CN107194575A - It is a kind of to handle the vehicle autonomy dispatching method that express delivery increases pickup demand newly - Google Patents

It is a kind of to handle the vehicle autonomy dispatching method that express delivery increases pickup demand newly Download PDF

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CN107194575A
CN107194575A CN201710356856.6A CN201710356856A CN107194575A CN 107194575 A CN107194575 A CN 107194575A CN 201710356856 A CN201710356856 A CN 201710356856A CN 107194575 A CN107194575 A CN 107194575A
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vehicle
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pickup
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CN107194575B (en
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谷振宇
刘国荣
白晓辉
郑家佳
吕健成
朱雪莲
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Chongqing University
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Abstract

The present invention proposes a kind of vehicle autonomy dispatching method for handling the newly-increased pickup demand of express delivery, belongs to Vehicular intelligent Optimum Scheduling Technology field.The problem of quick response, pickup can not be made not in time to the newly-increased pickup demand occurred in delivery process for express company, propose that a kind of decision-making by way of multiple distribution vehicles coordinate autonomy goes out the optimal insertion position of newly-increased pickup demand, and the vehicle autonomy dispatching method of local path optimization is carried out to the route after insertion.This method to implement step as follows：1. receive newly-increased pickup demand；2. insert feasibility checking；3. more than car coordinate autonomous decision-making and go out optimal vehicle；4. bicycle local path optimizes.

Description

It is a kind of to handle the vehicle autonomy dispatching method that express delivery increases pickup demand newly
Technical field
The invention belongs to Vehicular intelligent Optimum Scheduling Technology field, it is related to a kind of vehicle for handling the newly-increased pickup demand of express delivery Autonomous dispatching method.
Background technology
Customer demand under e-commerce environment shows the characteristics of in large scale, degree of dynamism is high.Dynamic need when Between on random appearance and random distribution spatially so that express company only enters to these dynamic needs at line delay batch Reason, could reduce high no-load ratio, obtain scale and benefit, but it is so further reduce that logistics distribution services when Effect property.Particularly in delivery process, client can at any time submit or cancel and post part order, requires pickup, express delivery as early as possible If company, which can not make customer demand quick response and change original express delivery in time, fetches and delivers part scheme, continue to continue to use over Centralized data processing method and dispatching method, will not only improve CSAT, can also further increase enterprise Logistics distribution cost.
Multi-agent system (Multi-Agent System, MAS) is the set of multiple intelligent body compositions, can be by big Complicated system Construction is into the system being easily managed that is small, communicating and coordinate each other, with adaptability, self-organizing and good Good coordination performance, can complete numerous and diverse overall operation by coordination mode.Efficient dynamic operation can be carried out using MAS, It is highly suitable for solving the vehicle dispatching problem in logistics distribution.
MAS technologies are introduced into logistics distribution system, it is possible to achieve the autonomous scheduling of distribution vehicle.
Vehicle autonomy scheduling is a kind of distributed autonomous Logistic Scheduling method, is different from traditional centralized logistics and adjusts Degree, the decision-maker of vehicle autonomy scheduling is the vehicle intelligent body in distribution vehicle, and is no longer home-delivery center or control centre. Vehicle intelligent body enjoys autonomy, can independently be judged and decision-making according to real time information.More traditional centralized logistics is adjusted Degree, vehicle autonomy scheduling copes with large-scale transport task, adapts to traffic, the dynamic change of road network, can handle in time The client requirement information occurred at random.
Under vehicle autonomy dispatching method, home-delivery center or control centre are mainly responsible for transmission information and coordinate each vehicle Task is preferably completed, and macro adjustments and controls are carried out to whole distribution vehicles；For the management of single distribution vehicle, by each vehicle On vehicle intelligent body be managed respectively.
Under vehicle autonomy dispatching method, vehicle intelligent body can be according to real-time road condition information and demand information, independently to car Driving path makes Dynamic Programming；For the newly-increased pickup demand occurred at random, vehicle intelligent body can independently judge whether can This demand is inserted into present running route, and optimal insertion position of making decisions on one's own out；For there are many distribution vehicles all It can receive the situation of same pickup demand, the adaptability having using multiple agent, self-organizing and good coordination performance, Autonomous decision mode can be coordinated by many cars, the complex decision for solving to select optimal vehicle from multiple feasible pickup vehicles is asked Topic.
