
Optionally, for every ant, initialization information prime matrix A of the ant in x-th of targetxIncluding U × V
Element, wherein the total orders that U includes for the order data, total transport power number that V includes for the transport power data, the U ×
V element is filled with preset constant.
Optionally, described during each ant transfer, to give up object vector all in the ant colony in the ant colony
The ant dominated includes to obtain Noninferior Solution Set：In the ant colony during each ant transfer, based on ant group algorithm
Calculate and update the state-transition matrix and Pheromone Matrix of each ant in the ant colony, wherein for each of every ant
Target, the state-transition matrix are used to describe the newest matching result of order and transport power of the ant under the target,
The Pheromone Matrix is for describing the ant newest transition probability of each order between transport power under the target；For
Every ant calculates object vector of the ant in each target according to the state-transition matrix；Give up in each target
Object vector be inferior to the ant of the corresponding object vector of other ants in ant colony, to obtain the Noninferior Solution Set, wherein
For the ant in the Noninferior Solution Set, the ant is at least in the object vector in a target better than in the Noninferior Solution Set
Other ants.
Optionally, in the ant colony during each ant transfer, the state transfer of each ant in the ant colony is updated
The update cycle of matrix and Pheromone Matrix is as unit of step-length when each ant transfer.
Optionally, during ant transfer, for each target of every ant, by updated Pheromone Matrix
Initialization information prime matrix when being shifted as the ant next time.
Optionally, for every ant in the ant colony, the ant is according to certainty probability or randomness probability
Shifted, wherein it is described according to certainty probability carry out transfer refer to according to the ant Pheromone Matrix instruction
Maximum probability direction is shifted, described to carry out transfer according to randomness probability and refer to and shifted according to random direction, described
Pheromone Matrix is used to describe newest transition probability of each order of the ant between transport power.
Optionally, the ant includes according to the process that certainty probability or randomness probability are shifted：The ant
A random number is extracted out of pre-set interval；When the random number is less than preset threshold, shifted according to certainty probability；
Otherwise, it is shifted according to randomness probability.
Optionally, the preset termination condition includes：The transfer number of each ant reaches preset loop in the ant colony
Number.
Optionally, the complete vehicle logistics data are by carrying out pretreatment acquisition, the acquisition vehicle to initial data
Logistics data includes：Obtain the initial data；According to initial data described in preset standard value range screening, to reject the original
The data of corresponding preset standard value range are not met in beginning data；The complete vehicle logistics are obtained according to the initial data after screening
Data.
Optionally, the target is selected from：Maximize shipped quantity；It maximizes and loads Commercial Vehicle urgency level；Maximum makeup
Carry large and medium-sized Commercial Vehicle quantity.
Optionally, the quantity M of the candidate allocation scheme is determined according to the order data.
In order to solve the above technical problems, the embodiment of the present invention also provides a kind of complete vehicle logistics based on multiple target ant group algorithm
Dispatching device, including：Module is obtained, for obtaining complete vehicle logistics data, the complete vehicle logistics data include order data and fortune
Force data；Initial load module, for being based on M candidate allocation scheme of the complete vehicle logistics data acquisition, wherein M >=1；It is more
The candidate allocation scheme is denoted as ant by target ant group algorithm optimization module, and the set that M ant is constituted is denoted as ant colony,
In the ant colony during each ant transfer, give up the ant that all object vectors are dominated in the ant colony, to obtain
Noninferior Solution Set, projection of the ant in each target are denoted as the object vector corresponding to the target；Module is chosen, when described
When the transfering state of ant colony meets preset termination condition, optimal tune is chosen from the Noninferior Solution Set of acquisition according to business scenario
Degree scheme.
In order to solve the above technical problems, the embodiment of the present invention also provides a kind of storage medium, it is stored thereon with computer and refers to
The step of enabling, the above method executed when the computer instruction is run.
In order to solve the above technical problems, the embodiment of the present invention also provides a kind of terminal, including memory and processor, it is described
The computer instruction that can be run on the processor is stored on memory, the processor runs the computer instruction
The step of Shi Zhihang above method.
Compared with prior art, the technical solution of the embodiment of the present invention has the advantages that：
The embodiment of the present invention provides a kind of complete vehicle logistics dispatching method based on multiple target ant group algorithm, including：It obtains whole
Vehicle logistics data, the complete vehicle logistics data include order data and transport power data；Based on the complete vehicle logistics data acquisition M
Candidate allocation scheme, wherein M >=1；The candidate allocation scheme is denoted as ant, the set that M ant is constituted is denoted as ant
Group gives up the ant that all object vectors are dominated in the ant colony, in the ant colony during each ant transfer to obtain
Noninferior Solution Set is taken, projection of the ant in each target is denoted as the object vector corresponding to the target；When the ant colony
When transfering state meets preset termination condition, optimal scheduling side is chosen from the Noninferior Solution Set of acquisition according to business scenario
Case.Compared with existing using the artificial implementation for carrying out complete vehicle logistics scheduling, the scheme of the embodiment of the present invention with it is intelligentized from
Dynamic solution scheme substitutes existing manual mode of operation, and each candidate allocation scheme that will acquire is denoted as an ant, in conjunction with ant
The principle of group's algorithm obtains one group of noninferior solution by corresponding to different target by the transfer of every ant during continuous iteration
The Noninferior Solution Set of composition is chosen from the optional program according to the specific requirement of business scenario optimal as optional program
Scheduling scheme.It will be appreciated by those skilled in the art that the scheme of the embodiment of the present invention is by multiple target ant group algorithm come to complete vehicle logistics
Scheduling is accurately described and is deduced, and the optional program finally obtained can flexibly correspond to different scheduling scenarios, fully consider
All requirements avoid generating failure scheduling, to improve the system effectiveness of vehicle scheduling system, it is ensured that vehicle scheduling system
System can be run without any confusion.Further, the scheme of the embodiment of the present invention can not only improve operation efficiency, additionally it is possible to ensure
The solution of optimal case, reduces cost, increases customer satisfaction degree.
Further, described to include based on M candidate allocation scheme of the complete vehicle logistics data acquisition：Loop iteration it is random
The order data and transport power data are matched, for each iteration, when Order splitting finishes and matched transport power is minimum, or
Person, when capacity deployment finishes, candidate allocation that the matching result of allocated order and transport power is obtained as current iteration
Scheme；The candidate allocation scheme that all previous iteration obtains is screened based on default constraint condition, to obtain the M candidate allocation side
Case.It will be appreciated by those skilled in the art that carrying out order and transport power using greedy algorithm in the initialization loading process of the present embodiment
Preliminary matches, directly to calculate a feasible scheduling scheme, then in multiple target ant group algorithm through this embodiment
The feasible scheduling scheme is carried out by excellent deduction, to obtain optimal case.
