CN108830528A - Express mail Distribution path planing method based on time-space attribute - Google Patents
Express mail Distribution path planing method based on time-space attribute Download PDFInfo
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Abstract
The present invention relates to logistic industry data mining technology fields, disclose a kind of express mail Distribution path planing method based on time-space attribute, include the following steps：1) Customer Location data are obtained；2) initial clustering is carried out according to spatial position proximity to client；3) space length of client and time window distance are normalized, then calculate the time-space matrix between each client；4) using the resulting each customers' cluster centre of the initial clustering of acquisition as the initial cluster center of second of cluster, secondary cluster is carried out to client；5) according to secondary cluster as a result, carrying out path planning to the client in cluster areas.The present invention not only allows for spatial position propinquity when clustering to express mail dispatching client, while also contemplating the time window propinquity between express mail dispatching client, this makes the dispatching of the express mail in same customers client's similarity higher；Cluster result is applied in the path planning of vehicle, path planning is carried out in each cluster areas, effectively raise client response speed and reduces dispatching expense.
Description
Technical field
The present invention relates to logistic industry data mining technology field, specially a kind of planing method of express mail Distribution path.
Background technique
Logistics intuitively understand be subject displacement, both include the change of subject spatially, also wrap
Include its continuity in time.Logistics is defined as the movable a part of supply chain, be in order to meet client need and to commodity,
Planning, implementation and the control that efficient, the inexpensive flowing and storage of service and relevant information from the place of production to area of consumption carry out
Process.Can be understood as the appropriate time, place and it is appropriate under conditions of, in the appropriate way and appropriately by appropriate product
Cost be supplied to appropriate consumer.And express mail dispatching provides logistics service, therefore meets customer to logistics service
Demand is the premise of dispatching.People are in logistics distribution service request, and the requirement to service time is common one, also
As more and more common requirement, i.e. people require logistics distribution service that must complete in certain special time period, the specific time
Section is also referred to as time windows constraints.Naturally it is considered that if we gather the adjacent customer adjacent with spatial position of time window
Class reaches to coming together to comprehensively consider and then be dispensed again and not only meets customer service time requirement, but also reduces expense as far as possible
Purpose.
In data mining, cluster is also a critically important concept.The set of physics or abstract object is divided by class
As object form multiple classes process be known as cluster.By clustering the set that cluster generated is one group of data object, these
Object and the object in the same cluster are similar to each other, different with the object in other clusters.Express mail dispatching client is carried out herein
After cluster, customer in same class have spatial position adjacent and time window similar in characteristic.Cluster side involved in herein
Method is division methods, and most of division methods are to give the number of partitions to be constructed based on distance, and division methods create first
One initialization divides, and a kind of re-positioning technology of iteration is then used, by the way that object is moved to another group from a group
To be divided.The general preparation of one good division is：Object in the same cluster is as close to each other as possible or related, and
Object in different clusters is away as far as possible or difference.The division of client is dispensed for express mail, the client position in the same cluster
It is close to set adjacent and time window, close so-called time window is that express mail distribution vehicle can be within certain a period of time to the cluster
Client services, and the Customer Location in different clusters is relatively far apart, but time window is likely to be similar.
The fast development of electric business results in surging for express delivery amount, although Express firm is continuing to increase the throwing to logistics net
Enter, but Courier Service quality is not caught up with, corporate profit margin decline, mainly there are problems for terminal transport link.It is existing to match
It sees off and coverage is rule of thumb a little divided into road area one by one, the express delivery dispatching personnel in each road area are then pre- according to this
The region first distributed carries out parcel services.But this subregion can be such that client is unevenly distributed, and may cause package delay and hand over
It pays.In delivery process, the quality made house calls is ignored.So a reasonable road Division mode is needed at this time, so that
Client's points in road area are substantially uniform, and the service range in each road area reaches unanimity.And each road area is planned in real time optimal
Driving path enables express delivery dispatching personnel to adjust distribution project according to customer demand.
Summary of the invention
In view of this, the purpose of the present invention is to provide a kind of express mail Distribution path planing method based on time-space attribute,
Data source is reliable, method is easily achieved, and analysis result is easily explained.
In order to achieve the above objectives, the present invention provides the following technical solutions：
Express mail Distribution path planing method based on time-space attribute, includes the following steps：
1) Customer Location data are obtained according to customer order content；
2) initial clustering is carried out according to spatial position proximity to client；
3) space length of client and time window distance are normalized, then calculate the space-time between each client
Distance；
4) the resulting each customers' cluster centre of initial clustering for obtaining step 2) clusters initial as second
Cluster centre carries out secondary cluster to client；
5) according to the secondary cluster of step 4) as a result, carrying out path planning to the client in cluster areas.
