CN109615137A - The Optimization Method for Location-Selection dispensed for cloud under cloud logistics environment - Google Patents
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Abstract
The invention discloses the Optimization Method for Location-Selection that cloud under a kind of logistics environment for cloud dispenses, and are related to logistics distribution technical field.The Optimization Method for Location-Selection includes: the position for obtaining all distribution points in setting regions, to obtain the dispatching point set in setting regions；It is multiple clusters by dispatching point set random division；For any one cluster, select a distribution point as cluster centre；Update cluster centre；For any one updated cluster centre, judge whether that with the cluster centre before corresponding update be the same distribution point；In the case where judging any one updated cluster centre with the cluster centre before corresponding update for the same distribution point, all cluster centre is exported, using as home-delivery center.The Optimization Method for Location-Selection can more rapidly, more accurately obtain alternative distribution point, to improve logistic efficiency and cost of serving.
Description
Technical field
The present invention relates to logistics distribution technical fields, and in particular, to the choosing that cloud dispenses under a kind of logistics environment for cloud
Location optimization method.
Background technique
The logistics centers location problem and cloud logistics service mould faced in internet and big data background Xia Xin retailer
Formula development is new retailer's bring opportunities and challenges, analyzes the validity of home-delivery center, and considering cost and Service Quality
Address Selection of Distributing Center is evaluated and is selected on the basis of amount, a kind of more optimized Address Selection of Distributing Center is provided,
It is able to ascend the success rate of new retailer's innovation, enterprise is helped to provide better service for client.
Summary of the invention
The object of the present invention is to provide the Optimizing Site Selection method that cloud under a kind of logistics environment for cloud dispenses, the Optimizing Site Selections
Method can more rapidly, more accurately obtain alternative distribution point, to improve logistic efficiency and cost of serving.
To achieve the goals above, the present invention provides the Optimizing Site Selection sides that cloud under a kind of logistics environment for cloud dispenses
Method, which includes: to obtain the position of all distribution points in setting regions, to obtain the dispatching in setting regions
Point set；It is multiple clusters by dispatching point set random division, wherein each cluster includes multiple distribution points；For any one cluster:
Select a distribution point as cluster centre；Calculate separately remaining distribution point to cluster centre pseudo range, to obtain in cluster
Pseudo range set, the user that wherein pseudo range is defined as remaining distribution point enlivens quantity and the user of cluster centre is active
The average value of quantity；Calculate the average value of multiple pseudo ranges in cluster in pseudo range set；Obtain the void with cluster centre
Quasi-distance closest to average value distribution point, to update cluster centre；For any one updated cluster centre, judgement is
The no cluster centre with before corresponding update is the same distribution point；Judgement at least exist updated cluster centre with
In the case that cluster centre before corresponding update is not the same distribution point, dispatching point set is repartitioned；Sentencing
Break in the case that any one updated cluster centre and the cluster centre before corresponding update be the same distribution point, by institute
Some cluster centres output, using as home-delivery center.
Preferably, dispatching point set repartition specifically including: for any one non-cluster center distribution point,
Calculate separately non-cluster center distribution point to any one cluster updated cluster centre pseudo range, with obtain virtually away from
From set；Non-cluster center distribution point is divided into cluster corresponding with the minimum pseudo range in pseudo range set, with weight
It is new to divide dispatching point set.
Preferably, pseudo range is indicated using formula (1):
Di=((b0+bi)/2)*10-3Formula (1)
Wherein, DiFor the pseudo range of i-th of distribution point to cluster centre, b0Quantity, b are enlivened for the user of cluster centrei
User for i-th of distribution point enlivens quantity.
Above-mentioned technical proposal, by using pseudo range replace traditional Euclidean distance, can more rapidly, it is more acurrate
The alternative distribution point of acquisition, to improve logistic efficiency and cost of serving.
Other features and advantages of the present invention will the following detailed description will be given in the detailed implementation section.
