CN107220731A - A kind of logistics distribution paths planning method - Google Patents
A kind of logistics distribution paths planning method Download PDFInfo
- Publication number
- CN107220731A CN107220731A CN201710367004.7A CN201710367004A CN107220731A CN 107220731 A CN107220731 A CN 107220731A CN 201710367004 A CN201710367004 A CN 201710367004A CN 107220731 A CN107220731 A CN 107220731A
- Authority
- CN
- China
- Prior art keywords
- dispatching
- distribution path
- path
- ant
- primary
- 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.)
- Pending
Links
Classifications
-
- 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
- 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
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- 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
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Economics (AREA)
- Biophysics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Artificial Intelligence (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Quality & Reliability (AREA)
- Evolutionary Biology (AREA)
- Development Economics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Tourism & Hospitality (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Genetics & Genomics (AREA)
- Game Theory and Decision Science (AREA)
- Physiology (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention relates to a kind of logistics distribution paths planning method, in order to improve the performance and ability of searching optimum of algorithm, make up ant group algorithm and the respective defect of genetic algorithm, introduce the intersection of genetic algorithm, mutation operation, the precocity in local search procedure can be prevented effectively from, early Convergent Phenomenon, and utilize the random search of genetic algorithm, quickly, global convergence produces the initial solution for the problem that to be solved, and the initial solution is converted into the initial information element distribution of ant group algorithm, then the concurrency of ant group algorithm is utilized, the features such as positive feedback mechanism and high solution efficiency, seeks optimal solution, overcome the problem of ant group algorithm initial information element is deficient not enough, obtain time efficiency and solution efficiency all relatively good heuritic approaches.
Description
Technical field
The present invention relates to a kind of logistics distribution paths planning method, belong to logistics distribution technical field.
Background technology
The business of express mail logistics is usually the post network for borrowing generic services, passes through the express delivery logistics of different regions
Outlet carries out the dispatching of mail.In fact, postal car is when carrying out delivery operation, the selection in its path is typically all to lean on postal car
The dispatching experience of driver, the Path selection foundation without science, the selection for Distribution path is compared blindly, or even postal car sometimes
The logistics cost for being least rational Distribution path, much increasing dispatching link of driver now, so that express delivery logistics is looked forward to
The income that industry cannot be satisfied with.Therefore, in actual logistics distribution, Path selection of the postal car between outlet turns into shadow
Ring the key factor of express delivery logistics distribution cost height.It is whether reasonable to dispatching speed, cost, benefit shadow that Distribution path is arranged
Sound is very big, and it is that express delivery logistics postal car high efficiency, high-quality completion are matched somebody with somebody that postal car path, which is optimized, using scientific and rational method
Send the key point of task.
What the routing problem of express delivery logistics postal car needed to study is：How in complicated transportation network quick, science
Suitable traffic route is found, the effective select permeability for solving postal car path in express delivery logistics distribution process, to reach most
The reduction distribution time of limits, saves the cost produced in delivery process；Punctual transport can be realized, by the finger of customer requirement
Mail is sent in client's hand by section of fixing time, and accomplishes human nature service, can more meet requirement of the current consumer to express delivery logistics；
While delivery operation is completed, making the totle drilling cost of delivery process reduces, and improves the performance of enterprises, optimizes allocation of resources, and improves thing
Flow horizontal.In the algorithm of numerous solution distribution vehicle routing problems, ant group algorithm is progressively applied to vehicle routing optimization neck
Domain, the focus as the problem of scholar's research optimum organization in recent years, but ant group algorithm is solving the optimal of practical problem
Not only search time is long during path, and is easily trapped into local optimum, it is therefore desirable to ant group algorithm is changed in theoretic
Enter to be very important.
The research of Vehicle Routing Problems has important theory significance, is had wide range of applications in solving practical problems.
The algorithm of nineteen fifty-nine solution Vehicle Routing Problems obtained the concern of scholars after being suggested, until, Clarke in 1964
A kind of Danting-Ramser methods innovatory algorithm-heuristic Danting-Ramser saving algrithms are proposed with Wright, thus
Vehicle Routing Problems have obtained the favor of numerous scholars.With the continuous research and exploration of scholars, Vehicle Routing Problems are ground
Study carefully method to constantly update and perfect, domestic increasing scholar, which begins one's study, solves VRP algorithm.
Path optimization's strategy based on chaotic neural network, needing to provide with reference to supply chain allows the number of serv-fail twice
Learn model, it is proposed that a kind of hybrid neural networks derivation algorithm, solve Vehicle Routing Problem with Stochastic Demands in supply chain.But
This method vehicle overload operation, utilization ratio is low, and management method falls behind, with the continuous expansion of scope of the enterprise, delivery industry
Business amount and dispatching site are gradually increased, and this method can not only complete dispatching business, can also return enterprise and bring huge
Cost, cause the waste of manpower and financial resources, seriously constrain the development of enterprise.
Based on the Path Planning of multistage path optimization, ant group algorithm path optimization in nformation grid is overcome easy
It is absorbed in the weakness of single path deadlock, strengthens the positive feedback of ant group algorithm search, efficient convergent advantage, it is to avoid algorithm is too early or mistake
Evening terminates and influences the overall performance of partitioning algorithm so that nformation grid node scheduling can be carried out according to task amount and path performance
Effective distribution.But this method calculates pheromones scarcity at initial stage, solving speed is slow, and when problem scale is larger, ant colony is calculated
Method search speed when searching for optimal path is excessively slow, it is necessary to which high-quality solution can just be obtained by taking considerable time.
