CN109583660A - A kind of implementation method of dynamic order-picking policy - Google Patents
A kind of implementation method of dynamic order-picking policy Download PDFInfo
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Abstract
The invention discloses a kind of implementation method of dynamic order-picking policy, the goods yard for next picking list that the path of current picking list is passed through is recorded, goods yard set A is obtained；If goods yard set A is not empty set, then judge in the set A of goods yard with the presence or absence of some goods yard, and by subtract the goods yard can make current picking list next picking list the picking time it is shorter, if judging result is to exist, the picking time that next picking list of current picking list is made in the set A of goods yard is reached into shortest goods yard and is set to the goods yard for needing to adjust；While completing the picking task of current picking list, corresponding the needed picking object shifting in the goods yard for needing to adjust in next picking list of current picking list is put into warehouse entrance, the cargo is picked at warehouse entrance when completing the picking task of next picking list of current picking list, realizes dynamic order-picking policy.
Description
Technical field
The present invention relates to the dynamic order-picking policies and its implementation in a kind of warehouse.
Background technique
With the fast development of electric business and retail business, requirement of the home-delivery center for warehousing and storage activities is continuously improved, wherein picking
Goods operation is the core link of warehouse homework, and the height of picking efficiency directly affects the working efficiency of entire home-delivery center, optimization
Picking path is the key that improve picking efficiency.Meanwhile the characteristics of according to electric business cargo multi items, small lot, it is understood that there may be on
A moment warehouse adjusted becomes out-of-date because of the variation of contained commodity in product demand, LK algorithm and order at lower a moment
The phenomenon that, the extensive mode cost for adjusting goods yard is too high, can not adapt to this situation, and traditional static goods yard distribution and its
Picking method for optimizing route has certain limitation again, it is therefore desirable to more flexible, flexible, small-scale dynamic goods yard adjustment with
Picking mode, to achieve the purpose that reduce picking path, shorten the picking time.
Dynamic picking proposes the thought of " goods yard adjustment and picking path " collaboration optimization, and picking process and goods yard were adjusted
Journey is combined into one, i.e., carries out goods yard while picking and adjust operation, and the warehouse excessive for goods yard adjustment frequency can be reduced goods
Position adjustment time and improve efficiency, realize simplify warehousing and storage activities process, reduce logistics cost, accelerate circulation of goods, improve enterprise
Economic benefit.
Research for dynamic picking path optimization, the optimization algorithm that domestic scholars propose at present includes: genetic algorithm, mould
Quasi- annealing algorithm, ant group algorithm, particle swarm algorithm etc..Genetic algorithm is a kind of probability search method of overall importance, is calculated using heredity
Method cataloged procedure is excessively complicated and is easy to fall into local optimum.Simulated annealing is a kind of random search algorithm, it is searched
Rope process introduces enchancement factor, can jump out locally optimal solution and reach globally optimal solution, but search speed is slower.
Have not yet to see the report to strategy and implementation method for picking single action state picking path planning.
Summary of the invention
The purpose of the present invention is to provide a kind of implementation methods of dynamic order-picking policy.
In order to achieve the above objectives, the invention adopts the following technical scheme:
1) goods yard for recording next picking list that the path (i.e. picking path) of current picking list is passed through, obtains goods yard collection
Close A；
If 2) set A in goods yard is not empty set, judge with the presence or absence of some goods yard in the set A of goods yard, and by subtracting this
Goods yard can make the picking time of next picking list of current picking list shorter, if judging result is to exist, by goods yard set A
In make the picking time of next picking list of current picking list reach shortest goods yard and be set to need the goods yard that adjusts；
3) it while completing the picking task of current picking list, will need to adjust in next picking list of current picking list
Corresponding the needed picking object shifting in goods yard to be put into warehouse entrance (temporary using an empty shelf is placed beside warehouse entrance
Storage), the cargo is picked at warehouse entrance when completing the picking task of next picking list of current picking list, is realized dynamic
State order-picking policy.
