CN108932564A - A kind of method and system of the path planning for determining collection with integrated consistency vehicle - Google Patents
A kind of method and system of the path planning for determining collection with integrated consistency vehicle Download PDFInfo
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- CN108932564A CN108932564A CN201810729060.5A CN201810729060A CN108932564A CN 108932564 A CN108932564 A CN 108932564A CN 201810729060 A CN201810729060 A CN 201810729060A CN 108932564 A CN108932564 A CN 108932564A
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Abstract
The invention discloses a kind of methods for determining path planning of the collection with integrated consistency vehicle, which comprises determines that the consistency vehicle route scheme sum aggregate for path planning matches integrated vehicle route scheme；Match integrated vehicle route scheme according to the consistency vehicle route scheme and the collection, building collection is with integrated consistency vehicle route scheme；According to the collection with integrated consistency vehicle route scheme, determines and minimize vehicle overall travel time model；The minimum vehicle overall travel time model is solved using heuristic algorithm, obtains calculated result；The minimum vehicle overall travel time model is solved according to decision rule, obtains the result of decision；Compare the calculated result and the result of decision, when the difference of the calculated result and the result of decision is less than threshold value, the calculated result and the difference accurately solved determine its validity；Select output result of the smallest calculated result of difference as collection with integrated consistency vehicle route scheme.
Description
Technical field
The present invention relates to reverse logistic technical fields, more particularly, to one kind for determining collection with integrated consistency
The method and system of the path planning of vehicle.
Background technique
Reverse logistic plays increasingly important role in current logistics with keen competition.The return of goods are reverse logistics
The increasingly increased one of the main reasons of importance.By taking the shopping at network industry of China as an example, averagely return of goods rate is about 10%.Famous
" during double 11 " shopping at network sections (on November 11st, 2017), day cat (i.e. the shopping online store of Largest In China) is in this day pin
The volume of selling reaches 26,700,000,000 dollars, and about 800,000,000 1,200 ten thousand package (China Internet observation, 2017) is brought in 24 hours.So
And the public may be unaware that these package in about 30% need from customer return shop.Booming shopping online is to small
Package transportation industry brings opportunity, while also bringing huge challenge for its positive logistics and Reverse Logistics.Many parcels
The effect of positive logistics can be played well by wrapping up in carrier, however, their abilities in reverse logistic are likely to become weighing apparatus
The major criterion of its service level is measured, even determines that can they survive in logistics with keen competition sometimes.
Collection is with the basic problem that integrated vehicle routing problem (VRPSDC) is in reverse logistic.In order to further change
Customer service in kind reverse logistic, small packet carrier are specifically contemplated that the consistency of service, it means that these companies
By providing service in roughly the same time by identical driver private connection is established, the relationship with client is improved with this.
Therefore, it is necessary to a kind of technologies, to realize the technology for determining path planning of the collection with integrated consistency vehicle.
Summary of the invention
The present invention provides a kind of for determining the method and system of path planning of the collection with integrated consistency vehicle, with
It solves the problems, such as how to determine and determines path planning of the collection with integrated consistency vehicle.
To solve the above-mentioned problems, the present invention provides a kind of for determining path rule of the collection with integrated consistency vehicle
The method drawn, which comprises
Determine that the consistency vehicle route scheme sum aggregate for path planning matches integrated vehicle route scheme；
Match integrated vehicle route scheme according to the consistency vehicle route scheme and the collection, building collection is with integration
Consistency vehicle route scheme；
According to the collection with integrated consistency vehicle route scheme, determines and minimize vehicle overall travel time model；
The minimum vehicle overall travel time model is solved using heuristic algorithm, obtains calculated result；
The minimum vehicle overall travel time model is solved according to decision rule, obtains the result of decision；
Compare the calculated result and the result of decision, when the difference of the calculated result and the result of decision is less than
When threshold value, the calculated result and the difference accurately solved determine its validity；Select the smallest calculated result of difference as
Output result of the collection with integrated consistency vehicle route scheme.
Preferably, the determining minimum vehicle overall travel time model, comprising:
Establish the minimum vehicle overall travel time model:
Wherein i, j are client's point index；
N is client's point set；
N+For client's point set and parking lot { 0 }；
D is date index；
D is date set；
K is vehicle index；
K is vehicle set；
tijFor the hourage between two client's points i and j；
βijkdIt is equal to 1 if vehicle k accessed client point j after access client's point i on d at once for variable 0 or 1；It is no
Then it is equal to 0.
Preferably, the determining minimum vehicle overall travel time model, comprising:
The constraint condition for minimizing vehicle overall travel time model is set are as follows:
It is interviewed when ensuring in all days parking lots all 0；
Ensure that client needs consolidating the load or is accessed once just when servicing with goods；
Ensure in given number of days, capacity of the capacity of carriage between any two client no more than each car；
Establish client's point consolidating the load and the traffic constraints with loads；
Ensure only one tight preceding client of each client and a next client；
Ensure no matter when client needs to service, they are serviced by identical driver；
It determines the arrival time of each client, also is used to eliminate the sub- road of daily route；
Ensure that each car must return to parking lot before maximum hourage；
Ensure any two days d1And d2Reaching time-difference be no more than maximum reaching time-difference；
Define the thresholding of decision variable.
Preferably, described that the minimum vehicle overall travel time model is solved using heuristic algorithm, obtain meter
Result is calculated, includes:
Heuristic algorithm is updated using record to solve the minimum vehicle overall travel time model, is obtained and is calculated knot
Fruit.
Preferably, the process of the record update heuristic algorithm includes:
Initiation parameter and variable；
Template route is constructed, feasible initial solution is generated；
The template route is updated, the updated template route overall travel time increases the deterioration shifting for being no more than threshold value
It is dynamic；
By iterative algorithm, the current solution of the template route is improved；From the current solution, client is inserted into cost
Minimum position, and establish and the smallest day route of the overall travel time for currently solving corresponding template route；It determines to institute
State the feasibility that client's point is inserted into template route.
Preferably, described that the minimum vehicle overall travel time model is solved using heuristic algorithm, obtain meter
Result is calculated, includes:
The minimum vehicle overall travel time model is solved using variable neighborhood local search heuristic algorithm, is obtained
Take calculated result.
Preferably, the process of the variable neighborhood local search heuristic algorithm includes:
The template route of the smallest day route is determined by variable neighborhood local search heuristic algorithm and inversion operations search；
The specified contiguous range in specified region is scanned for, the template route of multiple updates is obtained；
According to the template route of multiple updates, calculated result corresponding with the template route of multiple updates is obtained；
From the corresponding calculated result of template route of the multiple update, the smallest calculated result of overall travel time is chosen
For Optimal calculation result.
Preferably, described that the minimum vehicle overall travel time model is solved using heuristic algorithm, obtain meter
Result is calculated, includes:
The minimum vehicle overall travel time model is solved using the TABU search heuristic algorithm based on template,
Obtain calculated result.
Preferably, the process of the TABU search heuristic algorithm based on template includes:
Locally optimal solution is searched for using the TABU search heuristic algorithm based on template, and the locally optimal solution is deposited
It stores up to taboo list；
Calculated result other than taboo list described in iterative search carries out the search of the different effectively directions of search.
