CN107194576B - Dynamic scheduling method for processing newly-added pickup requirement in express delivery process - Google Patents

Dynamic scheduling method for processing newly-added pickup requirement in express delivery process Download PDF

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CN107194576B
CN107194576B CN201710356879.7A CN201710356879A CN107194576B CN 107194576 B CN107194576 B CN 107194576B CN 201710356879 A CN201710356879 A CN 201710356879A CN 107194576 B CN107194576 B CN 107194576B
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scheduling
time
requirement
pickup
newly added
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CN107194576A (en
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谷振宇
刘国荣
白晓辉
郑家佳
朱雪莲
吕健成
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Chongqing University
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    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group
    • 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
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Abstract

The invention provides a dynamic scheduling method for processing a newly-added pickup requirement in an express delivery process, and belongs to the technical field of vehicle intelligent optimization scheduling. Aiming at the problem that the no-load rate of delivery vehicles of the current express company is high and the requirement for picking up a new express item cannot be met in time, the express simultaneous pick-up and delivery dynamic scheduling method combining batch scheduling and emergency scheduling is provided. The method comprises the following concrete implementation steps: 1. the distribution center receives a newly added pickup requirement; 2. selecting a scheduling mode; 3. determining a scheduling time; 4. determining a scheduling range; 5. determining the insertion position of the newly added pick-up requirement, wherein the newly added pick-up requirement cannot be inserted into the vehicle assigned by the distribution center of the current route; 6. and carrying out local path optimization within the scheduling range.

Description

Dynamic scheduling method for processing newly-added pickup requirement in express delivery process
Technical Field
The invention provides a dynamic scheduling method for processing a newly-added pickup requirement in an express delivery process, and belongs to the technical field of vehicle intelligent optimization scheduling.
Background
With the development of electronic commerce, customers have more and more requirements for getting on-site and taking out packages, and can submit orders at any time, and express companies are expected to provide services with higher speed and better quality. When an express company sends or receives a package, the express company needs to make a quick response to the random dynamic demands and adjust the existing route. Different from the traditional scheduling problem of picking and delivering goods simultaneously, the dynamic scheduling problem of picking and delivering goods simultaneously for express delivery in the electronic commerce environment faces to customers which are vast consumers and are not business customers, the quantity is more, the distribution is more dispersed, the occurrence randomness is stronger, the unit order cost is higher, and the profit is lower. For express companies directly facing consumers in the e-commerce environment, on one hand, customers are extremely dispersed, the no-load rate of delivery vehicles is high, and the transportation capacity resources of the companies cannot be effectively utilized; on the other hand, in the face of a newly added pick-up order, if the frequency of departure is too high, the cost of an enterprise is further increased, but the frequency is too low, the waiting time after a client submits the order is too long, and the customer satisfaction is reduced. How to solve the contradiction between the vehicle use efficiency and the timeliness of getting a piece at the door becomes the problem that express companies are urgent to face.
The dynamic vehicle scheduling problem can be divided into three types, namely a dynamic scheduling problem of a goods taking vehicle, a dynamic scheduling problem of a goods delivery vehicle, a dynamic scheduling problem of a mixed vehicle for taking and delivering and the like. The dynamic scheduling problem of the picking and delivering hybrid vehicle considers the delivering and picking processes in the transporting process as a whole without the requirement of the order of picking and delivering, thereby reducing the idle load rate of the vehicle in the going or returning process and improving the vehicle transporting benefit.
There are two processing strategies for the dynamic scheduling problem: one is a re-optimization strategy, that is, according to real-time dynamic requirements, all demand points are re-optimized by using a static algorithm, which requires frequent calling of the static algorithm, and therefore, the calculation time of the algorithm is high. In addition, since the re-optimization strategy regenerates the scheduling scheme each time, the stability of plan execution is not facilitated. The other is a local optimization strategy, i.e. the original scheduling scheme is adjusted locally according to the received real-time information, and although this strategy may be inferior to the re-optimization strategy, the efficiency and the practicability are high. The re-optimization strategy is suitable for the case that the number of newly added clients is large and the real-time requirement is slightly low, and the local optimization strategy is suitable for the case that the number of newly added clients is small and the real-time requirement is high.
Disclosure of Invention
In order to solve the problems of high idle load rate and untimely picking up and delivering of the express delivery, the invention combines batch scheduling with emergency scheduling, provides a dynamic scheduling method for processing the newly added picking up requirements in the express delivery process, can realize reasonable scheduling of vehicles, reduces the picking up and delivering cost of express enterprises, and improves the response speed of the newly added picking up requirements, thereby improving the customer satisfaction.