Newly-increased pickup demand is completed using the vehicle dispensed, logistics time can be greatly shortened, logistics is reduced and match somebody with somebody Send cost；Dispatched by vehicle autonomy, coordinating autonomous decision-making by multiple distribution vehicles goes out optimal pickup vehicle, can greatly improve To the response speed of customer order, the CSAT of enterprise is improved.Vehicle autonomy scheduling can more rationally, more effectively in fact The configuration of existing logistic resources and raising logistics response speed and efficiency, the need for having adapted to e-commerce development.
The content of the invention
The present invention can not make quick response to the newly-increased pickup demand occurred in delivery process, visit for express company Pickup not in time the problem of, propose it is a kind of multiple distribution vehicles coordinate it is autonomous by way of decision-making go out newly-increased pickup demand most Good insertion position, and the vehicle autonomy dispatching method of local path optimization is carried out to the route after insertion.
A kind of vehicle autonomy dispatching method for handling the newly-increased pickup demand of express delivery comprises the following steps：
Step 1 distribution vehicle receives newly-increased pickup demand：
The newly-increased pickup demand of client is submitted to home-delivery center first, and then sending it to this by home-delivery center newly-increased takes Vehicle near part demand.
Be located at time instant τ, client submits newly-increased pickup demand u, it is desirable to pickup service time window be (ETu,LTu), ETuFor the beginning service time of u earliest permission, LTuFor the u beginning service time allowed the latest, truRepresent in time instant τ, Any vehicle krTo the time of vehicle operation of newly-increased pickup demand u positions, then newly-increased pickup demand u car can be received Set KuFor：
Ku={ kr|tru≤LTu-τ}
Step 2 increases the insertion feasibility checking of pickup demand newly：
Distribution vehicle is received after newly-increased pickup demand, and insertion feasibility checking is carried out first, and whether calculate will can increase newly Pickup demand is inserted into this circuit, further, is comprised the following steps：
Counted out provided with a client as n route, vehicle eventually passes back to home-delivery center from home-delivery center.I, j are Two adjacent static client's points, s on this routeiFor service time of the vehicle at client's point i, tijFor from client point i to Client's point j time of vehicle operation, btjIt is vehicle in client's point j beginning service time, btj' represent newly-increased pickup demand u It is inserted into after client point i and j, vehicle NEW BEGINNING service time, defined variable PF at client's point jjPosition is inserted into reflect Client's point after putting starts the incrementss of service time, PF afterwards before insertionjComputational methods are as follows：
btj=max { ETj,bti+si+tij}
btj'=max { ETj,btu+su+tuj}
PFj=btj′-btj
1≤j≤n+1
2-1：Update Current vehicle position, the demand information and newly-increased pickup demand information that not yet service；
2-2：Select newly-increased pickup demand to be tested.When counting the early start service for the newly-increased pickup demand having been received by Between, select wherein to allow successively to start service time it is earliest as newly-increased pickup demand u to be verified；
2-3：Determine feasible insertion position.Judge whether u can be plugged into circuit between any client's point i and j；Further Comprise the following steps：
2-3-1:After judging that u is inserted between point i and j, whether u itself time window meets：
btu≤LTu
2-3-2:After judging that u is inserted between point i and j, whether the time window requirement of subsequent clients meets：
btl+PFl≤LTl,j≤l≤n
2-3-3：If meeting two above condition, the insertion cost Cost that u is inserted into feasible location is calculated, if discontented Foot, makes Cost be equal to a maximum M, alpha+beta=1, α, and beta, gamma is the constant not less than 0；
Cost=α (diu+duj-dij)+β·(btj′-btj)-γdou
2-4：It is determined that optimal insertion position.Feasible location minimum selection Cost is used as optimal insertion position；
Step 3 sets up many cars and coordinates autonomous decision model, selects optimal pickup vehicle：
Each vehicle is by newly-increased pickup demand insertion feasibility checking, and route is optimal feasible where determining each car Behind position, by information transfer and the coordinated decision between multiple vehicles, it is determined that optimal pickup vehicle.
Further, comprise the following steps：
3-1 receives same newly-increased pickup demand and constitutes concilliation panel by the vehicle of feasible insertion position, if can not Row insertion position, is individually sent a car to complete unscheduled successfully newly-increased pickup demand by home-delivery center；
3-2 coordinates autonomous decision model according to many cars, compares the decision function evaluation of estimate of each vehicle in concilliation panel, really Fixed optimal pickup vehicle.
