CN109102122A - It is a kind of based on NSGAII packet transaction on a large scale with the method and system of capacity-constrained - Google Patents
It is a kind of based on NSGAII packet transaction on a large scale with the method and system of capacity-constrained Download PDFInfo
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Abstract
The present invention provides a kind of method based on NSGAII packet transaction Vehicle Routing Problems with capacity-constrained on a large scale, comprising steps of data prediction；Parameter initialization defines primary iteration number and maximum number of iterations, and initializes a feasible solution and be used as with reference to solution；Judge whether the number of iterations reaches the maximum number of iterations；Grouping is optimized with reference to solution to described by multi-objective Algorithm NSGAII；Processing is optimized to the subproblem after grouping on the first leading surface by tabu search algorithm；It updates described with reference to solution, accumulation the number of iterations；It is exported described with reference to solution as optimal solution.
Description
Technical field
The present invention relates to field of intelligent transportation technology, and in particular to one kind is based on NSGAII packet transaction on a large scale with capacity
The method and system of constraint.
Background technique
In current electronic commerce times, global logistics industry has new development trend.The core of modern logistics service
Target be in logistics overall process with the smallest overall cost to meet customer the needs of.The quick emergence of present electric business and industry
Demand, for warehouse logistics dispense this important link demand and require also be continuously improved, third company is in market
It is also played an increasingly important role in industry, or even can assist in businessman and provide extensive service in terminal and channel end.
With the development of logistic industry, the client for needing to service is more and more, causes vehicle scheduling scale increasing.
Vehicle Routing Problem (Vehicle Routing Problem, abbreviation VRP) refers to a series of specific positions and needs
The client's point for the amount of asking, calls a certain number of vehicles, from central warehouse, selects optimal traffic route and accesses in an orderly manner
Each client's point reaches most in the case where meeting specific constraint condition so that cargo reaches client's point as early as possible and transports total cost
Low requirement.
Traditional vehicle routes solution, mainly according to logistics experience, has certain randomness and contingency, does not have
It is standby scientific.With the development of science and technology, there are mainly four types of algorithms for solution Vehicle Routing Problem at present: one, bright based on glug
Day relaxation, column-generation and three kinds of Dynamic Programming tactful accurate algorithms, the example scale that this algorithm can solve are very small；
Two, way construct heuritic approach, certain node selection principle or Vehicle routing principle in problem, this algorithm need by
Demand point is included in the solution of way route one by one；Three, way improves heuritic approach, first determines a feasible way, that is, one
A starting solution, do always to this starting solution improves later, until cannot improve；Four, general heuritic approach, traditional area
The optimum solution of domain search method can only obtain local optimum solution often because of the limitation of starting solution characteristic or method for searching, general starting
Hairdo algorithm is heuristic solution of new generation, receives method comprising tabu search algorithm, simulated annealing, genetic algorithm and door
Deng can effectively solve the problems, such as to fall into local optimum.Vehicle when above-mentioned four classes method not can effectively solve larger
Routing issue.
Summary of the invention
In view of the deficiencies of the prior art, the present invention provide it is a kind of based on NSGAII packet transaction on a large scale with capacity-constrained
Method and system, to can effectively solve the problem that Vehicle Routing Problem when the client node for needing to service especially more.
In order to achieve the above object, the present invention is achieved by the following technical programs:
A method of based on NSGAII packet transaction on a large scale with capacity-constrained, comprising steps of
Step 1: with reference to solution pretreatment；
Step 2: parameter initialization, defines primary iteration number and maximum number of iterations, and initializes a feasible solution and make
For with reference to solution；
Step 3: judging whether the number of iterations reaches the maximum number of iterations；If so, executing step 7；Otherwise it holds
Row step 4；
Step 4: optimizing grouping with reference to solution to described by multi-objective Algorithm NSGAII；
Step 5: optimizing processing to the subproblem after grouping on the first leading surface by tabu search algorithm；
Step 6: updating described with reference to solution, accumulation the number of iterations and return step three；
Step 7: being exported described with reference to solution as optimal solution.
Further, the step 1 is specifically included with reference to solution coding and with reference to solution decoding；Described be encoded to reference to solution makes
Reference solution is encoded with natural number coding, code length is equal to client node number and usable number of vehicles is added to add 1.
Further, the step 4 specifically includes the following steps:
Step A, individual UVR exposure；
Step B, individual decoding；
Step C, initialization population；
Step D, suitable individual is selected；
Step E, Evolution of Population.
Further, the step 5 specifically includes the following steps:
Step F, the sequence for generating client in a subproblem at random is inserted into warehouse section by vehicle principle as fully loaded as possible
Point generates initial solution, empties taboo list, and Tabu Length is arranged；
Step G, by the initial solution, candidate solution is generated by searching operators, and calculate the fitness value of each candidate solution；
Step H, the best candidate solution of fitness value will be selected in all candidate solutions.
