CN109492800A - A kind of vehicle routing optimization method for Automatic Warehouse - Google Patents
A kind of vehicle routing optimization method for Automatic Warehouse Download PDFInfo
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Abstract
The present invention provides a kind of method of vehicle routing optimization determined for Automatic Warehouse storage outbound whole story position.Hybrid algorithm of this method based on simulated annealing and TABU search, the planning problem determined for the storage outbound whole story position in vehicle route, constraint condition is set, initial solution is acquired using simulated annealing, so that the initial solution that tabu search algorithm can be randomly generated to avoid selection, the initial solution for directly quoting simulated annealing carries out double optimization, to obtain the global solution of better effect.This method applies to a kind of vehicle routing optimization that whole story position determines, belongs to warehouse homework scheduling field.After optimization, vehicle traverses all warehouse compartments in warehouse from specified storage position, has by each warehouse compartment and only once, finally reaches specified outbound position.In conjunction with the actual vehicle route dispatch situation in warehouse, the optimization method is practical, and availability is good.
Description
Technical field
The invention belongs to warehouse logistics automatic dispatching fields, are related to a kind of vehicle routing optimization for Automatic Warehouse
Method.
Background technique
With the fast development of logistics storage sector, the demand of past storage mode and goods and materials of today production and circulation
It compares, lag situation occurs, therefore tiered warehouse facility is able to be paid attention to and used by more and more people.Tiered warehouse facility
Operating efficiency depends on many factors, the allocation strategy including warehouse goods yard, the optimum choice of vehicle route and consideration storehouse
Combination design of library software and hardware etc..Accordingly, with respect to stereo warehouse Shelves Optimization, vehicle path planning (Vehicle Routing
Problem, VRP) the problem of model and algorithm research become storage sector automatic dispatching research emphasis.
Automatic Warehouse scheduling problem is one to travelling salesman (Traveling Salesman Problem, TSP) problem
Kind extension, belongs to most difficult one of combinatorial optimization problem.Golden, Magnanti and Nguyan in 1972 with
" vehicle routing " is that title studies Vehicle Routing Problems.Thereafter, Golden and Stewart will not be really in 1978
Determine probability theory and introduces VRP problem.To in the 1990s, having benefited from the rapid development of personal computer, VRP problem is obtained into one
Step research.Similarly, also there is the heuritic approach of all kinds of solution hybrid optimization problems in this period, these algorithms also by with
To solve the problems, such as VRP.The algorithm that Laporte and Gendreau et al. had studied VRP in 1998 uses, such as hereditary
Algorithm, ant group algorithm, neural network algorithm, simulated annealing, TABU search etc..And it also proposed travelling salesman's probability problem
PTSP, vehicle route probability problem PVRP and Stochastic Vehicle Routing Problem SVRP, in addition there are also Time Dependent traveling salesman problem TD-
TSP, Time Dependent Vehicle Routing Problems TD-VRP and Dynamic Vehicle Routing Problems.
Comparison is external, and studies in China Vehicle Routing Problems are started late, and just begin one's study VRP at the end of the nineties, later
It is gradually taken seriously, more and more people start to further investigate.The time of nearest more than ten years experienced the hair in external several stages
Exhibition.No matter from the research of algorithm itself, there is considerable and extensive research again with respect to the extension of vehicle route, every year about vehicle
The Research Literature quantity of routing problem is doubled and redoubled.
Summary of the invention
Drawbacks described above present in warehouse logistics automatic dispatching for the prior art, the present invention provides one kind to be used for
The method of the vehicle routing optimization of Automatic Warehouse.Problem to be solved by this invention is different from typical TSP problem, research pair
As the Vehicle Routing Problem for Automatic Warehouse, it is often referred to the vehicle road in the case where the whole story position determination of storage outbound
Diameter scheduling.Using vehicle routing optimization method of the invention, the segmented areas in warehouse is traversed from specified storage position
All warehouse compartments in warehouse, have and only once by each warehouse compartment, finally reach specified outbound position.