Detailed description of the invention
The drawings are intended to provide a further understanding of the invention, and constitutes part of specification, with following tool
Body embodiment is used to explain the present invention together, but is not construed as limiting the invention.In the accompanying drawings:
Fig. 1 is the stream of the Optimization Method for Location-Selection dispensed for cloud under cloud logistics environment according to an embodiment of the present invention
Cheng Tu.
Specific embodiment
Below in conjunction with attached drawing, detailed description of the preferred embodiments.It should be understood that this place is retouched
The specific embodiment stated is merely to illustrate and explain the present invention, and is not intended to restrict the invention.
In traditional logistics distribution, logistics cost accounts for relatively high, and loglstics enterprise is smaller and very at random, and shortage is mutually fitted
The flow basis facility answered, especially wisdom logistics system and intelligent circulation systems.As materials stream informationization level is continuously improved,
Technology of Internet of things continues to develop, and the technologies such as GIS, GPS/BDS and RFID are widely applied, and produces in logistics progress big
The data of amount, the information that each links such as order, client, income, dispatching person including short distance dispatching are related to.Using newest
Information technology, motion profile of the available article in logistics distribution process, thus collecting path data.Cloud logistics service mould
Formula is put forward based on the various aspects such as big data, Internet of Things, novel information technology.Cloud logistics service mode can be effectively right
Magnanimity is mostly that information resources carry out logistics service optimization, and for reducing, inventory cost, raising logistic efficiency, improving service quality has
Important influence.Compared with traditional logistics distribution, cloud logistics service is to perceive logistics resource information by Internet of Things, in cloud
It calculates under environment by logistic resources information, that is, logistics big data etc. of perception, by virtualizing, servicing chemical conversion cloud.By to logistics
Big data is analyzed, and the links of dispatching are carried out with the analysis and planning of digitization, obtains relevant useful information, for
Send to be elected location provide decision support.Big data is pre-processed, stored and is managed using the big datas tool such as Hadoop and spark
Then reason carries out big data analysis and excavation, big data shows and apply, search out frequent path information therein, obtains phase
The knowledge that flows to of article is closed, enterprise can clearly learn the path message of article, optimize logistics links, to reduce cost.
How effective information is refined from the information resources of magnanimity and properly assign the task to executable unit, to optimize
Logistics cost and time are an important features of cloud logistics service.There is scholar to establish dynamic virtual business tie-up partner selection
With the new model for cooperateing with transportation dispatching integrated, proposes a kind of unique dynamic coloring body surface and show that the heredity with genetic manipulation is calculated
Method, in order to be able to find the optimal solution of a synthtic price index, then demonstrate proposed method in typical case research
Validity.
End distribution project includes the specific links such as Distribution path setting, dispatching personal scheduling, means of distribution selection.Pass through
It monitors goods flow information in real time and adjusts Distribution path in time in conjunction with Customer Location information, traffic information, traffic information etc.；It is logical
Dispatching personnel are crossed in way information, dispense the distance between personnel and dispatching website information etc., reasonable arrangement dispenses personnel；By dividing
Cargo property (such as cargo size, shape, type), the information such as individual demand of client are analysed, suitable means of distribution is selected.
The addressing that integrated center is matched in storehouse needs according to the actual situation to select, common site selecting method have gravity model appoach,
Baumol-Wolfe method and CFLP method etc..If Location of Distribution Centre problem is solved the problems, such as using traditional exact algorithm
A possibility that very little, therefore solved the problems, such as using heuritic approach.Therefore, how home-delivery center's choosing efficiently and is reasonably carried out
Location is the popular problem of comparison in academia and its application field research.There is scholar to propose two stage retail business dispatching
The decision model of center location, considers the objective and subjective criterion (multiple criteria) of decision process respectively, and demonstrates this method
Correctness.Some scholars propose a kind of with the non-linear new adaptive particle swarm optimization with time-varying acceleration factor of inertia weight
Algorithm is proposed to solve the problems, such as Location of Distribution Centre.On the other hand, genetic algorithm, particle swarm algorithm and ant group algorithm can be used
Select suitable path that cargo is sent in time in consumer's hand.There is scholar to propose and demonstrates a kind of super inspiration based on evolution
Algorithm (EH-DVRP) solves dynamic vehicle scheduling route problem.There are also the logistics distribution mathematical modulos that scholar constructs belt restraining
Type proposes a kind of Wavelet chaotic neural network model for solving logistics distribution route optimization problem.