A dynamic scheduling method for processing newly added pickup requirements in an express delivery process comprises the following specific steps:
step 1: and selecting a scheduling mode of batch scheduling or emergency scheduling according to the scheduling interval time, the quantity of newly added parts taking requirements and the emergency degree, and determining the scheduling time. Further comprising the steps of:
1-1 identifies whether there is an emergency need. If so, adopting an emergency scheduling mode and determining the scheduling time of the emergency scheduling mode. Further comprising the steps of:
1-1-1 identifies whether there is an emergency need:
setting the order submitting time of the newly added pickup requirement i as STiThe longest waiting time for the express company to commit to the customer is Ts，LTiFor the latest allowed start service time of client i, STi+Ts＝LTiThe vehicle running time from the distribution center to the position of the newly added pickup requirement i is toiThe emergency reserve time is trFor a newly added pickup requirement i of a vehicle which is not scheduled to pick up a pickup, the requirement i is assumed to be incapable of finishing scheduling in a batch scheduling mode, and the latest scheduling time is determined
Figure GDA0002489249380000021
Figure GDA0002489249380000022
The method of identifying whether there is an emergency need is as follows:
assume that the current time is tnowIf t isnowLatest scheduling time greater than newly-added pickup requirement i
Figure GDA0002489249380000023
Namely, it is
Figure GDA0002489249380000024
The newly added pickup requirement i is an emergency requirement, and emergency scheduling is required.
1-1-2 determining scheduling time of emergency scheduling mode
After the emergency demand is identified, until the demand i finishes scheduling, t is more than or equal tonowAt any future time tfThe emergency scheduling time of each demand i, i.e. the dynamic scheduling time t in the emergency scheduling modeE(n) is:
Figure GDA0002489249380000025
1-2, under the condition of no emergency demand, judging whether the dispatching starting condition of the batch dispatching mode is met, if so, adopting the batch dispatching mode to determine the dispatching time of the batch dispatching mode. Further comprising the steps of:
the batch scheduling mode comprises two sub-modes: a batch scheduling T mode and a batch scheduling Q mode.
Let τ (n) be the scheduling time, T, of the nth dynamic schedulingmaxFor dynamic scheduling of maximum interval time, QmaxFor the maximum accumulated quantity of the newly added parts,
Figure GDA0002489249380000026
is shown at t1To t2And adding the accumulated quantity of the piece taking demands at the moment.
1-2-1, judging whether the scheduling condition of the batch scheduling T mode is met:
Figure GDA0002489249380000027
1-2-2 if the scheduling condition in 1-2-1 is satisfied, the scheduling time T of the batch scheduling T mode can be determinedT(n) the following:
Figure GDA0002489249380000032
1-2-3, if the scheduling condition in the 1-2-1 is not met, judging whether the scheduling condition of the batch scheduling Q mode is met:
Figure GDA0002489249380000031
1-2-4 if the scheduling condition in 1-2-3 is met, the scheduling time t of the batch scheduling Q mode can be determinedQ(n) the following:
Figure GDA0002489249380000033
1-3 selection of scheduling mode and determination of scheduling time:
when selecting the scheduling mode, the scheduling time of which scheduling mode is satisfied first, i.e. which scheduling mode is used, and the scheduling time of the dynamic scheduling can be determined, the selection of the dynamic scheduling mode and the determination of the scheduling time are shown in fig. 2,
further, the start time τ (n) of the nth dynamic scheduling may be determined as follows:
τ(n)＝Min{tT(n)，tQ(n)，tE(n)}
the newly added pickup requirements processed in the emergency scheduling mode are not limited to the emergency requirements, but also include all newly added pickup requirements received after last dynamic scheduling, that is, the emergency scheduling mode is triggered by the identified emergency requirements, and when scheduling is performed, all unprocessed pickup requirements at the beginning of emergency scheduling are processed in a batch scheduling mode.
Two key parameters in the batch scheduling mode: dynamically scheduling maximum interval time TmaxAnd the maximum accumulated quantity Q of newly added and taken part demandsmaxIs not fixed but is determined according to the number of newly added fetching member demands in each time interval in a working day, Tmax、QmaxThe values of the two key parameters can be determined according to the following method:
counting the occurrence time and the number of newly added pickup requirements of a plurality of working days, dividing one working day into a plurality of time periods based on the analysis of historical data, analyzing and predicting the number P of the newly added pickup requirements in each time period, and fitting a P-t relation curve reflecting the number of the newly added pickup requirements in each time period;
determining the scheduling interval time T according to the actual process of dynamic schedulingmaxA relation function T between the value and the number P of newly added pick-up requirements in each time period in a working daymaxF (p). To reflect the dynamically scheduled maximum interval time TmaxThe relation between the value and the quantity P of newly added pickup requests is drawnSystem of TmaxA schematic diagram of the relationship P, as shown in fig. 3, the more newly added pick-up pieces in a working day, the shorter the interval time between two adjacent dynamic schedules is, and if the newly added pick-up pieces are less, the interval time between two dynamic schedules can be properly extended, but not longer than the service commitment time T for the express company to complete pick-up pieces at home after the requirement is submittedsI.e. Tmax≤Ts。
Determining the maximum accumulated quantity Q of newly added part demands according to the actual process of dynamic schedulingmaxIs taken value and the relation function Q of the newly added pick-up required quantity PmaxF (p). To reflect the dynamically scheduled maximum interval QmaxThe value of (A) and the quantity P of newly added pickup requirements, and drawing QmaxP-P relationship diagram, as shown in FIG. 4, scheduling interval QmaxThe quantity P of newly added pick-up requirements is in positive correlation, and in the actual scheduling process, Q can be estimated according to the predicted value of the newly added pick-up requirementsmaxThe value of (a).
