CN110264100A - A kind of multi-field model logistics transportation dispatching method, device and equipment - Google Patents
A kind of multi-field model logistics transportation dispatching method, device and equipment Download PDFInfo
- Publication number
- CN110264100A CN110264100A CN201910568291.7A CN201910568291A CN110264100A CN 110264100 A CN110264100 A CN 110264100A CN 201910568291 A CN201910568291 A CN 201910568291A CN 110264100 A CN110264100 A CN 110264100A
- Authority
- CN
- China
- Prior art keywords
- logistics transportation
- harmony
- model
- client
- field
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
- G06Q10/0835—Relationships between shipper or supplier and carriers
- G06Q10/08355—Routing methods
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Operations Research (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
This application discloses a kind of multi-field model logistics transportation dispatching method, device, equipment and readable storage medium storing program for executing, during realizing the scheduling of multi-field model logistics transportation, multiple bicycle field logistics transportation scheduling problems are converted by multi-field model logistics transportation scheduling problem according to clustering strategy, furthermore, optimal vehicle route is scanned for using the harmonic search algorithm based on artificial fish school algorithm, have the feature that the speed of service is fast, convergence capabilities are strong, Searching efficiency is high, has been obviously improved the execution efficiency and reliability of logistics transportation scheduling process.
Description
Technical field
This application involves logistics transportation scheduling field, in particular to a kind of multi-field model logistics transportation dispatching method, is set device
Standby and readable storage medium storing program for executing.
Background technique
Logistics is article from supply into the physical flow process for receiving ground, according to actual needs, by transport, storage,
The functions such as handling, packaging, circulation and process, dispatching, information processing, which combine, realizes the process of user's requirement.Logistics
Industry is considered as the basic industry of the national economic development in the world, and development degree is to measure modernization of the country degree and comprehensive
Close the one of the important signs that of national power.
On the one hand logistics transportation needs to guarantee to meet client to commodity, service as the movable a part of demand supply chain
And the demand of relevant information, it on the other hand needs to improve conevying efficiency, reduce transportation cost.The running of logistics not only determines
The overall operation cost of business enterprise, and the stability and harmony of entire business system running are directly influenced, therefore need
It will be to from supply to the reasonable planning of transportational process progress and control for receiving ground.As it can be seen that logistics transportation scheduling is transported in logistics
It is very crucial step during defeated.
However, the logistics transportation scheduling intricate, traditional due to factor in need of consideration in logistics transportation scheduling process
Scheme takes a long time, inefficiency, it is difficult to meet current demand.
Summary of the invention
The purpose of the application is to provide a kind of multi-field model logistics transportation dispatching method, device, equipment and readable storage medium storing program for executing,
Logistics transportation scheduling scheme to solve traditional takes a long time, inefficiency, it is difficult to the problem of meeting current demand.
In a first aspect, this application provides a kind of multi-field model logistics transportation dispatching methods, comprising:
Multi-field model logistics transportation scheduling model is obtained, and the multi-field model logistics transportation scheduling model is converted into multiple lists
Parking lot logistics transportation scheduling model, wherein the multi-field model logistics transportation scheduling model be describe multiple parking lots vehicle it is common
Complete the model of the delivery task of multiple client's points；
For each bicycle field logistics transportation scheduling model, search operation, and root are executed according to harmonic search algorithm
The optimal harmony during current iteration is determined according to target fitness function, wherein the target fitness function is for measuring
The length of Distribution path corresponding with harmony；
When current iteration number is not up to maximum number of iterations, new harmony is generated according to artificial fish-swarm algorithm, is gone forward side by side
Enter following iteration process；
When the current iteration number reaches the maximum number of iterations, bicycle field logistics transportation scheduling mould is obtained
The optimal harmony of the target of type；
Optimal vehicle corresponding with the optimal harmony of target of each bicycle field logistics transportation scheduling model is determined respectively
Path, using the logistics transportation scheduling result as the multi-field model logistics transportation scheduling model.
Preferably, described that the multi-field model logistics transportation scheduling model is converted into multiple bicycle fields logistics transportation scheduling mould
Type, comprising:
Determine the parking lot in the multi-field model logistics transportation scheduling model and client's point；
For each client's point, the distance between client's point and each parking lot are determined；
According to the distance and cohesion objective function, determine intimate between client's point and each parking lot
Degree；
Client's point is distributed to the maximum parking lot of the cohesion, until all client's point is assigned, is obtained
To client's point in each parking lot, using as multiple bicycle field logistics transportation scheduling models.
It is preferably, described to distribute client's point to the maximum parking lot of the cohesion, comprising:
Under conditions of meeting the limitation of vehicle transport mileage and load limit, client's point is distributed to the cohesion
Maximum parking lot.
It is preferably, described that new harmony is generated according to artificial fish-swarm algorithm, comprising:
New harmony is generated according to artificial fish-swarm algorithm；
According to adaptive re-configuration police, tone fine tuning is carried out to the new harmony.
Preferably, described according to adaptive re-configuration police, tone fine tuning is carried out to the new harmony, comprising:
Generate random number；
When the random number is less than the probability of tone fine tuning, the tone fine tuning is adjusted according to adaptive Tuning function
Probability and bandwidth, and the operation of the tone fine tuning is executed to one group of harmony.