Based on another aspect of the present invention, provide a kind of for determining path planning of the collection with integrated consistency vehicle
System, the system comprises:
Determination unit, for determining that the consistency vehicle route scheme sum aggregate for path planning matches integrated vehicle path
Scheme；
Construction unit, for matching integrated vehicle route scheme according to the consistency vehicle route scheme and the collection,
Building collection is with integrated consistency vehicle route scheme；
Modeling unit, for, with integrated consistency vehicle route scheme, determining according to the collection and minimizing the total trip of vehicle
Row time model；
First computing unit, for being solved using heuristic algorithm to the minimum vehicle overall travel time model,
Obtain calculated result；
Second computing unit, for being solved according to decision rule to the minimum vehicle overall travel time model,
Obtain the result of decision；
As a result unit, for the calculated result and the result of decision, when the calculated result and the decision
As a result when difference is less than threshold value, the calculated result and the difference accurately solved determine its validity；Select difference minimum
Output result of the calculated result as collection with integrated consistency vehicle route scheme.
Preferably, the modeling unit is for the determining minimum vehicle overall travel time model, comprising:
Establish the minimum vehicle overall travel time model:
Wherein i, j are client's point index；
N is client's point set；
N+For client's point set and parking lot { 0 }；
D is date index；
D is date set；
K is vehicle index；
K is vehicle set；
tijFor the hourage between two client's points i and j；
βijkdIt is equal to 1 if vehicle k accessed client point j after access client's point i on d at once for variable 0 or 1；It is no
Then it is equal to 0.
Preferably, the modeling unit minimizes vehicle overall travel time model for determining, comprising:
The constraint condition for minimizing vehicle overall travel time model is set are as follows:
It is interviewed when ensuring in all days parking lots all 0；
Ensure that client needs consolidating the load or is accessed once just when servicing with goods；
Ensure in given number of days, capacity of the capacity of carriage between any two client no more than each car；
Establish client's point consolidating the load and the traffic constraints with loads；
Ensure only one tight preceding client of each client and a next client；
Ensure no matter when client needs to service, they are serviced by identical driver；
It determines the arrival time of each client, also is used to eliminate the sub- road of daily route；
Ensure that each car must return to parking lot before maximum hourage；
Ensure any two days d1And d2Reaching time-difference be no more than maximum reaching time-difference；
Define the thresholding of decision variable.
Preferably, first computing unit is used for using heuristic algorithm to the minimum vehicle overall travel time model
It is solved, obtains calculated result, comprising:
Heuristic algorithm is updated using record to solve the minimum vehicle overall travel time model, is obtained and is calculated knot
Fruit.
Preferably, the process of the record update heuristic algorithm includes:
Initiation parameter and variable；
Template route is constructed, feasible initial solution is generated；
The template route is updated, the updated template route overall travel time increases the deterioration shifting for being no more than threshold value
It is dynamic；
By iterative algorithm, the current solution of the template route is improved；From the current solution, client is inserted into cost
Minimum position, and establish and the smallest day route of the overall travel time for currently solving corresponding template route；It determines to institute
State the feasibility that client's point is inserted into template route.
Preferably, first computing unit is used for using heuristic algorithm to the minimum vehicle overall travel time model
It is solved, obtains calculated result, comprising:
The minimum vehicle overall travel time model is solved using variable neighborhood local search heuristic algorithm, is obtained
Take calculated result.
Preferably, the process of the variable neighborhood local search heuristic algorithm includes:
The template route of the smallest day route is determined by variable neighborhood local search heuristic algorithm and inversion operations search；
The specified contiguous range in specified region is scanned for, the template route of multiple updates is obtained；
According to the template route of multiple updates, calculated result corresponding with the template route of multiple updates is obtained；
From the corresponding calculated result of template route of the multiple update, the smallest calculated result of overall travel time is chosen
For Optimal calculation result.
Preferably, first computing unit is used for using heuristic algorithm to the minimum vehicle overall travel time model
It is solved, obtains calculated result, comprising:
The minimum vehicle overall travel time model is solved using the TABU search heuristic algorithm based on template,
Obtain calculated result.
Preferably, the process of the TABU search heuristic algorithm based on template includes:
Locally optimal solution is searched for using the TABU search heuristic algorithm based on template, and the locally optimal solution is deposited
It stores up to taboo list；
Calculated result other than taboo list described in iterative search carries out the search of the different effectively directions of search.
Technical solution of the present invention provide a kind of method for determining path planning of the collection with integrated consistency vehicle and
System, method comprise determining that the consistency vehicle route scheme sum aggregate for path planning with integrated vehicle route scheme；Root
Match integrated vehicle route scheme according to consistency vehicle route scheme sum aggregate, building collection is with integrated consistency vehicle route side
Case；According to collection with integrated consistency vehicle route scheme, determines and minimize vehicle overall travel time model；Utilize heuristic algorithm
It is solved to vehicle overall travel time model is minimized, obtains calculated result；According to decision rule to the minimum total trip of vehicle
Row time model is solved, and the result of decision is obtained；Compare calculated result and the result of decision, when calculated result and the result of decision
Output result when difference is less than threshold value, using calculated result as identified collection with integrated consistency vehicle route scheme.
The technical scheme is that improving the level of customer service in reverse logistic, collection is proposed with integrated consistency vehicle road
Diameter problem ConVRPSDC, it combines two famous VRP variants, i.e., collection matches integrated vehicle routing problem VRPSDC and one
Cause property Vehicle Routing Problems ConVRP.Technical solution of the present invention proposes a kind of MILP mould for minimizing vehicle overall travel time
Type, and propose the derivation algorithm for solving the model.Numerical experiment also demonstrates validity and the institute of proposed derivation algorithm
The validity of the model of proposition.
Detailed description of the invention
By reference to the following drawings, exemplary embodiments of the present invention can be more fully understood by:
Fig. 1 is the method for determining path planning of the collection with integrated consistency vehicle according to embodiment of the present invention
Flow chart；
Fig. 2 is to be schemed according to the collection of embodiment of the present invention with integration consistency Vehicle Routing Problems ConVRPSDC；
Fig. 3 is four kinds of local search approach schematic diagrames according to embodiment of the present invention；And
Fig. 4 is the system for determining path planning of the collection with integrated consistency vehicle according to embodiment of the present invention
Structure chart.
Specific embodiment
Exemplary embodiments of the present invention are introduced referring now to the drawings, however, the present invention can use many different shapes
Formula is implemented, and is not limited to the embodiment described herein, and to provide these embodiments be at large and fully disclose
The present invention, and the scope of the present invention is sufficiently conveyed to person of ordinary skill in the field.Show for what is be illustrated in the accompanying drawings
Term in example property embodiment is not limitation of the invention.In the accompanying drawings, identical cells/elements use identical attached
Icon note.
Unless otherwise indicated, term (including scientific and technical terminology) used herein has person of ordinary skill in the field
It is common to understand meaning.Further it will be understood that with the term that usually used dictionary limits, should be understood as and its
The context of related fields has consistent meaning, and is not construed as Utopian or too formal meaning.