Hybrid ant colony path optimization strategy based on mutual information similarity, in order to represent optimal path and road subject to registration
Mutual information entropy between footpath, adds a new similarity factor of influence in the probability operator of ant group algorithm, so as to
The ability of searching optimum of the former algorithm of increase, while search speed that can be with accelerating algorithm in solution space applies the algorithm in travelling
In business's problem, according to the specific environment of traveling salesman problem, a certain degree of abbreviation is carried out to the formula of hybrid ant colony, made
Algorithm is obtained when solving problems, corresponding time complexity reduction.But this method is when that search is proceeded by is certain
After degree, the solution that all ants are found can be close with some or some locally optimal solutions, so as to stagnation behavior occur, causes ant
Group's algorithm can not continue to search further for solution space, be unfavorable for finding global optimal solution.
The content of the invention
The technical problems to be solved by the invention are to provide a kind of raising algorithm performance and search capability, and when can be balanced
Between efficiency and solution efficiency logistics distribution paths planning method.
In order to solve the above-mentioned technical problem the present invention uses following technical scheme：The present invention devises a kind of logistics distribution road
Footpath planing method, the planning for completing each dispatching point Distribution path, initializes N=1, then performs following steps first：
Step A. obtains each bar difference under n-th circulation with each any dispatching point for starting point respectively according to ant group algorithm
The primary Distribution path of all dispatching points of covering, the quantity of the lower primary Distribution path of this time circulation is equal to default first quantity, and
The dispatching point quantity passed through respectively in the primary Distribution path of each bar is equal to the sum of all dispatching points；Meanwhile, based on n-th
The acquisition of the primary Distribution path of each bar under circulation, for all dispatching points, updates the letter on Distribution path between each dispatching point
Breath element；Subsequently into step B；
Step B. is less than preset path length threshold for the primary Distribution path of each bar under n-th circulation, selection path length
The primary Distribution path of each bar of value, circulates down the intermediate Distribution path of each bar, subsequently into step C as n-th；
Step C. uses genetic algorithm, under being circulated for n-th the intermediate Distribution path of each bar intersected, mutation operation,
The primary optimal Distribution path of wall scroll is obtained, primary optimal Distribution path is circulated down as n-th, subsequently into step D；
Step D. judges whether N is equal to default maximum cycle Nmax, it is then to enter step E；Otherwise the value for N is entered
Row Jia 1 and updated, and return to step A；
Step E. selects the primary optimal dispatching road of shortest path length for primary optimal Distribution path under each circulation
Footpath, as optimal Distribution path, the dispatching for completing each dispatching point.
As a preferred technical solution of the present invention, the step A comprises the following steps：
Step A1. is directed to all dispatching points, and according to ant group algorithm, K ant is randomly dispersed in into any dispatching point respectively
On, and it is sky to build each dispatching taboo list of corresponding each ant respectively, subsequently into step A2；Wherein, K is equal to default the
One quantity；
Step A2. is respectively for each ant, and the dispatching point according to where ant dispenses taboo excluding corresponding to the ant
In table on the premise of dispatching point, the next transfer for obtaining the ant by state transition probability formula dispenses point, and this is dispensed
Point is added among the path corresponding to the ant, while the dispatching point is added in the dispatching taboo list corresponding to the ant, such as
This circulation performs this step aforesaid operations, until ant institute covers all dispatching points by path, is derived from each ant
Corresponding path, that is, obtain the primary Distribution path of K bars under n-th circulation, subsequently into step A3 respectively；
Step A3. is according to ant group algorithm, and the primary Distribution path of K bars under being circulated based on obtained n-th updates each dispatching
Pheromones between point on Distribution path, subsequently into step B.
As a preferred technical solution of the present invention, the step C comprises the following steps：
Step C1. is directed to all dispatching points, and different numerical value are respectively adopted and are marked, and obtaining each dispatching point, institute is right respectively
The dispatching mark value answered, subsequently into step C2；
Dispatching mark values of the step C2. according to corresponding to each dispatching point difference, builds each bar middle rank under n-th circulation and matches somebody with somebody
The dispatching mark value sequence that path difference is corresponding is sent, subsequently into step C3；
Step C3. uses genetic algorithm, and each dispatching mark value sequence under being circulated for n-th is intersected, made a variation
Operation, obtains the primary optimal dispatching mark value sequence of wall scroll, subsequently into step C4；
Dispatching mark values of the step C4. according to corresponding to each dispatching point difference, obtains primary optimal dispatching mark value sequence
Primary optimal Distribution path under the corresponding path of row, i.e. n-th circulation, subsequently into step D.
It is as follows as a preferred technical solution of the present invention, in addition to step B-C, after execution of step B, into step
After rapid B-C, execution of step B-C, into step C；
The beginning that step B-C. adds home-delivery center's node to n-th circulation in the intermediate Distribution path of each bar, updates
The intermediate Distribution path of each bar under n-th circulation；
In the step C, the intermediate Distribution path of each bar under being circulated for n-th, using genetic algorithm, is keeping each bar
Start in intermediate Distribution path node it is constant in the case of, intersected for the intermediate Distribution path of each bar, mutation operation, acquisition
The primary optimal Distribution path of wall scroll, primary optimal Distribution path is circulated down as n-th.