Further, described during each ant transfer, to give up object vector all in the ant colony in the ant colony
The ant dominated includes to obtain Noninferior Solution Set：In the ant colony during each ant transfer, based on ant group algorithm
Calculate and update the state-transition matrix and Pheromone Matrix of each ant in the ant colony, wherein for each of every ant
Target, the state-transition matrix are used to describe the newest matching result of order and transport power of the ant under the target,
The Pheromone Matrix is for describing the ant newest transition probability of each order between transport power under the target；For
Every ant calculates object vector of the ant in each target according to the state-transition matrix；Give up in each target
Object vector be inferior to the ant of the corresponding object vector of other ants in ant colony, to obtain the Noninferior Solution Set, wherein
For the ant in the Noninferior Solution Set, the ant is at least in the object vector in a target better than in the Noninferior Solution Set
Other ants.The principle of the scheme combination ant group algorithm of the present embodiment, will be between order and transport power documented by allocation plan
The situation of change of matching relationship be equivalent to the transfer of ant, to calculate each ant during every ant transfer in ant colony
State-transition matrix and Pheromone Matrix, be not inferior to other ants therefrom to select the object vector at least one target
Noninferior Solution Set described in the ant of the object vector of ant on the object and composition.It will be appreciated by those skilled in the art that being based on this implementation
The scheme of example can be approached using the positive feedback mechanism of ant colony to optimal case, it is ensured that different application scenarios can be final
Suitable noninferior solution (i.e. optimal case) is corresponded in the Noninferior Solution Set of acquisition.
Detailed description of the invention
Fig. 1 is a kind of flow chart of complete vehicle logistics dispatching method based on multiple target ant group algorithm of the embodiment of the present invention；
Fig. 2 is the flow chart of a specific embodiment of step S101 in Fig. 1；
Fig. 3 is the flow chart of a specific embodiment of step S102 in Fig. 1；
Fig. 4 is the flow chart of a typical case scene of Fig. 3；
Fig. 5 is the flow chart of a specific embodiment of step S103 in Fig. 1；
Fig. 6 is the flow chart of a typical case scene of Fig. 5；
Fig. 7 is a kind of structural representation of complete vehicle logistics dispatching device based on multiple target ant group algorithm of the embodiment of the present invention
Figure.
Specific embodiment
It will be appreciated by those skilled in the art that as described in the background art, traditional complete vehicle logistics scheduling method does not fully consider
Specific scheduling scenario does not carry out loading optimization to task object, does not also fully consider the constraint of order of input itself
Demand, but operation plan (i.e. scheduling scheme) is formed simply by the mode of manual allocation order to vehicle.Timing by
The deficiency existing for manual dispatching in existing complete vehicle logistics scheduling scheme leads to the few, scheduling scheme in the presence of consideration Variable Factors
The non-optimal, shortcomings such as transport capacity resource utilization rate is low, order reaction speed is full, are unable to satisfy business contract in practical applications
The constraint angularly proposed damages the stackholders of task various aspects, but will be due to ignoring some scheduling systems
In reality factor and generate inefficient solution, influence the normal operation of whole system, cause inefficiency, system perturbations.
On the other hand, existing complete vehicle logistics scheduling method do not comb aims of systems and carry out it is various plan as a whole it is excellent
Change, limit the promotion of system capability and the maximization to various aspects interests, or each target of Scheduling System cannot be weighed,
It causes to attend to one thing and lose sight of another, put the cart before the horse, it is even more impossible to carry out global pool optimization from the angle of time.
In order to realize intelligentized automatic calculation vehicle scheduling scheme, present inventor studies discovery：
Solution throughway generally, for typical multi-objective problem is to carry out mathematical modeling to it, is abstracted as
The optimization problem of numerical function.But in practical applications, due to practical factor complexity, these functions would generally show different
Mathematical feature, as objective function and constraint function whether continuously differentiable, if having convexity matter etc., so that calculated result is likely difficult to
Meet physical condition.So in most cases, needing to carry out near-optimal calculating by the method that numerical value calculates.That is, needle
To current application scene, need to find out numerical function approximate optimal solution within acceptable time and accuracy rating.And it is heuristic
Requirement of the algorithm for objective function and constraint condition is more loose, does not require to reach accurate optimal solution, thus become currently compared with
For popular solution.
As a kind of specific solution of heuritic approach, ant group algorithm is a kind of machine for finding path optimizing in figure
Rate type algorithm.Its Inspiration Sources finds the behavior of shortest path during Food Recruiment In Ants.Specifically, before ant is on path
Into when can select next step path according to the pheromone concentration that previous ant secretes, select the probability and information of a paths
The concentration of element is proportional.The collective behavior of ant colony constitutes positive feedback mechanism as a result, that is, the ant that certain paths is passed by is got over
More, subsequent ant selects the probability in the path bigger.Ant group algorithm timing is according to this phenomenon, and manually ant simulates ant colony
Behavior, to realize optimizing.
On the other hand, there may be the case where conflicting with each other when carrying out multiple target solution, therefore, multiple-objection optimization is asked
Topic very likely can not find unique globally optimal solution so that optimal on all target stricti jurise.
Thus, herein described scheme will not be for one or more mesh based on multiple solutions that multiple target ant group algorithm generates
Mark is further optimized, that is, the optimal solution for a target that the scheme based on the embodiment of the present invention generates is to surplus
As long as other remaining targets reach be unlikely to deterioration degree, therefore this multiple solution can be classified as Noninferior Solution Set (or
Pareto disaggregation).
In order to solve technical problem described in background technique, scheme disclosed in the present application by the Dynamic Programming of belt restraining with
The mode that multiple-objection optimization combines realizes intelligent, automation complete vehicle logistics scheduling, it can be considered that variable as much as possible
Factor, the scheduling scheme optimal conducive to acquisition, are greatly improved transport capacity resource utilization rate, improve order reaction speed.
Specifically, the embodiment of the present invention provides a kind of complete vehicle logistics dispatching method based on multiple target ant group algorithm, packet
It includes：Complete vehicle logistics data are obtained, the complete vehicle logistics data include order data and transport power data；Based on the complete vehicle logistics number
According to M candidate allocation scheme of acquisition, wherein M >=1；The candidate allocation scheme is denoted as ant, the collection that M ant is constituted
Conjunction is denoted as ant colony, in the ant colony during each ant transfer, gives up what all object vectors in the ant colony were dominated
Ant, to obtain Noninferior Solution Set, projection of the ant in each target is denoted as the object vector corresponding to the target；Work as institute
When stating the transfering state of ant colony and meeting preset termination condition, chosen from the Noninferior Solution Set of acquisition according to business scenario optimal
Scheduling scheme.
It will be appreciated by those skilled in the art that the scheme of the embodiment of the present invention is existing with intelligentized automatic calculation scheme substitution
Manual mode of operation, each candidate allocation scheme that will acquire are denoted as an ant, in conjunction with the principle of ant group algorithm, are constantly changing
Transfer during generation by every ant obtain one group by the Noninferior Solution Set that the noninferior solution for corresponding to different target forms be used as to
Scheme is selected, and then optimal scheduling scheme is chosen from the optional program according to the specific requirement of business scenario.
Further, the scheme of the embodiment of the present invention accurately retouches complete vehicle logistics scheduling by multiple target ant group algorithm
It states and deduces, the optional program finally obtained can flexibly correspond to different scheduling scenarios, fully consider all requirements, avoid
Failure scheduling is generated, to improve the system effectiveness of vehicle scheduling system, it is ensured that the vehicle scheduling system can be without any confusion
Ground operation.
Further, the scheme of the embodiment of the present invention can not only improve operation efficiency, additionally it is possible to ensure asking for optimal case
Solution, reduces cost, increases customer satisfaction degree.