It preferably, is to work as with next picking list of current picking list if goods yard set A is empty set in the step 2)
Preceding picking list, goes to step 1).
Preferably, in the step 2), if judging result is that there is no be to work as with next picking list of current picking list
Preceding picking list, goes to step 1).
Preferably, before the step 1), based on static order-picking policy, according to the goods yard of picking list each in a cycle
Distribution is respectively to the path of corresponding picking list according to picking time most short carry out path optimization.
Preferably, in the step 2), to the path of next picking list of current picking list according to reducing some goods yard
Picking task remaining goods yard picking time most short carry out path optimization, so that it is determined that making next picking of current picking list
Single picking time reaches shortest goods yard, and updates the path of the picking list (the corresponding picking time is most short)；Then according to step
It is rapid 3) to carry out picking；Then step 1) is gone to for current picking list with next picking list of current picking list；And so on, directly
The active path planning of the picking task of all picking lists and sorting in completion a cycle.
Preferably, before the step 1), using the picking time most short target as path optimization, the picking road established
The objective function of diameter optimization problem mathematical model indicates are as follows:
Preferably, it in the step 2), using the most short target as path optimization of remaining goods yard picking time, is established
The objective function of routing problem mathematical model indicates are as follows:
Wherein, the constraint condition of mathematical model includes:
ZrFor r-th of picking single total picking time under static order-picking policy；Z″r+1It is picked for r-th of the next of picking list
Total picking time behind s-th of goods yard that manifest removal is adjusted；dijIt, can be by for the distance between any two goods yard in warehouse
Goods yard Position Number is calculated；xijThe path (variable to be solved) passed through for picking；V is that picking personnel are averaged the speed of travel；
trThe time required to picking list r initialization operation (be scanned, check, confirmation etc.)；tuSingle goods is averagely picked for picking personnel
Time needed for position；Sr,kThe goods yard number for needing to pick for r-th of picking list；er,sExpression picks r-th of the next of picking list
S-th of goods yard that the picking time of manifest shortens；K is the maximum value of the coding in the goods yard for needing to pick in picking list, and S is r
The quantity in the different goods yards for making the picking time of the picking list shorten in next picking list that a picking list picks.
Preferably, the routing problem is solved using blending heredity simulated annealing.
It preferably, is object element by picking nonoculture, according to picking path in the blending heredity simulated annealing
Goods yard picking sequence carries out integer coding；The number of iterations T is 500~2000, and population scale N is 80~100, initial temperature T0For
40000~50000, annealing coefficient a value range is (0,1), mutation probability pmValue range is (0.1,1), crossover probability pcIt takes
Being worth range is (0.01,1).
The beneficial effects of the present invention are embodied in:
The present invention is that picking process and goods yard adjust the adjustment of dynamic goods yard and picking optimization method, root that process is combined into one
According to needing partial cargo in adjustment picking list in real time to pick order, using front and back picking list path possible lap and
Warehouse has the characteristics that all public entrances in picking path, to next picking list during the task of current picking list carries out
The position that picks of partial cargo (such as the cargo in some goods yard) be adjusted, it is long with the LK algorithm for shortening picking list
Degree, the picking path for solving the picking list determined in static goods yard are difficult to the deficiency advanced optimized.
Further, the present invention passes through most short for target foundation with the picking time to the path optimization in dynamic order-picking policy
Mathematical model solves model using blending heredity simulated annealing, on the basis of dynamic picking path planning, into
One step has saved the picking time, improves picking efficiency.
Further, sequence adjustment is being picked according to picking time most short progress cargo or/and is not adjusting it in the present invention
Before, the picking task consuming time of picking list is optimized, so as to by continuing to optimize so that picking in a cycle
The picking time of manifest minimizes.
Detailed description of the invention
Fig. 1 is dynamic picking path optimization flow chart.
Fig. 2 is dual zone type layout mapping storage.
Fig. 3 is blending heredity simulated annealing flow chart.