Fig. 1 is the method for determining path planning of the collection with integrated consistency vehicle according to embodiment of the present invention
Flow chart.In order to improve the customer service in reverse logistic, the application embodiment is by consistency Vehicle Routing Problems
(ConVRP) sum aggregate is combined with integrated vehicle routing problem (VRPSDC), defines a kind of new Vehicle Routing Problems change
Body.This new variant is referred to as " collection is with integrated consistency Vehicle Routing Problems (ConVRPSDC) ", constructs one thus
Mixed-integer programming model.In order to solve this problem, propose based on record updating method (RTR), variable neighborhood local search
(LSVNS) and three kinds of heuritic approaches of the TABU search based on template.Numerical experiment demonstrates proposed algorithm and is proposed
Model validity.The result shows that advantage is had for small-scale example based on the heuritic approach of RTR, and in
Etc. scales, most preferably based on the heuritic approach of LVSNS, it can solve have 40 clients, 5 days examples in 10 seconds.
In addition, the heuritic approach based on LVSNS can be can solve the extensive example of 199 clients in three hours.
Embodiment of the present invention provides a kind of method for determining path planning of the collection with integrated consistency vehicle, side
Method comprises determining that the consistency vehicle route scheme sum aggregate for path planning with integrated vehicle route scheme；According to consistent
Property vehicle route scheme sum aggregate match integrated vehicle route scheme, building collection is with integrated consistency vehicle route scheme；According to
Collection determines with integrated consistency vehicle route scheme and minimizes vehicle overall travel time model；Using heuristic algorithm to minimum
Change vehicle overall travel time model to be solved, obtains calculated result；According to decision rule to minimum vehicle overall travel time
Model is solved, and the result of decision is obtained；Compare calculated result and the result of decision, when the difference of calculated result and the result of decision is small
When threshold value, the calculated result and the difference accurately solved determine its validity；The smallest calculated result of difference is selected to make
To collect the output result with integrated consistency vehicle route scheme.For example, by heuritic approach calculated result and accurate solution ratio
Compared with selecting output result of the smallest calculated result of difference as collection with integrated consistency vehicle route scheme；Comparison decision
Rules results and algorithm calculated result, when the difference of the calculated result and the result of decision is less than threshold value, it was demonstrated that practical
Application value.As shown in Figure 1, a kind of for determining the method 100 of path planning of the collection with integrated consistency vehicle, comprising:
Preferably, in step 101: determining that the consistency vehicle route scheme sum aggregate for path planning matches integrated vehicle
Route scheme.
Preferably, in step 102: matching integrated vehicle route scheme, building according to consistency vehicle route scheme sum aggregate
Collection is with integrated consistency vehicle route scheme.
Preferably, in step 103: according to collection with integrated consistency vehicle route scheme, determining and minimize the total trip of vehicle
Row time model.
Preferably, it determines and minimizes vehicle overall travel time model, comprising:
It establishes and minimizes vehicle overall travel time model:
Wherein i, j are client's point index；
N is client's point set；
N+For client's point set and parking lot { 0 }；
D is date index；
D is date set；
K is vehicle index；
K is vehicle set；
tijFor the hourage between two client's points i and j；
βijkdIt is equal to 1 if vehicle k accessed client point j after access client's point i on d at once for variable 0 or 1；It is no
Then it is equal to 0.
Preferably, it determines and minimizes vehicle overall travel time model, comprising:
The constraint condition for minimizing vehicle overall travel time model is set are as follows:
It is interviewed when ensuring in all days parking lots all 0；
Ensure that client needs consolidating the load or is accessed once just when servicing with goods；
Ensure in given number of days, capacity of the capacity of carriage between any two client no more than each car；
Establish client's point consolidating the load and the traffic constraints with loads；
Ensure only one tight preceding client of each client and a next client；
Ensure no matter when client needs to service, they are serviced by identical driver；
It determines the arrival time of each client, also is used to eliminate the sub- road of daily route；
Ensure that each car must return to parking lot before maximum hourage；
Ensure any two days d1And d2Reaching time-difference be no more than maximum reaching time-difference；
Define the thresholding of decision variable.
Preferably, in step 104: being solved, obtained to vehicle overall travel time model is minimized using heuristic algorithm
Calculated result.
Preferably, it is solved using heuristic algorithm to vehicle overall travel time model is minimized, obtains calculated result, packet
It includes:
Heuristic algorithm is updated using record to solve to vehicle overall travel time model is minimized, and obtains calculated result.
Preferably, the process of record update heuristic algorithm includes:
Initiation parameter and variable；
Template route is constructed, feasible initial solution is generated；
Template route is updated, updated template route overall travel time increases the deterioration movement for being no more than threshold value；
By iterative algorithm, the current solution of template route is improved；
From current solution, client is inserted into the minimum position of cost, and establish template route corresponding with currently solving
The smallest day route of overall travel time；
Determine the feasibility that client's point is inserted into template route.
Preferably, it is solved using heuristic algorithm to vehicle overall travel time model is minimized, obtains calculated result, packet
It includes:
It is solved using variable neighborhood local search heuristic algorithm to vehicle overall travel time model is minimized, obtains meter
Calculate result.
Preferably, the process of variable neighborhood local search heuristic algorithm includes:
The template route of the smallest day route is determined by variable neighborhood local search heuristic algorithm and inversion operations search；
The specified contiguous range in specified region is scanned for, the template route of multiple updates is obtained；
According to the template route of multiple updates, calculated result corresponding with the template route of multiple updates is obtained；
From the corresponding calculated result of template route of multiple updates, choosing the smallest calculated result of overall travel time is most
Excellent calculated result.
Preferably, it is solved using heuristic algorithm to vehicle overall travel time model is minimized, obtains calculated result, packet
It includes:
It is solved, is obtained to vehicle overall travel time model is minimized using the TABU search heuristic algorithm based on template
Calculated result.
The process for being preferably based on the TABU search heuristic algorithm of template includes:
Locally optimal solution is searched for using the TABU search heuristic algorithm based on template, and locally optimal solution is stored to taboo
Table；
Calculated result other than iterative search taboo list carries out the search of the different effectively directions of search.
Preferably, in step 105: being solved, obtained to vehicle overall travel time model is minimized according to decision rule
The result of decision.
Preferably, in step 106: comparing calculated result and the result of decision, when the difference of calculated result and the result of decision is small
When threshold value, the calculated result and the difference accurately solved determine its validity；The smallest calculated result of difference is selected to make
To collect the output result with integrated consistency vehicle route scheme.For example, by heuritic approach calculated result and accurate solution ratio
Compared with selecting output result of the smallest calculated result of difference as collection with integrated consistency vehicle route scheme；Comparison decision
Rules results and algorithm calculated result, when the difference of the calculated result and the result of decision is less than threshold value, it was demonstrated that practical
Application value.