It will be appreciated by those skilled in the art that multiple target ant group algorithm is applied to the more of belt restraining by the scheme of the embodiment of the present invention
Objective optimization is scheduled the solution of scheme.For example, the scheme based on the embodiment of the present invention, can load number to maximize
Amount maximizes loading order urgency level, maximizes the targets of the targets as algorithm such as the large and medium-sized Commercial Vehicle quantity of loading；It can be with
By prestowage constraint, the constraint of intention destination, the constraints as algorithm such as city numbers constraint can be spelled；In above-mentioned target and constraint
On the basis of optimizing is iterated to scheduling scheme using multiple target ant group algorithm, can include a variety of scheduling scenarios to obtain one group
Scheme disaggregation.Further, for different scheduling scenarios, final tune can be selected using different target weight parameters
Degree scheme, rather than same set of parameter is used, so that the scheduling scheme obtained is most suitably adapted for the optimal side of current scheduling scene
Case.
It is understandable to enable above-mentioned purpose of the invention, feature and beneficial effect to become apparent, with reference to the accompanying drawing to this
The specific embodiment of invention is described in detail.
Fig. 1 is a kind of flow chart of complete vehicle logistics dispatching method based on multiple target ant group algorithm of the embodiment of the present invention.
Wherein, complete vehicle logistics scheduling can refer to vehicle from factory, via dispatching website and dealer to the object during End-Customer
Flow path planning, prestowage and Transport capacity dispatching.The scheme of the embodiment of the present invention can be adapted for complete vehicle logistics scheduling application scenarios, with
Determine the optimal scheduling scheme for being directed to specific business scenario (alternatively referred to as scheduling scenario).
Specifically, with reference to Fig. 1, the complete vehicle logistics dispatching method based on multiple target ant group algorithm described in the present embodiment be can wrap
Include following steps：
Step S101 obtains complete vehicle logistics data, and the complete vehicle logistics data include order data and transport power data.
Step S102 is based on M candidate allocation scheme of the complete vehicle logistics data acquisition, wherein M >=1.
The candidate allocation scheme is denoted as ant by step S103, the set that M ant is constituted is denoted as ant colony, in institute
During stating in ant colony each ant transfer, give up the ant that all object vectors are dominated in the ant colony, it is non-bad to obtain
Disaggregation, projection of the ant in each target are denoted as the object vector corresponding to the target.
Step S104, when the transfering state of the ant colony meets preset termination condition, according to business scenario from acquisition
Optimal scheduling scheme is chosen in the Noninferior Solution Set.
More specifically, the order data can be used for describing information relevant to vehicle to be scheduled.For example, described
Order data may include vehicle to be dispensed, destination, due date, order urgency level etc..
Further, the transport power data can be used for the means of transport for describing to can be used for transporting the vehicle to be scheduled
Prestowage information.For example, the transport power data may include the quantity of the means of transport, struck capacity etc..Preferably, described
Means of transport may include sedan-chair fortune vehicle.
Further, the complete vehicle logistics data can also include node data, need when for describing to formulate scheduling scheme
Dispatching website, the dealer to be passed through etc..
Further, the complete vehicle logistics data can also include contextual data, need when for describing to formulate scheduling scheme
The points for attention to be considered.For example, the preferential dispatching of the priority ranking of specific indent, specific vehicle requires etc..
Preferably for every order data, the order data may include multiple fields, and the field may include
Distributor information, customer information, vehicle vehicle, vehicle personal settings content, term of delivery etc..Similar, every is transported
Force data, the transport power data also may include multiple fields, and the field may include loading quota, sedan-chair fortune vehicle quantity, sedan-chair
Fortune vehicle can fill vehicle vehicle etc..
Further, the complete vehicle logistics data can be by carrying out pretreatment acquisition, the original to initial data
Beginning data equally may include order data, transport power data, node data, contextual data etc..For example, can be obtained from dealer
The order data, node data and contextual data are taken, obtains the transport power data from logistics side.
It will be appreciated by those skilled in the art that the initial data can be integrated from multi-data source, and each data source is in typing
Very likely there is shortage of data when respective data, the problems such as data fill in mistake.On the other hand, due to traditional complete vehicle logistics
Scene relies on artificial experience to form operation plan mostly, and the data generated in scheduling process are all recorded in unstructured manner
Get off, thus can generate and be mixed into more extraneous data and wrong data.Thus, the stage is obtained in primary data, it can be to obtaining
The initial data taken is cleaned, and to exclude wrong data conflicting in initial data, is extracted needed for executing subsequent algorithm
Useful information, so that it is guaranteed that the reliability and reasonability of the complete vehicle logistics data itself.
As a non-limiting embodiment, with reference to Fig. 2, the step S101 be may include steps of：
Step S1011 obtains the initial data.
Step S1012 is not inconsistent according to initial data described in preset standard value range screening with rejecting in the initial data
Close the data of corresponding preset standard value range.
Step S1013 obtains the complete vehicle logistics data according to the initial data after screening.
Specifically, the preset standard range can be corresponded with the field.In practical applications, institute can be directed to
It states each of initial data field and corresponding preset standard range is set, when the field of the initial data of acquisition is not met pair
When the preset standard range answered, the data are rejected, with the validity for the data for ensuring finally to remain.
By taking the loading quota field of the transport power data as an example, can preset the loading quota can only be selected from fixation
Value set { 8,10 } judges the initial data mistake, proposes that this is original if the numerical value obtained is not belonging to the fixed value set
Data.
Preferably, the corresponding preset standard value range of the initial data can be by providing the data of the initial data
Provider presets；Alternatively, the provider of the complete vehicle logistics dispatching method as described in the present embodiment formulates, the data
Provider can according to need the specific value for adjusting the preset standard value range.
Further, the candidate allocation scheme can be used for describing the matching result that original state places an order with transport power,
The matching result is scheduling scheme, and the original state refers to the state at the beginning of executing multiple target ant group algorithm.That is,
After clearing up the initial data, it can be loaded based on the data (the i.e. described complete vehicle logistics data) after cleaning, it will
On Order splitting to sedan-chair fortune vehicle, to carry out subsequent multiple target ant group algorithm.Preferably, the M candidate allocation scheme can
Initialization with Ant colony parameter when using as subsequent ant group algorithm.
For example, greedy algorithm can be formulated by the experience of manual dispatching, first according to vehicle size in order data and
Then the priority of urgency level scheduling order selects currently appearing to be best loading pattern progress prestowage, thus directly
A feasible scheduling scheme is calculated as the candidate allocation scheme.Wherein, the feasible scheduling scheme needs to meet
Default constraint condition.
Further, the quantity M of the candidate allocation scheme can be according to order data determination.For example, needle
10 candidate allocation schemes available to 100 order datas.
As a non-limiting embodiment, with reference to Fig. 3, the step S102 be may include steps of：
Step S1021, loop iteration ground order data and transport power data described in random fit, for each iteration, when ordering
It is singly assigned and when matched transport power is minimum, alternatively, when capacity deployment finishes, by the matching of allocated order and transport power
As a result the candidate allocation scheme obtained as current iteration.
Step S1022 screens the candidate allocation scheme that all previous iteration obtains based on default constraint condition, to obtain the M
A candidate allocation scheme.
In a preferred embodiment, the random fit can refer to：The order for including by the order data is by urgency level
Sequence successively extracts an order according to the sequence of urgency level from high to low when each loop iteration, and from the transport power number
A transport power is randomly selected in starts matching operation.