It is the existing task quantity of calculating vehicle, remaining loading space, increased distance, follow-up according to vehicle current information Client's point delay time at stop value added and net cycle time, set up decision function as follows：
WhereinRepresent CTNormalized, if CT={ CT1,CT2,…,CTn, CTMiddle arbitrary element CTrNormalization at Reason method is as follows：
Wherein ωTRepresent in decision function CbestC in each decision variableTShared weight, successively to CTMiddle each element normalizing Change is handled, and is obtainedSimilarly can be to Cg、Cn、CCNormalized, is obtained
Specific correlation function definition procedure is as follows：
If 1) newly-increased pickup demand u is inserted into vehicle kiThe circuit at place, the net cycle time T required for calculatingi',
According to the most long working time T of distribution vehiclemaxDetermine net cycle time control variable CT:
CT=Tmax-Ti′
2) vehicle kiIf not receiving newly-increased pickup demand u, according to original distribution project, part task is fetched and delivered in completion, is counted
Calculate remaining loading space RG when returning to home-delivery centeri, calculate loading capacity control variable Cg：
RGi=(Gmax-∑gj)
Wherein GmaxFor vehicle i dead weights, gjThe weight of part or the weight of pickup are sent for vehicle client point j, if gj< 0, then to send the weight of part, if gj>0 weight for pickup, M represents a maximum；
3) vehicle kiIf not receiving newly-increased pickup demand u, according to original distribution project, existing pickup quantity is counted
With send number of packages amountCalculate existing task amount decision variable Cn：
If 4) newly-increased pickup demand u is inserted into vehicle kiIn the circuit at place, calculate insertion after apart from value added
DaAnd subsequent clients point delay time at stop value added Td, calculate distribution project and change cost variable CC:
CC=α Da+β·Td, alpha+beta=1
Da=D '-D
Td=∑ dtj′-∑dtj
Wherein D, D ' represent to insert u fore-aft vehicle operating ranges, dt respectivelyj、dtj' insertion u fore-aft vehicles k is represented respectivelyi Delay time at stop on its travel route at any client's point j, atjThe time at j, delay of the vehicle at j are reached for vehicle Time Calculation method is as follows：
dtj=max { 0, atj-LTj}
Step 4 bicycle local path optimizes：
If coordinating autonomous decision-making through excessive car, it is optimal pickup vehicle to determine vehicle i, and vehicle i is according to bicycle dynamic route Optimized model, to the route re-optimization after insertion new demand, part order is fetched and delivered in adjustment, generates new route or travel by vehicle.
Path planning in step 4 is bicycle path planning, belongs to TSP problems, but from unlike typical TSP problems：
Typical TSP problems are a closed loops, and vehicle eventually passes back to starting point from starting point；And the present invention is dynamic Vehicle route is optimized under state environment, its starting point is not fixed, it is necessary to obtain current vehicle position in optimization, and The starting point in path is used as using current location.Therefore, each ant is placed on together by the present invention when using ant colony optimization for solving path One starting point is current vehicle position, is able to carry out with the route scheme for ensureing generation.
When the route after to the newly-increased pickup demand of insertion carries out the optimization of bicycle local path, bicycle dynamic route is set up excellent Change model, then it is solved using ant group algorithm, generate new route or travel by vehicle.
The bicycle dynamic path optimization model that the present invention is set up is as follows：
IwAll client's point sets not yet serviced
IθDistribution vehicle position
c1Vehicle running cost coefficient
c2Vehicle is earlier than ETiReach client's point i penalty coefficient
c3Vehicle is later than LTiReach client's point i penalty coefficient
ETiThe beginning service time LT that client's point i allows earliestiThe beginning service time that client's point i allows the latest
atiReach client's point i time
wtiAdvanceing to needs the time of wait up to client's point i
tijFrom client point i to client's point j running time
dijFrom client point i to client's point j distance
Decision variable：
Object function：
Constraints：
atj=ati+wti+si+tij
wti=max (ETi-ati,0) (5)
(1) formula is object function, represents to minimize vehicle running cost, CSAT cost；(2) formula, (3) formula ensure Each client's point can only be by a car service, and can only be accessed once；(4) formula limits for vehicle dead weight；(5) formula The calculation formula of client's point time and stand-by period are reached for vehicle；
As shown in Fig. 2 the ant group algorithm flow that the present invention is used for carrying out bicycle path planning is described as follows:
A) initiation parameter
Initialize relevant parameter：Ant colony scale m, pheromones significance level factor-alpha, heuristic function significance level factor-beta, letter The plain volatilization factor ρ of breath, pheromone release total amount Q, maximum iteration Nmax, iterations initial value N0=1.