Further, the step 2) specifically comprises the following steps：
21) Euclidean distance of client between any two is calculated
22) each client is classified as one kind；
23) two nearest classes of distance are merged into a new class；；
24) space D (i, j) of newly-generated cluster and every other cluster is recalculated, if meeting cluster result
Number is more than preset value, is thened follow the steps 3), no to then follow the steps 23).The preset value of clusters number is counted according to corresponding client
And distribution vehicle number determines.
Further, the step 3) specifically comprises the following steps：
31) by following formula, space length and time window distance to client are normalized：
Wherein,For space length,The average value of time-space matrix between a client,For time window away from
From,For the average value of time window distance；
32) by following formula, the time-space matrix between each client is calculated：
Wherein,For the time-space matrix between client's point i and j, ω1For weight shared by space length, ω2For the time
Window is apart from shared weight, ω1≥0、ω2≥0、ω1+ω2=1.
Further, for step 31) and 32) described in time window distance measurement, it is assumed that the time window of client i be [a,
B], the time window of client j is [c, d]；
If the time that dispatching person reaches client i is t ∈ [a, b], the service time at client i is si, from client i to
The running time of client j is tij, then the time for reaching client j is t'=[a+si+tij,b+si+tij]；Remember a'=a+si+tij,
B'=b+si+tij；
Then the calculation formula of time window distance is：
Further, the step 4) specifically comprises the following steps：
41) using the resulting customers' cluster centre of step 2) initial clustering as initial cluster center；
42) each point is calculated to the time-space matrix of each initial cluster center, arrives initial cluster center distance most according to point
It is short to will click on capable classification；
43) new central point is recalculated according to the classification determined；
44) if cluster centre knots modification is greater than predetermined value, then follow the steps 42), makes a reservation for if cluster centre knots modification is less than
Value, then entire cluster process terminates.
Further, the step 5) specifically comprises the following steps：
51) vehicle routing optimization model is established：
52) path optimization model of each cluster areas is solved using genetic algorithm.
The beneficial effects of the present invention are：
1) when clustering to express mail dispatching client, spatial position propinquity is not only allowed for, while being also contemplated fast
Part dispenses the time window propinquity between client, this makes the dispatching of the express mail in same customers client's similarity higher；
2) during cluster, using the method clustered twice, by the resulting each client group center of initial clustering
As the initial cluster center of second cluster, influence of the selection of effective solution initial cluster center to cluster result；
3) cluster result is applied in the path planning of vehicle, path planning is carried out in each cluster areas, effectively
Improve client response speed and reduce dispatching expense.
Detailed description of the invention
In order to keep the purpose of the present invention, technical scheme and beneficial effects clearer, the present invention provides following attached drawing：
Fig. 1 is the flow diagram of the express mail Distribution path planing method based on time-space attribute；
, Fig. 2 is client's initial distribution figure；
Fig. 3 is Customer clustering result figure；
Fig. 4 is the vehicle running path of cluster areas one.
Specific embodiment
Below in conjunction with attached drawing, a preferred embodiment of the present invention will be described in detail, but illustrated embodiment not as
Limitation of the invention.
Referring to Fig. 1-4, the express mail Distribution path planing method based on time-space attribute of the present embodiment includes the following steps：
1) client to be clustered is chosen, Customer Location data are obtained；
2) different location of each client's distribution spatially, client is initially gathered according to spatial position proximity
Class；Specifically comprise the following steps：
21) Euclidean distance of 100 clients between any two is calculated, i.e.,
22) each client is classified as one kind, 100 classes is obtained, every class only includes an object.Between class and class away from
From being exactly the distance between client that they are included；
23) two are found apart from nearest cluster and is merged into a class, the number of such class just reduces one；
24) spacing for recalculating newly-generated cluster and every other cluster executes if meeting termination condition
Step 3), it is no to then follow the steps 23).In the present embodiment, according to corresponding client points and distribution vehicle number, 6 are finally obtained
Class.When cluster result reaches 6 class, algorithm terminates.
Each cluster centre of 6 group clusters obtained is as shown in table 1：
1 initial cluster center of table
3) space length of client and time window distance are normalized, then calculate the space-time between each client
Distance；Specifically comprise the following steps：
31) space length and time window distance have different units, in order to eliminate this influence, to the space of client
Distance and time window distance are normalized respectively, and normalization can pass through all space lengths and time window distance difference
Divided by obtained by a respective characteristic quantity of characterization.It is specific as follows：
Wherein,For space length,The average value of time-space matrix between a client,For time window away from
From,For the average value of time window distance；
32) different weights is assigned to space length and time window distance to weight, calculate the when Ullage between each client
From：
Wherein,For the time-space matrix between client's point i and j, ω1For weight shared by space length, ω2For the time
Window is apart from shared weight, ω1≥0、ω2≥0、ω1+ω2=1.