As a preferred technical solution of the present invention, the step C comprises the following steps：
Step C1. is directed to home-delivery center's node and all dispatchings point, and different numerical value are respectively adopted and are marked, are dispensed
Dispatching mark value corresponding to Centroid and each dispatching point difference, subsequently into step C2；
Dispatching mark values of the step C2. according to corresponding to home-delivery center's node and each dispatching point difference, builds n-th and follows
The corresponding dispatching mark value sequence of the intermediate Distribution path difference of each bar under ring, subsequently into step C3；
Each dispatching under step C3. is circulated for n-th marks value sequence, using genetic algorithm, matches somebody with somebody keeping each
Send in the case that first dispatching mark value is constant in mark value sequence, intersected, become for each dispatching mark value sequence
ETTHER-OR operation, obtains the primary optimal dispatching mark value sequence of wall scroll, subsequently into step C4；
Dispatching mark values of the step C4. according to corresponding to each dispatching point difference, obtains primary optimal dispatching mark value sequence
Primary optimal Distribution path under the corresponding path of row, i.e. n-th circulation, subsequently into step D.
A kind of logistics distribution paths planning method of the present invention, using above technical scheme compared with prior art, tool
There is following technique effect：
(1) the logistics distribution paths planning method that the present invention is designed, the searching of Rapid Science in complicated transportation network
Suitable traffic route, to reduce distribution time to greatest extent, saves the cost produced in delivery process；
(2) the logistics distribution paths planning method that the present invention is designed, it is possible to achieve punctual transport, according to the finger of customer requirement
Mail is sent in client's hand by section of fixing time, and accomplishes human nature service, can more meet requirement of the current consumer to express delivery logistics；
(3) the logistics distribution paths planning method that the present invention is designed, while delivery operation is completed, makes delivery process
Totle drilling cost is reduced, and is improved the performance of enterprises, is optimized allocation of resources, and improves logistics level；
(4) the logistics distribution paths planning method that the present invention is designed, modified two-step method searching algorithm solves ant group algorithm and sought
Look for the solving speed existed during optimal path slow, do not catch up with problem scale expansion, be absorbed in the defects such as local optimum.
Brief description of the drawings
Fig. 1 is the schematic diagram of logistics distribution paths planning method designed by the present invention.
Embodiment
The embodiment of the present invention is described in further detail with reference to Figure of description.
Ant group algorithm is the accumulation and selection optimal path of more newly arriving by pheromones, with distribution, parallel, global convergence
Ability, but search pheromones at initial stage are deficient, the algorithm speed of service is slow；By contrast, genetic algorithm has quick global search
Ability, but its randomness is stronger, it is impossible to and guarantee can rapidly and accurately converge on optimal solution every time.Genetic algorithm and ant group algorithm
It is each to have respective advantage by oneself, and the combination of two kinds of algorithms has very strong complementarity, therefore genetic algorithm is introduced into ant herein
Group's algorithm will be greatly enhanced the performance and search capability of algorithm.
In order to improve the performance and ability of searching optimum of algorithm, ant group algorithm and the respective defect of genetic algorithm are made up, I
Introduce intersection, the mutation operation of genetic algorithm, precocious, the early Convergent Phenomenon in local search procedure can be prevented effectively from.
The initial solution for the problem that to be solved is produced using the random search of genetic algorithm, quick, global convergence, and the initial solution is turned
The initial information element distribution of ant group algorithm is turned to, the concurrency of ant group algorithm, positive feedback mechanism and solution efficiency is then utilized
High the features such as, seeks optimal solution, overcomes the problem of ant group algorithm initial information element is deficient not enough, obtains time efficiency and solution
Efficiency all relatively good heuritic approaches.
Ant group algorithm is gone to last city from first city and covered behind all cities, complete for ant group algorithm
Into the access to whole cities in model, taboo list is filled in this case, and every ant can be drawn by calculating
The path length passed by, shortest path is selected after being contrasted two-by-two.Then constantly repeat empty taboo list, fresh information element this
Two processes, until untill you more preferable solution do not occur in the number of times of traversal needed for completing or certain cyclic process.
Using ant colony optimization for solving path planning practical problem, the ant colony with certain amount ant should be generated first, often
Ant selects next transfering node, until setting up one from start node according to the pheromone concentration on path
Solution；Ant solves fine or not degree the pheromones that are directly proportional of quality to solving is discharged on the path of process according to finding；, every
Ant starts new solution procedure, until finding satisfactory solution.
In designed logistics distribution paths planning method of the invention, the application based on ant group algorithm is calculated while introducing heredity
Method, wherein, genetic algorithm is improved ant group algorithm and mainly realized by following specific steps：
1st, replicate：I.e. after each round search, the optimal solution of parent is copied into the next generation so that the optimum individual of algorithm
It can be continued in the algorithm
2nd, encode：The model of Vehicle Routing Problems is encoded, the mathematical data in model is converted into gene data,
For each client's point, it is identified respectively using number different from each other；It is same using mutual not phase for each dispensing vehicle
Same numerical value is marked respectively.
3rd, intersect：Crossover operation in genetic algorithm can increase population diversity, and crossover operation is introduced into ant group algorithm
Search space can effectively be expanded, be prevented effectively from and be absorbed in locally optimal solution., will be optimal after terminating by ant group algorithm search
Solution and suboptimal solution are intersected, until effectively being solved.