B) solution space is built
Each ant is placed on same starting point i.e. current vehicle position, (w=1,2 ..., m), are pressed to each ant w According to transition probabilityClient's point of its next arrival is calculated, until all ants have accessed all client's points.
Wherein, τij(t) it is heuristic function, ηij(t)=1/dij, represent that ant is transferred to client's point j phase from client's point i Prestige degree；CPw(w=1,2 ..., are m) set of ant w client's points to be serviced, and α is the pheromones significance level factor, and β is to open Send a letter several significance level factors.
C) fresh information element
Calculate the path length L of each ant processw(w=1,2 ..., m), the optimal solution in record current iteration number of times. Meanwhile, the pheromone concentration on each client's point access path is updated according to equation below.
WhereinRepresent the pheromone concentration that the w ant discharges on client point i and client's point j access paths；Δ τijRepresent the pheromone concentration sum that all ants discharge on client point i and client's point j access paths.
Wherein Q is constant, represents the pheromones total amount that ant circulation primary is discharged；LwIt is the w ant by path Length.
D) judge whether to terminate
If iterations N<Nmax, then N=N+1 is made, the record sheet of ant process, and return to step 2 is emptied；Otherwise, terminate Calculate, export optimal solution.
The beneficial effects of the present invention are：
The shortcoming that traditional centralized dispatching method wastes time and energy effectively is compensate for, for increasing pickup demand newly, without Can home-delivery center's lot size scheduling, distribution vehicle can independently judge receive pickup demand；Using the adaptability of MAS technologies, from group Knit with good coordination performance, can avoid existing multiple vehicles and fight for same pickup demand, solve from multiple feasible pickup cars Select the complicated decision-making problems of optimal vehicle；Efficient dynamic operation is carried out using intelligent body, can be according to current intelligence, in time Vehicle running path is updated and optimized.
Vehicle autonomy dispatching method proposed by the present invention can significantly shorten express delivery dispatching and pickup times, carry significantly The high response speed to client's dynamic order, improves CSAT, and reduce logistics cost.
Brief description of the drawings
Fig. 1 vehicle autonomy scheduling processes
Fig. 2 ant group algorithm flow charts
The scheduling of Fig. 3 vehicle autonomies newly-increased pickup demand position and vehicle location route map when starting
Embodiment
Present invention is described in detail below in conjunction with embodiment and accompanying drawing.
In order to illustrate vehicle autonomy dispatching method of the present invention, it is necessary to generate static distribution route as scheduling process Basis.Therefore, in embodiment, vehicle leaves each car before home-delivery center oneself needs what is completed according to what is had determined Part demand is fetched and delivered, using bicycle dynamic path optimization model, using ant group algorithm these demand points are carried out with the road of single unit vehicle Footpath is planned, so as to obtain initial distribution route.
Bicycle dynamic path optimization model is as follows：
IwAll client's point sets not serviced
IθDistribution vehicle position
c1Vehicle running cost coefficient
c2Vehicle is earlier than ETiReach client's point i penalty coefficient
c3Vehicle is later than LTiReach client's point i penalty coefficient
ETiThe beginning service time that client's point i allows earliest
LTiThe beginning service time that client's point i allows the latest
atiReach client's point i time
wtiAdvanceing to needs the time of wait up to client's point i
tijFrom client point i to client's point j running time
dijFrom client point i to client's point j apart from decision variable：
Object function：
Constraints：
atj=ati+wti+si+tij
wti=max (ETi-ati,0) (5)
(1) formula is object function, represents to minimize vehicle running cost, CSAT cost；(2) formula, (3) formula ensure Each client's point can only be by a car service, and can only be accessed once；(4) formula limits for vehicle dead weight；(5) formula The calculation formula of client's point time and stand-by period are reached for vehicle；
Ant colony optimization for solving process is as follows：
A) initiation parameter
Initialize relevant parameter：Ant colony scale m=50, pheromones significance level factor-alpha=1, heuristic function significance level Factor-beta=5, pheromones volatilization factor ρ=0.1, pheromone release total amount Q=1, maximum iteration Nmax=200, iteration time Number initial value N0=1.