Step 2: determining a scheduling range:
after the scheduling start time τ (n) is determined, the scheduling range of the current scheduling needs to be determined according to the location of the newly added pickup requirement entering the current scheduling.
And determining that the newly added pickup requirement set for scheduling at this time is U at the scheduling time tau (n), wherein the number of newly added pickup requirements is m, m is more than or equal to 1, and the service time window of any newly added pickup requirement U is (ET)u,LTu) U ∈ U, any vehicle k satisfying the following conditionsrScheduling range SR capable of being divided into newly-added part demands uu，
tru≤LTu-τ(n)
Wherein t isruIndicates that an arbitrary vehicle k is present at time τ (n)rAnd the vehicle running time to the position of the newly added pickup requirement u. Solving the scheduling range of each newly-added pickup requirement in the newly-added pickup requirement set U in sequence, wherein the scheduling range of the current scheduling is as follows:
Figure GDA0002489249380000041
and 3, determining the insertion position, wherein the vehicle assigned by the distribution center can not be inserted into the newly added pick-up requirement of the current route.
Further comprising the steps of:
let U be the newly added pick-up requirement to be distributed, U ∈ U, i, j be two adjacent distributed demand points on the same line, btjRepresents the start service time, bt ', of the vehicle at demand point j'jIndicates that after u is inserted into demand points i and j, the vehicle will start a new service time, bt, at demand point jjAnd bt'jThe calculation method is as follows:
btj＝max{ETj,bti+si+tij}
bt′j＝max{ETj,btu+su+tuj}
1≤j≤n+1
wherein s isiRepresenting the service time, t, of the vehicle at the point of demand iijIndicating the vehicle travel time from demand point i to demand point j.
To reflect the amount of increase in the service time of the demand point after the position to be inserted before and after the insertion, a variable PF is definedj，PFjThe calculation method of (2) is as follows:
PFj＝bt′j-btj
3-1: updating newly added pickup requirement information, un-served requirement information and in-transit vehicle information;
3-2: and selecting a new pick-up requirement to be distributed. Counting the earliest initial service time ET of newly added and taken part requirementuSelecting a newly-added pickup requirement to be allocated, wherein the service starting time is earliest allowed:
min{ETu}
3-3: and determining the feasible positions. Judging whether u can be inserted between any demand points i and j in the line; further comprising the steps of:
3-3-1, judging whether the time window of u is satisfied after u is inserted between any demand points i and j:
btu≤LTu
3-3-2, judging whether the time window of the subsequent client is satisfied after u is inserted between i and j:
btl+PFl≤LTl,j≤l≤n
3-3-3: if the two conditions are met, calculating an insertion Cost for inserting the newly-added pickup requirement u to be distributed into a feasible position, and if the two conditions are not met, making the Cost equal to a maximum value M:
Cost＝α·(diu+duj-dij)+β·(bt′j-btj)-γdou
α + β is 1, α, γ is a constant not less than 0;
3-4: the optimal insertion position is determined. Selecting a feasible position with the minimum insertion Cost for inserting the newly-added pickup requirement u to be distributed into different routes as an optimal insertion position;
3-5: checking whether the newly added pickup requirement in the period is completely distributed, if so, finishing the scheduling, and if not, turning to 3-6;
3-6: and checking whether the distribution center has unused available vehicles, if so, independently dispatching the vehicles by the distribution center to finish the unallocated demands, and if not, moving the unallocated demands to the next dispatching cycle.
And 4, step 4: after inserting the new requirement of taking part into the current line, the scheduling range SR of the nth schedulingτ(n)Performing local path optimization on each line in the system, and performing local path optimization operation O between the lines1Then performing an in-line local path optimization operation O2Further comprising the steps of:
4-1 inter-line local path optimization operation O1：
O1Operations directed only to the pickup requirement, including remove operation O11And reinsertion operation O12Two operations. At O1In operation, O is first carried out11Operating, then carrying out O12And (5) operating.
Removing operation O11: according to the removal probability PrDetermining the needRemoving the selected pick-up requirement r according to the removed pick-up requirement;
reinsertion operation O12: and repeating the step 2 to re-determine the scheduling range, and then repeating the step 3 to insert the piece taking requirement r into the line in the scheduling range.
Removal probability P of pickup requirementrThe determination method comprises the following steps:
Figure GDA0002489249380000051
Figure GDA0002489249380000052
bt″j＝max{ETj,btr+sr+trj}
Figure GDA0002489249380000053
wherein C isrThe removal cost of the pick-up requirement r, bt ″)jTo remove the start service time of the front delivery vehicle at the next demand j of the pick-up demand r,
Figure GDA0002489249380000054
to remove the pick-up demand r and then deliver the vehicle's start service time at the demand point j, α + β is 1, α is a constant not less than 0;
4-2 in-line local path optimization operation O2：
And re-planning paths of all uncompleted demand points in the line including the pickup demand and the delivery demand, and adjusting the sequence of service of the demand points.