As a change case, the random fit can refer to：The sedan-chair fortune vehicle for including by the transport power data is big by vehicle
Small sequence successively extracts a transport power according to the descending sequence of vehicle when each loop iteration, and from the order data
In randomly select an order and start matching operation.
As another change case, the random fit can also refer to：Respectively from the order numbers when each loop iteration
According to an order is respectively randomly selected in transport power data and a transport power starts matching operation.
Next in the third above-mentioned random fit mode as an example, in conjunction with Fig. 4 candidate allocation scheme described in the present embodiment
Acquisition process be specifically addressed.
In a typical application scenarios, with reference to Fig. 4, it is possible, firstly, to execute step a101, with from the transport power data
In randomly select a transport power and start inner iteration.
Specifically, the process of the inner iteration may include steps of：Step a102, traversing the order data includes
Order, be screened out from it with the transport power meet load constraint all orders；Whether step a103 judges the transport power
It is fully loaded.
Preferably, the loading constraint can refer to size constraint, that is, can transport power load order.
More specifically, the step a102 may include steps of：Step a1021, from the order data with
Machine extracts an order；Step a1022 loads the order to the transport power；Step a1023 judges the transport power and institute
State whether order meets the loading constraint.
When the judging result of the step a1023 is negative, that is, described in being unsatisfactory for when the transport power and the order
When loading constraint, the step a1021 is re-executed, an order is randomly selected from the order data again, until
This order randomly selected and the transport power meet it is described load constraint (namely until the step a1023 judging result
For certainly) when, it step a1024 is executed, is finished with judging whether order that the order data includes traverses.
When the judging result of the step a1024 is negative, that is, when the order that the order data includes does not traverse
When finishing, the step a1021 to step a1023 is continued to execute, one is randomly selected from the order data with continuation and orders
List simultaneously judges whether the transport power and this order randomly selected meet the loading constraint, until the order data includes
Order all traversal finish (namely until the step a1024 judging result be affirm).
When the judging result of the step a1024 be affirmative when, the step a103 is executed, to judge that the transport power is
It is no fully loaded.
When the judging result of the step a103 is negative, that is, executing step when the transport power underload
A104, with empty the matching result of the transport power and re-execute the inner iteration (namely re-execute the step a102 and
Step a103), until the result of this inner iteration is that the transport power is fully loaded (i.e. until the judging result of the step s103 is willing
Fixed) when, step a105 is executed, to judge whether the order that the order data includes is assigned.
When the judging result of the step a105 is negative, that is, when the order that the order data includes is unallocated
Finish, and the transport power data transport power that includes is unallocated when finishing, continue to execute the step a101 to step a105 (i.e. after
It is continuous to randomly select a transport power from the transport power data and execute the inner iteration), up to what the order data included orders
The capacity deployment that Dan Jun is assigned or the transport power data include finishes, to complete one cycle iteration.
When the judging result of the step a105 is affirmative, that is, when the order that the order data includes distributes
When finishing, step a106 can be executed, to compare whether the quantity for the transport power that this loop iteration determines is less than last circulation
The quantity for the transport power that iteration determines.
If the quantity for the transport power that this loop iteration determines is less than what (may include being equal to) last loop iteration determined
The quantity of transport power, that is, if the judging result of the step a106 is affirmative, the fortune that this loop iteration can be determined
The candidate allocation scheme that the matching result of power and order is obtained as current iteration.
Otherwise, if the quantity for the transport power that this loop iteration determines is greater than the number for the transport power that last loop iteration determines
Amount, that is, executing since the step a101 again, if the judging result of the step a106 be to negate with again
Obtain the random fit result of order data and transport power data.
As a change case, during loop iteration, when the transport power that the transport power data include is assigned,
The step a106 can be executed.
Further, the default constraint condition can be selected from：Prestowage constraint；The constraint of intention direction；City numbers can be spelled
Constraint.Wherein, the intention direction can refer to the destination in intention city namely the order, can spell city etc..Actually answering
In, those skilled in the art also can according to need the particular content for adjusting the default constraint condition.
Further, multivariable, discreteness, high dimension, data volume and solution space based on vehicle scheduling problem it is big and
It is required that the features such as calculating time is short, the scheme proposed adoption ant group algorithm of the present embodiment is scheduled the solution of scheme.
Specifically, can be according to aims of systems under the premise of meeting various constraints for given order and transport power
The order for carrying out the greedy algorithm based on priori tentatively loads and (executes the step S102).In several preliminary loading patterns
On the basis of, by the iteration optimization of ant group algorithm, generate the scheme for gradually maximizing task object.Wherein, just using ant colony
Feedback mechanism is approached to optimal case；The selection strategy combined is selected using certainty selection and randomness, avoids algorithm
Stagnation behavior；Local rule update is carried out to the ant that all completions are once shifted and is used optimal ant is recycled every time
The overall situation, which updates, to be avoided falling into local optimum；Information update is carried out to the walked path of the optimal ant in per generation, has been limited in
In lower bound section, avoid converging on locally optimal solution；Object vector is carried out to the scheme that per generation generates and compares generation Noninferior Solution Set
Optional program disaggregation.
Further, basic ant group algorithm model can use traveling salesman problem (Travelling salesman
Problem, abbreviation TSP) description.For the set on the side of given n urban node and connecting node, find out one most short
Loop circuit so that the loop circuit is in each node only by primary.
Specifically, there are two fundamentals in Basic Ant Group of Algorithm：Node transition rule and Pheromone update rule
Then.
For node transition rule, human oasis exploited (may be simply referred to as ant) randomly chooses some node as initialization section
Then point is transferred to next node via the node, until completing the loop circuit by all nodes.
Corresponding, the node transition rule of every ant is：
Wherein, node where kth ant is i, and the probability for being transferred to j is pij；η (i, j) indicates heuristic information, general to select
It is taken as the inverse of urban node distance；Be without node set；α, β respectively indicate the ginseng of pheromones and heuristic information
The number factor；τ (i, j) indicates the pheromones total amount on path (i, j).
The pheromone updating rule can be summarized as following formula：
τ (i, j)=(1- ρ) τ (i, j)+Δ τ (i, j)；
Wherein, m human oasis exploited completes route, the release pheromone on its route by n-1 selection.Only for kth
The pheromones that ant discharges when (i, j) is shifted, the total Pheromone update amount of the route are Δ τ (i, j).Meanwhile introducing information
The maintenance dose for being adjusted to pheromones of plain Volatilization mechanism, volatility coefficient ρ, pheromones adds newly-added information element burst size.
Since multiple target ant group algorithm is to find out one group of Noninferior Solution Set, Pheromone Matrix and heuristic information matrix include
The possible position of all Plato (Pareto) optimal solution, therefore need for different targets using multiple corresponding information
Prime matrix and heuristic information matrix.
In the present embodiment, the candidate allocation scheme is denoted as ant, the set that M ant is constituted is denoted as ant colony,
Every ant in the ant colony can be assigned i initialization information prime matrix and heuristic information matrix, and i is number of targets,
The initialization information prime matrix and heuristic information matrix and target correspond, wherein for each target of every ant,
The heuristic information matrix is used to describe the initial matching of order and transport power of the ant under the target as a result, described first
Beginningization Pheromone Matrix is for describing initial transition probabilities of the ant in order each under the target between each transport power.