Since under big data environment, storage, transport and dispatching Optimizing Mode in new retail logistics service have it solely
Vertical feature and intension, existing model and derivation algorithm are unable to satisfy the object of dynamic change in electric business logistics under big data environment
Flow resource allocation.In these relevant algorithms, particle swarm algorithm usually handles bad in face of discrete optimization problem, is easier
Fall into local optimum.Traditional heuritic approach can only be calculated again for small data set.In addition, traditional Dijkstra away from
The distance between facility home-delivery center and distributing node from expression.However in practical application, need to analyze mobile social networking
The many factors such as data, media data, transportation cost, haulage time, customer satisfaction, propose on this basis virtually away from
From.In contrast, the K-means algorithm in clustering algorithm, algorithm is scalable and efficiency is very high.It can be to large batch of data
Carry out parallel processing.Institute using the document of K-Means clustering method usually can only consider transport, handle the correlations such as time at present
Attribute, however the duplicate number of channels in order product item is not accounted for but, also lack and initial cluster center is reasonably selected, and
And it is easily trapped into local optimum.
In order to solve above-mentioned problems of the prior art, following be used for is provided in embodiments of the present invention
The Optimization Method for Location-Selection that cloud dispenses under cloud logistics environment.
Fig. 1 is the stream of the Optimization Method for Location-Selection dispensed for cloud under cloud logistics environment according to an embodiment of the present invention
Cheng Tu.As shown in Figure 1, in one embodiment of the present invention, the addressing that cloud dispenses under a kind of logistics environment for cloud is provided
Optimization method, the Optimization Method for Location-Selection may include:
In step s101, the position of all distribution points in setting regions is obtained, to obtain the dispatching in setting regions
Point set；
It in step s 102, is multiple clusters by dispatching point set random division, wherein each cluster includes multiple distribution points；
For any one cluster:
In step s 103, select a distribution point as cluster centre；
In step S104, calculate separately remaining distribution point to cluster centre pseudo range, with obtain in cluster virtually away from
From set, the user that wherein pseudo range is defined as remaining distribution point enlivens quantity and the user of cluster centre enlivens quantity
Average value；
In step s105, the average value of multiple pseudo ranges in cluster in pseudo range set is calculated；
In step s 106, the distribution point with the pseudo range of cluster centre closest to average value is obtained, to update cluster
Center；
In step S108, for any one updated cluster centre, judge whether and gathering before corresponding update
Class center is the same distribution point；
In step s 110, judgement at least exist updated cluster centre in the cluster before corresponding update
In the case that the heart is not the same distribution point, dispatching point set is repartitioned；
In step S111, judging that any one updated cluster centre is with the cluster centre before corresponding update
In the case where the same distribution point, all cluster centre is exported, using as home-delivery center.
In one embodiment of the present invention, dispatching point set repartition can specifically include:
For any one non-cluster center distribution point, non-cluster center distribution point is calculated separately to any one cluster more
The pseudo range of cluster centre after new, to obtain pseudo range set；
Non-cluster center distribution point is divided into cluster corresponding with the minimum pseudo range in pseudo range set, with weight
It is new to divide dispatching point set.
Pseudo range can for example be indicated using formula (1):
Di=((b0+bi)/2)*10-3Formula (1)
Wherein, DiFor the pseudo range of i-th of distribution point to cluster centre, b0Quantity, b are enlivened for the user of cluster centrei
User for i-th of distribution point enlivens quantity.
In optimal enforcement mode of the invention, pseudo range is for example also conceivable to shipment and delivery cost, dispatching duration and visitor
The factors such as family satisfaction.
Above embodiment replaces traditional Euclidean distance by using pseudo range, it is contemplated that the use of distribution point
The influence of quantity, shipment and delivery cost, dispatching duration and customer satisfaction many factors is enlivened at family, can more rapidly, more accurately obtain
Alternative distribution point is obtained, to improve logistic efficiency and cost of serving.