Second aspect, this application provides a kind of multi-field model logistics transportation dispatching devices, comprising:
Conversion module: mould is dispatched for obtaining multi-field model logistics transportation scheduling model, and by the multi-field model logistics transportation
Type is converted to multiple bicycle field logistics transportation scheduling models, wherein the multi-field model logistics transportation scheduling model is that description is multiple
The vehicle in parking lot completes the model of the delivery task of multiple client's points jointly；
Iteration module: it for being directed to each bicycle field logistics transportation scheduling model, is executed according to harmonic search algorithm
Search operation, and determine according to target fitness function the optimal harmony during current iteration, wherein the target fitness
Function is used to measure the length of Distribution path corresponding with harmony；
Harmony update module: for when current iteration number is not up to maximum number of iterations, according to artificial fish-swarm algorithm
New harmony is generated, and enters following iteration process；
The optimal harmony determining module of target: for obtaining when the current iteration number reaches the maximum number of iterations
To the optimal harmony of target of the bicycle field logistics transportation scheduling model；
Scheduling result determining module: most for determining and each bicycle field logistics transportation scheduling model target respectively
The corresponding optimal vehicle route of excellent harmony dispatches knot using the logistics transportation as the multi-field model logistics transportation scheduling model
Fruit.
Preferably, the conversion module includes:
Parameter determination unit: for determining parking lot and client's point in the multi-field model logistics transportation scheduling model；
Distance determining unit: it for being directed to each client's point, determines between client's point and each parking lot
Distance；
Cohesion determination unit: for according to the distance and cohesion objective function, determine client's point with it is each
Cohesion between the parking lot；
Allocation unit: for distributing client's point to the maximum parking lot of the cohesion, until all clients
Point is assigned, and obtains client's point in each parking lot, using as multiple bicycle field logistics transportation scheduling models.
Preferably, the harmony update module includes:
Harmony generation unit: for generating one group of harmony according to artificial fish-swarm algorithm；
Harmony fine-adjusting unit: for carrying out tone fine tuning to the new harmony according to adaptive re-configuration police.
The third aspect, this application provides a kind of multi-field model logistics transportation controlling equipments, comprising:
Memory: for storing computer program；
Processor: for executing the computer program, to realize a kind of multi-field model logistics transportation scheduling as described above
The step of method.
Fourth aspect is stored with computer on the readable storage medium storing program for executing this application provides a kind of readable storage medium storing program for executing
Program, for realizing a kind of multi-field model logistics transportation dispatching method as described above when the computer program is executed by processor
The step of.
A kind of multi-field model logistics transportation dispatching method, device, equipment and readable storage medium storing program for executing provided herein, in reality
During existing multi-field model logistics transportation scheduling, convert multi-field model logistics transportation scheduling problem to according to clustering strategy more
A bicycle field logistics transportation scheduling problem, in addition, using the harmonic search algorithm based on artificial fish school algorithm to optimal vehicle
Path scans for, and has the feature that the speed of service is fast, convergence capabilities are strong, Searching efficiency is high, has been obviously improved logistics transportation
The execution efficiency and reliability of scheduling process.
Detailed description of the invention
It, below will be to embodiment or existing for the clearer technical solution for illustrating the embodiment of the present application or the prior art
Attached drawing needed in technical description is briefly described, it should be apparent that, the accompanying drawings in the following description is only this Shen
Some embodiments please for those of ordinary skill in the art without creative efforts, can be with root
Other attached drawings are obtained according to these attached drawings.
Fig. 1 is a kind of implementation flow chart of multi-field model logistics transportation dispatching method embodiment one provided herein；
Fig. 2 is a kind of implementation flow chart of multi-field model logistics transportation dispatching method embodiment two provided herein；
Fig. 3 is that a kind of the simulation experiment result of multi-field model logistics transportation dispatching method embodiment two provided herein is shown
It is intended to；
Fig. 4 is a kind of functional block diagram of multi-field model logistics transportation dispatching device embodiment provided herein；
Fig. 5 is a kind of structural schematic diagram of multi-field model logistics transportation controlling equipment embodiment provided herein.
Specific embodiment
In order to make those skilled in the art more fully understand application scheme, with reference to the accompanying drawings and detailed description
The application is described in further detail.Obviously, described embodiments are only a part of embodiments of the present application, rather than
Whole embodiments.Based on the embodiment in the application, those of ordinary skill in the art are not making creative work premise
Under every other embodiment obtained, shall fall in the protection scope of this application.
Logistics transportation scheduling is very crucial step during logistics transportation, however, due to logistics transportation scheduling process
In factor in need of consideration it is intricate, traditional logistics transportation scheduling scheme takes a long time, inefficiency, it is difficult to meet current
Demand.For this problem, the application provides a kind of multi-field model logistics transportation dispatching method, device, equipment and readable storage medium storing program for executing,
It has been obviously improved the execution efficiency and reliability of logistics transportation scheduling process.
A kind of multi-field model logistics transportation dispatching method embodiment one provided by the present application is introduced below, referring to Fig. 1,
Embodiment one includes:
Step S101: multi-field model logistics transportation scheduling model is obtained, and the multi-field model logistics transportation scheduling model is turned
It is changed to multiple bicycle field logistics transportation scheduling models；
During logistics transportation scheduling, above-mentioned multi-field model logistics transportation scheduling model refers specifically to describe multiple parking lots
Vehicle completes the model of the delivery task of multiple client's points jointly, and bicycle field logistics transportation scheduling model refers specifically to describe single vehicle
Complete the model of the delivery task of multiple client's points in field.It illustrates, it is " multiple in multi-field model logistics transportation scheduling model
Client's point " is not equal to " multiple client's points " in the logistics transportation scheduling model of bicycle field, the scheduling of each bicycle field logistics transportation
" multiple client's points " involved in model is also not necessarily identical.