For step 31) and 32) described in time window distance measurement, it is assumed that the time window of client i be [a, b], client
The time window of j is [c, d], dispatching person successively services client i and j, when dispatching person successively services client i and j, a≤
c.The time window of client i and client j include three kinds of relationships at this time：
Situation one：The case where including or intersecting, i.e. c > b is not present in the time window of client i and client j；
Situation two：The time window of client i and client j intersect, i.e. a < c < b&d > b；
Situation three：Client i includes the time window of client j, i.e. a < c&d < b.
If the time that dispatching person reaches client i is t ∈ [a, b], the service time at client i is si, from client i to
The running time of client j is tij, then the time for reaching client j is t'=[a+si+tij,b+si+tij].Remember a'=a+si+tij,
B'=b+si+tij。
Three kinds of division modes of time window distance：
One：If dispatching person, which is later than client j at the time of reaching client j from client i, can receive the latest time serviced,
Dispatching person cannot simultaneously service client i and client j, and the time window distance of client i and j are set as infinitely great；
Two：At the time of if dispatching person reaches client j from client i within the scope of the time window that client j can receive service,
The time window distance of client i and client j are then set as 0；
Three：If service could be provided for it by having to pass through the waiting of a period of time after dispatching person's arrival client j, will be objective
The time window distance of family i and client j is set as the corresponding waiting time.
According to the three of time window distance kinds of division modes, the time-space matrix for calculating three kinds of client's time windows is as follows：
Situation one：
Situation two：
Situation three：
Then the calculation formula of time window distance is：
In the present embodiment, ω is chosen1=0.5, ω2=0.5.Since 100 client's amounts are larger, calculate here initial poly-
The time-space matrix of 17 clients of class 1, cluster 1 time-space matrix it is as shown in table 2 below：
Table 2 clusters 1 time-space matrix
4) the resulting each customers' cluster centre of initial clustering for obtaining step 2) clusters initial as second
Cluster centre carries out secondary cluster to client；
In view of time window propinquity, if spatial position a good distance off is not suitable for cluster and arrives together, if spatial position connects
Close customer, if time window difference is also not suitable for greatly gathering in one kind very much, so the spatial position that comprehensively consider client is adjacent
Nearly property and time window propinquity, are usedIndicate its time-space matrix, and we dispense express mail the final purpose of Customer clustering
It is that other in each class is made to put the distance and minimum for arriving the cluster centre point, then objective function F is expressed as：
Wherein, n indicates customer's number, and k indicates clusters number；
Specifically comprise the following steps：
41) using the resulting customers' cluster centre of step 2) initial clustering as initial cluster center；
42) each point is calculated to the time-space matrix of each initial cluster center, arrives initial cluster center distance most according to point
It is short to will click on capable classification；
43) new central point is recalculated according to the classification determined；
44) if cluster centre knots modification is greater than predetermined value 1E-6, then follow the steps 42), if cluster centre knots modification is less than
Or being equal to predetermined value 1E-6, then entire cluster process terminates.
The 6 class cluster results finally obtained are as follows：
The final cluster result of table 3
After handling client, final cluster result has been obtained.The initial distribution situation of client as shown in Fig. 2,
Final cluster result is as shown in Figure 3.
5) according to the secondary cluster of step 4) as a result, carrying out path planning to the client in cluster areas.Specifically include as
Lower step：
51) vehicle routing optimization model is established：
Symbol description：
c:The expense of dispensing vehicle unit operating range；
dij:Distance of the client i to client j；
tij:From client i to the running time of client j；
ai:Earliest arrival time as defined in client i；
bi:Arrival time the latest as defined in client i；
mi:The demand goods weight of client i；
vi：The demand goods volume of client i；
ti:The time of dispensing vehicle arrival client i；
p：Long-run cost rate when dispensing vehicle reaches ahead of time；
q：Long-run cost rate when dispensing vehicle delays to reach；
Q:The maximum load quality of dispensing vehicle；
V:The maximum load volume of dispensing vehicle；
fi:The service time of client i；
N:The client's number for needing to service.