Specifically, the heuristic information matrix and initialization information prime matrix combine, in that case it can be decided that in the ant colony
The position and direction of each ant first step transfer.
Remember the set shipment for the order that the order data includesset={ shipmentu, u ∈ U, wherein U is institute
State the total orders that order data includes.Similar, remember the set Trailer for the sedan-chair fortune vehicle that the transport power data includeset=
{Trailerv, v ∈ V, wherein V is total transport power number that the transport power data include (i.e. total sedan-chair transports vehicle number).
As a non-limiting embodiment, for every ant, the heuristic information matrix can be based on following formula
It indicates：
Bx=(buv)；
Wherein, BxFor the heuristic information matrix of x-th of target of the ant, 1≤x≤i, buvFor the heuristic information square
The element of u row v column in battle array, u are u-th of order in the order data, and 1≤u≤U, v are in the transport power data
V-th of transport power, 1≤v≤V, works as buvIndicate that u-th of order and v-th of transport power match (i.e. u-th in the ant when=1
Order is loaded by v-th of transport power), work as buvIndicate that u-th of order and v-th of transport power do not match that (i.e. in the ant when=0
U-th of order is not loaded by v-th of transport power).
Optionally, for every ant, initialization information prime matrix A of the ant in x-th of targetxIt may include U
× V element, wherein the U × V element is filled with preset constant.For example, the preset constant can be 1, Ye Jisuo
It is 1 that ant, which is stated, in initial transition probabilities of each order between each transport power in x-th of target.
In the present embodiment, the order and transport power for including for an allocation plan, can be by the order and the fortune
The variation of the matching result of power is the transfer of ant.The continuous transfer that the present embodiment is exactly based on ant constantly to change entrucking
State, until the performance evaluation in corresponding target is optimal.
As a non-limiting embodiment, with reference to Fig. 5, the step S103 be may include steps of：
Step S1031 calculates based on ant group algorithm and updates the ant colony in the ant colony during each ant transfer
In each ant state-transition matrix and Pheromone Matrix, wherein for each target of every ant, state transfer
Matrix is used to describe the newest matching result of order and transport power of the ant under the target, and the Pheromone Matrix is used for
The ant newest transition probability of each order between transport power under the target is described.
Step S1032 calculates mesh of the ant in each target according to the state-transition matrix for every ant
Mark vector.
Step S1033, give up the object vector in each target be inferior to the corresponding targets of other ants in ant colony to
The ant of amount, to obtain the Noninferior Solution Set, wherein for the ant in the Noninferior Solution Set, the ant is at least at one
Object vector in target is better than other ants in the Noninferior Solution Set.
It will be appreciated by those skilled in the art that the principle of the scheme combination ant group algorithm of the present embodiment, it will be recorded in allocation plan
Order and transport power between the situation of change of matching relationship be equivalent to the transfer of ant, thus every ant transfer in ant colony
Period calculates the state-transition matrix and Pheromone Matrix of each ant, therefrom to select the target at least one target
Vector is not inferior to Noninferior Solution Set described in the ant of the object vector of other ants on the object and composition.Those skilled in the art
Understand, the scheme based on the present embodiment, can be approached using the positive feedback mechanism of ant colony to optimal case, it is ensured that different applications
Scene can correspond to suitable noninferior solution (i.e. optimal case) in the Noninferior Solution Set finally obtained.
Specifically, the state-transition matrix is initially the heuristic information matrix.
It is possible to further there is formula：State-transition matrix=Pheromone Matrix × heuristic information matrix.
In the step S1031, M ant starts random transferring parallel, one step of every transfer can obtain M it is new
Scheme, and the Pheromone Matrix of each self refresh oneself, pheromones square when pheromones and last time based on this ant shift
Battle array updates regular operation according to above- mentioned information element and obtains new Pheromone Matrix.That is, each ant transfer in the ant
Period, for each target of every ant, when updated Pheromone Matrix can be shifted as the ant next time
Initialization information prime matrix.
As an optional scheme, when M ant transfer finishes, (i.e. all orders and all transport power are equal in every ant
Matched primary) after, therefrom select optimal ant, and using the newest Pheromone Matrix of the optimal ant as following next time
The initial information prime matrix of all ants when ring.
Further, in the ant colony during each ant transfer, the state for updating each ant in the ant colony turns
The update cycle for moving matrix and Pheromone Matrix can be as unit of step-length when each ant transfer.That is, ant is every
Primary (often making a move) is shifted, the state-transition matrix and Pheromone Matrix of the primary ant are updated.
Further, for each target of every ant, above-mentioned node transition rule and pheromones can be based on more
New rule calculates and updates corresponding state-transition matrix and Pheromone Matrix.
In the step S1032, what is reflected since the state-transition matrix is practical is newest allocation plan, because
And projection (i.e. object vector) of the allocation plan in corresponding target can be calculated, the projection can be a quantization
As a result, for measuring the superiority and inferiority degree of the allocation plan on the object.
As a non-limiting embodiment, for every ant in the ant colony, the ant be can be according to true
What qualitative probabilistic or randomness probability were shifted, wherein it is described according to certainty probability carry out transfer refer to according to the ant
Ant Pheromone Matrix instruction maximum probability direction shifted, it is described according to randomness probability carry out transfer refer to according to
Machine direction is shifted, and it is general that the Pheromone Matrix is used to describe newest transfer of each order of the ant between transport power
Rate.
In a preferred embodiment, the ant can wrap according to the process that certainty probability or randomness probability are shifted
It includes：The ant extracts a random number out of pre-set interval；It is general according to certainty when the random number is less than preset threshold
Rate is shifted；Otherwise, it is shifted according to randomness probability.
For example it is assumed that the pre-set interval is (0,1) and the preset threshold is 0.9, for an ant, this transfer
When a number is randomly selected from the pre-set interval, if the numerical value be 0.2, this transfer according to randomness probability shift；
If the numerical value is 0.98, this transfer is shifted according to certainty probability.
Further, for every ant, aforesaid operations be can carry out before each transfer, with determination be according to
Randomness probability or the transfer of certainty probability.
Further, the preset termination condition may include：The transfer number of each ant reaches pre- in the ant colony
If cycle-index.Preferably, the preset loop number, which can be, determines according to the order data and transport power data, mesh
Be to ensure that the scheduling scheme that finally obtains is a convergent result.
Further, the target can be selected from：Maximize shipped quantity；It maximizes and loads Commercial Vehicle urgency level；Most
Big makeup carries large and medium-sized Commercial Vehicle quantity.In practical applications, those skilled in the art can also adjust the target as needed
Particular content and quantity.
Further, the business scenario can be used for the particular demands for describing to be directed to the scheduling scheme, as order is tight
Vehicle requirement for the vehicle that anxious degree, order include etc..The business scenario can be associated with the target.
In a typical application scenarios, with reference to Fig. 6, M candidate allocation side is being obtained based on scheme described in above-mentioned Fig. 4
After case (i.e. M ant), step b101 can be continued to execute, so that every ant is assigned to the letter of quantity identical as number of targets
Prime matrix and heuristic information matrix are ceased, and calculates separately the different state-transition matrixes of every ant.
Further, step b102 is executed, every ant is shifted according to certainty or randomness probability, every transfer
One step updates respective Pheromone Matrix.