The present invention also provides a kind of based on NSGAII packet transaction on a large scale with the system of capacity-constrained, comprising:
Preprocessing module, for reference to solution pretreatment；
Parameter initialization module is used for parameter initialization, defines primary iteration number and maximum number of iterations, and initialize
One feasible solution is used as with reference to solution；
Judgment module, for judging whether the number of iterations reaches the maximum number of iterations；If so, entering output mould
Block；Otherwise enter grouping module；
Grouping module, for optimizing grouping with reference to solution to described by multi-objective Algorithm NSGAII；
Processing module, for optimizing place to the subproblem after grouping on the first leading surface by tabu search algorithm
Reason；
Parameter updating module, it is described with reference to solution for updating, it accumulates the number of iterations and enters judgment module；
Output module, for being exported described with reference to solution as optimal solution.
Further, the preprocessing module includes with reference to solution encoding submodule and with reference to solution decoding sub-module.
Further, the grouping module includes:
Individual UVR exposure submodule is used for individual UVR exposure；
Individual decoding sub-module, for individual decoding；
Initialization of population submodule is used for initialization population；
Individual screening submodule, for selecting suitable individual；
Evolution of Population submodule is used for Evolution of Population.
Further, the processing module includes:
Initialization submodule, for generating the sequence of client in a subproblem at random, by the original that vehicle is as fully loaded as possible
It is then inserted into warehouse node, generates initial solution, empties taboo list, Tabu Length is set；
Candidate solution handles submodule, for generating candidate solution by searching operators, and calculate each time for the initial solution
Select the fitness value of solution；
Candidate solution screens submodule, for will select the best candidate solution of fitness value in all candidate solutions.
Compared with prior art, the invention has the following advantages:
Current most of algorithms for solving the problems, such as the Vehicle Routing Problem with capacity limit perhaps can mention on small-scale
For relatively good solution, but more excellent solution can not be just provided when problem scale becomes larger.The present invention, will by using the mode of grouping
Big problem is converted into minor issue solution, and difficult point is transferred to and how to be grouped, and solves this using multi-objective optimization algorithm
Difficult point.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
It obtains other drawings based on these drawings.
Fig. 1 is inventive algorithm flow chart；
Fig. 2 is present system structure chart；
Fig. 3 is grouping module structure chart in the present invention；
Fig. 4 is processing module structure chart in the present invention.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
Every other embodiment obtained without creative efforts, shall fall within the protection scope of the present invention.
The embodiment of the present invention provide it is a kind of based on NSGAII packet transaction on a large scale with the method and system of capacity-constrained.
Defining the network characterisation is G (V, E), V={ v1,v2,…,vi,…,vnIndicate all nodes in the network
Set, viIndicate i-th of node；N is the sum of node；v1Indicate warehouse node, remaining node is client node.E=
{eij| i=1,2 ..., n；J=1,2 ..., n indicate any two node between path distance set；eijIt indicates i-th
Node viWith j-th of node vjThe distance between；It is constrained to vehicle capacity-constrained.
Step 1, with reference to solution encoding and decoding:
Step 1.1: with reference to solution coding
Reference solution is encoded using natural number coding, code length is equal to client node number and adds usable vehicle
Number adds one again；Individual UVR exposure is X={ g1,g2,...,gi,...,gm},gi=0 indicates that i-th of node is warehouse node, gi
={ 1,2,3,4 ... } indicate client node；
Step 1.2: with reference to solution decoding
Step 1.2.1: initialization traversal individual parameter i is 1, and record subscript parameters k is 1；
Step 1.2.2: finding the node between the subscript i, k to i for the node that next gene place value is 0 is a vehicle clothes
All client nodes of business；
Step 1.2.3: being assigned to k for i, execute step 1.2.2, until the value of i is greater than individual UVR exposure length.To obtain n
Paths c={ c1,c2,...,cr,...,cn, wherein crIndicate r paths；
Step 2, initialization
Step 2.1: definition maximum number of iterations is T, and primary iteration number is t=1；
Step 2.2: initializing a feasible solution X using greedy thoughtgAs reference solution；
It updates in step 3, evolutionary process with reference to solution
It is grouped during evolution using multi-objective Algorithm (NSGAII) optimization, then right by tabu search algorithm (Tabu)
Subproblem on first leading surface optimizes, then goes to update with reference to solution, solves band capacity-constrained on a large scale by nesting optimization
Vehicle Routing Problem；
Step 3.1:NSGAII optimization grouping
Step 3.1.1: individual UVR exposure and decoding
Block encoding is encoded using the decimal between 0~1 and no more than the quotient of number of path and packet count, and code length is