Decision variable：
The known time t for reaching client's point ii, then the time t that client j is reached from client i can be obtainedjFor： tj=ti+fi+
ti,j·di,j+max[(ai-ti),0]
Objective function：
Constraint condition：
Objective function (1) indicates that total running time of distribution vehicle, objective function (2) indicate the dispatching of distribution vehicle
Cost；Constraint condition (3) indicates to go to service by a dispensing vehicle as top n client o'clock；Constraint condition (4) indicates each visitor
Family point must all be serviced；Constraint condition (5) indicates that distribution vehicle from home-delivery center and finally returns that home-delivery center；
Constraint condition (6) indicates that the cargo that distribution vehicle is transported is limited no more than the weight of vehicle；Constraint condition (7) expression is matched
Volumetric constraint of the cargo for sending vehicle to be transported no more than vehicle；Constraint condition (8) indicates the visitor for also needing to be serviced in total
Amount is N.
52) path optimization model of each cluster areas is solved using genetic algorithm, obtains current vehicle delivery side
Case.The route scheme of acquisition such as the following table 4, the vehicle delivery scheme planned cluster 1 are as shown in Figure 4.
4 vehicle running path of table
Sample result analysis：
By examples detailed above as it can be seen that carrying out initial clustering according to spatial position propinquity to express mail dispatching client first, then
The spatial position propinquity and time window propinquity for comprehensively considering customer carry out second and cluster, and effective solution is initial poly-
Influence of the selection at class center to cluster result.And using a kind of proposed express mail dispatching based on time-space attribute
Customer clustering method cluster express mail dispatching client and then to client's progress path planning in corresponding region, can be with
Accelerate the response speed to customer demand, article is sent to client on time, quickly, improve customer satisfaction degree, reduces dispatching
Cost.
Finally, it is stated that preferred embodiment above is only used to illustrate the technical scheme of the present invention and not to limit it, although logical
It crosses above preferred embodiment the present invention is described in detail, however, those skilled in the art should understand that, can be
Various changes are made to it in form and in details, without departing from claims of the present invention limited range.
Claims (6)
1. the express mail Distribution path planing method based on time-space attribute, it is characterised in that include the following steps：
1) Customer Location data are obtained；
2) initial clustering is carried out according to spatial position proximity to client；
3) space length of client and time window distance are normalized, then calculate the time-space matrix between each client；
4) the resulting each customers' cluster centre of initial clustering for obtaining step 2) is as the initial clustering of second of cluster
Center carries out secondary cluster to client；
5) according to the secondary cluster of step 4) as a result, carrying out path planning to the client in cluster areas.
2. the express mail Distribution path planing method according to claim 1 based on time-space attribute, which is characterized in that the step
It is rapid 2) to specifically comprise the following steps：
21) distance of client between any two is calculated；
22) each client is classified as one kind；
23) two nearest classes of distance are merged into a new class；
24) spacing of newly-generated cluster and every other cluster is recalculated, if it is more than default for meeting cluster result number
3) value, thens follow the steps, no to then follow the steps 23).
3. the express mail Distribution path planing method based on time-space attribute as claimed in claim 2, it is characterised in that：The step
3) specifically comprise the following steps：
31) by following formula, space length and time window distance to client are normalized：
Wherein,For space length,The average value of time-space matrix between a client,For time window distance,For the average value of time window distance；
32) by following formula, the time-space matrix between each client is calculated：
Wherein,For the time-space matrix between client's point i and j, ω1For weight shared by space length, ω2For time window distance
Shared weight, ω1≥0、ω2≥0、ω1+ω2=1.
4. the express mail Distribution path planing method based on time-space attribute as claimed in claim 3, it is characterised in that：
For step 31) and 32) described in time window distance measurement, it is assumed that the time window of client i be [a, b], client j when
Between window be [c, d]；
If the time that dispatching person reaches client i is t ∈ [a, b], the service time at client i is si, from client i to client j's
Running time is tij, then the time for reaching client j is t'=[a+si+tij,b+si+tij]；Remember a'=a+si+tij, b'=b+si+
tij；
Then the calculation formula of time window distance is：
5. the express mail Distribution path planing method based on time-space attribute as claimed in claim 3, it is characterised in that：The step
4) specifically comprise the following steps：
41) using the resulting customers' cluster centre of step 2) initial clustering as initial cluster center；
42) calculate the time-space matrix that each point arrives each initial cluster center, according to point to initial cluster center distance it is most short by
Point is classified；
43) new central point is recalculated according to the classification determined；
44) if cluster centre knots modification is greater than predetermined value, then follow the steps 42), if cluster centre knots modification is less than predetermined value,
Entire cluster process terminates.
6. the express mail Distribution path planing method based on time-space attribute as claimed in claim 5, it is characterised in that：The step
5) specifically comprise the following steps：
51) vehicle routing optimization model is established：
52) path optimization model of each cluster areas is solved using genetic algorithm.
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