Further, in the ant colony during each ant transfer, every ant often makes a move, and executes step b103,
To give up the ant dominated completely in ant colony according to object vector, the corresponding newest allocation plan of remaining ant is selected into non-
Inferior solution collection is as optional program.
Then, step b104 is executed, whether termination condition is currently met with judgement.For example, the termination condition can refer to
Whether the cycle-index of the step b102 to step b104 reaches preset loop number.
When the judging result of the step b104 is affirmative, that is, terminating the algorithm when meeting the termination condition
Module obtains optional program set (the i.e. described Noninferior Solution Set).
Otherwise, i.e., when the judging result of the step b104 be negative when, with this circulation obtain optimal ant into
The update of row information prime matrix calculates state-transition matrix by Pheromone Matrix, updates ant colony, weight by state-transition matrix
The step b102 to step b104 is newly executed, so rolling iteration, until meeting the termination condition.
Further, when the judging result of the step b104 be affirmative when, step b105 is continued to execute, according to reality
The business scenario on border exports optimal tune in conjunction with more each scheme target value size of mode that optional program collection shares weighted sum
Degree scheme.
It will be appreciated by those skilled in the art that the scheme of the present embodiment makes full use of the global optimizing ability of ant group algorithm and parallel
Search capability improves the real-time of intelligent scheduling, also, the scheme of the present embodiment can satisfy different scheduling business scenarios
Carry out the solution of optimal scheduling scheme.
By upper, using the scheme of the present embodiment, existing manual mode of operation is substituted with intelligentized automatic calculation scheme,
The each candidate allocation scheme that will acquire is denoted as an ant, in conjunction with the principle of ant group algorithm, by every during continuous iteration
The transfer of ant obtains Noninferior Solution Set that one group is made of the noninferior solution for corresponding to different target as optional program, Jin Ergen
Optimal scheduling scheme is chosen from the optional program according to the specific requirement of business scenario.
It will be appreciated by those skilled in the art that the scheme of the embodiment of the present invention is by multiple target ant group algorithm come to complete vehicle logistics tune
Degree is accurately described and is deduced, and the optional program finally obtained can flexibly correspond to different scheduling scenarios, is fully considered each
Aspect requirement avoids generating failure scheduling, to improve the system effectiveness of vehicle scheduling system, it is ensured that the vehicle dispatches system
It can run without any confusion.
Further, the scheme of the embodiment of the present invention can not only improve operation efficiency, additionally it is possible to ensure asking for optimal case
Solution, reduces cost, increases customer satisfaction degree
Fig. 7 is a kind of structural representation of complete vehicle logistics dispatching device based on multiple target ant group algorithm of the embodiment of the present invention
Figure.It will be appreciated by those skilled in the art that the complete vehicle logistics dispatching device 7 described in the present embodiment based on multiple target ant group algorithm is (following
Referred to as complete vehicle logistics dispatching device 7) for implementing above-mentioned Fig. 1 to method and technology scheme described in embodiment illustrated in fig. 6.
Specifically, in the present embodiment, the complete vehicle logistics dispatching device 7 may include：Module 71 is obtained, for obtaining
Complete vehicle logistics data, the complete vehicle logistics data include order data and transport power data；Initial load module 72, for being based on institute
State M candidate allocation scheme of complete vehicle logistics data acquisition, wherein M >=1；Multiple target ant group algorithm optimization module 73, by the time
It selects allocation plan to be denoted as ant, the set that M ant is constituted is denoted as ant colony, in the ant colony during each ant transfer,
Give up the ant that all object vectors are dominated in the ant colony, to obtain Noninferior Solution Set, the ant is in each target
Projection be denoted as the object vector corresponding to the target；Module 74 is chosen, when the transfering state of the ant colony meets preset termination
When condition, optimal scheduling scheme is chosen from the Noninferior Solution Set of acquisition according to business scenario.
More specifically, the initial load module 72 can execute following steps：Loop iteration described in random fit
Order data and transport power data, for each iteration, when Order splitting finishes and matched transport power is minimum, alternatively, working as transport power
When being assigned, candidate allocation scheme that the matching result of allocated order and transport power is obtained as current iteration；It is based on
Default constraint condition screens the candidate allocation scheme that all previous iteration obtains, to obtain the M candidate allocation scheme.
Preferably, the complete vehicle logistics dispatching device 7 can also include：Constraints module 76, it is described for storing and providing
Default constraint condition.
Further, the loop iteration order data and transport power data described in random fit may include：From described
A transport power is randomly selected in transport power data and starts inner iteration, and the process of the inner iteration includes：Traverse the order data
Including order, be screened out from it with the transport power meet load constraint all orders；Judge whether the transport power is fully loaded with；
It when the transport power underload, empties the matching result of the transport power and re-executes the inner iteration, until this inner iteration
Result be the transport power full load, judge whether the order that the order data includes is assigned；When the order data
Including Order splitting do not finish, and the transport power data transport power that includes is unallocated when finishing, and continues from the transport power data
In randomly select a transport power and execute the inner iteration, until the capacity deployment that the transport power data include finishes or the order
The Order splitting that data include finishes, to complete one cycle iteration.
Further, the candidate allocation that the matching result of allocated order and transport power is obtained as current iteration
Scheme may include：The number for the transport power that the quantity and last loop iteration for comparing the transport power that this loop iteration determines determine
Amount；If the quantity for the transport power that this loop iteration determines is less than the quantity for the transport power that last loop iteration determines, this is followed
The candidate allocation scheme that the matching result of transport power and order that ring iterative determines is obtained as current iteration.
Further, the order that the traversal order data includes, is filled with being screened out from it to meet with the transport power
Carrying all orders constrained may include：An order is randomly selected from the order data；Judge the transport power and described
Whether order meets the loading constraint；When the transport power and the order are unsatisfactory for loading constraint, again from described
An order is randomly selected in order data, until this order randomly selected meets the loading with the transport power and constrains
When, judge whether order that the order data includes traverses and finishes；It is finished when the order that the order data includes does not traverse
When, continue to randomly select an order from the order data and judge whether are the transport power and this order for randomly selecting
Meet the loading constraint, until the order that the order data includes all is traversed and finished.
Further, the default constraint condition can be selected from：Prestowage constraint；The constraint of intention direction；City numbers can be spelled
Constraint.
Further, every ant in the ant colony can be assigned i initialization information prime matrix and inspire letter
Matrix is ceased, i is number of targets, and the initialization information prime matrix and heuristic information matrix and target correspond, wherein for every
Each target of ant, the heuristic information matrix be used to describe order and transport power of the ant under the target just
Beginning matching result, the initialization information prime matrix are used to describe the ant in order each under the target between each transport power
Initial transition probabilities.
Further, for every ant, the heuristic information matrix can be indicated based on following formula：Bx=(buv)；Wherein, BxFor the heuristic information matrix of x-th of target of the ant, 1≤x≤i, buvFor the inspiration
The element that u row v is arranged in information matrix, u are u-th of order in the order data, and 1≤u≤U, U are the order numbers
According to the total orders for including, v be the transport power data in v-th of transport power, 1≤v≤V, V be the transport power data include it is total
Transport power number, works as buvIt indicates that u-th of order matches with v-th of transport power in the ant when=1, works as buvIt indicates when=0 in institute
U-th of order in ant is stated not match that with v-th of transport power.