More specifically, above-mentioned multi-field model logistics transportation scheduling model can be described as: there are multiple parking lots, each parking lot has
One or more vehicle, the vehicle in multiple parking lots complete the delivery task of several client's points jointly, and each car is from corresponding vehicle
Field is set out, and after all client's points that the vehicle is responsible for, returns to original parking lot.It is suitable that the purpose of model is to select
Stroke route so that total kilometres are most short, to save energy consumption, improve dispatching efficiency.It is understood that each vehicle is vehicle-mounted
Amount need to be greater than or equal to total cargo demand of its all client's point being responsible for, and all client's points can only be passed through by a vehicle,
And passing through number is one.
As a kind of specific embodiment, the multi-field model logistics transportation can be dispatched according to clustering strategy
Model conversion is multiple bicycle field logistics transportation scheduling models.
Step S102: it is directed to each bicycle field logistics transportation scheduling model, search is executed according to harmonic search algorithm
It operates, and determines the optimal harmony during current iteration according to target fitness function；
As described above, the purpose of multi-field model logistics transportation scheduling model and bicycle field logistics transportation scheduling model is to select
Suitable stroke route is selected so that total kilometres are most short, therefore, above-mentioned target fitness function is mainly used for measuring corresponding with harmony
Distribution path length.
Harmonic search algorithm is a kind of novel meta-heuristic algorithm, it is analogous to music band in the process to compose music
In, each musician adjusts the tone of each musical instrument, ceaselessly according to oneself memory and experience to seek most beautiful harmony
Process.
Step S103: it when current iteration number is not up to maximum number of iterations, is generated newly according to artificial fish-swarm algorithm
Harmony, and enter following iteration process；
The present embodiment generates new harmony after each iterative process, according to artificial fish-swarm algorithm.As a kind of tool
The embodiment of body can first generate a random number, when determining that random number is less than preset threshold, according to from harmony data base
The one group of harmony randomly selected generates new harmony；When determining that the random number is more than or equal to preset threshold, according to artificial fish-swarm
Algorithm generates new harmony.On this basis, new harmony can be finely adjusted, using the harmony after fine tuning as changing next time
For the basis of search process.
Step S104: when the current iteration number reaches the maximum number of iterations, bicycle field logistics is obtained
The optimal harmony of the target of transportation dispatching model；
Above-mentioned maximum number of iterations be it is pre-set, specific value can be set accordingly according to actual conditions
It sets.
Step S105: determination is corresponding with the optimal harmony of target of each bicycle field logistics transportation scheduling model respectively
Optimal vehicle route, using the logistics transportation scheduling result as the multi-field model logistics transportation scheduling model.
The present embodiment provides a kind of multi-field model logistics transportation dispatching method, in the mistake for realizing the scheduling of multi-field model logistics transportation
Cheng Zhong converts the scheduling of multiple bicycle fields logistics transportation for multi-field model logistics transportation scheduling problem according to clustering strategy and asks
Topic, in addition, scanning for using the harmonic search algorithm based on artificial fish school algorithm to optimal vehicle route, has operation
The feature that speed is fast, convergence capabilities are strong, Searching efficiency is high has been obviously improved the execution efficiency of logistics transportation scheduling process and reliable
Property.
Start that a kind of multi-field model logistics transportation dispatching method embodiment two provided by the present application, embodiment is discussed in detail below
Two one realize based on the above embodiment, and have carried out expansion to a certain extent on the basis of example 1.
Referring to fig. 2, embodiment two specifically includes:
Step S201: multi-field model logistics transportation scheduling model is obtained, and initializes relevant parameter；
Specifically, multi-field model logistics transportation scheduling model is as follows in the present embodiment:
Wherein, D (unit: km) indicate haulage vehicle institute by path total length, M expression parking lot quantity, KmIt is (single
Position :) indicate the haulage vehicle quantity that parking lot m is possessed；N (unit: a) indicates the quantity of client's point,(m=1,
2,...,M；I, j=0,1 ..., N, unit: km) indicate haulage vehicle by client's point i travel to the straight line of client's point j away from
From, particularly,(or) indicate parking lot m to client j distance,(m=1,2 ..., M；I, j=0,1 ..., N；k
=1,2 ..., Km) it is non-zero i.e. 1 variable,When indicate parking lot m vehicle k drive towards client's point j through client's point i.
The process of initialization relevant parameter specifically includes two parts, and respectively initialization multi-field model logistics transportation dispatches mould
The parameter of type, and initialization harmonic search algorithm parameter, are introduced with regard to the two parts separately below:
Initialize the parameter of multi-field model logistics transportation scheduling model: initialization parking lot quantity M, the haulage vehicle of parking lot m are total
Number Km, wherein the maximum load of the kth haulage vehicle of parking lot m be(unit: ton), client point quantity N, wherein the i-th (i, j
=1,2 ..., N) a client's point demand load-carrying is wi(unit: ton)；
Initialize the parameter of harmonic search algorithm: harmonic search algorithm harmony data base size HMS, data base probability
HMCR, fine tuning probability minimum value PARmin, fine tuning maximum probability value PARmax, tone finely tune initial bandwidth bw0With creation number
Tmax, field range visual, single travelling the maximum distance range, number of attempt try_number of Artificial Fish, algorithm are worked as
Preceding the number of iterations gn (being initially 0).