Reverse logistic plays increasingly important role in current logistics with keen competition.The return of goods are reverse logistics
The increasingly increased one of the main reasons of importance.By taking the shopping at network industry of China as an example, averagely return of goods rate is about 10%.Famous
" during double 11 " shopping at network sections (on November 11st, 2017), day cat (i.e. the shopping online store of Largest In China) is in this day pin
The volume of selling reaches 26,700,000,000 dollars, and about 800,000,000 1,200 ten thousand package (China Internet observation, 2017) is brought in 24 hours.So
And the public may be unaware that these package in about 30% need from customer return shop.Booming shopping online is to small
Package transportation industry brings opportunity, while also bringing huge challenge for its positive logistics and Reverse Logistics.Many parcels
The effect of positive logistics can be played well by wrapping up in carrier, however, their abilities in reverse logistic are likely to become weighing apparatus
The major criterion of its service level is measured, even determines that can they survive in logistics with keen competition sometimes.
Collection is with the basic problem that integrated vehicle routing problem (VRPSDC) is in reverse logistic.In order to further change
Customer service in kind reverse logistic, small packet carrier are specifically contemplated that the consistency of service, it means that these companies
By providing service in roughly the same time by identical driver private connection is established, the relationship with client is improved with this.
The factor of Consistency service has been taken into account another well-known consistency Vehicle Routing Problems (ConVRP).The application is logical
It crosses and proposes a kind of new VRP variant in conjunction with ConVRP and VRPSDC, it is referred to as, and " collection is asked with integrated consistency vehicle route
Topic " (ConVRPSDC, as shown in Figure 2).In view of the consistency of service, which can be improved package shipment company in positive logistics
With the service level in reverse logistic.
In ConVRPSDC, have | D | the dispatching of day and consolidating the load demand for services and | K | homogeneity vehicle.At specific one day
The each client for needing to service at most receives primary service from any vehicle, this also means that each client needs while receiving
With goods service and consolidating the load service.| D | in a few days, each client must receive service in the substantially same time at same driver,
Maximum reaching time-difference (difference of arrival time and earliest arrival time the latest) is no more than L chronomere.In every day, vehicle
Capacity is no more than Q, and time of vehicle operation is no more than T.Purpose is to pass through minimum | D | day vehicle overall travel time set
Count fleet's route.
Before explaining the model, it is described as follows some constraints:
(1) all vehicle homogeneities can use in parking lot.
(2) all vehicles must leave parking lot at daily 0, and return to parking lot at the end.
(3) every client receives primary service (driver's consistency) from same vehicle daily.
(4) every client receives the time difference of service no more than maximum reaching time-difference (time consistency) daily.
(5) capacity of carriage is no more than vehicle capacity.
(6) overall travel time of vehicle is limited no more than time of vehicle operation.
Model building method is as follows:
Index and set
I, j client's point index
N client's point set
N+Client's point set and parking lot { 0 }
The d date indexes
The D date gathers
K vehicle index
K vehicle set
Input variable
The capacity of Q each car
The maximum running time of t each car
L maximum reaching time-difference (earliest arrival time and the latest reaching time-difference)
tijHourage between two client's points i and j
pidThe d days consolidating the load demands at client's point i
qidMatch goods demand at client's point i within d days
tp idThe d days consolidating the load service times at client's point i
tq idMatch goods service time at client's point i within d days
hid0-1 variable is equal to 1 if client i needed consolidating the load on d or with goods service；Otherwise it is equal to 0
wid0-1 variable, if client i needed to be equal to 1 with goods service on d；Otherwise it is equal to 0
vid0-1 variable is equal to 1 if client i needed consolidating the load service on d；Otherwise it is equal to 0
Decision variable
αidThe time of client's point i is reached within d days, if client i did not needed service on d and is equal to 0
βijkd0-1 variable is equal to 1 if vehicle k accessed client point j after access client's point i on d at once；
Otherwise it is equal to 0
γikd0-1 variable is equal to 1 if client point i was accessed by vehicle k on d；Otherwise it is equal to 0
ξijdD days vehicle-mounted consolidating the load amounts between client point i and client's point j
D days vehicle-mounted between client point i and client's point j to match goods amount
Mathematical model
st
Objective function (1) minimizes the overall travel time of all vehicles in day.Constraint (2) and constraint (3) ensure in institute
There is day parking lot to be all accessed at 0.Constraint (4) ensures that client needs consolidating the load or is accessed once just when servicing with goods.About
Beam (5) ensures that in given number of days, the capacity of carriage between any two client is no more thanConstraint (6) and constraint (7) are
Consolidating the load and traffic constraints with loads.Constraint (8) ensures only one tight preceding client of each client and a next client.About
Beam (9) ensures no matter when client needs to service, they are serviced by identical driver.Constraint (10) and constraint (11) determine
The arrival time of each client also is used to eliminate the sub- road of daily route.If it is intended to allowing idling of vehicle, then constraint (11)
It can delete.Constraint (12) ensures that each car must return to parking lot before maximum hourage T.Constraint (13) ensures any two
Its d1And d2Reaching time-difference be no more than L.Constraint (14-18) defines the thresholding of decision variable.
The model proposed in the application can solve small-scale example with CPLEX, in order to solve medium-scale and big rule
The example of mould, present applicant proposes the derivation algorithm based on RTR, the derivation algorithm based on LVSNS and the taboos based on template to search
Demand resolving Algorithm.(advanced module) has been used for reference the advantages of existing VRP related algorithm in the design of these methods, is such as used
The improvement that Yellow is proposed saves mileage method (Yellow-CW) and generates template route, uses greedy operation building | D | day route,
The feasibility of program checkout insertion client is checked using the feasibility that Wasson et al. (2008) propose.In addition, the application proposes
Tolerant process, to improve the quality of solution.
Algorithm based on RTR, the application list the algorithm frame based on RTR.The basic thought of RTR algorithm is calculated by CW
The feasible solution that method obtains is set out, and then searches for neighborhood with perturbation strategy is reinitialized by randomized local search, and receive not
It is inferior to the solution of record, to approach optimal solution.The algorithm of the application is the RTR algorithm proposed based on Li et al. people, uses Wassan
Et al. propose displacement, exchange, part displacement and inverse manner, improve the quality of solution.The frame of this method is given in algorithm 1
Frame.
As shown in figure 3, a client point i is moved to another route from a route in shifting function；It is grasped in exchange
In work, route two client point i and j affiliated before exchanging to them each other；In local shifting function, client's point i
It is moved to the other positions of the route；It is reversed in the direction of inversion operations, route.