B) solution space is built
Each ant is placed on same starting point i.e. current vehicle position, (w=1,2 ..., m), are pressed to each ant w According to transition probabilityClient's point of its next arrival is calculated, until all ants have accessed all client's points.
Wherein, τij(t) it is heuristic function, ηij(t)=1/dij, represent that ant is transferred to client's point j phase from client's point i Prestige degree；CPw(w=1,2 ..., are m) set of ant w client's points to be serviced, and α is the pheromones significance level factor, and β is to open Send a letter several significance level factors.
C) fresh information element
Calculate the path length L of each ant processw(w=1,2 ..., m), the optimal solution in record current iteration number of times. Meanwhile, the pheromone concentration on each client's point access path is updated according to equation below.
WhereinRepresent the pheromone concentration that the w ant discharges on client point i and client's point j access paths；Δ τijRepresent the pheromone concentration sum that all ants discharge on client point i and client's point j access paths.
Wherein Q is constant, represents the pheromones total amount that ant circulation primary is discharged；LwIt is the w ant by path Length.
D) judge whether to terminate
If iterations N<Nmax, then N=N+1 is made, the record sheet of ant process, and return to step 2 is emptied；Otherwise, terminate Calculate, export optimal solution.
So that part demand client's point is fetched and delivered in 30 express deliveries as an example newly pickup is increased to a kind of processing express delivery proposed by the present invention below The vehicle autonomy dispatching method of demand is described in detail：
Home-delivery center's coordinate serial number 0, coordinate is (35,35), normal working hours window in the case of not working overtime for (0, 230), vehicle average overall travel speed is set as 1, and dead weight is 200, service time s=10, c1、c2、c3Value difference For 1,1.5,2.The receipts that distribution vehicle carries out quick despatch to 30 client's points are sent, and wherein 1-29 is known client's point, Ke Hudian 30 be newly-increased pickup client's point.giFor the express delivery weight at client's point i, if express delivery weight gi>0, then express delivery demand i is to send part to need Ask, if express delivery weight gi<0, then express delivery demand i is pickup demand.Coordinate (the x of each client's pointi,yi), time window (ETi,LTi) And express delivery weight giIt is as shown in table 1 below：
Table 1 each client's point coordinates, time window and express delivery weight
Sequence number xi yi ETi LTi gi
0 35 35 0 230 0
1 15 30 20 107 -26
2 25 30 54 153 -3
3 30 25 70 208 -23
4 15 10 32 137 -20
5 30 5 30 154 -8
6 10 20 54 105 -19
7 20 20 117 160 -8
8 5 5 42 145 -16
9 24 12 25 172 -5
10 23 3 115 158 -7
11 11 14 31 138 -18
12 6 38 29 189 -16
13 32 12 78 133 -7
14 21 24 17 123 -28
15 14 37 21 97 -11
16 11 31 68 143 17
17 16 22 74 117 11
18 4 18 79 118 35
19 28 18 84 111 16
20 15 19 130 194 1
21 22 22 18 181 2
22 18 24 41 199 12
23 26 27 88 121 27
24 25 24 14 83 20
25 22 27 119 160 21
26 25 21 122 153 22
27 19 21 32 93 10
28 20 26 38 137 9
29 18 18 28 195 17
30 12 24 59 102 13
According to the bicycle dynamic path optimization model built in embodiment, a driving road is clicked through to client using ant group algorithm Footpath is planned, can obtain route or travel by vehicle.
In embodiments of the present invention, it is a kind of to handle the vehicle autonomy dispatching method that express delivery increases pickup demand newly, specific implementation Step is as follows：
Step 1 distribution vehicle receives newly-increased pickup demand：
Assuming that home-delivery center is in time instant τ=5, newly-increased pickup demand is connected to, serial number 30, demand information is sent out by home-delivery center Distribution vehicle is given, the vehicle set K of newly-increased pickup demand 30 is received30For：
K30={ kr|tr30≤LT30-τ}
Statistics receives the vehicle of newly-increased pickup demand 30, updates these vehicle position datas, and with where each vehicle Position is correspondence virtual client point position, the early start service time ET of virtual client pointiCurrent time τ is taken, is started the latest Service time LTiValue is the service time LT the latest of the next client of route where vehiclei+1, the information of virtual client point is such as Shown in table 2 below：
The virtual client point information of table 2
Update at current time, client's point information that each vehicle is not yet serviced, the route information for obtaining each vehicle is as follows Shown in table 3, the position of newly-increased pickup demand and vehicle location route are as shown in Figure 3 during vehicle autonomy scheduling beginning.