Step S202: multi-field model logistics transportation scheduling model is converted into multiple bicycle field logistics transportation scheduling models；
Above-mentioned conversion process specifically includes: calculating the distance between each parking lot and each client's point；According to this distance and intimately
Degree function determines the cohesion between parking lot and client's point；Client's point is assigned to according to cohesion descending sequence each
Parking lot is assigned, cluster leaves it at that until whole client's points are assigned to corresponding parking lot.It is understood that will
Client's point is assigned to before parking lot, is needed first to verify whether to meet transport mileage limitation and load limit, is divided if meeting
Match, otherwise re-starts distribution.Above-mentioned cohesion function is as follows:
Wherein,(m=1,2 ... M；J=1,2 ..., N) indicate distance, DOI (m, j) indicates cohesion, m ∈ M, j
∈ N, α is mileage weighting coefficient in formula, and β is load-carrying weighting coefficient, MmThe client terminal quantity of parking lot m, w have been distributed in expressionjIt indicates
The material requirement amount (unit: ton) of client's point j.
Step S203: random initializtion harmony data base；
Above-mentioned harmony data base includes multiple harmony, the Distribution path of the corresponding vehicle of each harmony.Harmony data base
The mode of random initializtion is more, can specifically be chosen according to actual conditions, the present embodiment is not specifically limited.As one
Kind specific embodiment, can select chaos intialization method to initialize harmony data base, specifically initialize
Journey will be introduced in greater detail below, herein not reinflated description.
Step S204: the fitness value of each harmony in harmony data base is determined；
Specifically, determining client's point that corresponding vehicle needs to service according to harmony for each harmony in harmony data base
And sequence, and determine therefrom thatWherein, (m=1,2 ..., M；I, j=0,1 ..., N；K=1,2 ..., Km)；Final root
The fitness value of the harmony is calculated according to fitness function, fitness function is as follows:
Step S205: according to fitness value, global optimum's harmony and its fitness value are determined, and determines worst harmony and its
Fitness value；
Step S206: generating new harmony, and current iteration number adds one；
As a kind of specific embodiment, the process of the new harmony of above-mentioned generation includes: to generate the first random number；If the
One random number is less than data base probability HMRC, then randomly selects one group of harmony from harmony data base, and according to this group of harmony
Generate new harmony；If the first random number is more than or equal to data base probability HMRC, generated according to artificial fish-swarm algorithm new
Harmony；Generate the second random number；If the second random number be less than fine tuning probability P AR, according to fine tuning function to new harmony into
Row fine tuning；If the second random number is less than fine tuning probability P AR, without any processing.The new harmony of final output.
Step S207: when the fitness value of new harmony is greater than the fitness value of worst harmony, more according to new harmony
New worst harmony；
Step S208: judging whether current iteration number reaches maximum number of iterations, if so, entering step S209, otherwise
Enter step S205；
Step S209: the output optimal harmony of target and its fitness value export corresponding vehicle route.To be transported as logistics
Defeated scheduling result.
As described above, each harmony corresponds to the driving path of certain vehicle in the present embodiment, specifically, the present embodiment uses one
The decoding policy that kind etc. divides harmony codomain to combine with maximum position method parses harmony, which defines such as
Under:
P-th of harmony is denoted as Xp=[xp1,xp2,...,xpq,...,xpN], the present embodiment is to harmony XpCarry out inner part
Group generates ∑ KmA set, i.e. Cmj.Further according to maximum position method, to the element in each set according to xpqSize descending row
Column, the second dimension value of each element is client's point that corresponding vehicle needs to service and suitable in each set after the completion of arrangement
Sequence.CmjIt is as follows:
Cmj={ (xpq,q)|j-1≤xpq< j } (4)
Wherein, m=1,2 ..., M, p=0,1 ..., HMS, q=0,1 ..., N, k=1,2 ..., Km。
For above-mentioned decoding policy is further described, it is exemplified below: assuming that parking lot number is A, possesses 4 transports
Vehicle services 8 client's points.In certain iteration of algorithm obtained one group of harmony be X=[3.6,2.4,0.7,1.8,
2.6,3.2,1.4,3.3].It is available that internal grouping is carried out to harmony inside X: CA1={ (0.7,3) }, CA2=(1.8,4),
(1.4,7) }, CA3={ (2.4,2), (2.6,5) }, CA4={ (3.6,1), (3.2,6), (3.3,8) }.
It can be obtained after sorting to each set: CA1={ (0.7,3) }, CA2={ (1.8,4), (1.4,7) }, CA3=(2.6,
5), (2.4,2) }, CA4={ (3.6,1), (3.3,8), (3.2,6) }.
Logistics transportation scheduling scheme corresponding to harmony X finally can be obtained, i.e. 1 path of vehicle is A-3-A；2 path of vehicle
For A-4-7-A；3 path of vehicle is A-5-2-A；4 path of vehicle is A-1-8-6-A.
In above-mentioned steps S203, there are many random initializtion harmony data base method, as an alternative embodiment, this
Embodiment uses chaos intialization method, and process is as follows:
The first step, it is random to generate original chaotic vector；
Original chaotic vector Y0=[y01,y02,...,y0j,...,y0N], wherein y0j∈ (0,1), j=1,2 ..., N.
Second step generates HMS chaos vector according to original chaotic vector sum objective function；
I-th of vector in above-mentioned HMS chaos vector are as follows: Yi=[yi1,yi2,...,yij,...,yiN], objective function are as follows:
y(i+1)j=μ yij(1-yij) (5)
Wherein i=1,2 ..., HMS, furthermore in order to reach Complete Chaos state, μ value is 4 in the present embodiment.
Third step is obtained by HMS chaos DUAL PROBLEMS OF VECTOR MAPPING of generation into the value range of logistics transportation scheduling problem
HMS meet the vector of the present embodiment decoding policy；
Above-mentioned HMS i-th of the vector X met in the vector of the present embodiment decoding policyi=[xi1,xi2,...,
xij,...,xiN], wherein i=1,2 ..., HMS, Xi=Yi*K。
HMS vector is put into harmony data base HM, obtains the initial value of harmony data base by the 4th step.