Further, for every ant, initialization information prime matrix A of the ant in x-th of targetxMay include
U × V element, wherein U is the total orders that the order data includes, and V is total transport power number that the transport power data include,
U × V the element is filled with preset constant.
Further, the multiple target ant group algorithm optimization module 73 can execute following steps：It is each in the ant colony
During the transfer of ant, the state-transition matrix and pheromones of each ant in the ant colony are calculated and updated based on ant group algorithm
Matrix, wherein for each target of every ant, the state-transition matrix is for describing the ant under the target
Order and transport power newest matching result, the Pheromone Matrix exists for describing the ant each order under the target
Newest transition probability between transport power；For every ant, the ant is calculated in each target according to the state-transition matrix
On object vector；Give up the ant that the object vector in each target is inferior to the corresponding object vector of other ants in ant colony
Ant, to obtain the Noninferior Solution Set, wherein for the ant in the Noninferior Solution Set, the ant is at least in a target
Object vector better than other ants in the Noninferior Solution Set.
Further, the complete vehicle logistics dispatching device 7 can also include：Object vector comparison module 75 is used for basis
State-transition matrix of the every ant in each target calculates corresponding object vector, and chooses noninferior solution therefrom to form
State Noninferior Solution Set.Specifically, whether the object vector comparison module 75 can be used for describing scheduling scheme by other dispatching parties
Case is dominated, and then selects the Noninferior Solution Set.
Further, in the ant colony during each ant transfer, the state for updating each ant in the ant colony turns
The update cycle for moving matrix and Pheromone Matrix can be as unit of step-length when each ant transfer.
It further,, can be by updated information for each target of every ant during ant transfer
Initialization information prime matrix prime matrix is shifted as the ant next time when.
Further, for every ant in the ant colony, the ant be can be according to certainty probability or random
Property probability shifted, wherein it is described to carry out transfer according to certainty probability and refer to Pheromone Matrix according to the ant
The maximum probability direction of instruction is shifted, described to carry out transfer according to randomness probability and refer to and turned according to random direction
It moves, the Pheromone Matrix is used to describe newest transition probability of each order of the ant between transport power.
Further, the ant may include according to the process that certainty probability or randomness probability are shifted：Institute
It states ant and extracts a random number out of pre-set interval；When the random number be less than preset threshold when, according to certainty probability into
Row transfer；Otherwise, it is shifted according to randomness probability.
Further, the preset termination condition may include：The transfer number of each ant reaches pre- in the ant colony
If cycle-index.
Further, the complete vehicle logistics data can be by carrying out pretreatment acquisition to initial data, described to obtain
Modulus block 71 can execute following steps：Obtain the initial data；According to initial data described in preset standard value range screening,
To reject the data for not meeting corresponding preset standard value range in the initial data；It is obtained according to the initial data after screening
The complete vehicle logistics data.
Further, the target can be selected from：Maximize shipped quantity；It maximizes and loads Commercial Vehicle urgency level；Most
Big makeup carries large and medium-sized Commercial Vehicle quantity.
Further, the quantity M of the candidate allocation scheme can be according to order data determination.
Working principle, more contents of working method about the complete vehicle logistics dispatching device 7, are referred to above-mentioned figure
1 associated description into Fig. 6, which is not described herein again.
By upper, using the scheme of the present embodiment, initial data, will be effectively usable after the acquisition module 71 cleaning
Order information, capacity information, nodal information, scene description pass to initial load module 72；Initial load module 72 is according to constraint
The requirement of module 76 carries out greed loading according to ad hoc rules by priori and forms preliminary load mode (the i.e. described M candidate
Allocation plan)；Multiple target ant group algorithm optimization module 73 is using initial load result as starting point, combining target vector comparison module
75, it is iterated optimization using ant group algorithm, and then calculate one group of optional program for being directed to different target, finally by selection mould
Block 74 filters out optimal scheduling scheme according to scheduling scenario.
It will be appreciated by those skilled in the art that the scheme of the present embodiment is completed based on the multiple target ant group algorithm optimization module 73
The process that order and transport power match under each constraint condition carries out scheme optimizing, combining target vector using ant group algorithm
Comparison module 75 is scheduled the choice of scheme, finally obtains the dispatching party optimal on particular characteristic for meeting constraints module
Case.
Further, a kind of storage medium is also disclosed in the embodiment of the present invention, is stored thereon with computer instruction, the calculating
Above-mentioned Fig. 1 is executed to method and technology scheme described in embodiment illustrated in fig. 6 when machine instruction operation.Preferably, the storage is situated between
Matter may include non-volatile (non-volatile) memory or non-transient (non-transitory) memory etc.
Computer readable storage medium.The storage medium may include ROM, RAM, disk or CD etc..
Further, a kind of terminal, including memory and processor is also disclosed in the embodiment of the present invention, deposits on the memory
The computer instruction that can be run on the processor is contained, the processor executes above-mentioned when running the computer instruction
Fig. 1 is to method and technology scheme described in embodiment illustrated in fig. 6.
Although present disclosure is as above, present invention is not limited to this.Anyone skilled in the art are not departing from this
It in the spirit and scope of invention, can make various changes or modifications, therefore protection scope of the present invention should be with claim institute
Subject to the range of restriction.
Claims (21)
1. a kind of complete vehicle logistics dispatching method based on multiple target ant group algorithm, which is characterized in that including：
Complete vehicle logistics data are obtained, the complete vehicle logistics data include order data and transport power data；
Based on M candidate allocation scheme of the complete vehicle logistics data acquisition, wherein M >=1；
The candidate allocation scheme is denoted as ant, the set that M ant is constituted is denoted as ant colony, each ant in the ant colony
During ant shifts, give up the ant that all object vectors are dominated in the ant colony, to obtain Noninferior Solution Set, the ant exists
Projection in each target is denoted as the object vector corresponding to the target；
When the transfering state of the ant colony meets preset termination condition, according to business scenario from the Noninferior Solution Set of acquisition
Choose optimal scheduling scheme.
2. complete vehicle logistics dispatching method according to claim 1, which is characterized in that described to be based on the complete vehicle logistics data
Obtaining M candidate allocation scheme includes：
Loop iteration ground order data and transport power data described in random fit, for each iteration, when Order splitting finish and
When the transport power matched is minimum, alternatively, the matching result of allocated order and transport power is changed as this when capacity deployment finishes
The candidate allocation scheme that generation obtains；
The candidate allocation scheme that all previous iteration obtains is screened based on default constraint condition, to obtain the M candidate allocation scheme.
3. complete vehicle logistics dispatching method according to claim 2, which is characterized in that loop iteration ground random fit institute
It states order data and transport power data includes：
Randomly selected since the transport power data transport power and inner iteration, the process of the inner iteration includes：Traversal institute
The order that order data includes is stated, meets all orders for loading constraint with the transport power to be screened out from it；Judge the fortune
Whether power is fully loaded with；
It when the transport power underload, empties the matching result of the transport power and re-executes the inner iteration, until in this
The result of iteration is the transport power full load, judges whether the order that the order data includes is assigned；
It is finished when the order that the order data includes is unallocated, and the transport power data transport power that includes is unallocated when finishing,
Continuation randomly selects a transport power from the transport power data and executes the inner iteration, until the transport power that the transport power data include
It is assigned or Order splitting that the order data includes finishes, to complete one cycle iteration.