Each vehicle route information of table 3
Step 2 increases the insertion feasibility checking of pickup demand newly：
In the embodiment of the present invention, by taking route 1 as an example, the insertion feasibility for increasing pickup demand newly is verified, it is feasible to insert The evaluation of estimate for entering position and insertion cost is as shown in table 4 below：
The feasible insertion position of 4 route of table 1 and insertion cost
Minimum insertion cost is 9.589 in route 1, optimal insertion position the 3rd client's point and the 4th client's point it Between, i.e., between client's point 12 and client's point 16, client's point 30 is inserted into route after route 1 is：
θ1-15-12-[30]-16-1-2-23-25-26-3-0
The feasible insertion position of other routes can similarly be obtained, the optimal insertion position of route 2 is between client's point 6 and 18, The optimal insertion position of route 3 is between client's point 22 and 17.
In embodiments of the present invention, there are many distribution vehicles can be inserted into itself circuit by newly-increased pickup demand 30 In, it is therefore desirable to coordinate autonomous decision-making technique using many cars, select optimal pickup vehicle.
Step 3 sets up many cars and coordinates autonomous decision model, selects optimal pickup vehicle：
Each distribution vehicle inserts feasibility checking by newly-increased pickup demand, it is determined that after optimal feasible location, by multiple Information transfer and decision-making between vehicle, it is determined that optimal pickup vehicle.
According to vehicle current information, the existing task quantity of vehicle is calculated, remaining loading space, increased distance, follow-up Client's point delay time at stop value added and net cycle time, set up decision function as follows：
The decision variable weight of table 5
Decision variable weight is set in the embodiment of the present invention as shown in table 5, Tmax=230, μ=1.5, α=0.3,
β=0.7, each variable calculating process of decision function is as follows：
If 5) newly-increased pickup demand u is inserted into vehicle kiThe circuit at place, the net cycle time T required for calculatingi',
According to the most long working time T of distribution vehiclemaxDetermine net cycle time control variable CT:
CT=Tmax-Ti′
6) vehicle kiIf not receiving newly-increased pickup demand u, according to original distribution project, part task is fetched and delivered in completion, is counted
Calculate remaining loading space RG when returning to home-delivery centeri, calculate loading capacity control variable Cg：
RGi=(Gmax-∑gj)
Wherein GmaxFor vehicle i dead weights, gjThe weight of part or the weight of pickup are sent for vehicle client point j, if gj< 0, then to send the weight of part, if gj>0 weight for pickup, M represents a maximum；
7) vehicle kiIf not receiving newly-increased pickup demand u, according to original distribution project, existing pickup quantity is counted
With send number of packages amountCalculate existing task amount decision variable Cn：
If 8) newly-increased pickup demand u is inserted into vehicle kiIn the circuit at place, calculate insertion after apart from value added
DaAnd subsequent clients point delay time at stop value added Td, calculate distribution project and change cost variable CC:
CC=α Da+β·Td, alpha+beta=1
In embodiments of the present invention, each decision function variable of 3 routes is calculated successively, 3 route line decisions Function result of calculation is as shown in table 6 below：
6 three route line decision function variable result of calculations of table
After normalized, result of calculation is as shown in table 7 below：
Three route line decision function variable result of calculation after the normalized of table 7
Analysis form can obtain Cbest=-0.053, will increase newly pickup demand be inserted into route 3 client's point 22 and 17 it Between, then the variation route for inserting client's point is：
24-14-28-22-(30)-17-19-9-10-5-13-0
Step 4 bicycle local path optimizes：
If coordinating autonomous decision-making through excessive car, vehicle i is chosen to be optimal pickup vehicle, and the vehicle is according to the dynamic road of bicycle Footpath Optimized model, to the route re-optimization after insertion new demand, part order is fetched and delivered in adjustment, generates new route or travel by vehicle.