The vehicle optimization objective function is:
Figure GDA0002489249380000061
Figure GDA0002489249380000062
the objective function represents a minimum vehicle travel cost and a customer satisfaction cost, where f1As a vehicle running cost factor, f2For vehicles earlier than ETiPenalty factor, f, for reaching demand point i3For the vehicle later than L TiPenalty factor, n, for reaching demand point ieTotal number of unserviced demands in each line, IeA set of requirements not yet served within each line.
In-line local path optimization operation O2For only the route of the vehicle with the changed route in step 3 and step 4-1, namely, the route of the newly added pickup demand point inserted in step 3 and the operation O executed in step 4-111And O12Route changed later, proceed to O2And (5) operating.
The invention has the beneficial effects that:
the scheduling mode can be intelligently selected according to the number, the emergency degree and the scheduling interval time of newly increased pickup requirements in each time period in a working day, the scheduling time is determined, the response speed to dynamic requirements is improved, and the logistics cost of home pickup of express companies is reduced by processing the newly increased pickup requirements in batches;
by defining the scheduling range, the negative influence of large-range route change caused by global scheduling on logistics distribution activities is avoided, the calculation time of dynamic scheduling is greatly reduced, and the feasibility of a generated scheduling scheme is ensured;
the method has the advantages that the newly added pickup requirements are completed by the vehicles in distribution, so that the time for customers to wait for the pickup service at home is shortened, the customer satisfaction is improved, the no-load rate is reduced, and the logistics cost is reduced;
after the new fetching requirement is inserted into the current route, the local route optimization among routes and the local route optimization in the routes within the scheduling range are carried out in sequence, the optimization performance of the generated local scheduling scheme is improved, and the influence of the route change on the demand which is not served yet is reduced.
Drawings
FIG. 1 dynamic scheduling flow chart
FIG. 2 is a schematic diagram of dynamic scheduling mode selection and scheduling time determination
FIG. 3TmaxP-P relation curve diagram
FIG. 4QmaxP-P relation curve diagram
FIG. 5 static Path planning route map
FIG. 6 is a chart of scheduling ranges for 1 st dynamic scheduling
FIG. 7 first order dynamic scheduling local path optimization
Detailed Description
The present invention will be described in detail with reference to the following examples and drawings.
(1) In order to illustrate the dynamic scheduling method of the present invention, a static delivery route needs to be generated as a basis for the dynamic scheduling process. Therefore, in the embodiment, a vehicle path planning model of the simultaneous delivery member is established, and vehicle path planning is performed on the static demand points, so that an initial distribution route is obtained.
The established simultaneous pick-and-place vehicle path planning model is as follows:
Iothe location of the distribution center
IwSet of all demand points not yet served
IθAt the beginning of the dispatch the set of vehicle positions is being delivered, Io∈Iθ
KθA collection of vehicles is being delivered at the beginning of a dispatch
KoVehicle aggregation at distribution center at the beginning of dispatch
K set of all vehicles, Ko+Kθ＝K
c1Coefficient of vehicle running cost
c2Cost factor for vehicle departure
c3Vehicle earlier than ETiPenalty factor for reaching demand point i
c4The vehicle is later than L TiPenalty factor for reaching demand point i
ETiWhen the demand point i is earliest allowed to start serviceWorkshop
LTiThe latest allowed starting service time of demand point i
atiTime to reach demand point i
wtiThe time for waiting for reaching the demand point i in advance
tijTravel time from demand point i to demand point j
dijDistance from demand point i to demand point j
Decision variables:
Figure GDA0002489249380000071
an objective function:
Figure GDA0002489249380000081
constraint conditions are as follows:
Figure GDA0002489249380000082
Figure GDA0002489249380000083
Figure GDA0002489249380000084
Figure GDA0002489249380000085
Figure GDA0002489249380000086
no+nθ＝k
(7)
(1) the formula is an objective function and represents the minimum vehicle running cost, departure cost and time penalty cost; (2) formula (3) ensures that each demand point is onlyCan be serviced by one vehicle and can only be accessed once; (4) formula is the maximum load limit of the vehicle, where giThe express delivery weight of the demand point i is shown, and G is the maximum load of the vehicle; (5) the formula is a calculation formula of the time of the vehicle reaching the demand point and the waiting time; (6) the formula represents that the departure number cannot exceed the total number of vehicles which do not drive away from the distribution center; (7) formula represents the total number limit of vehicles, where no、nθ、nkThe number of vehicles not dispatched from the distribution center, the number of vehicles being distributed, and the total number of vehicles are respectively provided.