Specifically, harmony data base HM initialization result is as follows:
In the step S206 of the present embodiment, the process for generating new harmony is specifically included:
The first step generates the random number rand (0,1) between (0,1), if rand (0,1) < HMCR, then from harmony data base
One group of harmony is selected at random, is denoted as Xnew, as shown in formula (6), go to third step；Otherwise second step is gone to.
Xnew=rand [X1,X2,...,XHMS] (6)
Second step generates new harmony according to artificial fish-swarm algorithm at random；
Third step generates the random number rand (0,1) between (0,1), if rand (0,1) < PAR, according to formula (7) to Xnew
It is finely adjusted；Otherwise XnewDo not change.
Xnew=Xnew±rand()×bw (7)
Above-mentioned second step specifically includes: taking XbestAs the position of an Artificial Fish in artificial fish-swarm algorithm, according to formula
(8) the position X of another Artificial Fish is generatedj, and compare XbestAnd XjFitness value, if position XjFitness value be better than
Position Xbest, then Artificial Fish is to position XjDirection, travelling primary with the step-length travelling for being no more than its single advancement maximum distance
Position is X afterwardsnew；Otherwise need to regenerate position Xj.If still not satisfying advance after making repeated attempts try_number times
Condition, then the Artificial Fish random walk is primary, and the position after travelling is assigned to Xnew, shown in the process such as formula (9).
Xj=Xbest+rand()*visual (8)
As a kind of specific embodiment, above-mentioned third step specifically can according to tone finely tune strategy to new harmony into
Row fine tuning, and in tone trim process, tone fine tuning probability and tone fine tuning bandwidth can be adjusted adaptively, such as formula (10) and
Shown in formula (11):
Wherein, PARminTo finely tune probability minimum value, PARmaxTo finely tune maximum probability value, bw0Initial strip is finely tuned for tone
Width, Tmax are creation number, and gn is algorithm current iteration number.
As it can be seen that a kind of multi-field model logistics transportation dispatching method provided in this embodiment, is dispatched for multi-field model logistics transportation
Problem converts the scheduling of multiple bicycle fields logistics transportation for multi-field model logistics transportation scheduling problem by clustering strategy and asks
Topic, reduces difficulty in computation；Optimal path is searched for using the harmonic search algorithm based on artificial fish school algorithm, and using certainly
The tone fine tuning probability and tone of adaptive strategy dynamic adjustment harmonic search algorithm finely tune bandwidth.Therefore, fast with the speed of service,
Convergence capabilities are strong, the high feature of Searching efficiency, have been obviously improved the execution efficiency and reliability of logistics transportation scheduling scheme.
To prove the present embodiment with more superiority, the application is directed to the logistics for possessing 3 haulage vehicles, 20 client's points
Demands Vehicle Routing Problems carry out Multi simulation running to the multi-field model logistics transportation dispatching method of conventional harmonic search algorithm and the present embodiment
Comparative experiments, the obtained shortest path figure of multi-field model logistics transportation dispatching method of the present embodiment is as shown in figure 3, simulation result
Comparison is as shown in table 1.
Table 1
As it can be seen from table 1 relative to conventional harmonic search algorithm, the multi-field model logistics transportation dispatching party of the present embodiment
The VMT Vehicle-Miles of Travel that method searches is shorter, and search process time-consuming is shorter.It was therefore concluded that compared to based on normal
The scheme that harmonic search algorithm realizes logistics transportation scheduling is advised, the multi-field model logistics transportation dispatching method of the present embodiment has operation
Speed is fast, convergence capabilities are strong, the high feature of Searching efficiency.
A kind of multi-field model logistics transportation dispatching device provided by the embodiments of the present application is introduced below, it is described below
A kind of multi-field model logistics transportation dispatching device can correspond to each other ginseng with a kind of above-described multi-field model logistics transportation dispatching method
According to.
Referring to fig. 4, which includes:
Conversion module 401: it is dispatched for obtaining multi-field model logistics transportation scheduling model, and by the multi-field model logistics transportation
Model conversion is multiple bicycle field logistics transportation scheduling models, wherein the multi-field model logistics transportation scheduling model is that description is more
The vehicle in a parking lot completes the model of the delivery task of multiple client's points jointly；
Iteration module 402: it for being directed to each bicycle field logistics transportation scheduling model, is held according to harmonic search algorithm
Row search operation, and determine according to target fitness function the optimal harmony during current iteration, wherein the target adapts to
Degree function is used to measure the length of Distribution path corresponding with harmony；
Harmony update module 403: for being calculated according to artificial fish-swarm when current iteration number is not up to maximum number of iterations
Method generates new harmony, and enters following iteration process；
The optimal harmony determining module 404 of target: for when the current iteration number reaches the maximum number of iterations,
Obtain the optimal harmony of target of the bicycle field logistics transportation scheduling model；
Scheduling result determining module 405: for the determining mesh with each bicycle field logistics transportation scheduling model respectively
The corresponding optimal vehicle route of optimal harmony is marked, using the logistics transportation scheduling as the multi-field model logistics transportation scheduling model
As a result.
In some specific embodiments, the conversion module 401 includes:
Parameter determination unit: for determining parking lot and client's point in the multi-field model logistics transportation scheduling model；
Distance determining unit: it for being directed to each client's point, determines between client's point and each parking lot
Distance；
Cohesion determination unit: for according to the distance and cohesion objective function, determine client's point with it is each
Cohesion between the parking lot；
Allocation unit: for distributing client's point to the maximum parking lot of the cohesion, until all clients
Point is assigned, and obtains client's point in each parking lot, using as multiple bicycle field logistics transportation scheduling models.