According to active path planning model in step 1, using ant group algorithm to the route re-optimization after insertion new demand, The route or travel by vehicle after local path optimization can be obtained：
24-14-28-22-(30)-17-19-13-5-10-9-0
The optimization process of 8 route of table 3
According to table 8 shown in the optimization process of route 3：
Compared to the route after the newly-increased pickup demand of insertion, optimize by local path, vehicle travels total distance and total time Increase (being respectively 2.39% and 1.09%) by a small margin, but totle drilling cost declines to a great extent (43.22%), illustrates that local path optimizes Vehicle running cost can further be reduced.
Compared to original route, insertion increases pickup demand newly and after local path optimizes, and vehicle travels total distance and total Although the time can increase (being respectively 11.23% and 5.90%), totle drilling cost increase is not set excessively to occur in that on the contrary larger The decline (34.80%) of amplitude, illustrates newly-increased pickup demand being inserted into the vehicle dispensed and to carry out local path excellent Change, the stand-by period and delay time at stop of client can be substantially reduced, so as to increase customer satisfaction degree, meanwhile, using dispensing Middle vehicle completes newly-increased pickup demand, is directly sent a car pickup compared with from home-delivery center, can substantially reduce logistics cost.
To sum up, can effectively to compensate for traditional centralized dispatching method time-consuming for vehicle autonomy dispatching method proposed by the present invention Laborious shortcoming, for newly-increased pickup demand, without home-delivery center's lot size scheduling, can distribution vehicle can independently judge connect By pickup demand；Using the adaptability of MAS systems, self-organizing and good coordination performance, it can avoid there are multiple vehicles and strive Same pickup demand is robbed, solves to select the complicated decision-making problems of optimal vehicle from multiple feasible pickup vehicles；Entered using intelligent body The efficient dynamic operation of row, can be updated and optimize to vehicle running path in time according to current intelligence.It is proposed by the present invention Vehicle autonomy dispatching method can significantly shorten express delivery dispatching and pickup times, greatly improve the sound to client's dynamic order Speed is answered, CSAT is improved, and reduce logistics cost.
Finally illustrate, preferred embodiment above is merely illustrative of the technical solution of the present invention and unrestricted, although logical Cross above preferred embodiment the present invention is described in detail, it is to be understood by those skilled in the art 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 (2)

1. a kind of handle the vehicle autonomy dispatching method that express delivery increases pickup demand newly, it is characterised in that following steps：
Step 1 distribution vehicle receives newly-increased pickup demand：
The newly-increased pickup demand of client is submitted to home-delivery center first, and then sending it to the newly-increased pickup by home-delivery center needs Seek neighbouring vehicle.
Be located at time instant τ, client submits newly-increased pickup demand u, it is desirable to pickup service time window be (ETu,LTu), ETuFor u Earliest permission beginning service time, LTuFor the u beginning service time allowed the latest, truRepresent in time instant τ, any car KrTo the time of vehicle operation of newly-increased pickup demand u positions, then newly-increased pickup demand u vehicle set can be received KuFor：
Ku={ kr|tru≤LTu-τ}
Step 2 increases the insertion feasibility checking of pickup demand newly：
Distribution vehicle is received after newly-increased pickup demand, insertion feasibility checking is carried out first, whether calculating can be by newly-increased pickup Demand is inserted into this circuit, further, is comprised the following steps：
Counted out provided with a client as n route, vehicle eventually passes back to home-delivery center from home-delivery center.I, j are this Two adjacent static client's points, s on routeiFor service time of the vehicle at client's point i, tijFor from client point i to client Point j time of vehicle operation, btjIt is vehicle in client's point j beginning service time, bt 'jRepresent to insert newly-increased pickup demand u To after client point i and j, vehicle NEW BEGINNING service time, defined variable PF at client's point jjCome reflect be inserted into position it Client's point afterwards starts the incrementss of service time, PF afterwards before insertionjComputational methods are as follows：
btj=max { ETj,bti+si+tij}
bt′j=max { ETj,btu+su+tuj}
PFj=bt 'j-btj
1≤j≤n+1
2-1：Update Current vehicle position, the demand information and newly-increased pickup demand information that not yet service；
2-2：Select newly-increased pickup demand to be tested.The early start service time for the newly-increased pickup demand having been received by is counted, Select wherein to allow successively to start service time it is earliest as newly-increased pickup demand u to be verified；
2-3：Determine feasible insertion position.Judge whether u can be plugged into circuit between any client's point i and j；Further comprise Following steps：
2-3-1:After judging that u is inserted between point i and j, whether u itself time window meets：
btu≤LTu
2-3-2:After judging that u is inserted between point i and j, whether the time window requirement of subsequent clients meets：
btl+PFl≤LTl,j≤l≤n
2-3-3：If meeting two above condition, the insertion cost Cost that u is inserted into feasible location is calculated, if it is not satisfied, order Cost is equal to a maximum M, alpha+beta=1, α, and beta, gamma is the constant not less than 0；
Cost=α (diu+duj-dij)+β·(bt′j-btj)-γdou
2-4：It is determined that optimal insertion position.Feasible location minimum selection Cost is used as optimal insertion position；
Step 3 sets up many cars and coordinates autonomous decision model, selects optimal pickup vehicle：
Each vehicle is by newly-increased pickup demand insertion feasibility checking, the optimal feasible location of route where determining each car Afterwards, by information transfer and the coordinated decision between multiple vehicles, it is determined that optimal pickup vehicle.