Example the problem of 100 demand points in the Solomon standard test set was selected as a test example. According to the characteristics that dynamic client positions are distributed randomly and the dispatching interval time of the distribution center is short in the electronic commerce environment, R1 data in a Solomon standard test set are selected, and R110 in the Solomon standard test set is selected randomly as a test data set. In this embodiment, 10 groups of data are randomly selected as the new pickup client data in r110, and the remaining 90 groups of data are used as the known client data.
The new pick-up client number is as follows:
17、30、32、50、53、60、61、67、71、91
in this embodiment, before the vehicle starts the delivery task, the vehicle path planning is performed on 90 sets of customer data in r110 by using the ant colony algorithm according to the simultaneous delivery path planning model, so as to generate a delivery route from the delivery center, and the delivery vehicle can sequentially complete the delivery task of the express delivery according to the delivery scheme. Initial delivery routes are generated based on customer location, time window, and demand as shown in table 1, a static planned route map is shown in figure 5,
TABLE 1 initial distribution route
Figure GDA0002489249380000091
(2) Step 1, selecting a scheduling mode, selecting a scheduling method of batch scheduling or emergency scheduling according to scheduling interval time, newly-added pickup required quantity and emergency degree, and determining scheduling time;
in this embodimentIn-set dynamic scheduling maximum interval time T max30, the maximum accumulated quantity Q of the newly added pickup requirements is 5, and the longest waiting time T of the home pickup of the express company committed to the customers＝90。
In the following, with reference to 10 sets of new pickup requirement data in r110, batch scheduling (T), batch scheduling (Q), and emergency scheduling will be described in groups of 3.
1) Setting that the newly-added pickup requirement received in the time period [0,30] is as follows:
17、61、91
t1＝30≥Tmax，
Figure GDA0002489249380000094
the dispatching condition of batch dispatching (T) is met, if there is no emergency demand, TT(1)＝t(0)+TmaxAt 30, the start time τ (1) of the 1 st dynamic scheduling may be determined as follows:
τ(1)＝t(1)＝t1＝30
2) let t2>t(1)＝t1At a time period [ t ]1,t2]The newly-added pickup requirement is received as follows:
30、32、50、53、60、67
Figure GDA0002489249380000092
the dispatching condition of batch dispatching (Q) is satisfied, if there is no emergency demand, tQ(2)＝t2Then the start time τ (2) of the 2 nd dynamic scheduling may be determined as follows:
τ(2)＝t(2)＝t2
3) setting the submission time ST of the newly added pickup requirement 7171＝16，Ts90, the vehicle travel time t from the distribution center to the location of the demand 71oi39.66, emergency reserved time trThe latest scheduling time for the newly added pick-up requirement 71 is 10:
Figure GDA0002489249380000093
assuming the current timeCarving tool
Figure GDA0002489249380000101
And is
Figure GDA0002489249380000102
If the scheduling condition of emergency scheduling is met, the emergency scheduling measures for the newly-added pickup requirement 71 must be considered, the emergency scheduling time is determined, and if the time period is within the time period
Figure GDA0002489249380000103
Only if the pick-up requirement 71 is a new pick-up requirement, the 3 rd dynamic scheduling time τ (3) is:
Figure GDA0002489249380000104
in this embodiment, 3 dynamic schedules can be determined during the vehicle delivery process, and the detailed information of the three dynamic schedules is shown in table 2:
TABLE 2 Tertiary dynamic scheduling details
Figure GDA0002489249380000105
(3) Step 2 determining the scheduling range
The embodiment takes the 1 st dynamic scheduling as an example to continue verification:
after the scheduling start time tau is determined, the scheduling range of the scheduling needs to be determined according to the coverage range of each newly added pickup requirement,
at time τ (1) ═ t 130, determining the set of newly-increased pickup requirements scheduled at this time as U ═ 17,61,91}, wherein the number m of newly-increased pickup requirements is 3, and the service time window of any newly-increased pickup requirement U is (ET)u,LTu) U ∈ U, any vehicle k satisfying the following conditionsrScheduling range SR capable of being divided into newly-added part demands uu，
tru≤LTu-τ(1)
Wherein t isruIs shown at the timeMoment τ (1), arbitrary vehicle krAnd the vehicle running time to the position of the newly added pickup requirement u. Solving the scheduling range of each newly-added pickup requirement in the newly-added pickup requirement set U in sequence, wherein the scheduling range of the current scheduling is as follows:
Figure GDA0002489249380000106
at the 1 st dynamic scheduling time tau (1), updating the current position data of each vehicle, and taking the current position of each vehicle as the corresponding virtual demand point position, wherein the earliest allowed service starting time ET of the virtual demand pointiThe current scheduling time τ (1) is taken to be 30, and the latest service starting time L T is takeniThe latest service time L T of the next client on the route of the vehicle is takeni+1The service time of the virtual demand point is 0. The vehicle with the serial number 0 indicates a vehicle which is not started at the distribution center, the vehicle position is the distribution center position (35,35), the time window is the distribution center service time window (0,230), the scheduling range of the 1 st dynamic scheduling is shown in fig. 6, and the information of the virtual demand point in the scheduling range is shown in the following table 3:
TABLE 3 virtual demand point information within scheduling Range
Figure GDA0002489249380000107
Figure GDA0002489249380000111
(4) Step 3, determining the insertion positions, wherein the vehicles which cannot be inserted into the newly added customers of the current route are assigned by the distribution center;
the embodiment takes the 1 st dynamic scheduling as an example to continue verification.