Fig. 3: four kinds of local search approach
The 1st row in direction calculation 1, the purpose of initial phase are the relevant parameter of statement and variable, and client is divided into
Two groups: frequent client and the frequent client of non-, and estimate the maximum capacity and journey time of template route.The process of initial phase
It is as follows:
The 4th row in direction calculation 1, the purpose in the stage are coveted by using Yellow-CW algorithm construction template route
Greedy operative configuration | D | day route, to generate feasible initial solution.The process for generating initial solution is as follows:
The 6th row in direction calculation 1, the purpose in diversified stage are modification template route Cr.The application uses four kinds of offices
Portion's searching method (shift, exchange, part displacement and reverse) searches for the neighborhood of current template route, receives all improvement shiftings
Dynamic and template route overall travel time increases the deterioration movement for being no more than threshold value.The program in diversified stage is as follows:
The 7th row in direction calculation 1, the purpose for improving the stage are that current solution is improved by iteration.In view of inversion operations
It can only be conducive to the insertion of client, and the quality of solution cannot be improved, therefore not will use in the improvement stage.The application uses three
Local search approach (shift, exchange, part displacement) searches for the neighborhood of current template route, and only receives improved shifting
It is dynamic.The program in improvement stage is as follows:
It is directed toward the 2nd row for generating initial solution and improves the 3rd row in stage, which is that client is iteratively inserted into cost
Minimum position constructs the smallest D days route of overall travel time for corresponding template route.Building | D | day route program such as
Under:
All algorithms are directed to the operation that client's point is inserted into same route, in VRPSDC, if there is any client
Mobile, capacity of carriage portion will fluctuate.Therefore, check that the feasibility of insertion operation is extremely important and challenging.Herein
In, we check that program reduces computation complexity using the feasibility that Wasson et al. (2008) use.
(1) if maxlodd+max (pi, qi)≤Q, client i are inserted into any position of route.
(2) if maxload+pi≤ Q and maxload+qi> Q, it is meant that at least road a Tiao Hu load > Q-qi,
First such arc road that forward direction is found is known as l1l2, client's point i cannot be inserted into arc road l1l2Position (including arc later
Road l1l2)。
(3) if maxload+pi> Q and maxload+qi≤ Q, it is meant that at least road a Tiao Hu load > Q-pi,
The such arc road of first reversely found is known as l1l2, client's point i cannot be inserted into arc road l1l2Position (including arc before
Road l1l2)。
(4) if maxload+min (pi, qi) > Q, client's point i cannot be inserted into any position of the route.
Algorithm 2: the algorithm based on LSVNS
This section outlines the algorithm based on LVSNS.The basic thought of LVSNS is then to pass through dynamic since feasible solution
Change neighbour structure collection and carry out expanded search range, search process is made to jump out local optimum to global optimum.The algorithm of the application is
Based on LVSNS, Yellow-CW algorithm, the tolerant process that greediness operation and the application propose, to improve the quality of solution are combined.
The frame of the algorithm is given in algorithm 2.
The 4th row in direction calculation 2, the purpose of educational stage are can to cause always to travel by LSVSN and inversion operations search
The template route of minimum D days time route.The program of educational stage is as follows:
It is directed toward the 3rd row of educational stage, the purpose in LVSNS stage is search CrNeighborhood to find better road model
Line, to preferably be solved.Three local search procedures (shifting, exchange and part shift) are used for three basic neighbourhoods
The search of structure.
The basic thought for the tabu search algorithm based on template that the application proposes is by some local optimums searched
Solution is put into taboo list, these objects (rather than absolute prohibition circulation) is avoided in further iterative search, to guarantee to search for
Different effective directions of search.The algorithm of the application is to combine classical TABU search and local search procedure (i.e. based on template
Displacement, exchange, part displacement and reversing).The frame of the algorithm is given in algorithm 3.
The application has carried out the validity of numerical experiment verifying institute climbing form type and algorithm.All experiments are all in Lenovo
The operation of ThinkStation P910 work station, it has, and there are two Xeon E5-2680V4CPU (28 kernel), the processing of 2.4GHz
Speed, the memory of 256GB and the system of Windows 7.Model and algorithm are solved using C# (VS2015) calling CPLEX 12.5.
0.7, L value, which is set, by P value in generating medium-scale example at random is set as 5.It is real on a small scale in amendment benchmark
In testing, daily consolidating the load service probability P value is set as 0.7, L value and is set as 5.In amendment benchmark large scale experiment, daily consolidating the load
Service probability P value is set as 0.7.
It is solved since the algorithm based on RTR and the algorithm based on LVSNS are depended on based on the priority principle of template route
The certainly consistency of arrival time, so maximum reaching time-difference L is not explained clearly.Therefore, the application runs the two
Algorithm, and the 2-opt operation proposed using Kovacs (2015) inverts the part of selected route, to improve time consistency.So
Afterwards, the application retains optimal solution (abandon all solutions that are greater than L) of the maximum reaching time-difference less than or equal to L.
The experiment that the application carries out includes small-scale, medium-scale and extensive example experiment, algorithm setting experiment and ratio
Compared with experiment.This section has two groups of experimental data sets, they the characteristics of it is as follows:
Amendment benchmark example: original ConVRP example includes the small-scale example generated at random and based on benchmark example
Extensive example.ConVRPSDC example is generated by adding consolidating the load demand and consolidating the load service time into ConVRP example.This
It includes data set A_small and data set A_large that application, which will correct benchmark example as data set A, data set A,.
The random example that generates: the application generates the small of this paper using the method and stochastic parameter for generating small-scale example
Scale and medium-scale example.It includes data set B_small and data that the application, which will be generated at random as data set B, data set B,
Collect B_medium.
In view of the setting of parameter, parking lot is located at (0,0).In the small-scale example and medium-scale example generated at random
In, table 1 lists Customer Location, the parameter with goods demand and consolidating the load demand.The characteristics of in view of different type client, the application
0.6,0.7,0.8,0.9, L value, which is set, by P value in generating small-scale example at random is set as 5.
Table 1: example (data set B) parameter setting is generated at random
The experimental result of small-scale example, medium-scale example and extensive example provides in table 2,3,4 and 5, wherein
Table 3 and table 5 are the experiments of A for data sets.In the experiment generated at random, each small example ID (such as (7-3) -0.6) table
Show that customer quantity 7, number of days 3, client need the probability 0.7 with goods or consolidating the load service daily.Each medium-scale example ID (such as
(10-4)) respectively indicate customer quantity 10, number of days 4.In the benchmark example experiment of modification, ID is the title of benchmark example.
ZcIt is the optimal solution that CPLEX is acquired；TcIt is the time of CPLEX operation；Zr, ZvAnd ZtIt is based on RTR algorithm, base respectively
In the solution that LSVNS algorithm and tabu search algorithm based on template acquire；Tr, TvAnd TtIt is to be based on based on RTR algorithm respectively
The calculating time of LSVNS algorithm and the Tabu-Search Algorithm based on template.NcIndicate example client number；NvrAnd NvvRespectively
It indicates based on RTR algorithm and vehicle number used in solution is acquired based on LSVNS algorithm.
Table 2: the performance (data set B_small) of three kinds of algorithms of small-scale example
Note: Gap1=(Zr-Zc)/Zc, Gap2=(Zv-Zc)/Zc, Gap3=(Zt-Zc)/Zc
As shown in table 2, CPLEX can solve the optimal solution of small-scale example, but the calculating time of CPLEX is with scale
Expansion and increase rapidly.In small-scale experiment, than the tabu search algorithm based on template and it is based on based on the algorithm of RTR
The algorithm of LVSNS has better effect.Although the tabu search algorithm based on template can also obtain optimal solution, it be based on
The average time ratio of the algorithm of RTR is 54.16.