Fig. 4 is that dynamic picking adjusts precedence diagram.
Fig. 5 is dynamic picking adjusts path figure；It wherein, is the goods yard being adjusted at cross-hatched markings.
Specific embodiment
The present invention is described in detail with reference to the accompanying drawings and examples.
(1) dynamic path optimization is carried out according to the thinking that picking process and goods yard adjustment are combined into one
Thinking is: when a large amount of picking lists need to handle, while carrying out picking to the current picking list in same period,
After adjustment in picking list certain cargo goods yard, optimize the LK algorithm of subsequent picking list, and so on, and then it is same to reach optimization
Picking efficiency is improved in the picking path of all picking lists in a cycle.Therefore the core of the dynamic order-picking policy proposed is
Appropriate adjustment is carried out in upper picking list to the goods yard of certain cargos in the picking list in same period during picking, is made
In same period all picking lists total picking path it is shorter, total the picking time it is less.
Implementation method are as follows: the setting one empty goods yard first beside warehouse entrance；Then it is designed using picking list as unit
The mathematical model (most short according to the picking time is objective function) of its optimal path, optimization 1 (in a cycle first)
The picking path of picking list；Then No. 2 (in a cycle second) pickings are found out singly in completion picks by way of No. 1 picking list
All goods yards, Ergodic judgement adjustment wherein which goods yard the picking time of No. 2 picking lists can be made maximumlly to reduce, into
When the picking of No. 1 picking list of row, by the empty shelf beside the cargo in this goods yard in passing band to entrance；Later according to weight
The path of No. 2 picking lists of new planning optimal path is found out in No. 3 picking lists (third in a cycle) by way of No. 2 pickings
Single all goods yards, and so on, until completing picking single task all in this period.The goods yard dynamic adjustable strategies are not
In the case where increasing current picking list LK algorithm, the path of the subsequent picking list of optimization of maximum possible is (most according to the picking time
Short is objective function), and can be simple and convenient to avoid secondary sorting, it is easy to implement, further improve picking efficiency.
By taking dual zone type warehouse as an example, specific adjustment process is as follows:
Referring to fig. 2, warehouse is by 1,2,3 three wide interconnection and ten longitudinal directions for the integral layout in dual zone type warehouse
Identical tunnel is constituted, and interconnection is respectively channel 1, channel 2 (intermediate channel), channel 3, and longitudinal tunnel is respectively tunnel 1, lane
Road 2, tunnel 3 ..., tunnel 10.In addition to the shelf of first row and last column are single shelf, other shelf are back-to-back type
The width of shelf, each tunnel is equal, and the lower left corner in warehouse is the entrance in warehouse, is into and out the ground that cargo is temporarily stored
Side.
Each goods yard is indicated by [a] [b] [c] three-dimensional array in warehouse, and wherein a is the tunnel number where goods yard, a
=1,2 ..., A；B is the shelf of the left and right sides in each tunnel, left side shelf, that is, b=0, the right shelf, that is, b=1；C is a column
The number in goods yard, c=1,2 ..., C on tunnel.According to calculated the characteristics of warehouse layout in warehouse between any two goods yard away from
From.In warehouse, [a] [b] [c] is numbered according to warehouse goods yard, it is assumed that the number of any two goods yard point is [ai][bi][ci]、
[aj][bj][cj], then the distance between the two goods yards are expressed as dij.Wherein:
1)The two conditions show that each product item of picking list occupies storehouse respectively
The side goods yard in a tunnel in library；
2)
An empty shelf is placed beside warehouse entrance, finds all next picking lists by current picking single path
Cargo, the cargo set are denoted as A, if judging to subtract which goods yard point can make the picking time of picking list r+1 in cargo set A
Reach most short, then this goods yard is set to the goods yard for needing to adjust.In picking by the cargo in goods yard for needing to adjust from existing
Warehousing displacement is put at empty shelf, and picking next time is taken cargo away from empty shelf and can be put (if any) when completing
The cargo in new adjustment goods yard, when each picking, at most adjust 1 goods yard.Illustrate by taking picking list r, picking list r+1 as an example, referring to
Fig. 1:
(1) optimal path of picking list r, picking list r+1 are solved (the picking time is most short).