Taking the demand point 17 as an example, the solution is performed. The feasible insertion sites and insertion costs are shown in table 4 below:
TABLE 4 feasible insertion locations and insertion costs
Figure GDA0002489249380000112
fi[j]Indicating that a newly added pick-up demand point is inserted into the vehicle kiOn the path riMedium demand point IiAnd a demand point Ii+1The latter insertion cost. If the time requirement of the newly added pick-up demand point cannot be met after the newly added pick-up demand point is inserted, fi[j]The value takes a maximum value M. Minimum insertion cost of f7[1]And (4) 1.545, namely, the newly added pickup requirement point 17 is inserted between the 1 st requirement point and the 2 nd requirement point of the route of the vehicle 7.
By thetaiIndicating that a vehicle k is being deliverediThe virtual demand point position where the new pick-up demand point is located is respectively solved, and the optimization scheme after inserting the new pick-up demand is shown as the following table 5:
table 5 route after inserting new pick-up requirement
Figure GDA0002489249380000113
(5) Step 4, after the requirement of the newly added part is inserted into the current line, local path optimization is carried out on the line in the scheduling range, and local path optimization operation O among the lines is carried out in sequence1And local path optimization operation O in line2：
The embodiment takes the 1 st dynamic scheduling as an example to continue verification:
and optimizing the line inserted with the newly added pick-up requirement by using a local path optimization method, wherein the route after the local path optimization in the 1 st dynamic scheduling is shown in fig. 7.
Demand point 58 of line 7 performs inter-line local path optimization operation O1Reinserted into the end of line 10; demand point 93 of line 10 goes to O1Operate, reinsert into line 9; 7. 9, 10 to carry out local path optimization operation O in the line2Adjusting the service sequence in which the demand point 100 of the line 9 passes through O2Operate, postpone the service sequence.
And (4) optimizing the local path in the step (4), and slightly reducing the total distance and the total time of the regenerated route scheme compared with the optimized scheme after the new requirement is inserted in the step (3), wherein the total cost has a reduction of 6.5% compared with the original scheme. The optimization results are shown in table 6 below:
TABLE 6 locally optimized routes
Figure GDA0002489249380000121
The embodiment verifies that the dynamic scheduling method for processing the newly added pickup requirement in the express delivery process has the following advantages:
the scheduling mode can be intelligently selected according to the number, the emergency degree and the scheduling interval time of newly increased pickup requirements in each time period in a working day, the scheduling time is determined, the response speed to dynamic requirements is improved, and the logistics cost of home pickup of express companies is reduced by processing the newly increased pickup requirements in batches;
by defining the scheduling range, the negative influence of large-range route change caused by global scheduling on logistics distribution activities is avoided, the calculation time of dynamic scheduling is greatly reduced, and the feasibility of a generated scheduling scheme is ensured;
the method has the advantages that the newly added pickup requirements are completed by the vehicles in distribution, so that the time for customers to wait for the pickup service at home is shortened, the customer satisfaction is improved, the no-load rate is reduced, and the logistics cost is reduced;
after the new fetching requirement is inserted into the current route, the local route optimization among routes and the local route optimization in the routes within the scheduling range are carried out in sequence, the optimization performance of the generated local scheduling scheme is improved, and the influence of the route change on the unserviceable requirement is reduced.
In summary, the dynamic scheduling method for processing the new pickup requirement in the express delivery process provided by the invention can intelligently select the scheduling mode and narrow the scheduling range, so that the new pickup requirement is effectively inserted into the route of the vehicle being delivered, and the route into which the new requirement is inserted is optimized. The optimization result shows that the invention can improve the response speed to the newly added pickup requirement on the premise of reducing the pickup cost, thereby improving the customer satisfaction.
Finally, it is noted that the above-mentioned preferred embodiments illustrate rather than limit the invention, and that, although the invention has been described in detail with reference to the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the invention as defined by the appended claims.

