CN109492828A - A kind of Distribution path optimization method for considering customer grade and distribution time and requiring - Google Patents
A kind of Distribution path optimization method for considering customer grade and distribution time and requiring Download PDFInfo
- Publication number
- CN109492828A CN109492828A CN201811519797.0A CN201811519797A CN109492828A CN 109492828 A CN109492828 A CN 109492828A CN 201811519797 A CN201811519797 A CN 201811519797A CN 109492828 A CN109492828 A CN 109492828A
- Authority
- CN
- China
- Prior art keywords
- artificial fish
- client
- individual
- distribution
- indicate
- 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
- 238000005457 optimization Methods 0.000 title claims abstract description 26
- 238000000034 method Methods 0.000 title claims abstract description 18
- 230000035772 mutation Effects 0.000 claims abstract description 21
- 108090000623 proteins and genes Proteins 0.000 claims abstract description 9
- 230000019637 foraging behavior Effects 0.000 claims description 21
- 230000006399 behavior Effects 0.000 claims description 19
- 230000000007 visual effect Effects 0.000 claims description 13
- 241000251468 Actinopterygii Species 0.000 claims description 12
- 230000000694 effects Effects 0.000 claims description 10
- 230000004069 differentiation Effects 0.000 claims description 8
- 230000006978 adaptation Effects 0.000 claims description 6
- 235000013305 food Nutrition 0.000 claims description 6
- 238000013178 mathematical model Methods 0.000 claims description 5
- 230000008859 change Effects 0.000 claims description 4
- 230000008569 process Effects 0.000 claims description 4
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 claims description 3
- 230000000977 initiatory effect Effects 0.000 claims description 3
- 230000000452 restraining effect Effects 0.000 claims description 3
- 230000008901 benefit Effects 0.000 abstract description 5
- 238000011161 development Methods 0.000 abstract description 4
- 230000007547 defect Effects 0.000 abstract description 2
- 238000009399 inbreeding Methods 0.000 abstract 1
- 230000006870 function Effects 0.000 description 10
- 230000002068 genetic effect Effects 0.000 description 6
- 238000011160 research Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 3
- 239000012634 fragment Substances 0.000 description 3
- 230000007246 mechanism Effects 0.000 description 2
- 239000002245 particle Substances 0.000 description 2
- 230000035945 sensitivity Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 239000003643 water by type Substances 0.000 description 2
- 201000004569 Blindness Diseases 0.000 description 1
- 241001465754 Metazoa Species 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- 240000005373 Panax quinquefolius Species 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 239000008280 blood Substances 0.000 description 1
- 210000004369 blood Anatomy 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000002716 delivery method Methods 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000002431 foraging effect Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 235000016709 nutrition Nutrition 0.000 description 1
- 230000035764 nutrition Effects 0.000 description 1
- 238000010206 sensitivity analysis Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 230000009182 swimming Effects 0.000 description 1
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
- 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)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Computational Linguistics (AREA)
- Biophysics (AREA)
- Mathematical Physics (AREA)
- Biomedical Technology (AREA)
- Software Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Computing Systems (AREA)
- Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- General Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses the Distribution path optimization methods that a kind of consideration customer grade and distribution time require, the vehicle delivery path optimization model for considering customer grade and client's distribution time requirement is established, and the model is solved based on improved Artificial Fish Swarm Algorithm.In view of standard intraocular's fish-swarm algorithm later stage of evolution there are solving precisions it is low, convergence rate is slow the disadvantages of, introduce elite conducting evolution strategy, staggered cross strategy and Heuristic Mutation strategy, pass through elite conducting evolution strategy, keep algorithm rapidly close to optimal solution, improves the local exploring ability of algorithm；Inbreeding is effectively overcomed while so that the gene of defect individual is obtained heredity by staggered cross strategy；The diversity that population can pointedly be improved by Heuristic Mutation strategy improves the global development ability of algorithm.The present invention can effectively ensure that being sent on time for Very Important Person order, effectively increase the satisfaction of client, to increase the economic benefit of enterprise.
Description
Technical field
The present invention relates to wisdom logistics and supply chain field, and in particular to a kind of consideration customer grade and distribution time requirement
Distribution path optimization method.
Background technique
Logistics is as the carrier of item circulation, the blood of e-commerce, it has also become the indispensable a part of enterprise development,
It is a kind of effective way increased customer satisfaction degree that cargo, which is sent to client, in time by client's time requirement.In this context, will
The factors such as customer grade and distribution time requirement are introduced into logistics distribution research, for maintenance customer relationship, are promoted
Dispensing efficiency and improving enterprise income has good facilitation, thus, seek a kind of consideration customer grade and distribution time
It is required that vehicle delivery method for optimizing route have good scientific meaning and social value.
Up to now, what focus of attention was more is still logistics distribution path optimization, and less or do not consider client etc.
The factors such as grade and distribution time requirement, thus be difficult to meet actual needs.Such as: Reihaneh et al. (A
branchandprice algorithm for a vehicle routing with demand allocation
Problem, European Journal of Operational Research, 2019) in Location of Distribution Centre, customer demand
On the basis of distribution, to minimize vehicle delivery path as optimization aim, and the branch price for being suitble to solve the problem is proposed
Algorithm.Fiber crops etc. (research of express delivery distribution vehicle path optimization, Traffic transport system engineering and information, 2017) are to minimize in dispatching
The total distribution time of heart vehicle is optimization aim, is carried out based on express delivery Distribution path optimization problem of the genetic algorithm to 20 distribution points
Research.