(2) input respectively picking list r, in picking list r+1 the cargo in need picked, arbitrarily take in picking list r+1
One goods yard S (r+1, j), a goods yard S (r, i) in picking list r.Judge whether i and j is in a tunnel and logical in centre
Road it is ipsilateral, if it is not, then goods yard j++, choose goods yard S (r+1, j) again；If so, taking the goods yard picking list r approach S (r, i)
Former and later two goods yards S (r, i-1) and S (r, i+1).
(3) judge goods yard S (r, i-1) and S (r, i+1) whether in same tunnel and in the ipsilateral of intermediate channel, if not,
Continue to judge goods yard S (r, i-1) and S (r, i+1) whether in different tunnels and in the ipsilateral of intermediate channel, if so, calculating S
(r, i) passes through from which side shelf, continues to judge this one side whether S (r+1, j) passes through in goods yard S (r, i), if it is obtain
Goods yard picking list r approach S (r+1, j), if it is not, then goods yard j++, returns to (2) step and choose S (r+1, j) again；If goods yard
S (r, i-1) and S (r, i+1) in same tunnel and in the ipsilateral of intermediate channel, directly judge goods yard S (r+1, j) whether this two
Between a goods yard, selection is to obtain the goods yard picking list r approach S (r+1, j).
(4) reduction amount for calculating the sorting time of picking list r+1 after removing the goods yard S (r+1, j), then updates and at most reduces
Amount, goods yard j++, return step (2), until finding out the at most corresponding goods yard of picking path reduction amount, goods yard i++, resumes step
(2) new goods yard S (r, i), S (r+1, j) are obtained, the above steps are repeated until finding by the picking list r on the path picking list r
+ 1 goods yard (and reducing the picking list r+1 picking time at most).
(5) picking task of picking list r is completed, while adjustment makes the picking list r+1 picking time reduce most correspondence goods
The cargo of position updates the optimal picking path of picking list r+1 (the picking time is most short).
(6) continue lower picking list to sort, picking list r++, return step (1) calculates, and is sequentially completed all picking lists
It sorts.
(2) dual zone type warehouse dynamic picking path optimization mathematical model is established
It is object element by picking nonoculture, dynamically to adjust goods yard using several picking lists of a cycle as research object
The target that picking task the time it takes is at least used as path optimization is completed, picking routing problem mathematical model is established.
(1) target of dynamic picking path optimization is to find a kind of picking mode, so that completing what picking task was spent
Time is minimum.
The parameter being related to first to concrete mathematical model statement is made as given a definition:
Since U be the activity duration terminated picking first picking list to the last one picking single task of this period；
R is current picking list；
R is period picking odd number amount；
dijFor the distance between any two goods yard；
xijThe path passed through for picking；
V is that picking personnel are averaged the speed of travel；
trIt is scanned, checks for r-th of picking list, the time required to the initialization operations such as confirmation；
tuTime needed for averagely picking single goods yard for picking personnel；
Sr,kFor the number in the goods yard that r-th of picking list needs to pick；
xa,c,kIndicate c-th of goods yard in k-th of product item, a-th of tunnel in warehouse that picking list needs to pick；
er,sIndicate s-th of goods yard for reducing the picking time of next picking list of r-th of picking list；
Gr,aFor the number of lanes of picking personnel walking.
Objective function is selected from:
Wherein, model by searching position, pick SKU, place SKU and other the time spent in merge, as often
A SKU's picks the time, therefore 3 parts of total time point: travel time that picking personnel are picked according to order, initialization behaviour
Make the time, each SKU picks the time.Then formula (1) is picking list with the most short function for target of total picking time.Formula (2) is
Under dynamic order-picking policy, the path optimization model of current picking list r is other than comprising the time in formula 1, also comprising needing
Adjust the picking time in goods yard.It is important that need to adjust the selection in goods yard to subsequent picking list, using looping to determine, find out can
So that the subsequent picking list picking time reduces at most corresponding goods yard.Formula (3) is to subtract after picking list r+1 is adjusted goods yard
Optimal path model.Formula (1), (2), in (3), i, j=0 are indicated since warehouse doorway.