Table 3: the performance (data set A_small) of amendment small-scale three kinds of algorithms of example of benchmark
Note: Gap1=(Zr-Zc)/Zc, Gap2=(Zv-Zc)/Zc, Gap3=(Zt-Zc)/Zc
Table 3 illustrates the experimental result of the small-scale example of benchmark, and scale is equivalent to the medium-scale example generated at random.
As shown in table 3, CPLEX can be in the hope of the optimal solution of 10 client's examples；And for the example of 12 or less clients, then it remains
The feasible solution that CPLEX is acquired in two hours.The result shows that solving quality based on the method for LVSNS and calculating time side
Mask is advantageous, and average gap is 1.21%, and average time is 0.97 second.
Table 4: the performance (data set B_medium) of three kinds of algorithms of the medium-scale example generated at random
Note: Gap1=(Zr-Zc)/Zc, Gap2=(Zv-Zc)/Zc, Gap3=(Zt-Zc)/Zc
As shown in table 4, CPLEX can only obtain the optimal solution of 10 client's examples.ZC in example remains CPLEX two
Feasible solution hour in a solution.From the results, it was seen that compared with the method based on RTR, the method based on LVSNS
Mean gap is 5.08%.In addition, the algorithm is calculating time side compared with CPLEX and based on the tabu search algorithm of template
Face also has significant advantage.
Table 5: the performance (data set A_large) of amendment extensive three kinds of algorithms of example of benchmark
As shown in table 5, in the extensive example that CPLEX can not be solved, the algorithm based on LSVNS and the calculation based on RTR
Method can acquire feasible solution in three hours.There is significant advantage on calculating the time based on the algorithm of RTR, however, being based on
The algorithm of LVSNS can be obtained preferably to be solved than the algorithm based on RTR, and average gap is 4.89%.
The application is compared experiment to compare algorithm that the application is proposed and without the simplification algorithm of tolerant process,
Study the validity of tolerant process.
Table 6: based on RTR algorithm and without the comparison based on RTR of tolerant process
As shown in table 6, the average gap of the solution obtained without the algorithm based on RTR of tolerant process is 3.78%, maximum
Gap is 19.3%, and average time ratio is 0.86.
Table 7: based on LSVNS algorithm and without the comparison based on LSVNS of tolerant process
As shown in table 7, the average gap for the solution that the algorithm based on LVSNS obtains is 4.28%, and maximum gap is 14.44%,
Average time ratio is 0.79.
It may be concluded that the algorithm without tolerant process will lead to and some be unsatisfactory for condition or template about from table 6 and table 7
Beam, but the template route that can obtain more preferable objective function is ignored.Tolerant process can make up this defect, improve solution
Quality.
The application is in order to more intuitively show the increase in view of consistency bring overall travel time, for the ease of company
Preferably weigh customer satisfaction with services and cost, the application is using the algorithm based on LVSNS to ConVRPSDC and VRPSDC
(not considering consistency) compares experiment.
Table 8:ConVRPSDC and VRPSDC comparison
As shown in table 8, the application is given the solution acquired based on LVSNS algorithm and does not consider to find most when consistency
Small overall travel time.Consider the overall travel time of consistency only than not considering that the overall travel time of consistency is slightly long (average
7.42%).
In order to illustrate the practical application meaning of the algorithm proposed, the application, which is tested, carrys out the intuitive decision rule of comparison
With the algorithm of the application.According to before as a result, showing good performance in three kinds of algorithms based on the algorithm of LVSNS.Cause
This, uses the algorithm in following comparison.About rival (decision rule), the application, which uses, is based on client's point and parking lot
The decision rule of distance priority grade.This real rule is intuitive, and is that practitioner is common.
The decision rule that the application establishes are as follows:
1: finding the client point i nearest apart from parking lot and client's point i is assigned to nearest vehicle k, k=1
2: finding client's point j that distance assigns client's point i nearest recently
3: check | D | day route feasibility, if feasible, assign client j give vehicle k；Otherwise, deleted from consolidating the load N
Assigned client's point, k=k+1 and returns to row 1
4: until all client's points have been assigned termination
Table 9: algorithm and decision rule comparison based on LSVNS
Table 9 is the comparison based on LVSNS algorithm and decision rule, and average gap is 39.18%, illustrates the reality of the algorithm
Border application value.
In order to improve the level of customer service in reverse logistic, the application has studied ConVRPSDC, and the application combines two
A famous VRP variant, i.e. VRPSDC and ConVRP.For this problem, it proposes and a kind of minimizes vehicle overall travel time
MILP model, and propose three kinds of derivation algorithms for solving the model.Numerical experiment also demonstrates proposed derivation algorithm
The validity of validity and the model proposed.The application highlights the necessity that service-conformance is considered in reverse logistic, and
It constructs for the first time and considers driver and arrival time consistency, and collection matches integrated VRP model.The application is theoretically
VRP family provides a new variant, and for practitioner, the VRRPSDC model proposed is also beneficial to improve inverse
To the service level of logistics.The angle that the application is designed from algorithm proposes and realizes algorithm based on RTR, is based on
The algorithm of LVSNS and tabu search algorithm based on template.The many advantages (advanced module) of the VRP related algorithm of the application,
Such as save mileage method (CW) using the improvement that Yellow (1970) propose and generate template route, use greediness operation building | D |
Day route, the feasibility for using Wasson et al. (2008) to propose check the feasibility of program checkout insertion client.In addition, this Shen
Tolerant process please be propose, to improve the quality of solution.The application numerical experiment provides some useful for researcher and practitioner
Enlightenment.The experimental results showed that being good in the case where solving some small-scale situations based on the algorithm of RTR；And for medium-scale,
It is preferably based on the heuritic approach of LVSNS, it can solve have 40 clients, 5 days examples in 10 seconds.In addition, being based on
The heuritic approach of LVSNS can be can solve the extensive example of 199 clients in three hours.
For the application embodiment by combining ConVRP and VRPSDC to propose a kind of new VRP variant, it is referred to as " collection
With integrated consistency Vehicle Routing Problems " (ConVRPSDC).In view of the consistency of service, which can be improved package shipment
Service level of the company in positive logistics and reverse logistic.
Fig. 4 is the system for determining path planning of the collection with integrated consistency vehicle according to embodiment of the present invention
Structure chart.As shown in figure 4, a kind of system for determining path planning of the collection with integrated consistency vehicle, system include:
Determination unit 401, for determining that the consistency vehicle route scheme sum aggregate for path planning matches integrated vehicle
Route scheme.
Construction unit 402, for matching integrated vehicle route scheme, building collection according to consistency vehicle route scheme sum aggregate
With integrated consistency vehicle route scheme.
Modeling unit 403, for, with integrated consistency vehicle route scheme, determining that minimizing vehicle always travels according to collection
Time model.Preferably, modeling unit 403 minimizes vehicle overall travel time model for determining, comprising:
Establish the minimum vehicle overall travel time model:
Wherein i, j are client's point index；
N is client's point set；
N+For client's point set and parking lot { 0 }；
D is date index；
D is date set；
K is vehicle index；
K is vehicle set；
tijFor the hourage between two client's points i and j；
βijkdIt is equal to 1 if vehicle k accessed client point j after access client's point i on d at once for variable 0 or 1；It is no
Then it is equal to 0.