Artificial fish-swarm algorithm is that Li Xiaolei et al. was proposed on the basis of animal population intelligent behavior research in 2002
A kind of novel side's Sheng optimization algorithm, the algorithm are exactly in this waters rich in nutrition according to the most place of fish existence number in waters
This most local feature of substance realizes optimizing to simulate the foraging behavior of the shoal of fish.Algorithm mainly utilizes the three big basic of fish
Behavior: behavior of looking for food, bunch and knock into the back is passed through using top-down optimizing mode since the bottom behavior of construction individual
The local optimal searching of each individual in the shoal of fish achievees the purpose that global optimum comes out in group's saliency.
Summary of the invention
The purpose of the present invention is to provide it is a kind of consideration customer grade and distribution time require Distribution path optimization method,
To improve the customer satisfaction and intelligent level of logistics supply chain.
To achieve the above object, the present invention adopts the following technical scheme:
A kind of Distribution path optimization method for considering customer grade and distribution time and requiring, successively the following steps are included:
(1) in the case where considering that customer grade and distribution time require, to the existing constraint in dispatching scene and to optimize
Target analyzed, obtain objective function and objective function be abstracted as to the mathematical model of belt restraining；
(2) initiation parameter: overall maximum evolutionary generation G_max, evolutionary generation counter t, fish way M, Artificial Fish
A counter n, it step-length step, the field range Visual of Artificial Fish, number of attempt Try_number, crowding factor delta, hands over
Pitch Probability pc, mutation probability pm, subscript c and m are differentiation effect, and non-variables, initialization population are individual；
(3) t=t+1 is enabled, global search is carried out；
(3-1) carries out fitness evaluation to all Artificial Fish individuals with the inverse of objective function, and optimal Artificial Fish is assigned
It is worth to bulletin board；
(3-2) enables n=n+1, carries out local search；
(3-2-1) each Artificial Fish individual passes through the foraging behavior that elite individual guides, bunches and behavior update of knocking into the back
Oneself；
(3-2-2) is updated to Artificial Fish individual if updated Artificial Fish individual is better than bulletin board, by bulletin board；
Otherwise, bulletin board remains unchanged；
(3-2-3) judges whether Artificial Fish counter n is less than fish way M, if n < M, return step (3-
2), if n >=M, (3-3) is entered step；
(3-3) generates random number r, if r < pc, then staggered cross strategy, p are carried outcIndicate crossover probability；
(3-4) generates random number r, if r < pm, then Heuristic Mutation strategy, p are carried outmIndicate mutation probability；
(3-5) is if evolutionary generation counter t is less than overall maximum evolutionary generation G_max, return step (3-1)；It is no
Then, optimal solution is exported.
Preferably, in the step (1), target to be optimized is to complete the row of the dispatching task vehicle of all customer orders
The sum of time, dispatching time in advance and period of delay punishment minimum is sailed, constraint condition is that distribution vehicle has proof load and finite volume, together
When, time in advance and period of delay penalty coefficient are associated with customer grade, i.e. penalty coefficient and the proportional pass of customer grade
System, mathematical model are defined as follows:
Wherein, formula (1) is objective function；Formula (2) to formula (7) are various constraint conditions, specifically: formula (2) indicates i-th
The order of client is once dispensed completion by distribution vehicle；Formula (3) indicates that in delivery process, i-th of client and j-th of client are not
It can forming circuit；Formula (4) and formula (5) limit distribution vehicle loaded goods no more than itself maximum load and maximum volume；
Formula (6) and formula (7) are the binary value region constraint of decision variable；
O indicates customer order set to be dispensed；R indicates that distribution vehicle completes the path of all customer order dispatching tasks
Set；R indicates subpath；dijIndicate that vehicle continuously dispenses the distance travelled needed for two customer orders, herein i, j ∈ O；v
Indicate the average overall travel speed of vehicle；HijrIndicate distribution vehicle at subpath r whether continuously across client i and client j mark
Remember, herein i, j ∈ O, r ∈ R, if distribution vehicle in r-th of subpath, continuously dispenses client i and client j,
Then Hijr=1, otherwise Hijr=0；SirIndicate whether client i belongs to subpath r, herein i ∈ O, r ∈ R, if i-th of client
Order completed in the r times dispatching, then Sir=1, otherwise Sir=0；SjrIndicate whether client j belongs to subpath r, herein j
∈ O, r ∈ R；EkIndicate the dispatching time in advance of client k；TkIndicate the dispatching period of delay of client k；λkAnd ρkRespectively indicate client k institute
Belong to the corresponding dispatching time in advance penalty coefficient of customer grade and dispatching period of delay penalty coefficient；WiAnd ViRespectively indicate i-th of client
The weight and volume of order；WjAnd VjRespectively indicate the weight and volume of j-th of customer order；QmaxAnd VmaxRespectively indicate dispatching
The maximum load and maximum volume of vehicle；OfAnd OlRespectively indicate first customer order and the last one customer order of dispatching；
Subscript i=1,2 ... ... i；J=1,2 ... ..., j.