(3) most short picking time corresponding optimal picking sequence is solved based on blending heredity simulated annealing.
Referring to Fig. 3, blending heredity simulated annealing: population is initialized using genetic algorithm, in iterative process
In, genetic algorithm is easy to fall into locally optimal solution, searches plain probability at random using simulated annealing, and search is made to jump out part most
Excellent solution obtains globally optimal solution, and the detailed step based on blending heredity simulated annealing is as follows.
(1) code Design of chromosome
It is encoded according to home-delivery center's picking path parameter set, picking path uses integer coding, and integer coding compares
Intuitively, intersection and mutation operation are carried out with can be convenient.With vector (c1, c2..., cK) indicate Chromosome G, wherein element (base
Cause) ciFor a mutual unduplicated natural number between [1, K] (K is the goods yard number for needing to sort).When i is 1, goods yard point is indicated
1；Goods yard point 2 is indicated when i is 2, then a group chromosome is randomly generated in remaining and so on, then use coded strings G:1,2,3,
4,5,6,7 ... K indicate picking path.
(2) initialization population
It is first randomly generated initial chromosome group, i.e., the picking sequence of picking list is generated at random, wherein each chromosome
Gene order increased according to physical meaning, population scale N=80.
(3) fitness is determined
It is calculated using the following equation the fitness of individual, the picking time is shorter, and the fitness of the individual is higher, is chosen to
Follow-on probability is also bigger
Fitness (x)=Cmax-F(x)
Wherein, F (x) is the target function value of chromosome, CmaxFor objective function maximum value in the same generation population, Fitness
It (x) is fitness function.
(4) selection operation
Population at individual is selected using roulette method.
(5) crossover operation
Cycle count variable gen=0 is enabled first, the use of the method for single point crossing is then T in temperatureNUnder conditions of carry out
Cross and variation passes through crossover probability PcJudge whether individual participates in crossover operation, intersects and occur to randomly choose out at two
Between individual.Such as two parent chromosomes of selection, [1,3,2,6,4,7,5] and [2,4,3,6,1,7,5] is randomly generated two
Natural number r1=2, r2=5, by two male parent chromosome r1And r2Between genetic fragment exchange, obtain after intersection [Isosorbide-5-Nitrae, 2,6,
1,7,5] and [2,3,3,6,4,7,5], crossover probability Pc=0.8.
(6) mutation operation
Gene mutation is carried out using simple point mutation method, in gene order [c1,c2,…,cK] in, randomly choose xiTo dash forward
What is become is greater or lesser, then by the value x ' of changeiIt is put back into sequence and resequences, mutation probability pm=0.4, it generates new
Population.
(7) simulated annealing
The parent of calculating simulation annealing operation and the fitness value of filial generation, i.e. Fitness (Xz) and Fitness (Cz), if T0
=50000 be initial temperature, and constant of the θ between [0,1], N is the number of iterations, then temperature computation formula is TN=T0*θN-1,
During simulated annealing, receive new state using certain probability that Metropolis criterion following formula provides.
(8) more new route
Judge cycle count variable gen < Maxgen, then gen=gen+1, return step (4) otherwise goes to step (7).
(9) if TN<Tend, then algorithm terminates, then returns to optimal solution；Otherwise cooling operation T is executedN=T0*θN-1, return to step
Suddenly (3).