Preferably, modeling unit 403 minimizes vehicle overall travel time model for determining, comprising:
The constraint condition for minimizing vehicle overall travel time model is set are as follows:
It is interviewed when ensuring in all days parking lots all 0；
Ensure that client needs consolidating the load or is accessed once just when servicing with goods；
Ensure in given number of days, capacity of the capacity of carriage between any two client no more than each car；
Establish client's point consolidating the load and the traffic constraints with loads；
Ensure only one tight preceding client of each client and a next client；
Ensure no matter when client needs to service, they are serviced by identical driver；
It determines the arrival time of each client, also is used to eliminate the sub- road of daily route；
Ensure that each car must return to parking lot before maximum hourage；
Ensure any two days d1And d2Reaching time-difference be no more than maximum reaching time-difference；
Define the thresholding of decision variable.
First computing unit 404 is obtained for being solved using heuristic algorithm to vehicle overall travel time model is minimized
Take calculated result.Preferably, the first computing unit 404 be used for using heuristic algorithm to minimize vehicle overall travel time model into
Row solves, and obtains calculated result, comprising:
Heuristic algorithm is updated using record to solve to vehicle overall travel time model is minimized, and obtains calculated result.
Preferably, the process of record update heuristic algorithm includes:
Initiation parameter and variable；
Template route is constructed, feasible initial solution is generated；
Template route is updated, updated template route overall travel time increases the deterioration movement for being no more than threshold value；
By iterative algorithm, the current solution of template route is improved；
From current solution, client is inserted into the minimum position of cost, and establish template route corresponding with currently solving
The smallest day route of overall travel time；
Determine the feasibility that client's point is inserted into template route.
Preferably, the first computing unit 404 is used to carry out using heuristic algorithm to vehicle overall travel time model is minimized
It solves, obtains calculated result, comprising:
It is solved using variable neighborhood local search heuristic algorithm to vehicle overall travel time model is minimized, obtains meter
Calculate result.
Preferably, the process of variable neighborhood local search heuristic algorithm includes:
The template route of the smallest day route is determined by variable neighborhood local search heuristic algorithm and inversion operations search；
The specified contiguous range in specified region is scanned for, the template route of multiple updates is obtained；
According to the template route of multiple updates, calculated result corresponding with the template route of multiple updates is obtained；
From the corresponding calculated result of template route of multiple updates, choosing the smallest calculated result of overall travel time is most
Excellent calculated result.
Preferably, the first computing unit 404 is used to carry out using heuristic algorithm to vehicle overall travel time model is minimized
It solves, obtains calculated result, comprising:
It is solved, is obtained to vehicle overall travel time model is minimized using the TABU search heuristic algorithm based on template
Calculated result.
The process for being preferably based on the TABU search heuristic algorithm of template includes:
Locally optimal solution is searched for using the TABU search heuristic algorithm based on template, and locally optimal solution is stored to taboo
Table；
Calculated result other than iterative search taboo list carries out the search of the different effectively directions of search.
Second computing unit 405 is obtained for being solved according to decision rule to vehicle overall travel time model is minimized
Take the result of decision.
As a result unit 406, for comparing calculated result and the result of decision, when the difference of calculated result and the result of decision is less than
Output result when threshold value, using calculated result as identified collection with integrated consistency vehicle route scheme.
For example, selecting the smallest calculated result of difference to match as collection by heuritic approach calculated result compared with accurate solution
The output result of integrated consistency vehicle route scheme；Comparison decision rules results and algorithm calculated result, when the calculating
When being as a result less than threshold value with the difference of the result of decision, it was demonstrated that practical application value.
The present invention is described by reference to a small amount of embodiment.However, it is known in those skilled in the art, as
Defined by subsidiary Patent right requirement, in addition to the present invention other embodiments disclosed above equally fall in it is of the invention
In range.
Normally, all terms used in the claims are all solved according to them in the common meaning of technical field
It releases, unless in addition clearly being defined wherein.All references " one // be somebody's turn to do [device, component etc.] " are explained with being all opened
For at least one example in device, component etc., unless otherwise expressly specified.The step of any method disclosed herein, does not all have
Necessity is run with disclosed accurate sequence, unless explicitly stated otherwise.
Claims (18)
1. a kind of method for determining path planning of the collection with integrated consistency vehicle, which comprises
Determine that the consistency vehicle route scheme sum aggregate for path planning matches integrated vehicle route scheme；
Match integrated vehicle route scheme according to the consistency vehicle route scheme and the collection, building collection is consistent with integration
Property vehicle route scheme；
According to the collection with integrated consistency vehicle route scheme, determines and minimize vehicle overall travel time model；
The minimum vehicle overall travel time model is solved using heuristic algorithm, obtains calculated result；
The minimum vehicle overall travel time model is solved according to decision rule, obtains the result of decision；
Compare the calculated result and the result of decision, when the difference of the calculated result and the result of decision is less than threshold value
When, the calculated result and the difference accurately solved determine its validity；The smallest calculated result of difference is selected to match as collection
The output result of integrated consistency vehicle route scheme.
2. according to the method described in claim 1, the determining minimum vehicle overall travel time model, comprising:
Establish the minimum vehicle overall travel time model:
Wherein i, j are client's point index；
N is client's point set；
N+For client's point set and parking lot { 0 }；
D is date index；
D is date set；
K is vehicle index；
K is vehicle set；
tijFor the hourage between two client's points i and j；
βijkdIt is equal to 1 if vehicle k accessed client point j after access client's point i on d at once for variable 0 or 1；Otherwise it is equal to
0。
3. according to the method described in claim 1, the determining minimum vehicle overall travel time model, comprising:
The constraint condition for minimizing vehicle overall travel time model is set are as follows:
It is interviewed when ensuring in all days parking lots all 0；
Ensure that client needs consolidating the load or is accessed once just when servicing with goods；
Ensure in given number of days, capacity of the capacity of carriage between any two client no more than each car；
Establish client's point consolidating the load and the traffic constraints with loads；
Ensure only one tight preceding client of each client and a next client；
Ensure no matter when client needs to service, they are serviced by identical driver；
It determines the arrival time of each client, also is used to eliminate the sub- road of daily route；
Ensure that each car must return to parking lot before maximum hourage；
Ensure any two days d1And d2Reaching time-difference be no more than maximum reaching time-difference；
Define the thresholding of decision variable.
4. according to the method described in claim 1, described utilize heuristic algorithm to the minimum vehicle overall travel time model
It is solved, obtains calculated result, comprising:
Heuristic algorithm is updated using record to solve the minimum vehicle overall travel time model, obtains calculated result.
5. according to the method described in claim 4, the process that the record updates heuristic algorithm includes:
Initiation parameter and variable；
Template route is constructed, feasible initial solution is generated；
The template route is updated, the updated template route overall travel time increases the deterioration movement for being no more than threshold value；
By iterative algorithm, the current solution of the template route is improved；From the current solution, it is minimum that client is inserted into cost
Position, and establish and the smallest day route of the overall travel time for currently solving corresponding template route；It determines to the sample
The feasibility of client's point is inserted into plate circuit line.