4th, make a variation：On the basis of crossover operation formation progeny population, appropriate variation can keep individual many in population
Sample, can improve the efficiency of algorithm again.Such as the coding S of optimum individual in population, we select to generate two differences at random herein
Natural number n1, n2, and meet n1, n2>1, the coding of the n-th 1 is mutually exchanged, then generate new coding S ' produce it is new
Distribution path.
After genetic algorithm and ant group algorithm are combined, the speed and search capability that algorithm finds optimal solution have obtained one
Fixed raising, specifically illustrates in terms of these three from state change rule, visibility, Pheromone update.
1st, node transition rule is improved：In order to prevent ant group algorithm to be easily trapped into locally optimal solution, in the mould of ant group algorithm
A new parameter F is introduced in type, when ant k accesses city j from city i, one is produced on interval [0,1] first at random
Number F, when f value is less than parameter F, ant selects next city according to parameter F.Node transition rule after improvement is as follows：
Add after parameter F, limit the influence for causing larger error solution occur because of the positive feedback row of ant, work as ant
When being conducted interviews to next city, the random ability of discovery of algorithm is enhanced, the ranged space for finding solution, success is expanded
Solve the defect that algorithm is absorbed in optimal solution in the range of some too early.
2nd, the improvement of visibility：The contrast between a city is defined apart from uij：uij=doi+doj-dij, wherein doi,doj,
dijThe center of city 0 is represented respectively to city i distance, and city j distance, distances of the city i to city j are arrived in city 0.uij's
The higher explanation of value should directly access city j after city i is accessed, therefore be by visibility is improved：
To avoid dij=0 and visibility is tended to be infinitely great, and when denominator is 1, ηij=1, coefficient does not work,
Constant c (c are added on denominator>1) influence that visibility is selected urban node can be ensured.
3rd, pheromone updating rule is improved：Improved in Pheromone update by pheromones volatility coefficient p introducing to still
The search engine meeting of non-access path, expands search space, it is to avoid occur stagnation behavior too early.Ant k is in search optimal path mistake
Pheromones between node i, j after node j is transferred to from node i, are carried out local updating by Cheng Zhong according to the following formula：τ(i,j)
←(1-ρ)·τ(i,j)+ρ·Δτ(i,j)
Wherein, Δ τ (i, j)=τ0
Improvement for above-mentioned genetic algorithm to ant group algorithm, it has been found that improve ant group algorithm and kept in overall structure
It is consistent with ant group algorithm, it is only necessary to increase the operation operators such as selection, intersection, the variation of genetic algorithm in a model.Improve ant colony
The algorithm flow of the VRP of algorithm is as shown below.
As shown in figure 1, a kind of designed logistics distribution paths planning method of the present invention, for completing each dispatching point dispatching
In the planning in path, practical application, we are specifically divided into two kinds of design considerations for the application of genetic algorithm, the first, losing
The consideration of home-delivery center's node it is not related in propagation algorithm, i.e., in application design genetic algorithm, acquisition is based only upon each dispatching point
Optimal Distribution path, then by home-delivery center's node, dispensed along optimal Distribution path；Second, in heredity calculation
Home-delivery center's node is considered in method, i.e., in application design genetic algorithm, is obtained based on home-delivery center's node and each dispatching point
Optimal Distribution path, then directly dispensed along optimal Distribution path.
We look first at the first corresponding embodiment, and N=1 is initialized first, following steps are then performed：
Step A. obtains each bar difference under n-th circulation with each any dispatching point for starting point respectively according to ant group algorithm
The primary Distribution path of all dispatching points of covering, the quantity of the lower primary Distribution path of this time circulation is equal to default first quantity, and
The dispatching point quantity passed through respectively in the primary Distribution path of each bar is equal to the sum of all dispatching points；Meanwhile, based on n-th
The acquisition of the primary Distribution path of each bar under circulation, for all dispatching points, updates the letter on Distribution path between each dispatching point
Breath element；Subsequently into step B.
Above-mentioned steps A specifically includes following steps：
Step A1. is directed to all dispatching points, and according to ant group algorithm, K ant is randomly dispersed in into any dispatching point respectively
On, and it is sky to build each dispatching taboo list of corresponding each ant respectively, subsequently into step A2；Wherein, K is equal to default the
One quantity.
Step A2. is respectively for each ant, and the dispatching point according to where ant dispenses taboo excluding corresponding to the ant
In table on the premise of dispatching point, the next transfer for obtaining the ant by state transition probability formula dispenses point, and this is dispensed
Point is added among the path corresponding to the ant, while the dispatching point is added in the dispatching taboo list corresponding to the ant, such as
This circulation performs this step aforesaid operations, until ant institute covers all dispatching points by path, is derived from each ant
Corresponding path, that is, obtain the primary Distribution path of K bars under n-th circulation, subsequently into step A3 respectively.
Step A3. is according to ant group algorithm, and the primary Distribution path of K bars under being circulated based on obtained n-th updates each dispatching
Pheromones between point on Distribution path, subsequently into step B.
Step B. is less than preset path length threshold for the primary Distribution path of each bar under n-th circulation, selection path length
The primary Distribution path of each bar of value, circulates down the intermediate Distribution path of each bar, subsequently into step C as n-th.