Claims (3)

1. A dynamic scheduling method for processing newly added delivery requirements in an express delivery process is characterized by comprising the following steps:
step 1: selecting a scheduling mode of batch scheduling or emergency scheduling according to the scheduling interval time, the quantity of newly-added pick-up requirements and the emergency degree, and determining scheduling time; further comprising the steps of:
1-1 identifying whether there is an emergency need; if so, adopting an emergency scheduling mode, and determining the scheduling time of the emergency scheduling mode; further comprising the steps of:
1-1-1 identifies whether there is an emergency need:
setting the order submitting time of the newly added pickup requirement i as STiThe longest waiting time for the express company to commit to the customer is TsThe vehicle running time from the distribution center to the position of the newly added pickup requirement i is toiThe emergency reserve time is trFor a newly added pickup requirement i of a vehicle which is not scheduled to pick up a pickup, the requirement i is assumed to be incapable of finishing scheduling in a batch scheduling mode, and the latest scheduling time is determined
Figure FDA0002535222830000011
Figure FDA0002535222830000012
The method of identifying whether there is an emergency need is as follows:
assume that the current time is tnowIf, iftnowLatest scheduling time greater than newly-added pickup requirement i
Figure FDA0002535222830000013
Namely, it is
Figure FDA0002535222830000014
The newly added pickup requirement i is an emergency requirement, and emergency scheduling is needed;
1-1-2 determining scheduling time of emergency scheduling mode
After the emergency demand is identified, until the demand i finishes scheduling, t is more than or equal tonowAt any future time tfThe emergency scheduling time of each demand i, i.e. the dynamic scheduling time t in the emergency scheduling modeE(n) is:
Figure FDA0002535222830000015
1-2, under the condition of no emergency demand, judging whether a starting scheduling condition of a batch scheduling mode is met, if so, adopting the batch scheduling mode to determine the scheduling time of the batch scheduling mode; further comprising the steps of:
the batch scheduling mode comprises two sub-modes: a batch scheduling T mode and a batch scheduling Q mode;
let τ (n) be the scheduling time, T, of the nth dynamic schedulingmaxFor dynamic scheduling of maximum interval time, QmaxFor the maximum accumulated quantity of the newly added parts,
Figure FDA0002535222830000016
is shown at t1To t2Adding the accumulated quantity of the piece taking requirements at the moment;
1-2-1, judging whether the scheduling condition of the batch scheduling T mode is met:
Figure FDA0002535222830000017
1-2-2 if meeting the scheduling condition in 1-2-1, determining the scheduling time T of the batch scheduling T modeT(n)：
Figure FDA0002535222830000018
1-2-3, if the scheduling condition in the 1-2-1 is not met, judging whether the scheduling condition of the batch scheduling Q mode is met:
Figure FDA0002535222830000021
1-2-4 if the scheduling condition in 1-2-3 is met, the scheduling time t of the batch scheduling Q mode can be determinedQ(n)：
Figure FDA0002535222830000022
1-3 selection of scheduling mode and determination of scheduling time:
when the scheduling mode is selected, the scheduling time of which scheduling mode is satisfied first, that is, which scheduling mode is adopted, and the scheduling time of the dynamic scheduling can be determined, further, the start time τ (n) of the nth dynamic scheduling can be determined as follows:
τ(n)＝Min{tT(n)，tQ(n)，tE(n)}
step 2: determining a scheduling range:
after the scheduling starting time tau (n) is determined, determining the scheduling range of the scheduling according to the position of a newly added pickup requirement entering the scheduling;
and determining that the newly added pickup requirement set for scheduling at this time is U at the scheduling time tau (n), wherein the number of newly added pickup requirements is m, m is more than or equal to 1, and the service time window of any newly added pickup requirement U is (ET)u,LTu)，u∈U，truAt time τ (n), any vehicle krThe vehicle travel time to the position u, and any vehicle k satisfying the following conditionsrCan be divided into newScheduling range SR of add-fetch requirement uu：
tru≤LTu-τ(n)
Solving the scheduling range of each newly-added pickup requirement in the newly-added pickup requirement set U in sequence, wherein the scheduling range of the current scheduling is as follows:
Figure FDA0002535222830000023
step 3, determining the insertion position, wherein the newly added pickup requirement which cannot be inserted into the current route is completed by the distribution center assigned vehicle;
and 4, step 4: after all newly-added part requirements are inserted into the current line, scheduling range SR of scheduling for the nth timeτ(n)Performing local path optimization on all lines in the system, and performing local path optimization operation O between the lines1Then performing an in-line local path optimization operation O2Further comprising the steps of:
4-1 inter-line local path optimization operation O1：
O1Operations directed only to the pickup requirement, including remove operation O11And reinsertion operation O12Two operations; at O1In operation, O is first carried out11Operating, then carrying out O12Operating;
removing operation O11: according to the removal probability PrDetermining a pickup requirement needing to be removed, and removing the selected pickup requirement r;
reinsertion operation O12: repeating the step 2, re-determining the scheduling range, then repeating the step 3, and inserting the pickup requirement r into the line in the scheduling range;
removal probability P of pickup requirementrThe determination method comprises the following steps:
Pr＝Cr/∑Cr
Figure FDA0002535222830000031
bt″j＝max{ETj,btr+sr+trj}
Figure FDA0002535222830000032
wherein C isrCost of removal for part-taking requirement r, dirDistance d from demand point i to demand point rrjDistance from demand point r to demand point j, dijIs the distance, bt ″, from demand point i to demand point jjTo remove the time to start service, ET, of the preceding delivery vehicle at a demand j following the pickup demand rjFor the earliest allowed start service time, bt, for demand point jrFor the time of service start of the vehicle at the point of demand r, srRepresenting the service time, t, of the vehicle at the demand point rrjRepresents the vehicle travel time from the demand point r to the demand point j,
Figure FDA0002535222830000033
to remove the delivery request r and then deliver the vehicle's beginning service time at the request point j, btiTime of service, s, for the start of the vehicle at the point of demand iiRepresenting the service time, t, of the vehicle at the point of demand iijRepresenting the vehicle travel time from the demand point i to the demand point j, α + β being 1, α being a constant not less than 0;
4-2 in-line local path optimization operation O2：
Re-planning paths of all uncompleted demand points in the line including a pickup demand and a delivery demand, and adjusting the sequence of service to the demand points;
in-line local path optimization operation O2For only the route of the vehicle with the changed route in step 3 and step 4-1, namely, the route of the newly added pickup demand point inserted in step 3 and the operation O executed in step 4-111And O12Route changed later, proceed to O2And (5) operating.