4. complete vehicle logistics dispatching method according to claim 3, which is characterized in that described by allocated order and transport power
The candidate allocation scheme that is obtained as current iteration of matching result include：
The quantity for the transport power that the quantity and last loop iteration for comparing the transport power that this loop iteration determines determine；
If the quantity for the transport power that this loop iteration determines is less than the quantity for the transport power that last loop iteration determines, this is followed
The candidate allocation scheme that the matching result of transport power and order that ring iterative determines is obtained as current iteration.
5. complete vehicle logistics dispatching method according to claim 3, which is characterized in that the traversal order data includes
Order, be screened out from it with the transport power meet load constraint all orders include：
An order is randomly selected from the order data；
Judge whether the transport power and the order meet the loading constraint；
When the transport power and the order are unsatisfactory for loading constraint, one is randomly selected from the order data again
Order, until judging that the order data includes when this order randomly selected meets loading constraint with the transport power
Order whether traverse and finish；
When the order that the order data includes, which does not traverse, to be finished, an order is randomly selected in continuation from the order data
And judge whether the transport power and this order randomly selected meet the loading constraint, until the order data includes
All traversal finishes order.
6. complete vehicle logistics dispatching method according to claim 2, which is characterized in that the default constraint condition is selected from：
Prestowage constraint；
The constraint of intention direction；
City numbers constraint can be spelled.
7. complete vehicle logistics dispatching method according to claim 1, which is characterized in that every ant in the ant colony is divided equally
Equipped with i initialization information prime matrix and heuristic information matrix, i is number of targets, the initialization information prime matrix and inspiration letter
It ceases matrix and target corresponds,
Wherein, for each target of every ant, the heuristic information matrix is for describing the ant under the target
Order and transport power initial matching as a result, the initialization information prime matrix is each under the target for describing the ant
Initial transition probabilities of the order between each transport power.
8. complete vehicle logistics dispatching method according to claim 7, which is characterized in that for every ant, the inspiration letter
Cease matrix is indicated based on following formula：
Bx=(buv)；
Wherein, BxFor the heuristic information matrix of x-th of target of the ant, 1≤x≤i, buvFor in the heuristic information matrix
U row v column element, u be the order data in u-th of order, 1≤u≤U, U be the order data include it is total
Order numbers, v are v-th of transport power in the transport power data, and 1≤v≤V, V are total transport power number that the transport power data include, when
buvIt indicates that u-th of order matches with v-th of transport power in the ant when=1, works as buvIt is indicated in the ant when=0
U-th of order is not matched that with v-th of transport power.
9. complete vehicle logistics dispatching method according to claim 7, which is characterized in that for every ant, the ant exists
The initialization information prime matrix A of x-th of targetxIncluding U × V element, wherein U is the blanket order that the order data includes
Number, V are total transport power number that the transport power data include, and the U × V element is filled with preset constant.
10. complete vehicle logistics dispatching method according to claim 1, which is characterized in that each ant in the ant colony
During ant shifts, gives up the ant that object vector all in the ant colony is dominated, include to obtain Noninferior Solution Set：
In the ant colony during each ant transfer, the shape of each ant in the ant colony is calculated and updated based on ant group algorithm
State transfer matrix and Pheromone Matrix, wherein for each target of every ant, the state-transition matrix is for describing institute
The newest matching result of order and transport power of the ant under the target is stated, the Pheromone Matrix exists for describing the ant
Newest transition probability of each order between transport power under the target；
For every ant, object vector of the ant in each target is calculated according to the state-transition matrix；
Give up the ant that the object vector in each target is inferior to the corresponding object vector of other ants in ant colony, to obtain
The Noninferior Solution Set, wherein for the ant in the Noninferior Solution Set, object vector of the ant at least in a target
Better than other ants in the Noninferior Solution Set.
11. complete vehicle logistics dispatching method according to claim 10, which is characterized in that each ant turns in the ant colony
During shifting, the update cycle of the state-transition matrix and Pheromone Matrix that update in the ant colony each ant is with each ant
Step-length when transfer is unit.
12. complete vehicle logistics dispatching method according to claim 10, which is characterized in that right during ant transfer
Initialization information in each target of every ant, updated Pheromone Matrix is shifted as the ant next time when
Prime matrix.
13. complete vehicle logistics dispatching method according to claim 1, which is characterized in that for every ant in the ant colony
Ant, the ant are shifted according to certainty probability or randomness probability, wherein described to carry out according to certainty probability
Transfer refers to that the maximum probability direction of the Pheromone Matrix instruction according to the ant is shifted, described according to randomness probability
It carries out transfer to refer to and shifted according to random direction, the Pheromone Matrix is used to describe each order of the ant in transport power
Between newest transition probability.
14. complete vehicle logistics dispatching method according to claim 13, which is characterized in that the ant is according to certainty probability
Or the process that randomness probability is shifted includes：
The ant extracts a random number out of pre-set interval；
When the random number is less than preset threshold, shifted according to certainty probability；Otherwise, it is carried out according to randomness probability
Transfer.
15. complete vehicle logistics dispatching method according to claim 1, which is characterized in that the preset termination condition includes：Institute
The transfer number for stating each ant in ant colony reaches preset loop number.
16. complete vehicle logistics dispatching method according to claim 1, which is characterized in that the complete vehicle logistics data are to pass through
Pretreatment acquisition is carried out to initial data, the acquisition complete vehicle logistics data include：
Obtain the initial data；
According to initial data described in preset standard value range screening, corresponding pre- bidding is not met in the initial data to reject
The data of quasi- value range；
The complete vehicle logistics data are obtained according to the initial data after screening.
17. according to claim 1 to complete vehicle logistics dispatching method described in any one of 16, which is characterized in that the target choosing
From：
Maximize shipped quantity；
It maximizes and loads Commercial Vehicle urgency level；
It maximizes and loads large and medium-sized Commercial Vehicle quantity.
18. according to claim 1 to complete vehicle logistics dispatching method described in any one of 16, which is characterized in that described candidate point
Quantity M with scheme is determined according to the order data.
19. a kind of complete vehicle logistics dispatching device based on multiple target ant group algorithm, which is characterized in that including：
Module is obtained, for obtaining complete vehicle logistics data, the complete vehicle logistics data include order data and transport power data；
Initial load module, for being based on M candidate allocation scheme of the complete vehicle logistics data acquisition, wherein M >=1；
The candidate allocation scheme is denoted as ant by multiple target ant group algorithm optimization module, and the set that M ant is constituted is denoted as
Ant colony gives up the ant that all object vectors are dominated in the ant colony in the ant colony during each ant transfer, with
Noninferior Solution Set is obtained, projection of the ant in each target is denoted as the object vector corresponding to the target；
Choose module, when the transfering state of the ant colony meets preset termination condition, according to business scenario from acquisition described in
Optimal scheduling scheme is chosen in Noninferior Solution Set.
20. a kind of storage medium, is stored thereon with computer instruction, which is characterized in that the computer instruction executes when running
The step of any one of claims 1 to 18 the method.
21. a kind of terminal, including memory and processor, be stored on the memory to run on the processor
Computer instruction, which is characterized in that perform claim requires any one of 1 to 18 when the processor runs the computer instruction
The step of the method.
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