Preferably, the step (3-2-1) specifically includes:
1. the foraging behavior of elite individual guidance:
In foraging behavior, the Artificial Fish after the guidance of elite individual is individual, and Artificial Fish position may be expressed as:
Xi_next=Xi+rand()·step·(Xj-Xi/||Xj-Xi||) (8)
And random behavior may be expressed as:
Xi_next=Xi+rand()·step (9)
Wherein, Xi_nextIndicate the position of the Artificial Fish individual after the guidance of elite individual, rand () indicates random letter
Number, step indicate step-length, XiAnd XjRespectively currently carry out the optimal Artificial Fish in the Artificial Fish individual and bulletin board of foraging behavior
Individual, subscript i=1,2 ... ... i；J=1,2 ... ..., j；
2. behavior of bunching:
Artificial Fish is assembled in groups, it may be assumed that in Artificial Fish X naturally during travelling for own existence and escape enemyi's
Number of partners in field range Visual is nf, and the center of partner is XcIf Yc/nf>δYi, then show partner
Center state is more excellent and less crowded, then Artificial Fish XiIt moves and moves a step towards the center of partner by formula (10), otherwise, hold
The foraging behavior of row elite individual guidance；
Xi_next=Xi+rand()·step·(Xc-Xi/‖Xc-Xi‖) (10)
Wherein, YiAnd YcRespectively indicate Artificial Fish XiAnd XcIndividual adaptation degree, subscript c and f be differentiation effect, not
Variable；
3. behavior of knocking into the back:
For the more foods of quick obtaining, Artificial Fish XiThe n in field range Visual can be explored as possiblefArtificial Fish
Optimal location XmoreIf, it may be assumed that Ymore/nf>δYi, show less crowded around optimal partner, then Artificial Fish XiBy formula (11) court
Optimal partner moves and moves a step；Otherwise, the foraging behavior of elite individual guidance is executed；
Xi_next=Xi+rand()·step·(Xmore-Xi/‖Xmore-Xi‖) (11)
Wherein, YmoreIndicate Artificial Fish XmoreIndividual adaptation degree, subscript m ore is differentiation effect, and non-variables.
Preferably, in the step (3-4), when executing Heuristic Mutation strategy, for the individual to make a variation, first
On genes of individuals position, two highest and lowest gene positions of expense, i.e. customer order are found out based on objective function, then to this
Two gene positions implement exchange, so that the fitness before and after making individual variation has large change.
The present invention establishes the vehicle delivery path optimization model for considering customer grade and client's distribution time requirement, and base
In improved Artificial Fish Swarm Algorithm (Improved Artificial Fish Swarm Algorithm, IAFS) to the model into
Row solves, and has the advantage that
First, customer grade is introduced in dispatching model, it can preferably guarantee that the high client of grade is preferentially dispensed,
Be conducive to safeguard the relationship with client.
Second, the elite individual guidance of this method is looked for food mechanism and staggered cross strategy, during having taken into account algorithm evolution
The depth of search, can bootstrap algorithm evolve to optimal or suboptimum direction.
Third, the population multiplicity based on heuristic mutation operator improves mechanism, population can be improved with faster speed
Diversity has taken into account the range of algorithm search, further improves the solving precision and convergence efficiency of algorithm.
The present invention can effectively ensure that being sent on time for Very Important Person order, so that the high satisfaction of client, is conducive to
Safeguard with big customer relationship, avoid big customer from being lost, also increase the economic benefit of enterprise indirectly, for promoted logistics with
The intelligent level of supply chain related fields has good exemplary role.
Detailed description of the invention
Fig. 1 is flow chart of the invention；
Fig. 2 is staggered cross strategy schematic diagram of the present invention；
Fig. 3 is Heuristic Mutation strategy schematic diagram of the present invention；
Fig. 4 is influence situation curve graph of each parameter of the present invention to IAFS performance；
Fig. 5 is that each algorithm solves effect contrast figure in embodiment of the present invention；
Fig. 6 is that the punctual rate comparison diagram of the dispatching of customer grade whether is considered in embodiment of the present invention.
Specific embodiment
Technical solution of the present invention is clearly and completely described below in conjunction with attached drawing, it is clear that described embodiment
Only section Example of the invention, instead of all the embodiments.Based on the embodiments of the present invention, the common skill in this field
Art personnel other all embodiments obtained without making creative work, belong to protection model of the invention
It encloses.
As shown in Figure 1, the invention discloses the Distribution path optimization sides that a kind of consideration customer grade and distribution time require
Method, specifically includes the following steps:
(1) in the case where considering that customer grade and distribution time require, to the existing constraint in dispatching scene and to optimize
Target analyzed, obtain objective function and objective function be abstracted as to the mathematical model of belt restraining.
The present invention carries out vehicle delivery path excellent in line on the basis of considering that customer grade and distribution time require
Change, then target to be optimized is to complete the running time of the dispatching task vehicle of all customer orders, dispatching time in advance and delay
The sum of phase punishment minimum, constraint condition have proof load and finite volume for distribution vehicle, meanwhile, the client to keep grade high obtains
Preferential dispatching, time in advance and period of delay penalty coefficient is associated with customer grade, i.e., penalty coefficient is in customer grade
Proportional relation, mathematical model are defined as follows:
Wherein, formula (1) is objective function；Formula (2) to formula (7) are various constraints, specifically: formula (2) indicates i-th of client
Order completion is once dispensed by distribution vehicle；Formula (3) indicates that in delivery process, i-th of client and j-th of client are unable to shape
At circuit；Formula (4) and formula (5) limit distribution vehicle loaded goods no more than itself maximum load and maximum volume；Formula (6)
It is the binary value region constraint of decision variable with formula (7).
O indicates customer order set to be dispensed；R indicates that distribution vehicle completes the path of all customer order dispatching tasks
Set；R indicates subpath；dijIndicate that vehicle continuously dispenses the distance travelled needed for two customer orders, herein i, j ∈ O；v
Indicate the average overall travel speed of vehicle；HijrIndicate distribution vehicle at subpath r whether continuously across client i and client j mark
Remember, herein i, j ∈ O, r ∈ R, if distribution vehicle in r-th of subpath, continuously dispenses client i and client j,
Then Hijr=1, otherwise Hijr=0；SirIndicate whether client i belongs to subpath r, herein i ∈ O, r ∈ R, if i-th of client
Order completed in the r times dispatching, then Sir=1, otherwise Sir=0；SjrIndicate whether client j belongs to subpath r, herein j
∈ O, r ∈ R；EkIndicate the dispatching time in advance of client k；TkIndicate the dispatching period of delay of client k；λkAnd ρkRespectively indicate client k institute
Belong to the corresponding dispatching time in advance penalty coefficient of customer grade and dispatching period of delay penalty coefficient；WiAnd ViRespectively indicate i-th of client
The weight and volume of order；WjAnd VjRespectively indicate the weight and volume of j-th of customer order；QmaxAnd VmaxRespectively indicate dispatching
The maximum load and maximum volume of vehicle；OfAnd OlRespectively indicate first customer order and the last one customer order of dispatching；
Subscript i=1,2 ... ... i；J=1,2 ... ..., j.
(2) initiation parameter: overall maximum evolutionary generation G_max, evolutionary generation counter t, fish way M, Artificial Fish
A counter n, it step-length step, the field range Visual of Artificial Fish, number of attempt Try_number, crowding factor delta, hands over
Pitch Probability pc, mutation probability pm, initialization population individual；
(3) t=t+1 is enabled, global search is carried out.
The step specifically includes:
(3-1) carries out fitness evaluation to all Artificial Fish individuals with the inverse of objective function, and optimal Artificial Fish is assigned
It is worth and gives bulletin board
The step is the prior art, is repeated no more.
(3-2) enables n=n+1, carries out local search.
(3-2-1) each Artificial Fish individual passes through the foraging behavior that elite individual guides, bunches and behavior update of knocking into the back
Oneself, specific implementation process is as follows:
1. the foraging behavior of elite individual guidance:
The present invention will be used to that Artificial Fish individual be guided to carry out foraging behavior from the elite individual of bulletin board, meanwhile, it is
It prevents from excessively guiding caused easy the drawbacks of falling into local optimum using elite individual, the present invention is only to those by setting
After fixed number of attempt, the Artificial Fish individual that state is not improved yet implements the foraging behavior guided based on elite individual,
If do not improved yet, random behavior is executed.In foraging behavior, the Artificial Fish after the guidance of elite individual is individual,
Its Artificial Fish position may be expressed as:
Xi_next=Xi+rand()·step·(Xj-Xi/||Xj-Xi||) (8)
And random behavior may be expressed as:
Xi_next=Xi+rand()·step (9)
Wherein, Xi_nextIndicate the position of the Artificial Fish individual after the guidance of elite individual, rand () indicates random letter
Number, step indicate step-length, XiAnd XjRespectively currently carry out the optimal Artificial Fish in the Artificial Fish individual and bulletin board of foraging behavior
Individual, subscript i=1,2 ... ... i；J=1,2 ... ..., j.
2. behavior of bunching:
Artificial Fish is assembled in groups, it may be assumed that in Artificial Fish X naturally during travelling for own existence and escape enemyi's
Number of partners in field range Visual is nf, and the center of partner is XcIf Yc/nf>δYi, then show partner
Center state is more excellent and less crowded, then Artificial Fish XiIt moves and moves a step towards the center of partner by formula (10), otherwise, hold
The foraging behavior of row elite individual guidance.
Xi_next=Xi+rand()·step·(Xc-Xi/‖Xc-Xi‖) (10)
Wherein, YiAnd YcRespectively indicate Artificial Fish XiAnd XcIndividual adaptation degree, subscript c and f be differentiation effect, not
Variable.
3. behavior of knocking into the back:
For the more foods of quick obtaining, Artificial Fish XiThe n in field range Visual can be explored as possiblefArtificial Fish
Optimal location XmoreIf, it may be assumed that Xmore/nf>δXi, show less crowded around optimal partner, then Artificial Fish XiBy formula (11) court
Optimal partner moves and moves a step；Otherwise, the foraging behavior of elite individual guidance is executed.
Xi_next=Xi+rand()·step·(Xmore-Xi/‖Xmore-Xi‖) (11)
Wherein, YmoreIndicate Artificial Fish XmoreIndividual adaptation degree, subscript m ore is differentiation effect, and non-variables.
(3-2-2) is updated to Artificial Fish individual if updated Artificial Fish individual is better than bulletin board, by bulletin board；
Otherwise, bulletin board remains unchanged；
(3-2-3) judges whether Artificial Fish counter n is less than fish way M, if n < M, return step (3-
2), if n >=M, (3-3) is entered step；
(3-3) generates random number r, if r < pc, then staggered cross strategy, p are carried outcIndicate crossover probability；
The implementation of Crossover Strategy helps the genetic fragment of defect individual being genetic to the next generation, calculates to be conducive to enhancing
Exploring ability of the method in local space.However, participating in intersection if two individuals intersect with the genetic fragment of position
In the identical situation of two parent individualities, two identical offspring individuals will be generated after intersection, thus are easily led to algorithm and fallen into
Local optimum.For this purpose, present invention employs staggered cross strategy, as shown in Fig. 2, the genetic fragment for participating in intersecting is located at two
The different location of a parent individuality, thus effectively prevented the case where identical parent individuality generates identical offspring individual generation, into
And it is lowered into local risk.
(3-4) generates random number r, if r < pm, then Heuristic Mutation strategy, p are carried outmIndicate mutation probability；
In standard intraocular's fish-swarm algorithm, during looking for food bunch behavior and the behavior of knocking into the back is easy makes the shoal of fish is opposite to collect
In, the diversity of population is reduced, to limit the global development ability of algorithm, algorithm is made easily to fall into local optimum.Thus,
The diversity of population is improved it is necessary to introduce mutation operator.Simultaneously, it is contemplated that implementing Mutation Strategy can give to a certain extent
Algorithm search brings blindness.For this purpose, the present invention proposes a kind of heuristic mutation operator, as shown in figure 3, for make a variation
Individual two highest and lowest gene positions of expense are found out based on objective function first in individual ownership gene position, i.e., it is objective
Then family order is implemented to exchange, so that the fitness before and after making individual variation has large change, effectively be mentioned to the two gene positions
The high diversity of population, has widened the range of solution space, to increase the probability for obtaining globally optimal solution.
(3-5) is if evolutionary generation counter t is less than overall maximum evolutionary generation G_max, return step (3-1)；It is no
Then, optimal solution is exported.
Embodiment one
The present embodiment designs Distribution path optimization problem of certain logistics distribution center with customer grade and distribution time requirement,
The optimal solution or suboptimal solution for meeting constraint condition are found out using the present invention.
It is illustrated according to above-mentioned technical proposal using certain logistics distribution center as application background.30 visitors are randomly generated
Family order is tested, and specific client order information is as shown in table 1, and the Item Information being related in order is listed in table 2, together
When, explanation has also been made in table 3 in time in advance relevant to customer grade and period of delay penalty coefficient.Between home-delivery center and client or
The distance between client and client obey on [20km, 250km] and are uniformly distributed, the average overall travel speed v of distribution vehicle, maximum
Load-carrying QmaxAnd maximum volume VmaxIt is respectively as follows: 50km/h, 5000kg and 450m3, test in Win10 system platform, 3.7GHz master
It is carried out under the Intel processor of frequency, 4GB memory and Matlab R2014b exploitation environment.
1 customer order information of table
2 Item Information of table
The penalty factor relevant to customer grade of table 3
1, Parameter Sensitivity Analysis
Different parameter configurations can generate the optimization performance of IAFS (technical solution of the present invention, abbreviation IAFS) different
It influences, such as: field range Visual, number of attempt Try_number, crowding factor delta and step-length step.In order to find compared with
Good parameter combination configuration, it is necessary to which the sensitivity influenced on Parameters variation on IAFS performance is analyzed and researched.Fig. 4 is shown
Influence situation of each parameter to IAFS performance, from fig. 4, it can be seen that working as field range Visual, number of attempt Try_
When numberr, crowding factor delta and step-length step are respectively set to 11,50,0.7 and 6, IAFS can obtain preferable optimization
Effect.Meanwhile from Fig. 4 it can further be seen that: initial solution can have an impact algorithm optimizing result, but in general,
Biggish field range makes Artificial Fish be able to observe that more partner, to be easy to jump out local extremum, but is unfavorable for part
Depth is explored, and convergence rate is influenced；Biggish number of attempt makes IAFS be easily trapped into local optimum；The biggish crowding factor,
Artificial Fish distribution is just more dispersed, makes IAFS be easy to flee from local optimum, but brings the randomness of artificial fish swimming, thus not
Conducive to fast convergence；And for fixed step size, it when larger, easily vibrates, when smaller, easily falls into local extremum, therefore, selection
Suitable step-length is equally most important to the performance of the optimization performance of IAFS.
2, compared with other evolution algorithms
For the performance for verifying IAFS algorithm proposed by the present invention, based on the order information and standard something lost in table 1, table 2 and table 3
Propagation algorithm (Genetic Algorithm, GA), standard particle group algorithm (Particle Swarm Optimization, PSO),
Standard intraocular's fish-swarm algorithm (Artificial Fish Swarm Algorithm, AFS) compares, the population of each algorithm
Scale is 50, and evolutionary generation is 600.Wherein, the crossover probability of GA and mutation probability are respectively 0.8,0.05；For PSO,
Inertia weight w is 1.3, Studying factors c1And c2It is 2；It is 14, number of attempt Try_ for AFS, field range Visual
Number is 40, crowding factor delta is 0.5, step-length step is 5；For IAFS, basic parameter is using parametric sensitivity point
Analyse the setting in chapters and sections, crossover probability pcWith mutation probability pmRespectively 0.8 and 0.05.According to the problems in embodiment one overview,
For the above-mentioned test example of the present invention, Fig. 5 is variation tendency of the optimal solution that finds out of each algorithm with evolutionary generation, meanwhile, for
Whether IAFS considers the Distribution path optimization problem of customer grade, and Fig. 6 is also compared.
For the generalization ability for further verifying IAFS algorithm, the different scales that consideration customer grade and distribution time are required
Customer order Distribution path optimization problem is tested, and the algorithm compared participation is respectively run 30 times, and table 4 gives various
Optimal solution and standard deviation of the algorithm in 30 calculating.
Each algorithm of 4 different scales order of table solves situation comparison
Can intuitively it be found out by Fig. 5, IAFS algorithm proposed by the present invention is in convergence rate and solves bright in quality
It is aobvious to be better than other three kinds of algorithms.For further, Fig. 6 focuses on whether to consider IAFS that the order dispatching of customer grade reaches on time
Rate is compared, and is disclosed in the case where considering customer grade, be can effectively ensure that being sent on time for Very Important Person order, from
And be conducive to safeguard the relationship of same big customer, it avoids big customer from being lost, also increases the economic benefit of enterprise indirectly.Table 4 from
The solution situation of different scales customer order dispatching, IAFS and GA, PSO and AFS is compared, with other three algorithm phases
Than IAFS is not only avoided that precocity, global convergence is effectively ensured, but also also improves convergence efficiency and solve quality, especially
It is particularly evident in extensive problem.Why IAFS is presented these advantages, mainly has benefited from elite individual proposed by the present invention
Foraging strategy, staggered cross strategy and Heuristic Mutation strategy are guided, these improve the quality of offspring individual and population
Diversity, thus enhance IAFS global development ability and local exploring ability, to help that IAFS is guided to find most
Excellent solution or suboptimal solution.
Claims (4)
1. a kind of Distribution path optimization method for considering customer grade and distribution time and requiring, which is characterized in that successively include with
Lower step:
(1) in the case where considering that customer grade and distribution time require, to mesh dispatching scene existing constraint and optimized
Mark is analyzed, and obtains objective function and objective function is abstracted as to the mathematical model of belt restraining；
(2) initiation parameter: overall maximum evolutionary generation G_max, evolutionary generation counter t, fish way M, Artificial Fish number
Counter n, step-length step, the field range Visual of Artificial Fish, number of attempt Try_number, crowding factor delta, intersect generally
Rate pc, mutation probability pm, subscript c and m are differentiation effect, and non-variables, initialization population are individual；
(3) t=t+1 is enabled, global search is carried out；
(3-1) carries out fitness evaluation to all Artificial Fish individuals with the inverse of objective function, and optimal Artificial Fish is assigned to
Bulletin board；
(3-2) enables n=n+1, carries out local search；
(3-2-1) each Artificial Fish individual passes through the foraging behavior that elite individual guides, bunch and the behavior of knocking into the back updates oneself；
(3-2-2) is updated to Artificial Fish individual if updated Artificial Fish individual is better than bulletin board, by bulletin board；It is no
Then, bulletin board remains unchanged；
(3-2-3) judges whether Artificial Fish counter n is less than fish way M, if n < M, return step (3-2), if n
>=M then enters step (3-3)；
(3-3) generates random number r, if r < pc, then staggered cross strategy, p are carried outcIndicate crossover probability；
(3-4) generates random number r, if r < pm, then Heuristic Mutation strategy, p are carried outmIndicate mutation probability；
(3-5) is if evolutionary generation counter t is less than overall maximum evolutionary generation G_max, return step (3-1)；Otherwise, defeated
Optimal solution out.
2. a kind of Distribution path optimization method for considering customer grade and distribution time and requiring as described in claim 1, special
Sign is: in the step (1), target to be optimized be complete the running time of the dispatching task vehicle of all customer orders,
The sum of time in advance and period of delay punishment minimum are dispensed, constraint condition has proof load and finite volume for distribution vehicle, meanwhile, it will mention
Early period and period of delay penalty coefficient are associated with customer grade, i.e., penalty coefficient is proportional to customer grade, number
Model is learned to be defined as follows:
Wherein, formula (1) is objective function；Formula (2) to formula (7) are various constraint conditions, specifically: formula (2) indicates i-th of client
Order completion is once dispensed by distribution vehicle；Formula (3) indicates that in delivery process, i-th of client and j-th of client are unable to shape
At circuit；Formula (4) and formula (5) limit distribution vehicle loaded goods no more than itself maximum load and maximum volume；Formula (6)
It is the binary value region constraint of decision variable with formula (7)；
O indicates customer order set to be dispensed；R indicates that distribution vehicle completes the path set of all customer order dispatching tasks
It closes；R indicates subpath；dijIndicate that vehicle continuously dispenses the distance travelled needed for two customer orders, herein i, j ∈ O；V table
Show the average overall travel speed of vehicle；HijrIndicate distribution vehicle at subpath r whether continuously across client i and client j mark
Remember, herein i, j ∈ O, r ∈ R, if distribution vehicle in r-th of subpath, continuously dispenses client i and client j,
Then Hijr=1, otherwise Hijr=0；SirIndicate whether client i belongs to subpath r, herein i ∈ O, r ∈ R, if i-th of client
Order completed in the r times dispatching, then Sir=1, otherwise Sir=0；SjrIndicate whether client j belongs to subpath r, herein j
∈ O, r ∈ R；EkIndicate the dispatching time in advance of client k；TkIndicate the dispatching period of delay of client k；λkAnd ρkRespectively indicate client k institute
Belong to the corresponding dispatching time in advance penalty coefficient of customer grade and dispatching period of delay penalty coefficient；WiAnd ViRespectively indicate i-th of client
The weight and volume of order；WjAnd VjRespectively indicate the weight and volume of j-th of customer order；QmaxAnd VmaxRespectively indicate dispatching
The maximum load and maximum volume of vehicle；OfAnd OlRespectively indicate first customer order and the last one customer order of dispatching；
Subscript i=1,2 ... ... i；J=1,2 ... ..., j.
3. a kind of Distribution path optimization method for considering customer grade and distribution time and requiring as described in claim 1, special
Sign is that the step (3-2-1) specifically includes:
1. the foraging behavior of elite individual guidance:
In foraging behavior, the Artificial Fish after the guidance of elite individual is individual, and Artificial Fish position may be expressed as:
Xi_next=Xi+rand()·step·(Xj-Xi/||Xj-Xi||) (8)
And random behavior may be expressed as:
Xi_next=Xi+rand()·step (9)
Wherein, Xi_nextIndicating the position of the Artificial Fish individual after the guidance of elite individual, rand () indicates random function,
Step indicates step-length, XiAnd XjRespectively currently carry out the optimal Artificial Fish in the Artificial Fish individual and bulletin board of foraging behavior
Body, subscript i=1,2 ... ... i；J=1,2 ... ..., j；
2. behavior of bunching:
Artificial Fish is assembled in groups, it may be assumed that in Artificial Fish X naturally during travelling for own existence and escape enemyiThe visual field
Number of partners in range Visual is nf, and the center of partner is XcIf Yc/nf>δYi, then show partner center
Location status is more excellent and less crowded, then Artificial Fish XiIt moves and moves a step towards the center of partner by formula (10), otherwise, execute essence
The foraging behavior of English individual guidance；
Xi_next=Xi+rand()·step·(Xc-Xi/‖Xc-Xi‖) (10)
Wherein, YiAnd YcRespectively indicate Artificial Fish XiAnd XcIndividual adaptation degree, subscript c and f are differentiation effect, and non-variables；
3. behavior of knocking into the back:
For the more foods of quick obtaining, Artificial Fish XiThe n in field range Visual can be explored as possiblefArtificial Fish it is optimal
Position XmoreIf, it may be assumed that Ymore/nf>δYi, show less crowded around optimal partner, then Artificial Fish XiBy formula (11) towards optimal
Partner moves and moves a step；Otherwise, the foraging behavior of elite individual guidance is executed；
Xi_next=Xi+rand()·step·(Xmore-Xi/‖Xmore-Xi‖) (11)
Wherein, YmoreIndicate Artificial Fish XmoreIndividual adaptation degree, subscript m ore is differentiation effect, and non-variables.
4. a kind of Distribution path optimization method for considering customer grade and distribution time and requiring as described in claim 1, special
Sign is: in the step (3-4), when executing Heuristic Mutation strategy, for the individual to make a variation, first in individual base
Because two highest and lowest gene positions of expense, i.e. customer order being found out based on objective function, then to the two genes on position
Exchange is implemented in position, so that the fitness before and after making individual variation has large change.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811519797.0A CN109492828B (en) | 2018-12-12 | 2018-12-12 | Distribution path optimization method considering customer grade and distribution time requirements |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811519797.0A CN109492828B (en) | 2018-12-12 | 2018-12-12 | Distribution path optimization method considering customer grade and distribution time requirements |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109492828A true CN109492828A (en) | 2019-03-19 |
| CN109492828B CN109492828B (en) | 2021-08-13 |
Family
ID=65710019
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811519797.0A Active CN109492828B (en) | 2018-12-12 | 2018-12-12 | Distribution path optimization method considering customer grade and distribution time requirements |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109492828B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109978471A (en) * | 2019-04-11 | 2019-07-05 | 聊城大学 | A kind of Cold Chain Logistics method for optimizing route with time window |
| CN110084390A (en) * | 2019-03-26 | 2019-08-02 | 河南科技学院 | A kind of more vehicles collaboration share-car method for optimizing route based on modified drosophila algorithm |
| CN111126770A (en) * | 2019-11-26 | 2020-05-08 | 浙江工业大学 | Task scheduling method of cross-layer shuttle storage system |
| CN111985700A (en) * | 2020-07-30 | 2020-11-24 | 中国外运股份有限公司 | Vehicle carrying single quantity balancing method and device for determining home delivery and loading |
| US11475394B2 (en) | 2021-02-22 | 2022-10-18 | Coupang Corp. | Method and apparatus for providing information |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101866384A (en) * | 2010-06-18 | 2010-10-20 | 杭州电子科技大学 | Improved artificial fish school optimization method based on vehicle path planning |
| CN102903038A (en) * | 2012-09-29 | 2013-01-30 | 威海智联信息网络有限公司 | Method and system for small object logistics distribution based on user track |
| US20140129139A1 (en) * | 2012-11-07 | 2014-05-08 | Intertrust Technologies Corporation | Vehicle charging path optimization systems and methods |
| CN105469201A (en) * | 2015-07-20 | 2016-04-06 | 浙江工业大学 | Method for logistics dispensing center work task processing and scheduling |
| CN106295864A (en) * | 2016-07-29 | 2017-01-04 | 广东工业大学 | A kind of method solving single home-delivery center logistics transportation scheduling problem |
| CN107169983A (en) * | 2017-04-13 | 2017-09-15 | 西安电子科技大学 | Multi-threshold image segmentation method based on cross and variation artificial fish-swarm algorithm |
| CN107682934A (en) * | 2017-11-21 | 2018-02-09 | 重庆邮电大学 | A kind of adaptive resource improves allocative decision in OFDM multi-user systems |
| CN108830431A (en) * | 2018-08-03 | 2018-11-16 | 广东工业大学 | A kind of Electricity price forecasting solution and relevant apparatus based on whale optimization algorithm |
| CN108846610A (en) * | 2018-06-19 | 2018-11-20 | 品信科技有限公司 | A kind of Distribution path method and device for planning |
-
2018
- 2018-12-12 CN CN201811519797.0A patent/CN109492828B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101866384A (en) * | 2010-06-18 | 2010-10-20 | 杭州电子科技大学 | Improved artificial fish school optimization method based on vehicle path planning |
| CN102903038A (en) * | 2012-09-29 | 2013-01-30 | 威海智联信息网络有限公司 | Method and system for small object logistics distribution based on user track |
| US20140129139A1 (en) * | 2012-11-07 | 2014-05-08 | Intertrust Technologies Corporation | Vehicle charging path optimization systems and methods |
| CN105469201A (en) * | 2015-07-20 | 2016-04-06 | 浙江工业大学 | Method for logistics dispensing center work task processing and scheduling |
| CN106295864A (en) * | 2016-07-29 | 2017-01-04 | 广东工业大学 | A kind of method solving single home-delivery center logistics transportation scheduling problem |
| CN107169983A (en) * | 2017-04-13 | 2017-09-15 | 西安电子科技大学 | Multi-threshold image segmentation method based on cross and variation artificial fish-swarm algorithm |
| CN107682934A (en) * | 2017-11-21 | 2018-02-09 | 重庆邮电大学 | A kind of adaptive resource improves allocative decision in OFDM multi-user systems |
| CN108846610A (en) * | 2018-06-19 | 2018-11-20 | 品信科技有限公司 | A kind of Distribution path method and device for planning |
| CN108830431A (en) * | 2018-08-03 | 2018-11-16 | 广东工业大学 | A kind of Electricity price forecasting solution and relevant apparatus based on whale optimization algorithm |
Non-Patent Citations (2)
| Title |
|---|
| 冀德刚 等: "聚类分析和改进鱼群算法在物流运输路径优化中的应用", 《物流技术》 * |
| 张素君 等: "基于混合离散人工蜂群算法的置换流水车间调度", 《河南大学学报（自然科学报）》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110084390A (en) * | 2019-03-26 | 2019-08-02 | 河南科技学院 | A kind of more vehicles collaboration share-car method for optimizing route based on modified drosophila algorithm |
| CN110084390B (en) * | 2019-03-26 | 2024-01-12 | 河南科技学院 | Multi-vehicle collaborative carpooling path optimization method based on improved drosophila algorithm |
| CN109978471A (en) * | 2019-04-11 | 2019-07-05 | 聊城大学 | A kind of Cold Chain Logistics method for optimizing route with time window |
| CN111126770A (en) * | 2019-11-26 | 2020-05-08 | 浙江工业大学 | Task scheduling method of cross-layer shuttle storage system |
| CN111126770B (en) * | 2019-11-26 | 2022-06-21 | 浙江工业大学 | Task scheduling method of cross-layer shuttle storage system |
| CN111985700A (en) * | 2020-07-30 | 2020-11-24 | 中国外运股份有限公司 | Vehicle carrying single quantity balancing method and device for determining home delivery and loading |
| CN111985700B (en) * | 2020-07-30 | 2024-01-05 | 中国外运股份有限公司 | Vehicle carrying bill quantity balancing method and device for determining home delivery |
| US11475394B2 (en) | 2021-02-22 | 2022-10-18 | Coupang Corp. | Method and apparatus for providing information |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109492828B (en) | 2021-08-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109492828A (en) | A kind of Distribution path optimization method for considering customer grade and distribution time and requiring | |
| Luo et al. | A novel hybrid shuffled frog leaping algorithm for vehicle routing problem with time windows | |
| Lan et al. | A two-phase learning-based swarm optimizer for large-scale optimization | |
| CN106845642B (en) | A kind of adaptive multi-target evolution method of belt restraining cloud workflow schedule | |
| Hu | Multi-objective genetic algorithm for berth allocation problem considering daytime preference | |
| CN109583660A (en) | A kind of implementation method of dynamic order-picking policy | |
| CN110739696A (en) | Integrated scheduling method for demand side resources and renewable energy in intelligent distribution network environment | |
| CN110033121B (en) | Multi-species co-evolution method for solving optimization problem of warehouse operation with corridor | |
| CN112650248A (en) | Routing inspection robot path planning method and system based on improved genetic algorithm | |
| Pop et al. | Review of bio-inspired optimization applications in renewable-powered smart grids: Emerging population-based metaheuristics | |
| CN110070212A (en) | A kind of polymorphic type harvester cooperative scheduling optimization method based on modified whale algorithm | |
| Simsir et al. | A metaheuristic solution approach to capacitied vehicle routing and network optimization | |
| CN115755963A (en) | Unmanned aerial vehicle group cooperative task planning method considering carrier delivery mode | |
| CN109359771A (en) | A kind of line haul vehicle and goods matching algorithm based on big data | |
| Perez et al. | Online and offline learning in multi-objective monte carlo tree search | |
| Fogarty et al. | Serious game modeling of caribou behavior across Lake Huron using cultural algorithms and influence maps | |
| Zhang et al. | Intelligent scheduling of public traffic vehicles based on a hybrid genetic algorithm | |
| CN111489005A (en) | Inventory path optimization method and device | |
| Lv et al. | Multi-compartment vehicle selection and delivery strategy with time windows under multi-objective optimization | |
| CN112862212B (en) | Multi-AGV (automatic guided vehicle) scheduling method, device and equipment based on improved sparrow search algorithm | |
| CN110658833B (en) | Multi-AUV real-time rescue task allocation algorithm in underwater environment | |
| CN113744296A (en) | Image edge detection method based on artificial slime bacterium population intelligence | |
| CN110910077B (en) | Logistics distribution method of improved artificial fish swarm algorithm | |
| CN105205347A (en) | Protein three-dimensional structure predicating method based on BSA-TS (bovine serum albumin-tabu search) algorithm | |
| CN113962496B (en) | Method for optimizing cooperative operation of two cranes in storage 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 | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20241008 Address after: 230000 b-1018, Woye Garden commercial office building, 81 Ganquan Road, Shushan District, Hefei City, Anhui Province Patentee after: HEFEI WISDOM DRAGON MACHINERY DESIGN Co.,Ltd. Country or region after: China Address before: 453003 90 east section of Hua Lan Road, Xinxiang, Henan. Patentee before: Henan Institute of Science and Technology Country or region before: China |