Further, comprise the following steps：
3-1 receives same newly-increased pickup demand and constitutes concilliation panel by the vehicle of feasible insertion position, if being inserted without feasible Enter position, individually sent a car to complete unscheduled successfully newly-increased pickup demand by home-delivery center；
3-2 coordinates autonomous decision model according to many cars, compares the decision function evaluation of estimate of each vehicle in concilliation panel, it is determined that most Good pickup vehicle.
Step 4 bicycle local path optimizes：
If coordinating autonomous decision-making through excessive car, it is optimal pickup vehicle to determine vehicle i, and vehicle i is according to bicycle dynamic path optimization Model, to the route re-optimization after insertion new demand, part order is fetched and delivered in adjustment, generates new route or travel by vehicle.
2. a kind of vehicle autonomy dispatching method for handling the newly-increased pickup demand of express delivery according to claim 1, its feature exists In：It is as follows that the autonomous decision model of many cars coordination in the step 3 sets up process：
According to vehicle current information, the existing task quantity of calculating vehicle, remaining loading space, increased distance, subsequent clients Point delay time at stop value added and net cycle time, set up decision function as follows：
WhereinRepresent CTNormalized, if CT={ CT1,CT2,…,CTn, CTMiddle arbitrary element CTrNormalized side Method is as follows：
Wherein ωTRepresent in decision function CbestC in each decision variableTShared weight, successively to CTAt middle each element normalization Reason, is obtainedSimilarly can be to Cg、Cn、CCNormalized, is obtained
Specific correlation function definition procedure is as follows：
If 1) newly-increased pickup demand u is inserted into vehicle kiThe circuit at place, the net cycle time T ' required for calculatingi, according to Send vehicle most long working time TmaxDetermine net cycle time control variable CT:
CT=Tmax-T′i
2) vehicle kiIf not receiving newly-increased pickup demand u, according to original distribution project, part task is fetched and delivered in completion, is calculated and is returned to dispatching Remaining loading space RG during centeri, calculate loading capacity control variable Cg：
RGi=(Gmax-∑gj)
Wherein GmaxFor vehicle i dead weights, gjThe weight of part or the weight of pickup are sent for vehicle client point j, if gj<0, then To send the weight of part, if gj>0 weight for pickup, M represents a maximum；
3) vehicle kiIf not receiving newly-increased pickup demand u, according to original distribution project, existing pickup quantity is countedWith send part QuantityCalculate existing task amount decision variable Cn：
If 4) newly-increased pickup demand u is inserted into vehicle kiIn the circuit at place, calculate insertion after apart from value added DaAnd it is follow-up Client's point delay time at stop value added Td, calculate distribution project and change cost variable CC:
CC=α Da+β·Td, alpha+beta=1
Da=D '-D
Td=∑ dt 'j-∑dtj
Wherein D, D ' represent to insert u fore-aft vehicle operating ranges, dt respectivelyj、dt′jInsertion u fore-aft vehicles k is represented respectivelyiAt it Delay time at stop on travel route at any client's point j, atjThe time at j, delay time at stop of the vehicle at j are reached for vehicle Computational methods are as follows：
dtj=max { 0, atj-LTj}。
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