Step C. uses genetic algorithm, under being circulated for n-th the intermediate Distribution path of each bar intersected, mutation operation,
The primary optimal Distribution path of wall scroll is obtained, primary optimal Distribution path is circulated down as n-th, subsequently into step D.
Above-mentioned steps C specifically includes following steps：
Step C1. is directed to all dispatching points, and different numerical value are respectively adopted and are marked, and obtaining each dispatching point, institute is right respectively
The dispatching mark value answered, subsequently into step C2.
Dispatching mark values of the step C2. according to corresponding to each dispatching point difference, builds each bar middle rank under n-th circulation and matches somebody with somebody
The dispatching mark value sequence that path difference is corresponding is sent, subsequently into step C3.
Step C3. uses genetic algorithm, and each dispatching mark value sequence under being circulated for n-th is intersected, made a variation
Operation, obtains the primary optimal dispatching mark value sequence of wall scroll, subsequently into step C4.
Dispatching mark values of the step C4. according to corresponding to each dispatching point difference, obtains primary optimal dispatching mark value sequence
Primary optimal Distribution path under the corresponding path of row, i.e. n-th circulation, subsequently into step D.
Step D. judges whether N is equal to default maximum cycle Nmax, it is then to enter step E；Otherwise the value for N is entered
Row Jia 1 and updated, and return to step A.
Step E. selects the primary optimal dispatching road of shortest path length for primary optimal Distribution path under each circulation
Footpath, as optimal Distribution path, the dispatching for completing each dispatching point.
Turning next to second of corresponding embodiment, N=1 is initialized first, following steps are then performed：
Step A. obtains each bar difference under n-th circulation with each any dispatching point for starting point respectively according to ant group algorithm
The primary Distribution path of all dispatching points of covering, the quantity of the lower primary Distribution path of this time circulation is equal to default first quantity, and
The dispatching point quantity passed through respectively in the primary Distribution path of each bar is equal to the sum of all dispatching points；Meanwhile, based on n-th
The acquisition of the primary Distribution path of each bar under circulation, for all dispatching points, updates the letter on Distribution path between each dispatching point
Breath element；Subsequently into step B.
Above-mentioned steps A specifically includes following steps：
Step A1. is directed to all dispatching points, and according to ant group algorithm, K ant is randomly dispersed in into any dispatching point respectively
On, and it is sky to build each dispatching taboo list of corresponding each ant respectively, subsequently into step A2；Wherein, K is equal to default the
One quantity.
Step A2. is respectively for each ant, and the dispatching point according to where ant dispenses taboo excluding corresponding to the ant
In table on the premise of dispatching point, the next transfer for obtaining the ant by state transition probability formula dispenses point, and this is dispensed
Point is added among the path corresponding to the ant, while the dispatching point is added in the dispatching taboo list corresponding to the ant, such as
This circulation performs this step aforesaid operations, until ant institute covers all dispatching points by path, is derived from each ant
Corresponding path, that is, obtain the primary Distribution path of K bars under n-th circulation, subsequently into step A3 respectively.
Step A3. is according to ant group algorithm, and the primary Distribution path of K bars under being circulated based on obtained n-th updates each dispatching
Pheromones between point on Distribution path, subsequently into step B.
Step B. is less than preset path length threshold for the primary Distribution path of each bar under n-th circulation, selection path length
The primary Distribution path of each bar of value, circulates down the intermediate Distribution path of each bar, subsequently into step B-C as n-th.
The beginning that step B-C. adds home-delivery center's node to n-th circulation in the intermediate Distribution path of each bar, updates
The intermediate Distribution path of each bar under n-th circulation, subsequently into step C；
Step C., using genetic algorithm, matches somebody with somebody for the intermediate Distribution path of each bar under n-th circulation keeping each bar middle rank
Send start in path node it is constant in the case of, intersected for the intermediate Distribution path of each bar, mutation operation, at the beginning of acquisition wall scroll
The optimal Distribution path of level, circulates down primary optimal Distribution path, subsequently into step D as n-th.
Above-mentioned steps C specifically includes following steps：
Step C1. is directed to home-delivery center's node and all dispatchings point, and different numerical value are respectively adopted and are marked, are dispensed
Dispatching mark value corresponding to Centroid and each dispatching point difference, subsequently into step C2.
Dispatching mark values of the step C2. according to corresponding to home-delivery center's node and each dispatching point difference, builds n-th and follows
The corresponding dispatching mark value sequence of the intermediate Distribution path difference of each bar under ring, subsequently into step C3.
Each dispatching under step C3. is circulated for n-th marks value sequence, using genetic algorithm, matches somebody with somebody keeping each
Send in the case that first dispatching mark value is constant in mark value sequence, intersected, become for each dispatching mark value sequence
ETTHER-OR operation, obtains the primary optimal dispatching mark value sequence of wall scroll, subsequently into step C4.
Dispatching mark values of the step C4. according to corresponding to each dispatching point difference, obtains primary optimal dispatching mark value sequence
Primary optimal Distribution path under the corresponding path of row, i.e. n-th circulation, subsequently into step D.
Step E. selects the primary optimal dispatching road of shortest path length for primary optimal Distribution path under each circulation
Footpath, as optimal Distribution path, the dispatching for completing each dispatching point.
Embodiments of the present invention are explained in detail above in conjunction with accompanying drawing, but the present invention is not limited to above-mentioned implementation
Mode, can also be on the premise of present inventive concept not be departed from the knowledge that those of ordinary skill in the art possess
Make a variety of changes.