6. according to the method described in claim 1, described utilize heuristic algorithm to the minimum vehicle overall travel time model
It is solved, obtains calculated result, comprising:
The minimum vehicle overall travel time model is solved using variable neighborhood local search heuristic algorithm, obtains meter
Calculate result.
7. according to the method described in claim 6, the process of the variable neighborhood local search heuristic algorithm includes:
The template route of the smallest day route is determined by variable neighborhood local search heuristic algorithm and inversion operations search；
The specified contiguous range in specified region is scanned for, the template route of multiple updates is obtained；
According to the template route of multiple updates, calculated result corresponding with the template route of multiple updates is obtained；
From the corresponding calculated result of template route of the multiple update, choosing the smallest calculated result of overall travel time is most
Excellent calculated result.
8. according to the method described in claim 1, described utilize heuristic algorithm to the minimum vehicle overall travel time model
It is solved, obtains calculated result, comprising:
The minimum vehicle overall travel time model is solved using the TABU search heuristic algorithm based on template, is obtained
Calculated result.
9. according to the method described in claim 8, the process of the TABU search heuristic algorithm based on template includes:
Search for locally optimal solution using the TABU search heuristic algorithm based on template, and by the locally optimal solution store to
Taboo list；
Calculated result other than taboo list described in iterative search carries out the search of the different effectively directions of search.
10. a kind of system for determining path planning of the collection with integrated consistency vehicle, the system comprises:
Determination unit, for determining that the consistency vehicle route scheme sum aggregate for path planning matches integrated vehicle path side
Case；
Construction unit, for matching integrated vehicle route scheme, building according to the consistency vehicle route scheme and the collection
Collection is with integrated consistency vehicle route scheme；
Modeling unit, for matching integrated consistency vehicle route scheme according to the collection, when determining that minimizing vehicle always travels
Between model；
First computing unit is obtained for being solved using heuristic algorithm to the minimum vehicle overall travel time model
Calculated result；
Second computing unit is obtained for being solved according to decision rule to the minimum vehicle overall travel time model
The result of decision；
As a result unit, for the calculated result and the result of decision, when the calculated result and the result of decision
Difference when being less than threshold value, the calculated result and the difference accurately solved determine its validity；Select the smallest meter of difference
Calculate output result of the result as collection with integrated consistency vehicle route scheme.
11. system according to claim 10, the modeling unit is for the determining minimum vehicle overall travel time
Model includes:
Establish the minimum vehicle overall travel time model:
Wherein i, j are client's point index；
N is client's point set；
N+For client's point set and parking lot { 0 }；
D is date index；
D is date set；
K is vehicle index；
K is vehicle set；
tijFor the hourage between two client's points i and j；
βijkdIt is equal to 1 if vehicle k accessed client point j after access client's point i on d at once for variable 0 or 1；Otherwise it is equal to
0。
12. system according to claim 10, the modeling unit minimizes vehicle overall travel time model for determining,
Include:
The constraint condition for minimizing vehicle overall travel time model is set are as follows:
It is interviewed when ensuring in all days parking lots all 0；
Ensure that client needs consolidating the load or is accessed once just when servicing with goods；
Ensure in given number of days, capacity of the capacity of carriage between any two client no more than each car；
Establish client's point consolidating the load and the traffic constraints with loads；
Ensure only one tight preceding client of each client and a next client；
Ensure no matter when client needs to service, they are serviced by identical driver；
It determines the arrival time of each client, also is used to eliminate the sub- road of daily route；
Ensure that each car must return to parking lot before maximum hourage；
Ensure any two days d1And d2Reaching time-difference be no more than maximum reaching time-difference；
Define the thresholding of decision variable.
13. system according to claim 10, first computing unit is used for using heuristic algorithm to the minimum
Vehicle overall travel time model is solved, and calculated result is obtained, comprising:
Heuristic algorithm is updated using record to solve the minimum vehicle overall travel time model, obtains calculated result.
14. system according to claim 13, the process that the record updates heuristic algorithm include:
Initiation parameter and variable；
Template route is constructed, feasible initial solution is generated；
The template route is updated, the updated template route overall travel time increases the deterioration movement for being no more than threshold value；
By iterative algorithm, the current solution of the template route is improved；
From the current solution, client is inserted into the minimum position of cost, and establish and currently solve corresponding road model with described
The smallest day route of the overall travel time of line；
Determine the feasibility that client's point is inserted into the template route.
15. system according to claim 10, first computing unit is used for using heuristic algorithm to the minimum
Vehicle overall travel time model is solved, and calculated result is obtained, comprising:
The minimum vehicle overall travel time model is solved using variable neighborhood local search heuristic algorithm, obtains meter
Calculate result.
16. the process of system according to claim 15, the variable neighborhood local search heuristic algorithm includes:
The template route of the smallest day route is determined by variable neighborhood local search heuristic algorithm and inversion operations search；
The specified contiguous range in specified region is scanned for, the template route of multiple updates is obtained；
According to the template route of multiple updates, calculated result corresponding with the template route of multiple updates is obtained；
From the corresponding calculated result of template route of the multiple update, choosing the smallest calculated result of overall travel time is most
Excellent calculated result.
17. system according to claim 10, first computing unit is used for using heuristic algorithm to the minimum
Vehicle overall travel time model is solved, and calculated result is obtained, comprising:
The minimum vehicle overall travel time model is solved using the TABU search heuristic algorithm based on template, is obtained
Calculated result.
18. the process of system according to claim 17, the TABU search heuristic algorithm based on template includes:
Search for locally optimal solution using the TABU search heuristic algorithm based on template, and by the locally optimal solution store to
Taboo list；
Calculated result other than taboo list described in iterative search carries out the search of the different effectively directions of search.
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| CN110991665A (en) * | 2019-11-21 | 2020-04-10 | 浙江工业大学 | Profit maximization integrated vehicle path planning method |
| CN111832783A (en) * | 2019-04-19 | 2020-10-27 | 顺丰科技有限公司 | Full cargo aircraft linkage transfer board early warning and decision-making method and system |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109919369A (en) * | 2019-02-26 | 2019-06-21 | 浙江财经大学 | A kind of battery-exchange station addressing and electric car paths planning method |
| CN111832783A (en) * | 2019-04-19 | 2020-10-27 | 顺丰科技有限公司 | Full cargo aircraft linkage transfer board early warning and decision-making method and system |
| CN110322066A (en) * | 2019-07-02 | 2019-10-11 | 浙江财经大学 | A kind of collaborative vehicle method for optimizing route based on shared carrier and shared warehouse |
| CN110322066B (en) * | 2019-07-02 | 2021-11-30 | 浙江财经大学 | Collaborative vehicle path optimization method based on shared carrier and shared warehouse |
| CN110991665A (en) * | 2019-11-21 | 2020-04-10 | 浙江工业大学 | Profit maximization integrated vehicle path planning method |
| CN110991665B (en) * | 2019-11-21 | 2023-05-12 | 浙江工业大学 | Profit maximization integrated vehicle path planning method |
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