According to one aspect of the present invention, a kind of vehicle routing optimization method of Automatic Warehouse is provided, for realizing
Vehicle route scheduling when the storage outbound whole story position of the Automatic Warehouse determines, method includes the following steps:
Step 1: random sequence is generated using the randperm function in Matlab software, as initial warehouse compartment sequence S0,
The vehicle route length D0 by warehouse compartment sequence traveling is calculated, initiation parameter defines initial value T0, decreasing ratio α, end value Tf,
Taboo list length tl, candidate solution number cl is arranged in taboo list Tlist；
Step 2: according to decline formula T (n+1)=α * T (n), if T (n+1)≤Tf, return to warehouse compartment sequence Sequence
With corresponding vehicle route length d_best, 6 are gone to step, otherwise perform the next step rapid；
Step 3: giving fixed step size L=lt, lt> 1, separately enables l=1, if l < lt, step 4 is executed, otherwise return step 2；
Step 4: upsetting warehouse compartment sequence number using the fliplr function in Matlab software and sort, calculate new warehouse compartment sequence number
Under vehicle route length D (n+1), if the vehicle route length of new warehouse compartment sequence number D (n+1) is compared with the length contracting before update
Short D (n), i.e. D (n) > D (n+1), update warehouse compartment sequence number Sequence and the corresponding vehicle route of new warehouse compartment sequence number is long
It spends D (n), and enables the number of iterations l=l+1, return to step 3, it is no to then follow the steps 5；
Step 5: according to discriminate e[D(n+1)-D(n)]/T(n)> rand judgement, meets discriminate, then updates warehouse compartment sequence and count
Calculate the vehicle route length under new warehouse compartment sequence, l=l+1；It is unsatisfactory for discriminate, is not updated, also enables l=l+1, and execute step
Rapid 3；
Step 6: introducing warehouse compartment sequence Sequence and corresponding vehicle route length d_best that step 2 generates, empty
Taboo list length tl, candidate solution number cl is arranged in taboo list Tlist；
Step 7: judging whether to meet termination condition the number of iterations Line=2000, that is, recycle 2000 times, if satisfied, terminating
And optimal warehouse compartment sequence Sequence_best and most short vehicle route length d_best2 are exported, it is no to then follow the steps 8；
Step 8: generating neighborhood solution using the neighbour structure of current warehouse compartment sequence Sequence, determine the whole story of storage outbound
Position, and cl candidate solution is determined in Sequence；
Step 9: candidate solution being judged whether to meet optimal warehouse compartment sequence Sequence and corresponding vehicle route length d_
Best, if satisfied, then with best warehouse compartment sequence Sequence and vehicle route length D (n), and with the corresponding taboo of Sequence
Object replacement enters the taboo object of taboo list earliest, while ' best so far ' state, then jumps with Sequence replacement
Step 8；Otherwise, 10 are entered step；
Step 10: judging the taboo situation of the corresponding each object of cl candidate solution, candidate solution is selected to concentrate non-taboo object
Corresponding optimum state is new current solution, while entering the taboo pair of taboo list earliest with corresponding taboo object replacement
As, then go to step 8.
In an embodiment wherein, initial value T defined in step 10Selection and warehouse compartment sequence permutation and search for more excellent vehicle
The search speed and convergence result of path length are associated, end value TfIt is associated with the number of iterations.
It is indicated in the taboo list Tlist of an embodiment wherein, step 1 by the function zeros of Matlab software, it is initial to prohibit
Avoid lengthIt is defined for the number of iterations to taboo object, N is the number of parameter in sequence number, is corresponded to certainly
Warehouse compartment sum in dynamicization warehouse, candidate solution numberCorresponding to candidate warehouse compartment sortord.
The rand of discriminate indicates the section (0,1) that Matlab software generates at random in an embodiment wherein, step 5
A random number.
In an embodiment wherein, the neighbour structure of step 8 determines using two elements in random exchange pool bit sequence
The whole story position of library outbound fixes sequence number first and last position, and return warehouse compartment sequence in step 2 is free of whole story point, in the step
Exchange is supplemented first and last position later at random in rapid.
In an embodiment wherein, random exchange pool bit sequence is taboo object, according to the Tabu Length set, to every
The operative memory of the secondary random exchange pool bit sequence of iteration, mode of operation are placed on taboo list in defined iterative steps, work as iteration
Number removes taboo list beyond range, taboo object is limited.
Detailed description of the invention
Reader is after having read a specific embodiment of the invention referring to attached drawing, it will more clearly understands of the invention
Various aspects.Wherein,
Fig. 1 shows a kind of vehicle routing optimization method of Automatic Warehouse being put in storage under the conditions of outbound whole story position determines
Flow chart.
Fig. 2A and Fig. 2 B is the subregion schematic diagram in certain non-ferrous metal warehouse.
Fig. 3 is the optimum results obtained using vehicle routing optimization method of the invention.
Specific embodiment
In order to keep techniques disclosed in this application content more detailed with it is complete, can refer to attached drawing and of the invention following
Various specific embodiments, identical label represents the same or similar component in attached drawing.However, those skilled in the art
It should be appreciated that embodiment provided hereinafter is not intended to limit the invention covered range.In addition, attached drawing is used only for
It is schematically illustrated, and is drawn not according to its full size.
With reference to the accompanying drawings, the specific embodiment of various aspects of the present invention is described in further detail.
Using certain non-ferrous metal Automatic Warehouse as background, in conjunction with the constraint condition in practical warehouse, propose a kind of for certainly
The method of the vehicle routing optimization in dynamicization warehouse, hybrid algorithm of this method based on simulated annealing and TABU search, for vehicle
The planning problem that storage outbound whole story position in path determines, is arranged constraint condition, is acquired initially using simulated annealing
Solution, so that the initial solution that tabu search algorithm can be randomly generated to avoid selection, directly quotes the initial solution of simulated annealing
Double optimization is carried out, to obtain the global solution of better effect.This method applies to a kind of vehicle route that whole story position determines
Optimization, belongs to warehouse homework scheduling field.After optimization, vehicle traverses all libraries in warehouse from specified storage position
Position, has and only once by each warehouse compartment, finally reaches specified outbound position.
Specific embodiments of the present invention carry out it is assumed hereinafter that:
(1) real process overhead traveling crane movement speed is divided into three parts, Acceleration of starting part, reaches after command speed at the uniform velocity
Motion process, and the down speeding procedure before designated position is reached, take average speed as the movement speed of overhead traveling crane here.
(2) failure is not present in overhead traveling crane operational process, i.e., when institute is through warehouse compartment, can successfully extract cargo.
(3) the cargo overhead traveling crane of each warehouse compartment can disposable discharging of goods, the coordinate in goods yard can be by (Xi, Yi, Zi) turn
For (Xi, Yi), i.e. the two dimensionization by three-dimensional coordinate.
(4) in view of the discrepancy lab environment of actual place, two exit passageways are such as set, barndoor gate does not set up library nearby
Position guarantees place, and an overhead traveling crane scheduling is used only.
Each to show 11 row goods yards to Mr. Yu's non-ferrous metal warehouse, one shares 40 rows, and warehouse is divided into altogether five pieces of regions
(Area A, Area B, Area C, AreaD, Area E), as shown, wherein red solid dot represents being put in storage out for a region
Position.
In conjunction with FIG. 1 to FIG. 3, specific implementation step of the invention is as follows:
Step 1: being directed to each region, do not consider inventory whole story position, remaining warehouse compartment point is calculated using simulated annealing
Method searches optimal initial solution.By taking the Area A of region as an example, a shared m warehouse compartment (does not include starting point, that is, chooses m-2 library
Position), more excellent warehouse compartment sequence string number and vehicle route length are generated by simulated annealing.Wherein, using in Matlab software
Randperm function generate random sequence and as initial warehouse compartment sequence S0 it is long to calculate the corresponding vehicle route of warehouse compartment sequence
Spend D0.Initiation parameter defines initial value T0=3000, decreasing ratio α=0.50, end value Tf=0.01；
Step 2: providing decline formula T (n+1)=α * T (n) and calculate, if T (n+1)≤Tf, return to more excellent warehouse compartment sequence
Otherwise Sequence and vehicle route length d_best, jump procedure 6 perform the next step rapid；By the vehicle route length of step 1
As the condition of tabu search algorithm special pardon criterion, and the warehouse compartment sequence string number generated by step 1, all add one, (such as
{ 5,3,2,4,1 } add one, obtain { 6,4,3,5,2 }) sequence number 1 and m ({ 6,4,3,5,2 } addition head is added in first and last position again
Last bit sets to obtain { 1,6,4,3,5,2,7 }), obtain new warehouse compartment sequence string；
Step 3: giving fixed step size L=lt, lt> 1, separately enables l=1.If l < lt, step 4 is executed, otherwise return step 2；
Step 4: upsetting warehouse compartment sequence number using the fliplr function in Matlab software and sort, calculate new warehouse compartment sequence number
Under vehicle route length D (n+1).If the vehicle route length of new warehouse compartment sequence number D (n+1) is compared with the length contracting before update
Short D (n), i.e. D (n) > D (n+1), update warehouse compartment sequence number Sequence and the corresponding vehicle route of new warehouse compartment sequence number is long
It spends D (n), and enables l=l+1, return to step 3, it is no to then follow the steps 5；Using tabu search algorithm, initial solution is step 2
The new warehouse compartment sequence string generated.If taboo list length is 60 the percent of warehouse compartment, taboo object is that the exchange of warehouse compartment sequence is grasped
Make.Neighbour structure using at random exchange two warehouse compartment modes, generate certain amount field solution, and calculate new vehicle path spend away from
From；
Step 5: according to discriminate e[D(n+1)-D(n)]/T(n)> rand judgement, meets discriminate, then updates warehouse compartment sequence and meter
Calculate new order vehicles path length, l=l+1；It is unsatisfactory for discriminate, is not updated, also enables l=l+1, and execute step 3；
Step 6: introducing return value Sequence and vehicle route length d_best that step 2 generates, empty taboo list
Taboo list length tl, candidate solution number cl is arranged in Tlist；
Step 7: judging whether to meet termination condition Line=2000, that is, recycle 2000 times.If satisfied, terminating and exporting most
The vehicle route d_best2 of excellent warehouse compartment sequence Sequence_best and warehouse compartment sequence；It is no to then follow the steps 8；
Step 8: generating neighborhood solution using the neighbour structure of current warehouse compartment sequence number Sequence, determine the beginning of storage outbound
Last bit is set, and cl candidate solution is determined in Sequence；
Step 9: judging whether the vehicle route for meeting optimal warehouse compartment sequence Sequence and the warehouse compartment sequence to candidate solution
d_best.If satisfied, then use optimal sequence Sequence and D (n), and with the corresponding taboo object of Sequence replace earliest into
Enter the taboo object of taboo list, while with Sequence replacement ' best so far ' state, then jump procedure 8；Otherwise, into
Row step 10；Wherein, first judge whether that meeting special pardon criterion (in this method, specially pardons criterion to receive to be better than simulated annealing
The more excellent solution Sequence and vehicle route length d_best being calculated), if meeting, select currently to solve as optimal warehouse compartment sequence
Otherwise column and vehicle route are new current solution selecting candidate solution to concentrate the corresponding optimum state of non-taboo object, exchange feelings
Condition is classified as taboo object and updates taboo list.
Step 10: judging the taboo situation of the corresponding each object of cl candidate solution, candidate solution is selected to concentrate non-taboo object
Corresponding optimum state is new current solution, while entering the taboo pair of taboo list earliest with corresponding taboo object replacement
As, then go to step 8.
Using this method, when the number of iterations is 2000, vehicle route length is 448.724.It is calculated with conventional TABU search
Method is compared, and the optimal solution calculated using simulated annealing substitutes the method generated at random, and effect significantly improves.Due to by whole story position
Determining influence, what optimization method of the invention obtained belongs to suboptimal solution, is determining whole story position with standard tabu search algorithm
In the case where setting, for the TSPLIB java standard library eil51 provided by Ruprecht-Karls-Universitat Heidelberg, effect of optimization is obvious.
Calculated result is as follows:
| Determine whole story point | It is maximum | It is minimum | It is average | Deviation ratio | Standard deviation | Iterative steps |
| TS | 535.7403 | 492.0961 | 516.2882 | 20.91% | 16.03 | 2000 |
| The method of the present invention | 489.1728 | 448.724 | 469.2680 | 9.8% | 12.23 | 2000 |
Above, a specific embodiment of the invention is described with reference to the accompanying drawings.But those skilled in the art
It is understood that without departing from the spirit and scope of the present invention, can also make to a specific embodiment of the invention each
Kind change and replacement.These changes and replacement are all fallen within the scope of the invention as defined in the claims.
Claims (6)
1. a kind of vehicle routing optimization method of Automatic Warehouse, for realizing the storage outbound whole story position of the Automatic Warehouse
When setting determining vehicle route scheduling, which is characterized in that the vehicle routing optimization method the following steps are included:
Step 1: generating random sequence using the randperm function in Matlab software, as initial warehouse compartment sequence S0, calculate
By the vehicle route length D0 that the warehouse compartment sequence travels, initiation parameter defines initial value T0, decreasing ratio α, end value Tf, taboo
Taboo list length tl, candidate solution number cl is arranged in table Tlist；
Step 2: according to decline formula T (n+1)=α * T (n), if T (n+1)≤Tf, return to warehouse compartment sequence Sequence and correspondence
Vehicle route length d_best, go to step 6, otherwise perform the next step rapid；
Step 3: giving fixed step size L=lt, lt> 1, separately enables l=1, if l < lt, step 4 is executed, otherwise return step 2；
Step 4: upsetting warehouse compartment sequence number using the fliplr function in Matlab software and sort, calculate under new warehouse compartment sequence number
Vehicle route length D (n+1), if the vehicle route length of new warehouse compartment sequence number D (n+1) shortens D compared with the length before update
(n), i.e. D (n) > D (n+1) updates the warehouse compartment sequence number Sequence and corresponding vehicle route length D of new warehouse compartment sequence number
(n), and the number of iterations l=l+1 is enabled, returns to step 3, it is no to then follow the steps 5；
Step 5: according to discriminate e[D(n+1)-D(n)]/T(n)> rand judgement, meets discriminate, then updates warehouse compartment sequence and calculate new
Vehicle route length under warehouse compartment sequence, l=l+1；It is unsatisfactory for discriminate, is not updated, also enables l=l+1, and execute step 3；
Step 6: introducing warehouse compartment sequence Sequence and corresponding vehicle route length d_best that step 2 generates, empty taboo
Taboo list length tl, candidate solution number cl is arranged in table Tlist；
Step 7: judge whether to meet termination condition the number of iterations Line=2000, that is, recycle 2000 times, if satisfied, terminate and it is defeated
Optimal warehouse compartment sequence Sequence_best and most short vehicle route length d_best2 out, it is no to then follow the steps 8；
Step 8: generating neighborhood solution using the neighbour structure of current warehouse compartment sequence Sequence, determine the whole story position of storage outbound
It sets, and determines cl candidate solution in Sequence；
Step 9: candidate solution is judged whether to meet optimal warehouse compartment sequence Sequence and corresponding vehicle route length d_best,
If satisfied, then with best warehouse compartment sequence Sequence and vehicle route length D (n), and with the corresponding taboo object of Sequence
Replacement enters the taboo object of taboo list earliest, while with Sequence replacement ' best so far ' state, then jump procedure
8；Otherwise, 10 are entered step；
Step 10: judging the taboo situation of the corresponding each object of cl candidate solution, candidate solution is selected to concentrate non-taboo object corresponding
Optimum state be new current solution, while with corresponding taboo object replacement earliest into the taboo object of taboo list,
Step 8 is gone to again.
2. the vehicle routing optimization method of Automatic Warehouse according to claim 1, which is characterized in that defined in step 1
Initial value T0Selection and warehouse compartment sequence permutation and the more excellent vehicle route length of search search speed and to restrain result associated,
End value TfIt is associated with the number of iterations.
3. the vehicle routing optimization method of Automatic Warehouse according to claim 1, which is characterized in that the taboo of step 1
Function zeros expression of the table Tlist by Matlab software, initial Tabu LengthFor the iteration time to taboo object
Number is defined, and N is the number of parameter in sequence number, corresponding to the warehouse compartment sum in Automatic Warehouse, candidate solution numberCorresponding to candidate warehouse compartment sortord.
4. the vehicle routing optimization method of Automatic Warehouse according to claim 1, which is characterized in that differentiate in step 5
The rand of formula indicates a random number in the section (0,1) that Matlab software generates at random.
5. the vehicle routing optimization method of Automatic Warehouse according to claim 1, which is characterized in that the neighborhood of step 8
Structure is determined the whole story position of storage outbound, fixes sequence number first and last position using two elements in random exchange pool bit sequence,
Return warehouse compartment sequence in step 2 is free of whole story point, is supplemented first and last position after random exchange in this step.
6. the vehicle routing optimization method of Automatic Warehouse according to claim 1, which is characterized in that exchange warehouse compartment at random
Sequence is taboo object, according to the Tabu Length set, operative memory to the random exchange pool bit sequence of each iteration, and operation
Mode is placed on taboo list in defined iterative steps, when the number of iterations removes taboo list beyond range, taboo object is limited.
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| CN114169560A (en) * | 2020-12-22 | 2022-03-11 | 四川合纵药易购医药股份有限公司 | Material scheduling control method for stereoscopic warehouse |
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