In some specific embodiments, the harmony update module 403 includes:
Harmony generation unit: for generating one group of harmony according to artificial fish-swarm algorithm；
Harmony fine-adjusting unit: for carrying out tone fine tuning to the new harmony according to adaptive re-configuration police.
The multi-field model logistics transportation dispatching device of the present embodiment for realizing multi-field model logistics transportation dispatching method above-mentioned,
Therefore the embodiment part of the visible multi-field model logistics transportation dispatching method hereinbefore of specific embodiment in the device, example
Such as, the optimal harmony determining module 404 of conversion module 401, iteration module 402, harmony update module 403, target, scheduling result are true
Cover half block 405 is respectively used to realize step S101, S102, S103, S104 in above-mentioned multi-field model logistics transportation dispatching method,
S105.So specific embodiment is referred to the description of corresponding various pieces embodiment, not reinflated introduction herein.
In addition, since the multi-field model logistics transportation dispatching device of the present embodiment is for realizing multi-field model logistics transportation above-mentioned
Dispatching method, therefore its effect is corresponding with the effect of the above method, which is not described herein again.
In addition, referring to Fig. 5, which includes: present invention also provides a kind of multi-field model logistics transportation controlling equipment
Memory 501: for storing computer program；
Processor 502: for executing the computer program, to perform the steps of
Multi-field model logistics transportation scheduling model is obtained, and the multi-field model logistics transportation scheduling model is converted into multiple lists
Parking lot logistics transportation scheduling model, wherein the multi-field model logistics transportation scheduling model be describe multiple parking lots vehicle it is common
Complete the model of the delivery task of multiple client's points；For each bicycle field logistics transportation scheduling model, according to and sonar surveillance system
Rope algorithm executes search operation, and the optimal harmony during current iteration is determined according to target fitness function, wherein described
Target fitness function is used to measure the length of Distribution path corresponding with harmony；Greatest iteration is not up in current iteration number
When number, new harmony is generated according to artificial fish-swarm algorithm, and enter following iteration process；Reach in the current iteration number
When the maximum number of iterations, the optimal harmony of target of the bicycle field logistics transportation scheduling model is obtained；It is determining and each respectively
The corresponding optimal vehicle route of the optimal harmony of target of a bicycle field logistics transportation scheduling model, using as more vehicles
The logistics transportation scheduling result of field logistics transportation scheduling model.
As a kind of specific embodiment, the processor 502 is executing the calculating loom journey in the memory 501
When sequence, specifically it may be implemented:
Determine the parking lot in the multi-field model logistics transportation scheduling model and client's point；For each client's point, really
Fixed the distance between client's point and each parking lot；According to the distance and cohesion objective function, the visitor is determined
Cohesion between family point and each parking lot；Client's point is distributed to the maximum parking lot of the cohesion, until complete
The point of client described in portion is assigned, and obtains client's point in each parking lot, to dispatch mould as multiple bicycle fields logistics transportation
Type.
As a kind of specific embodiment, the processor 502 is executing the calculating loom journey in the memory 501
When sequence, specifically it may be implemented:
Under conditions of meeting the limitation of vehicle transport mileage and load limit, client's point is distributed to the cohesion
Maximum parking lot.
As a kind of specific embodiment, the processor 502 is executing the calculating loom journey in the memory 501
When sequence, specifically it may be implemented:
New harmony is generated according to artificial fish-swarm algorithm；According to adaptive re-configuration police, sound is carried out to the new harmony
Adjust fine tuning.
As a kind of specific embodiment, the processor 502 is executing the calculating loom journey in the memory 501
When sequence, specifically it may be implemented:
Generate random number；When the random number is less than the probability of tone fine tuning, institute is adjusted according to adaptive Tuning function
The probability and bandwidth of tone fine tuning are stated, and executes the operation of the tone fine tuning to one group of harmony.
Finally, being stored with computer journey on the readable storage medium storing program for executing present invention also provides a kind of readable storage medium storing program for executing
Sequence, the step of when the computer program is executed by processor for realizing aforementioned multi-field model logistics transportation dispatching method.
Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with it is other
The difference of embodiment, same or similar part may refer to each other between each embodiment.For being filled disclosed in embodiment
For setting, since it is corresponded to the methods disclosed in the examples, so being described relatively simple, related place is referring to method part
Explanation.
The step of method described in conjunction with the examples disclosed in this document or algorithm, can directly be held with hardware, processor
The combination of capable software module or the two is implemented.Software module can be placed in random access memory (RAM), memory, read-only deposit
Reservoir (ROM), electrically programmable ROM, electrically erasable ROM, register, hard disk, moveable magnetic disc, CD-ROM or technology
In any other form of storage medium well known in field.
Scheme provided herein is described in detail above, specific case used herein is to the application's
Principle and embodiment is expounded, the present processes that the above embodiments are only used to help understand and its core
Thought；At the same time, for those skilled in the art, according to the thought of the application, in specific embodiment and application range
Upper there will be changes, in conclusion the contents of this specification should not be construed as limiting the present application.