Subtract one plus packet count with reference to the number of path in solution；Individual UVR exposure is X={ g1,g2,...,gi,...,gm}；
Step 3.1.2: individual decoding
Step 3.1.2.1: assuming that the preceding n progress ascending sorts of individual X are obtained road for n, packet count G by number of path
One sequence of diameter number；
Step 3.1.2.2: and then according to rear G-1 of genic value, the sequence of path number is cut, to obtain
One grouping c={ c in path1,c2,...,cr,...,cn, wherein crIndicate r-th of grouping；
Step 3.1.3: initialization population
Step 3.1.3.1: definition maximum number of iterations is maxgen, and primary iteration number is g=1, the number of population at individual
Mesh is pop, number of path n, packet count G；
Step 3.1.3.2: setting in population has pop individual { X1,X2..., Xg,…,Xpop}；XgIndicate g-th of individual；
Step 3.1.3.3: the random number generated between n 0~1, then random generation G-1 are not more than the integers of n/G, close
And g-th of individual X is obtained togetherg；
Step 3.1.3.4: it executes pop step 3.1.1.3 and obtains the coding of initial population
Step 3.1.4: selection suitable individual P'
If it is fine or not that random all individuals from selection individual in population or selection population carry out judgement grouping, obtain
Result be frequently not highly desirable, so from grouping the distance between and packets inner distance and grouping in client node number
To determine whether being suitable individual.Firstly, the distance between the grouping after individual decoding will greatly and packets inner distance wants small,
Because the bigger and packets inner distance of the distance between grouping wants small, indicate that the decoded grouping effect of the individual is better.Secondly
If the scale of obtained subproblem or larger solves the difficulty of subproblem too in view of the grouping of individual customer point is uneven
Greatly, desired effect is not achieved；
Step 3.1.4.1: according to step 3.1.2 to t for populationIn each individual solved
Code, obtains several path packets, wherein the inner distance of g-th of individual regards an objective function as；The outside of g-th of individual
The inverse of distance regards an objective function as；The sum of client's point number difference regards one as in the grouping that g-th of individual decoding obtains
A objective function.
Step 3.1.4.2: pop step 3.1.4.1 is executed, three target values of the i-th generation population are obtained, the i-th generation kind
Individual sorts according to the non-dominant minimum of corresponding three Target values progress in group, chooses the individual in the first leading surface as conjunction
Suitable individual P'={ P'1,P'2,...,P'g,...,P'num, P'gIndicate g-th of individual in P'；
Step 3.1.5, Evolution of Population:
Step 3.1.5.1: initialization primary iteration degree variables g=1；
Step 3.1.5.2: parent is selected using binary league matches, new filial generation child is generated using multiple point crossover mode；
Step 3.1.5.3: newly generated individual child is calculated according to step 3.1.4.1；
Three target function values of filial generation child are calculated again, are merged filial generation and parent, are selected by non-dominated ranking next
For the individual of population；
Step 3.1.5.3: being assigned to g for g+1, step 3.1.5.2 is repeated, until reaching maximum number of iterations；
Step 3.2: TABU search optimizes the vehicle routing of subproblem
Step 3.2.1: initialization
The sequence for generating client in a subproblem at random is inserted into warehouse node by vehicle principle as fully loaded as possible, raw
At initial solution, taboo list is emptied, Tabu Length is set；
Step 3.2.2: neighborhood search generates candidate solution
Initial solution is generated according to step 3.2.1, is waited by generations such as searching operators relocation, exchange, 2-opt
Choosing solution, and calculate the fitness value of each candidate solution (objective function is routing total distance here)；
Step 3.2.3: best candidate solution is selected
Select the best candidate solution of fitness value from all candidate solutions that step 3.2.2 is generated, by its with it is current best
Solution (i.e. searching algorithm starts the preferably solution found till now) is compared, if just not considering that it is better than currently preferably solving
It is no to be avoided, it is updated with this best candidate solution and is currently preferably solved, and the current solution as next iteration, then will
Taboo list is added in respective operations, currently preferably solves if be not better than, just from selecting in all candidate solutions not under taboo state
It preferably solves as new current solution, taboo list then is added in respective operations.
Step 3.2.4: judge termination condition
If meeting termination condition, stop and export currently preferably to solve immediately；Otherwise it continues searching, termination condition is maximum
The number of iterations；
Step 3.3: the solution that each sub-component obtains being merged, and calculates its fitness, with current reference solution fitness value
Compare, if being better than current reference solution, updates current reference solution, otherwise, do not update；
Step 3.4: t+1 being assigned to t, repeats step 3.1, until t > maxgen, exports optimal routing knot
Fruit.