Claims (5)
1. a kind of logistics distribution paths planning method, the planning for completing each dispatching point Distribution path, it is characterised in that just
Beginningization N=1, and perform following steps：
Step A. is obtained each bar under n-th circulation and is covered each by with each any dispatching point for starting point respectively according to ant group algorithm
The primary Distribution path of all dispatching points, the quantity of the lower primary Distribution path of this time circulation is equal to default first quantity, and each bar
The dispatching point quantity passed through respectively in primary Distribution path is equal to the sum of all dispatching points；Meanwhile, based on n-th circulation
Under the primary Distribution path of each bar acquisition, for all dispatching points, update the pheromones on Distribution path between each dispatching point；
Subsequently into step B；
Step B. is for the primary Distribution path of each bar under n-th circulation, and selection path length is less than preset path length threshold
The primary Distribution path of each bar, circulates down the intermediate Distribution path of each bar, subsequently into step C as n-th；
Step C. uses genetic algorithm, under being circulated for n-th the intermediate Distribution path of each bar intersected, mutation operation, acquisition
The primary optimal Distribution path of wall scroll, circulates down primary optimal Distribution path, subsequently into step D as n-th；
Step D. judges whether N is equal to default maximum cycle Nmax, it is then to enter step E；Otherwise the value for N Jia 1
Update, and return to step A；
Step E. selects the primary optimal Distribution path of shortest path length for primary optimal Distribution path under each circulation,
As optimal Distribution path, the dispatching for completing each dispatching point.
2. a kind of logistics distribution paths planning method according to claim 1, it is characterised in that：The step A includes as follows
Step：
Step A1. is directed to all dispatching points, and according to ant group algorithm, K ant is randomly dispersed on any dispatching point respectively, and
Each dispatching taboo list for building corresponding each ant respectively is sky, subsequently into step A2；Wherein, K is equal to default first number
Amount；
Step A2. is respectively for each ant, the dispatching point according to where ant, in dispatching taboo list corresponding to the ant is excluded
On the premise of dispatching point, the next transfer for obtaining the ant by state transition probability formula dispenses point, and the dispatching point is added
Enter among the path corresponding to the ant, while the dispatching point is added in the dispatching taboo list corresponding to the ant, so follow
Ring performs this step aforesaid operations, until ant institute covers all dispatching points by path, is derived from each ant difference
Corresponding path, that is, obtain the primary Distribution path of K bars under n-th circulation, subsequently into step A3；
Step A3. is according to ant group algorithm, and the primary Distribution path of K bars under being circulated based on obtained n-th updates each dispatching point
Between pheromones on Distribution path, subsequently into step B.
3. a kind of logistics distribution paths planning method is stated according to claim 1 or 2, it is characterised in that：The step C includes as follows
Step：
Step C1. is directed to all dispatching points, and different numerical value are respectively adopted and are marked, and obtains corresponding to each dispatching point difference
Mark value is dispensed, subsequently into step C2；
Dispatching mark values of the step C2. according to corresponding to each dispatching point difference, builds each bar middle rank dispatching road under n-th circulation
The corresponding dispatching mark value sequence of footpath difference, subsequently into step C3；
Step C3. uses genetic algorithm, and each dispatching mark value sequence under being circulated for n-th is intersected, mutation operation,
The primary optimal dispatching mark value sequence of wall scroll is obtained, subsequently into step C4；
Dispatching mark values of the step C4. according to corresponding to each dispatching point difference, obtains primary optimal dispatching mark value sequence institute
Primary optimal Distribution path under the circulation of corresponding path, i.e. n-th, subsequently into step D.
4. a kind of logistics distribution paths planning method according to claim 1 or claim 2, it is characterised in that：Also include step B-C such as
Under, after execution of step B, into step B-C, after execution of step B-C, into step C；
The beginning that step B-C. adds home-delivery center's node to n-th circulation in the intermediate Distribution path of each bar, updates n-th
The intermediate Distribution path of each bar under circulation；
In the step C, the intermediate Distribution path of each bar under being circulated for n-th, using genetic algorithm, is keeping each bar middle rank
Start in Distribution path node it is constant in the case of, intersected for the intermediate Distribution path of each bar, mutation operation, acquisition wall scroll
Primary optimal Distribution path, primary optimal Distribution path is circulated down as n-th.