Claims (10)
1. a kind of multi-field model logistics transportation dispatching method characterized by comprising
Multi-field model logistics transportation scheduling model is obtained, and the multi-field model logistics transportation scheduling model is converted into multiple bicycle fields
Logistics transportation scheduling model, wherein the multi-field model logistics transportation scheduling model is to describe the vehicle in multiple parking lots to complete jointly
The model of the delivery task of multiple client's points；
For each bicycle field logistics transportation scheduling model, search operation is executed according to harmonic search algorithm, and according to mesh
Mark fitness function determine the optimal harmony during current iteration, wherein the target fitness function for measure with and
The length of the corresponding Distribution path of sound；
When current iteration number is not up to maximum number of iterations, new harmony is generated according to artificial fish-swarm algorithm, and under entrance
One iterative process；
When the current iteration number reaches the maximum number of iterations, the bicycle field logistics transportation scheduling model is obtained
The optimal harmony of target；
Optimal vehicle road corresponding with the optimal harmony of target of each bicycle field logistics transportation scheduling model is determined respectively
Diameter, using the logistics transportation scheduling result as the multi-field model logistics transportation scheduling model.
2. multi-field model logistics transportation dispatching method as described in claim 1, which is characterized in that described by the multi-field model logistics
Transportation dispatching model conversion is multiple bicycle field logistics transportation scheduling models, comprising:
Determine the parking lot in the multi-field model logistics transportation scheduling model and client's point；
For each client's point, the distance between client's point and each parking lot are determined；
According to the distance and cohesion objective function, the cohesion between client's point and each parking lot is determined；
Client's point is distributed to the maximum parking lot of the cohesion, until all client's point is assigned, is obtained each
Client's point in a parking lot, using as multiple bicycle field logistics transportation scheduling models.
3. multi-field model logistics transportation dispatching method as claimed in claim 2, which is characterized in that described to distribute client's point
To the maximum parking lot of the cohesion, comprising:
Under conditions of meeting the limitation of vehicle transport mileage and load limit, client's point is distributed maximum to the cohesion
Parking lot.
4. multi-field model logistics transportation dispatching method as described in claim 1, which is characterized in that described according to artificial fish-swarm algorithm
Generate new harmony, comprising:
New harmony is generated according to artificial fish-swarm algorithm；
According to adaptive re-configuration police, tone fine tuning is carried out to the new harmony.
5. multi-field model logistics transportation dispatching method as claimed in claim 4, which is characterized in that the basis adaptively adjusts plan
Slightly, tone fine tuning is carried out to the new harmony, comprising:
Generate random number；
When the random number is less than the probability of tone fine tuning, the probability of the tone fine tuning is adjusted according to adaptive Tuning function
And bandwidth, and the operation of the tone fine tuning is executed to one group of harmony.
6. a kind of multi-field model logistics transportation dispatching device characterized by comprising
Conversion module: turn for obtaining multi-field model logistics transportation scheduling model, and by the multi-field model logistics transportation scheduling model
It is changed to multiple bicycle field logistics transportation scheduling models, wherein the multi-field model logistics transportation scheduling model is to describe multiple parking lots
Vehicle complete jointly multiple client's points delivery task model；
Iteration module: for being directed to each bicycle field logistics transportation scheduling model, search is executed according to harmonic search algorithm
It operates, and determines the optimal harmony during current iteration according to target fitness function, wherein the target fitness function
For measuring the length of Distribution path corresponding with harmony；
Harmony update module: for being generated according to artificial fish-swarm algorithm when current iteration number is not up to maximum number of iterations
New harmony, and enter following iteration process；
The optimal harmony determining module of target: for obtaining institute when the current iteration number reaches the maximum number of iterations
State the optimal harmony of target of bicycle field logistics transportation scheduling model；
Scheduling result determining module: for determining optimal with the target of each bicycle field logistics transportation scheduling model respectively and
The corresponding optimal vehicle route of sound, using the logistics transportation scheduling result as the multi-field model logistics transportation scheduling model.
7. multi-field model logistics transportation dispatching device as claimed in claim 6, which is characterized in that the conversion module includes:
Parameter determination unit: for determining parking lot and client's point in the multi-field model logistics transportation scheduling model；
Distance determining unit: for be directed to each client's point, determine between client's point and each parking lot away from
From；
Cohesion determination unit: for according to the distance and cohesion objective function, determine client's point with it is each described
Cohesion between parking lot；
Allocation unit: for distributing client's point to the maximum parking lot of the cohesion, until all client's points minute
With finishing, client's point in each parking lot is obtained, using as multiple bicycle field logistics transportation scheduling models.
8. multi-field model logistics transportation dispatching device as claimed in claim 6, which is characterized in that the harmony update module packet
It includes:
Harmony generation unit: for generating one group of harmony according to artificial fish-swarm algorithm；
Harmony fine-adjusting unit: for carrying out tone fine tuning to the new harmony according to adaptive re-configuration police.
9. a kind of multi-field model logistics transportation controlling equipment characterized by comprising
Memory: for storing computer program；
Processor: for executing the computer program, to realize a kind of multi-field model as described in claim 1-5 any one
The step of logistics transportation dispatching method.