It should be noted that, in this document, relational terms such as first and second and the like are used merely to a reality
Body or operation are distinguished with another entity or operation, are deposited without necessarily requiring or implying between these entities or operation
In any actual relationship or order or sequence.Moreover, the terms "include", "comprise" or its any other variant are intended to
Non-exclusive inclusion, so that the process, method, article or equipment including a series of elements is not only wanted including those
Element, but also including other elements that are not explicitly listed, or further include for this process, method, article or equipment
Intrinsic element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that
There is also other identical elements in process, method, article or equipment including the element.
The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although with reference to the foregoing embodiments
Invention is explained in detail, those skilled in the art should understand that: it still can be to aforementioned each implementation
Technical solution documented by example is modified or equivalent replacement of some of the technical features；And these modification or
Replacement, the spirit and scope for technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution.
Claims (8)
1. it is a kind of based on NSGAII packet transaction on a large scale with the method for capacity-constrained, which is characterized in that the method includes steps
It is rapid:
Step 1: with reference to solution pretreatment；
Step 2: parameter initialization, defines primary iteration number and maximum number of iterations, and a feasible solution is initialized as ginseng
Examine solution；
Step 3: judging whether the number of iterations reaches the maximum number of iterations；If so, executing step 7；Otherwise step is executed
Rapid four；
Step 4: optimizing grouping with reference to solution to described by multi-objective Algorithm NSGAII；
Step 5: optimizing processing to the subproblem after grouping on the first leading surface by tabu search algorithm；
Step 6: updating described with reference to solution, accumulation the number of iterations and return step three；
Step 7: being exported described with reference to solution as optimal solution.
2. it is according to claim 1 it is a kind of based on NSGAII packet transaction on a large scale with the method for capacity-constrained, feature
Be: the step 1 is specifically included with reference to solution coding and with reference to solution decoding；It is described to be encoded to reference to solution using natural number coding
Reference solution is encoded, code length is equal to client node number and usable number of vehicles is added to add 1.
3. it is according to claim 1 it is a kind of based on NSGAII packet transaction on a large scale with the method for capacity-constrained, feature
Be, the step 4 specifically includes the following steps:
Step A, individual UVR exposure；
Step B, individual decoding；
Step C, initialization population；
Step D, suitable individual is selected；
Step E, Evolution of Population.
4. it is according to claim 1 it is a kind of based on NSGAII packet transaction on a large scale with the method for capacity-constrained, feature
Be: the step 5 specifically includes the following steps:
Step F, the sequence for generating client in a subproblem at random is inserted into warehouse node by vehicle principle as fully loaded as possible,
Initial solution is generated, taboo list is emptied, Tabu Length is set；
Step G, by the initial solution, candidate solution is generated by searching operators, and calculate the fitness value of each candidate solution；
Step H, the best candidate solution of fitness value will be selected in all candidate solutions.
5. it is a kind of based on NSGAII packet transaction on a large scale with the system of capacity-constrained, which is characterized in that the system comprises:
Preprocessing module, for reference to solution pretreatment；
Parameter initialization module is used for parameter initialization, defines primary iteration number and maximum number of iterations, and initialize one
Feasible solution is used as with reference to solution；
Judgment module, for judging whether the number of iterations reaches the maximum number of iterations；If so, into output module；It is no
Then enter grouping module；
Grouping module, for optimizing grouping with reference to solution to described by multi-objective Algorithm NSGAII；
Processing module, for optimizing processing to the subproblem after grouping on the first leading surface by tabu search algorithm；
Parameter updating module, it is described with reference to solution for updating, it accumulates the number of iterations and enters judgment module；
Output module, for being exported described with reference to solution as optimal solution.
6. it is according to claim 5 it is a kind of based on NSGAII packet transaction on a large scale with the system of capacity-constrained, feature
Be: the preprocessing module includes with reference to solution encoding submodule and with reference to solution decoding sub-module.
7. it is according to claim 5 it is a kind of based on NSGAII packet transaction on a large scale with the system of capacity-constrained, feature
It is, the grouping module includes:
Individual UVR exposure submodule is used for individual UVR exposure；
Individual decoding sub-module, for individual decoding；
Initialization of population submodule is used for initialization population；
Individual screening submodule, for selecting suitable individual；
Evolution of Population submodule is used for Evolution of Population.
8. it is according to claim 5 it is a kind of based on NSGAII packet transaction on a large scale with the system of capacity-constrained, feature
It is, the processing module includes:
Initialization submodule is inserted for generating the sequence of client in a subproblem at random by vehicle principle as fully loaded as possible
Enter warehouse node, generate initial solution, empty taboo list, Tabu Length is set；
Candidate solution handles submodule, for generating candidate solution by searching operators, and calculate each candidate solution for the initial solution
Fitness value；
Candidate solution screens submodule, for will select the best candidate solution of fitness value in all candidate solutions.
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