It is described the prefered embodiments of the present invention in detail above in conjunction with attached drawing, still, the present invention is not limited to above-mentioned realities
The detail in mode is applied, within the scope of the technical concept of the present invention, a variety of letters can be carried out to technical solution of the present invention
Monotropic type, these simple variants all belong to the scope of protection of the present invention.It is further to note that in above-mentioned specific embodiment
Described in each particular technique feature can be combined in any appropriate way in the case of no contradiction, be
Avoid unnecessary repetition, the invention will not be further described in various possible combinations.
In addition, various embodiments of the present invention can be combined randomly, as long as it is without prejudice to originally
The thought of invention, it should also be regarded as the disclosure of the present invention.
Claims (3)
1. the Optimization Method for Location-Selection that cloud dispenses under a kind of logistics environment for cloud characterized by comprising
The position of all distribution points in setting regions is obtained, to obtain the dispatching point set in the setting regions；
It is multiple clusters by the dispatching point set random division, wherein each cluster includes multiple distribution points；
For any one cluster:
Select a distribution point as cluster centre；
Calculate separately remaining distribution point to the cluster centre pseudo range, to obtain pseudo range set in cluster, wherein institute
It states pseudo range and is defined as the user of remaining distribution point and enliven quantity and the user of the cluster centre enlivens quantity
Average value；
Calculate the average value of multiple pseudo ranges in the cluster in pseudo range set；
The distribution point with the pseudo range of the cluster centre closest to the average value is obtained, to update in the cluster
The heart；
For any one updated cluster centre, judge whether that with the cluster centre before corresponding update be same
One distribution point；
It is not same at least there is a updated cluster centre with the cluster centre before corresponding update in judgement
In the case where one distribution point, the dispatching point set is repartitioned；
Judging that any one updated described cluster centre matches with the cluster centre before corresponding update to be same
In the case where sending a little, all cluster centre is exported, using as home-delivery center.
2. Optimization Method for Location-Selection according to claim 1, which is characterized in that described to be carried out again to the dispatching point set
Division specifically includes:
For any one non-cluster center distribution point, the non-cluster center distribution point is calculated separately to any one described cluster
The updated cluster centre the pseudo range, to obtain pseudo range set；
The non-cluster center distribution point is divided into corresponding with the minimum pseudo range in the pseudo range set
In cluster, to repartition the dispatching point set.
3. Optimization Method for Location-Selection according to claim 2, which is characterized in that the pseudo range is indicated using formula (1):
Di=((b0+bi)/2)*10-3Formula (1)
Wherein, DiFor the pseudo range of i-th of distribution point to cluster centre, b0Quantity, b are enlivened for the user of the cluster centrei
User for i-th of distribution point enlivens quantity.
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| CN111882121A (en) * | 2020-07-15 | 2020-11-03 | 赛可智能科技(上海)有限公司 | Logistics path optimization method and device and computer readable storage medium |
| CN112819395A (en) * | 2019-11-15 | 2021-05-18 | 北京沃东天骏信息技术有限公司 | Distribution mode determining method, device, medium and equipment based on matrix representation |
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| CN114004386A (en) * | 2021-02-24 | 2022-02-01 | 成都知原点科技有限公司 | Virtual logistics transit station site selection and distribution path optimization method based on intelligent algorithm |
| CN114971502A (en) * | 2022-07-29 | 2022-08-30 | 白杨时代(北京)科技有限公司 | Site selection method and device for distribution center |
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| CN114004386A (en) * | 2021-02-24 | 2022-02-01 | 成都知原点科技有限公司 | Virtual logistics transit station site selection and distribution path optimization method based on intelligent algorithm |
| CN113935685A (en) * | 2021-10-12 | 2022-01-14 | 上海中通吉网络技术有限公司 | Method and system for realizing logistics tail end differential allocation |
| CN114971502A (en) * | 2022-07-29 | 2022-08-30 | 白杨时代(北京)科技有限公司 | Site selection method and device for distribution center |
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Application publication date: 20190412 |
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| RJ01 | Rejection of invention patent application after publication |