10. a kind of readable storage medium storing program for executing, which is characterized in that be stored with computer program, the meter on the readable storage medium storing program for executing
For realizing a kind of multi-field model logistics transportation tune as described in claim 1-5 any one when calculation machine program is executed by processor
The step of degree method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910568291.7A CN110264100B (en) | 2019-06-27 | 2019-06-27 | Multi-yard logistics transportation scheduling method, device, equipment and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910568291.7A CN110264100B (en) | 2019-06-27 | 2019-06-27 | Multi-yard logistics transportation scheduling method, device, equipment and readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110264100A true CN110264100A (en) | 2019-09-20 |
| CN110264100B CN110264100B (en) | 2022-03-25 |
Family
ID=67922334
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910568291.7A Active CN110264100B (en) | 2019-06-27 | 2019-06-27 | Multi-yard logistics transportation scheduling method, device, equipment and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110264100B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110689205A (en) * | 2019-10-09 | 2020-01-14 | 甘肃农业大学 | Vehicle path optimization method and device, readable storage medium and electronic equipment |
| CN111160747A (en) * | 2019-12-23 | 2020-05-15 | 北京百度网讯科技有限公司 | Scheduling method and device of robot taxi, electronic equipment and storage medium |
| CN112214024A (en) * | 2020-10-20 | 2021-01-12 | 浙江迈睿机器人有限公司 | AGV task allocation method, logistics sorting method and system |
| CN114264912A (en) * | 2021-10-29 | 2022-04-01 | 国网上海市电力公司 | Multi-source power distribution network fault positioning method based on improved Jaya algorithm |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150039361A1 (en) * | 2013-08-05 | 2015-02-05 | International Business Machines Corporation | Techniques for Managing Snow Removal Equipment Leveraging Social Media |
| CN104951850A (en) * | 2015-06-25 | 2015-09-30 | 广东工业大学 | Method for solving multiple-depot logistics transportation vehicle routing problem |
| CN107437126A (en) * | 2017-08-03 | 2017-12-05 | 广东工业大学 | A kind of logistics transportation dispatching method and device |
-
2019
- 2019-06-27 CN CN201910568291.7A patent/CN110264100B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150039361A1 (en) * | 2013-08-05 | 2015-02-05 | International Business Machines Corporation | Techniques for Managing Snow Removal Equipment Leveraging Social Media |
| CN104951850A (en) * | 2015-06-25 | 2015-09-30 | 广东工业大学 | Method for solving multiple-depot logistics transportation vehicle routing problem |
| CN107437126A (en) * | 2017-08-03 | 2017-12-05 | 广东工业大学 | A kind of logistics transportation dispatching method and device |
Non-Patent Citations (4)
| Title |
|---|
| 于宏涛: "启发式算法在生产与运作管理问题中的应用", 《中国博士学位论文全文数据库》 * |
| 周丹丹 等: "聚类分析的遗传算法在应急调度中的应用", 《计算机应用与软件》 * |
| 薛彬: "基于和声搜索算法对带有临时库存的越库车辆排序问题研究", 《中国优秀硕士学位论文全文数据库》 * |
| 黄帅: "改进和声搜索算法研究", 《中国优秀硕士学位论文全文数据库》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110689205A (en) * | 2019-10-09 | 2020-01-14 | 甘肃农业大学 | Vehicle path optimization method and device, readable storage medium and electronic equipment |
| CN111160747A (en) * | 2019-12-23 | 2020-05-15 | 北京百度网讯科技有限公司 | Scheduling method and device of robot taxi, electronic equipment and storage medium |
| CN111160747B (en) * | 2019-12-23 | 2022-07-22 | 北京百度网讯科技有限公司 | Scheduling method and device for unmanned robot taxi and electronic equipment |
| CN112214024A (en) * | 2020-10-20 | 2021-01-12 | 浙江迈睿机器人有限公司 | AGV task allocation method, logistics sorting method and system |
| CN114264912A (en) * | 2021-10-29 | 2022-04-01 | 国网上海市电力公司 | Multi-source power distribution network fault positioning method based on improved Jaya algorithm |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110264100B (en) | 2022-03-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110264100A (en) | A kind of multi-field model logistics transportation dispatching method, device and equipment | |
| US11318942B2 (en) | Travel speed control method, apparatus, computing device, and storage medium | |
| de Oliveira et al. | Clustering search for the berth allocation problem | |
| CN107833002B (en) | Multi-stage low-carbon logistics distribution network planning method based on cooperative multi-objective algorithm | |
| Cheng et al. | Solving a vehicle routing problem with time windows by a decomposition technique and a genetic algorithm | |
| Doppstadt et al. | The hybrid electric vehicle–traveling salesman problem | |
| CN111428931B (en) | Logistics distribution line planning method, device, equipment and storage medium | |
| CN105868949A (en) | Logistics distribution scheduling method and apparatus | |
| CN112995289B (en) | Internet of vehicles multi-target computing task unloading scheduling method based on non-dominated sorting genetic strategy | |
| CN108876024A (en) | Path planning, path real-time optimization method and device, storage medium | |
| CN113415288B (en) | Sectional type longitudinal vehicle speed planning method, device, equipment and storage medium | |
| CN106302172A (en) | Support Hash lookup and the storage of route querying, lookup method and device simultaneously | |
| CN112985443B (en) | Path planning method and device and terminal equipment | |
| CN109284852A (en) | A kind of Optimization Scheduling of the vehicle allocation process applied to logistics distribution center | |
| CN110427690A (en) | A kind of method and device generating ATO rate curve based on global particle swarm algorithm | |
| CN112085288A (en) | Logistics distribution route recommendation method and system based on two-stage optimization | |
| CN110264140A (en) | A kind of logistics transportation dispatching method, device and equipment with time window | |
| CN110930092B (en) | Distribution route adjusting method and device, electronic equipment and storage medium | |
| CN109032789A (en) | A kind of dispositions method of logistics network, device and server | |
| WO2019101742A1 (en) | A method of containership allocation-based stowage planning | |
| CN110503234A (en) | A kind of method, system and the equipment of logistics transportation scheduling | |
| CN109272151B (en) | Spark-based vehicle path planning algorithm optimization method | |
| CN103218667A (en) | Container wharf loading plan optimization method based on tabu search | |
| CN108921338A (en) | A kind of more vehicle shop logistics transportation dispatching methods | |
| CN110428089B (en) | Method, system and equipment for logistics transportation scheduling of bicycle yard |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |