CN108898237A - A kind of logistics of retail enterprise allocator based on genetic algorithm - Google Patents
A kind of logistics of retail enterprise allocator based on genetic algorithm Download PDFInfo
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- CN108898237A CN108898237A CN201810410485.XA CN201810410485A CN108898237A CN 108898237 A CN108898237 A CN 108898237A CN 201810410485 A CN201810410485 A CN 201810410485A CN 108898237 A CN108898237 A CN 108898237A
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Abstract
A kind of logistics of retail enterprise allocator based on genetic algorithm proposed by the present invention, includes the following steps：Establish vehicle dispatching model；Construct specific genetic algorithm；Input retail point demand for commodity information；Input retail point information and warehouse location information.The present invention is by the information and comprehensive income of the distribution vehicle of input distribution point as objective function, it is all arrived at etc. with distribution point as constraint function, exports optimal distribution project, more efficient dispatching is sold goods and materials, reduce logistics cost, the final overall efficiency for improving logistics distribution.
Description
Technical field
The present invention relates to logistics field, especially a kind of logistics of retail enterprise allocator based on genetic algorithm.
Background technique
Logistics distribution, i.e., from a kind of commodity circulation mode in terms of the management style of commodity circulation.It is a kind of circulation in modern times
Mode.The offering customers service that logistics distribution is located in as e-commerce, the characteristics of according to e-commerce, to entire logistics distribution
System carries out unified information management and scheduling, orders goods and requires according to user, carries out tally work in Logistic Base, and will prepare
Cargo deliver a kind of logistics mode of consignee.Logistics storage dispensing service is already as China E-Commerce Business core the most
Industry link is capable of providing a comprehensive perfect logistics storage dispensing solution and also becomes many medium and small sellers, electronics
The problem of commercial supplier's brand quotient must pay close attention to.
With the improvement of people's living standards, the service that resident can be provided 15 minutes life ranges is higher and higher, tradition
The forms such as mom-and-pop store, retail shop are no longer satisfied the demand of the people, it is therefore desirable to provide in time and update the retail of resident periphery
The type of goods that shop provides, it is therefore desirable to update more effective logistics system.
The mode of stocking up of tradition mom-and-pop store and retail shop is relatively simple at present, does not form unified logistics distribution optimization, because
And INTEGRATED LOGISTICS is at high cost and service ability is bad, while wasting a large amount of manpower, fuel and time.To these shops and
Simultaneously supplied goods is concentrated mainly in transportation cost problem Shi Gengxin, therefore scheduling and the paths planning method of retail business distribution vehicle
As its key factor.
Summary of the invention
The present invention is in view of the above-mentioned problems, disclose a kind of logistics of retail enterprise allocator based on genetic algorithm.
Specific technical solution is as follows：
A kind of logistics of retail enterprise allocator based on genetic algorithm, which is characterized in that steps are as follows：
(1) database is established, the distance between each target distribution point and adjacent distribution point are obtained, inputs warehouse, retail
The geographic information data of point, the demand data of all kinds of articles in retail point input road net data, calculate warehouse and retail point most
Short distance calculates the shortest distance of each retail point；
(2) according to warehouse, the direct distance in retail point constructs distance matrix；
(3) vehicle number needed according to distribution project, constraint condition are that the cargo of each car dispatching is no more than it
Useful load；
(4) calculate logistics overall cost, overall cost include logistic car cost of use and all logistics garages
Fuel oil expense during sailing；
(5) genetic algorithm is constructed；
(6) the scheduling dispatching model of logistics of retail enterprise is established；
(7) retail point and warehouse location information are initialized；
(8) retail point and warehouse location information are constructed；
(9) objective function of building model and limitation and condition, and calculate fitness function；
(10) it is optimized according to genetic algorithm, exports logistics distribution project.
Further, the cost of use of the logistic car includes vehicle usage charges and truck man wage.
Compared with the prior art, beneficial effects of the present invention are：
The present invention provides a kind of Multipurpose Optimal Method towards logistics of retail enterprise vehicle configuration and path planning, examines simultaneously
Considering dispatching fuel cost, distribution time and dispatching uses vehicle as overall cost, and parsing is optimized using genetic algorithm,
To achieve the purpose that cost optimization.The present invention can use this logistics route planning mode tissue distribution plan appropriate and row
Bus or train route line, in the case where meeting certain constraint condition, such as customer demand, vehicle load etc., realize cost minimization, the vehicle of dispatching
The targets such as minimum.
Detailed description of the invention
Fig. 1 is genetic algorithm schematic diagram of the present invention.
Specific embodiment
Clear to be more clear technical solution of the present invention, the present invention is described further with reference to the accompanying drawing,
The technical characteristic of any pair of technical solution of the present invention carries out the scheme that equivalencing is obtained with conventional reasoning and each falls within guarantor of the present invention
Protect range.
A kind of logistics of retail enterprise allocator based on genetic algorithm, includes the following steps：
(1) merchandising database is established：Database includes all kinds of commodity demand volumes for needing to transport daily, it is assumed that a shared K
Commodity are planted, the volume of k kind commodity needed for i-th of shop is Vi, k.
(2) retail point spatial geographic information library is established, m retail point is shared, provides its geographical coordinate.
(3) Traffic network database is established, road network matrix D i, j between m retail point are established, that refer to is exactly retail point i to zero
Sell the distance between point j.
(4) delivered payload capability of each car is Cap_Car, and calculating total vehicle demand Car_num amount is：INTINT function is round numbers.
(5) generalized cost function is constructed：Wherein a is that each car is daily
Average cost of use, b are the cost of use of every kilometer of vehicle.
(6) parametric variable space is encoded：Using natural number coding mode to chromosome coding.0 indicates in dispatching
The heart is dispensed from k vehicle to m client, by model conversion at the item chromosome of m+n+1.Such as 012056034
06 clients of expression are dispensed by 3 vehicles, and first car is dispensed into retail point point 1,2, and second car is dispensed into retail point point
5,6, third vehicle is dispensed into service point 3,4.
(7) it initializes：Number of the integer pop_size as chromosome is defined, and is randomly generated at the beginning of pop_size
Beginning chromosome.Under normal circumstances, it due to the complex nature of the problem, analytically generates feasible chromosome and is difficult.At this point, can
To adopt the two methods as the process of initialization, dependent on information provided by policymaker when specific implementation.
If policymaker can be denoted as V to an interior point in first feasible set0.A sufficiently large several M are defined, to guarantee to lose
Entire feasible set can be spread by passing operation, this big number in mutation operation not only during initialization using but also using.It presses
Pop_size chromosome is generated according to following method.In spaceIn, a direction d is randomly choosed, if V0+ M d can expire
Sufficient inequality, then by V0+ M d is as a chromosome.Otherwise, M is set to 0 to a random number between M, until V0+M d
Until feasible.Due to V0It is interior point, so the feasible solution for meeting inequality constraints can be found in limited step.It repeats above-mentioned
Process pop_size times, to generate pop_size initial chromosome V1, V2, V3, Vpop-size.
Evaluation function setting：F (i)=1/f (i), setting parameter concentrate the collection of the point between 0 and 0 to be combined into R, ifThen F (i)=0
Selection course:
Crossover process：Defined parameters P firstcFor the probability of crossover operation, this probability illustrates to be desired for P in populationc
Pop_size chromosome carries out crossover operation.For the parent for determining crossover operation, repeated from i=1 to pop_size following
Process；From random number r is generated in [0,1], if r < Pc, then V is selectediAs a parent.With V '1,V′2,V′3... it indicates
The parent selected above, and they are divided into immediately following pair：
(V′1,V′2),(V′3V′4),(V′5,V′6),…,
With (V '1,V′2) it is example to explain how to all of the above to progress crossover operation.Firstly, from open interval
A random number c is generated in (0,1), then, by following form in V '1With V '2Between carry out crossover operation, and after generating two
For X and Y：
X=cV '1+(1-c)V′2
Y=(1-c) V '1+cV′2
Mutation process：
Defined parameters PmAs the mutation probability in genetic system, this probability shows to be desired for P in totalitym pop_
Size chromosome is used to carry out mutation operation.
Following process is repeated by i=1 to pop_size similar to the process for selecting parent in crossover operation：From section
Random number r is generated in [0,1], if r < Pm, then selective staining body ViParent as variation.To each selection parent,
With V=(x1,x2... ..., xn) indicate, it makes a variation in following manner.?The direction d of middle random selection variation, if V
+ Md is infeasible, then, random number of the M between 0 and M is set, until feasible.Wherein M is initialization procedure definition
A sufficiently large number.If not finding feasible solution within previously given the number of iterations, M=0 is set.No matter M is
What is worth, and always replaces V with X=V+Md.
Algorithm terminates
By selection, intersection and mutation operation, a new population is obtained, next-generation evolution is ready for.To above-mentioned step
After the rapid cycle-index by giving, genetic algorithm is terminated.Finally obtain optimal dispatching vector V.
The logistics route planning mode provided through the invention selects optimal traffic route and vehicle to arrange of the invention
Beneficial effect is：It can use this logistics route planning mode tissue distribution plan appropriate and traffic route, meeting one
Under fixed constraint condition, such as customer demand, vehicle load etc., the targets such as the cost minimization of dispatching, vehicle be minimum are realized.
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
In the technical scope disclosed by the present invention, any changes or substitutions that can be easily thought of by anyone skilled in the art,
It should be covered by the scope of protection of the present invention.Therefore, protection scope of the present invention should be with the protection scope of claims
Subject to.
Claims (2)
1. a kind of logistics of retail enterprise allocator based on genetic algorithm, which is characterized in that steps are as follows：
（1）Database is established, the distance between each target distribution point and adjacent distribution point are obtained, inputs warehouse, retail point
Geographic information data, the demand data of all kinds of articles in retail point input road net data, calculate the most short distance in warehouse and retail point
From calculating the shortest distance of each retail point；
（2）According to warehouse, the direct distance in retail point constructs distance matrix；
（3）The vehicle number needed according to distribution project, constraint condition are the cargo of each car dispatching no more than its loading
Amount；
（4）The overall cost of logistics is calculated, overall cost includes that the cost of use of logistic car and all logistics garages cross
Fuel oil in journey takes；
（5）Construct genetic algorithm；
（6）Establish the scheduling dispatching model of logistics of retail enterprise；
（7）Initialize retail point and warehouse location information；
（8）Construct retail point and warehouse location information；
（9）The objective function of building model and limitation and condition, and calculate fitness function；
（10）It is optimized according to genetic algorithm, exports logistics distribution project.
2. a kind of logistics of retail enterprise allocator based on genetic algorithm as described in claim 1, which is characterized in that the object
The cost of use for flowing vehicle includes vehicle usage charges and truck man wage.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101051361A (en) * | 2007-05-18 | 2007-10-10 | 吉林大学 | Class genetic method for logistic optmum |
| CN101673382A (en) * | 2009-10-21 | 2010-03-17 | 北京交通大学 | Combined optimization method for agricultural chain-operation logistics delivering and loading-distribution |
| CN103440522A (en) * | 2013-08-31 | 2013-12-11 | 福州大学 | Vehicle dispatching method with genetic algorithm and MapReduce combined |
-
2018
- 2018-05-02 CN CN201810410485.XA patent/CN108898237A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101051361A (en) * | 2007-05-18 | 2007-10-10 | 吉林大学 | Class genetic method for logistic optmum |
| CN101673382A (en) * | 2009-10-21 | 2010-03-17 | 北京交通大学 | Combined optimization method for agricultural chain-operation logistics delivering and loading-distribution |
| CN103440522A (en) * | 2013-08-31 | 2013-12-11 | 福州大学 | Vehicle dispatching method with genetic algorithm and MapReduce combined |
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Application publication date: 20181127 |