2. The dynamic scheduling method for processing the requirement of newly adding a pickup in the express delivery process according to claim 1, wherein: the newly added pickup requirements processed in the emergency scheduling mode in step 1-1 are not limited to the emergency requirements themselves, but also include all newly added pickup requirements received after the last dynamic scheduling, that is, the emergency scheduling mode is triggered by the identified emergency requirements, and when scheduling is performed, all unprocessed pickup requirements at the beginning of the emergency scheduling are processed in a batch scheduling manner.
3. The dynamic scheduling method for processing the requirement of newly adding a pickup in the express delivery process according to claim 1, wherein: in step 1-2, two key parameters in the batch scheduling mode are as follows: dynamically scheduling maximum interval time TmaxAnd the maximum accumulated quantity Q of newly added and taken part demandsmaxIs not fixed but is determined according to the number of newly added fetching member demands in each time interval in a working day, Tmax、QmaxThe values of the two key parameters can be determined according to the following method:
counting the occurrence time and the number of newly added pickup requirements of a plurality of working days, dividing one working day into a plurality of time periods based on the analysis of historical data, analyzing and predicting the number P of the newly added pickup requirements in each time period, and fitting a P-t relation curve reflecting the number of the newly added pickup requirements in each time period;
determining T according to the actual process of dynamic schedulingmaxA relation function T between the value and the number P of newly added pick-up requirements in each time period in a working daymax(p); within a working day, the more newly added and taken pieces are, the shorter the interval time of two adjacent dynamic schedules is, if the newly added and taken pieces are less, the interval time of the dynamic schedules can be properly prolonged, but the service commitment time T of finishing home pickup after the express company submits the requirement is not exceededsI.e. Tmax≤Ts；
Determining the maximum accumulated quantity Q of newly added part demands according to the actual process of dynamic schedulingmaxIs taken value and the relation function Q of the newly added pick-up required quantity Pmax＝f(P)；QmaxThe quantity P of newly added pick-up requirements is in positive correlation, and in the actual scheduling process, Q can be estimated according to the predicted value of the newly added pick-up requirementsmaxThe value of (a).
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP6947044B2 (en) * 2018-01-12 2021-10-13 トヨタ自動車株式会社 Information systems, information processing methods and programs
CN108921467B (en) * 2018-06-15 2022-01-25 重庆城市管理职业学院 Intelligent agent distributed scheduling method for dynamically changing recipient information customer requirements
CN110659950B (en) * 2018-06-29 2022-05-27 阿里巴巴集团控股有限公司 Data processing method, device and machine readable medium
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2011162477A1 (en) * 2010-06-23 2011-12-29 동국대학교 산학협력단 System for providing food expiration date information using tti and method for providing food expiration date information using same
CN102609783A (en) * 2012-01-11 2012-07-25 东南大学 Construction optimizing and operating method of automatic fixed-point picking-up system of logistics express
CN104537426A (en) * 2014-11-20 2015-04-22 昆明理工大学 A method for optimized scheduling of an express delivery process
CN105069523A (en) * 2015-07-28 2015-11-18 昆明理工大学 Delivery vehicle scheduling method with time limitation
CN105243433A (en) * 2015-09-14 2016-01-13 浙江中烟工业有限责任公司 Configuration method of tobacco industry finished product logistics delivery resources

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2011162477A1 (en) * 2010-06-23 2011-12-29 동국대학교 산학협력단 System for providing food expiration date information using tti and method for providing food expiration date information using same
CN102609783A (en) * 2012-01-11 2012-07-25 东南大学 Construction optimizing and operating method of automatic fixed-point picking-up system of logistics express
CN104537426A (en) * 2014-11-20 2015-04-22 昆明理工大学 A method for optimized scheduling of an express delivery process
CN105069523A (en) * 2015-07-28 2015-11-18 昆明理工大学 Delivery vehicle scheduling method with time limitation
CN105243433A (en) * 2015-09-14 2016-01-13 浙江中烟工业有限责任公司 Configuration method of tobacco industry finished product logistics delivery resources

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