5. a kind of logistics distribution paths planning method is stated according to claim 4, it is characterised in that：The step C includes following step
Suddenly：
Step C1. is directed to home-delivery center's node and all dispatchings point, and different numerical value are respectively adopted and are marked, and obtains home-delivery center
Dispatching mark value corresponding to node and each dispatching point difference, subsequently into step C2；
Dispatching mark values of the step C2. according to corresponding to home-delivery center's node and each dispatching point difference, builds under n-th circulation
The corresponding dispatching mark value sequence of the intermediate Distribution path difference of each bar, subsequently into step C3；
Each dispatching under step C3. is circulated for n-th marks value sequence, using genetic algorithm, is keeping each dispatching mark
In the case of remembering that first dispatching mark value is constant in value sequence, for each dispatching mark, value sequence is intersected, make a variation behaviour
Make, the primary optimal dispatching mark value sequence of wall scroll is obtained, subsequently into step C4；
Dispatching mark values of the step C4. according to corresponding to each dispatching point difference, obtains primary optimal dispatching mark value sequence institute
Primary optimal Distribution path under the circulation of corresponding path, i.e. n-th, subsequently into step D.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710367004.7A CN107220731A (en) | 2017-05-23 | 2017-05-23 | A kind of logistics distribution paths planning method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710367004.7A CN107220731A (en) | 2017-05-23 | 2017-05-23 | A kind of logistics distribution paths planning method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107220731A true CN107220731A (en) | 2017-09-29 |
Family
ID=59945024
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710367004.7A Pending CN107220731A (en) | 2017-05-23 | 2017-05-23 | A kind of logistics distribution paths planning method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107220731A (en) |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107798423A (en) * | 2017-10-11 | 2018-03-13 | 南京邮电大学 | Vehicle path planning Simulation Experimental Platform based on multi-intelligence algorithm |
| CN108241375A (en) * | 2018-02-05 | 2018-07-03 | 景德镇陶瓷大学 | A kind of application process of self-adaptive genetic operator in mobile robot path planning |
| CN108510227A (en) * | 2018-03-23 | 2018-09-07 | 东华大学 | A kind of real-time planning system of vehicle-mounted logistics distribution based on machine learning |
| CN108510662A (en) * | 2017-11-16 | 2018-09-07 | 广州市沃希信息科技有限公司 | A kind of umbrella leasing charging method and system based on Quick Response Code |
| CN108844549A (en) * | 2018-07-04 | 2018-11-20 | 深圳凯达通光电科技有限公司 | A kind of reliable logistics distribution navigation system |
| CN108898340A (en) * | 2018-06-26 | 2018-11-27 | 易家网络科技有限公司 | The calculation method and system of logistics freight charges |
| CN109800910A (en) * | 2019-01-10 | 2019-05-24 | 浙江工业大学 | A kind of vehicle routing optimization method of the meta-heuristic algorithms based on TABU search |
| CN109919396A (en) * | 2019-04-01 | 2019-06-21 | 南京邮电大学 | A kind of route planning method of Logistics Oriented dispatching |
| CN109916419A (en) * | 2019-03-12 | 2019-06-21 | 哈尔滨工程大学 | A kind of hybrid genetic algorithm unmanned boat real-time route planing method of object-oriented |
| WO2019119457A1 (en) * | 2017-12-23 | 2019-06-27 | 深圳大学 | Model factor calculation-based method and device for dynamic vehicle pickup-and-delivery route planning |
| CN110135707A (en) * | 2019-04-25 | 2019-08-16 | 内蒙古工业大学 | A kind of optimization of garment sewing lines scheduling method |
| CN110276499A (en) * | 2019-06-28 | 2019-09-24 | 青岛立东生态有限公司 | A kind of refuse collection route optimization calculation method |
| CN110490503A (en) * | 2019-05-30 | 2019-11-22 | 湖南城市学院 | A kind of logistics delivery vehicle scheduling method based on mass data |
| CN110851755A (en) * | 2019-09-29 | 2020-02-28 | 口碑(上海)信息技术有限公司 | Method and device for acquiring delivery path information and electronic equipment |
| CN111709560A (en) * | 2020-05-29 | 2020-09-25 | 杭州电子科技大学 | Method for solving vehicle path problem based on improved ant colony algorithm |
| CN111722596A (en) * | 2019-03-20 | 2020-09-29 | 北京京东尚科信息技术有限公司 | Multistage scheduling method and device |
| CN112785190A (en) * | 2021-02-03 | 2021-05-11 | 杭州拼便宜网络科技有限公司 | E-commerce logistics diversified monitoring system based on multi-loop transportation algorithm |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103413209A (en) * | 2013-07-17 | 2013-11-27 | 西南交通大学 | Method for selecting multi-user and multi-warehouse logistics distribution path |
| CN103701702A (en) * | 2013-12-12 | 2014-04-02 | 杭州百富电子技术有限公司 | Dynamic routing algorithm in power line carrier communication |
| CN104751250A (en) * | 2015-04-16 | 2015-07-01 | 南通理工学院 | Method of finding optimal paths based on ant colony method |
-
2017
- 2017-05-23 CN CN201710367004.7A patent/CN107220731A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103413209A (en) * | 2013-07-17 | 2013-11-27 | 西南交通大学 | Method for selecting multi-user and multi-warehouse logistics distribution path |
| CN103701702A (en) * | 2013-12-12 | 2014-04-02 | 杭州百富电子技术有限公司 | Dynamic routing algorithm in power line carrier communication |
| CN104751250A (en) * | 2015-04-16 | 2015-07-01 | 南通理工学院 | Method of finding optimal paths based on ant colony method |
Non-Patent Citations (3)
| Title |
|---|
| 张昕 等: "基于改进蚁群算法的供应链物流配送路线规划", 《湖南税务高等专科学校学报》 * |
| 王慕抽: "基于混合蚁群算法的物流配送路径优化研究", 《物流科技》 * |
| 马江涛: "遗传混合算法路径规划研究", 《信息与电脑》 * |
Cited By (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107798423A (en) * | 2017-10-11 | 2018-03-13 | 南京邮电大学 | Vehicle path planning Simulation Experimental Platform based on multi-intelligence algorithm |
| CN108510662A (en) * | 2017-11-16 | 2018-09-07 | 广州市沃希信息科技有限公司 | A kind of umbrella leasing charging method and system based on Quick Response Code |
| WO2019119457A1 (en) * | 2017-12-23 | 2019-06-27 | 深圳大学 | Model factor calculation-based method and device for dynamic vehicle pickup-and-delivery route planning |
| CN108241375A (en) * | 2018-02-05 | 2018-07-03 | 景德镇陶瓷大学 | A kind of application process of self-adaptive genetic operator in mobile robot path planning |
| CN108241375B (en) * | 2018-02-05 | 2020-10-30 | 景德镇陶瓷大学 | Application method of self-adaptive ant colony algorithm in mobile robot path planning |
| CN108510227A (en) * | 2018-03-23 | 2018-09-07 | 东华大学 | A kind of real-time planning system of vehicle-mounted logistics distribution based on machine learning |
| CN108898340A (en) * | 2018-06-26 | 2018-11-27 | 易家网络科技有限公司 | The calculation method and system of logistics freight charges |
| CN108844549A (en) * | 2018-07-04 | 2018-11-20 | 深圳凯达通光电科技有限公司 | A kind of reliable logistics distribution navigation system |
| CN109800910A (en) * | 2019-01-10 | 2019-05-24 | 浙江工业大学 | A kind of vehicle routing optimization method of the meta-heuristic algorithms based on TABU search |
| CN109916419A (en) * | 2019-03-12 | 2019-06-21 | 哈尔滨工程大学 | A kind of hybrid genetic algorithm unmanned boat real-time route planing method of object-oriented |
| CN111722596A (en) * | 2019-03-20 | 2020-09-29 | 北京京东尚科信息技术有限公司 | Multistage scheduling method and device |
| CN109919396A (en) * | 2019-04-01 | 2019-06-21 | 南京邮电大学 | A kind of route planning method of Logistics Oriented dispatching |
| CN109919396B (en) * | 2019-04-01 | 2022-07-26 | 南京邮电大学 | Route planning method for logistics distribution |
| CN110135707A (en) * | 2019-04-25 | 2019-08-16 | 内蒙古工业大学 | A kind of optimization of garment sewing lines scheduling method |
| CN110135707B (en) * | 2019-04-25 | 2023-04-07 | 内蒙古工业大学 | Garment sewing assembly line optimization method |
| CN110490503A (en) * | 2019-05-30 | 2019-11-22 | 湖南城市学院 | A kind of logistics delivery vehicle scheduling method based on mass data |
| CN110276499A (en) * | 2019-06-28 | 2019-09-24 | 青岛立东生态有限公司 | A kind of refuse collection route optimization calculation method |
| CN110851755A (en) * | 2019-09-29 | 2020-02-28 | 口碑(上海)信息技术有限公司 | Method and device for acquiring delivery path information and electronic equipment |
| CN111709560A (en) * | 2020-05-29 | 2020-09-25 | 杭州电子科技大学 | Method for solving vehicle path problem based on improved ant colony algorithm |
| CN112785190A (en) * | 2021-02-03 | 2021-05-11 | 杭州拼便宜网络科技有限公司 | E-commerce logistics diversified monitoring system based on multi-loop transportation algorithm |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107220731A (en) | A kind of logistics distribution paths planning method | |
| CN103413209B (en) | Many client many warehouses logistics distribution routing resources | |
| CN107194513A (en) | A kind of optimization method for solving full channel logistics distribution | |
| Donati et al. | Time dependent vehicle routing problem with a multi ant colony system | |
| CN108053037B (en) | Power distribution network emergency repair strategy making method and device based on two-network fusion | |
| CN105608896B (en) | Traffic bottlenecks recognition methods in urban traffic network | |
| CN108764777A (en) | Electronic logistic car dispatching method with time window and system | |
| CN112382082A (en) | Method and system for predicting traffic running state in congested area | |
| CN104036324B (en) | Optimal design method of communication network capacity based on genetic algorithm | |
| CN103324982A (en) | Path planning method based on genetic algorithm | |
| CN108256969B (en) | Public bicycle leasing point dispatching area dividing method | |
| CN109800904A (en) | Assembled architecture prefabricated component Distribution path optimization method and system with time window | |
| CN107330030A (en) | A kind of hierarchical network construction method that storage is compressed towards magnanimity road net data | |
| Zhang et al. | Memetic algorithm with route decomposing for periodic capacitated arc routing problem | |
| CN101616074B (en) | Multicast routing optimization method based on quantum evolution | |
| CN110097218B (en) | Unmanned commodity distribution method and system in time-varying environment | |
| CN109559062A (en) | A kind of task distribution of cooperative logistical problem and paths planning method | |
| CN101777990A (en) | Method for selecting multi-objective immune optimization multicast router path | |
| CN109186619A (en) | A kind of intelligent navigation algorithm based on real-time road | |
| CN111709560A (en) | Method for solving vehicle path problem based on improved ant colony algorithm | |
| CN106096881A (en) | Storage consolidating the load mode vehicle path adverse selection method of operating | |
| CN109540165B (en) | Heuristic search expressway network constraint routing algorithm | |
| WO2023245740A1 (en) | Fourth-party logistics transportation edge planning method based on ant colony optimization algorithm | |
| CN105913213A (en) | Reverse logistics recycling vehicle scheduling method under storage commodity collection mode | |
| CN111445094B (en) | Express vehicle path optimization method and system based on time requirement |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170929 |