(4) dynamic picking path optimization instance analysis
If there are three same lateral channels and ten identical vertical passages in certain dual zone type warehouse, interconnection is respectively channel
1, channel 2 (intermediate channel), channel 3, longitudinal tunnel are respectively tunnel 1, tunnel 2, tunnel 3 ..., tunnel 10, first row and most
The shelf of latter column are single shelf, other shelf are back-to-back type shelf, and the goods yard quantity of every row's shelf is 40, are shared
800 storage spaces.The entrance in warehouse can walk in the lower left corner (Fig. 2), sorter along channel both direction, and sorter's rises
Initial point and terminating point are the entrance in warehouse, and see Table 1 for details for the general parameter setting in warehouse.With MATLAB to objective function into
Row solves.
The general parameter in 1. warehouse of table
By taking 10 orders (picking list) as an example, blending heredity simulated annealing is respectively adopted to dynamic order-picking policy and biography
The static policies of system carry out picking path optimization.
Total picking time is 2128.4s, total path 3118m, picking sequence under static policies are as follows:
One: 102-118-103-113-114-110-104-105-112-111-116-117-101-115- 109- of order
108-107-106
Order two: 208-207-206-202-209-203-204-205-210-201
Order three: 307-309-303-306-310-313-311-312-302-301-308-305-304
Order four: 407-405-406-408-401-404-403-402
Five: 505-507-510-508-506-502-509-504-516-512-514-515-513-511- 501- of order
503
Six: 601-603-607-606-605-610-609-617-616-614-613-620-619-611- 615- of order
618-612-604-608-602
Order seven: 702-707-711-708-709-713-710-714-712-706-703-704-701-705
Eight: 805-804-801-803-802-806-807-808-810-809-811-813-815-817- 816- of order
814-812
Order nine: 902-901-909-904-908-905-906-911-907-910-903
Ten: 1007-1009-1011-1005-1013-1008-1006-1010-1002-1012-1004-1 003- of order
1001
Total picking time is 2024.8 seconds under dynamic strategy, and total picking path is 2952m, and the calculated result of dynamic picking is such as
Table 2.By taking No. 1 picking list and No. 2 picking lists as an example, in dynamic picking, point of No. 2 picking list goods yards in No. 1 picking single path
Cloth as shown in figure 4, after dynamic picking adjustment No. 1 picking single path as shown in figure 5,10 orders whole picking sequence are as follows:
One: 102-118-103-106-105-107-109-108-110-111-113-112-114-207- 101- of order
117-115-116-104
Order two: 208-202-205-201-209-312-210-206-204-203
Order three: 302-301-303-305-403-310-313-311-307-309-308-306-304
Order four: 402-406-512-408-407-405-404-401
Five: 503-501-504-508-502-506-509-510-516-514-515-617-513-511- 507- of order
505
Six: 602-701-601-603-604-611-612-614-613-616-615-618-619-620- 609- of order
610-605-606-607-608
Order seven: 705-707-812-711-712-714-713-710-709-708-706-703-704-702
Eight: 805-804-808-810-906-814-816-817-815-813-809-811-807-806- 802- of order
803-801
Order nine: 901-902-904-905-908-909-1011-910-911-907-903
Order ten: 1001-1003-1005-1002-1006-1008-1013-1012-1010-1009-1007-1 004
In this example, total picking time of all picking lists is as follows in a cycle:
2 dynamic picking calculated result of table
When quantity on order is 50, calculated result is as shown in table 3.
Order calculated result is opened in 3. dynamic picking 50 of table
As shown in Table 3, the LK algorithm of whole cycle has obtained further optimization, picking road after optimizing through the invention
Diameter and time further shorten, and improve picking efficiency, while solving the problems, such as goods yard adjustment, further demonstrate the present invention
Feasibility, for practical warehouse picking operation have provide foundation.
When picking list is 10,50,100 3 kind, genetic algorithm and blending heredity simulated annealing is respectively adopted
The time and path length of static picking and dynamic order-picking policy are compared and analyzed, algorithm simulating experimental result is shown in Table 4, table
5, table 6.
The comparing result of two kinds of algorithms when 4. order volume of table is 10
The comparing result of two kinds of algorithms when 5. order volume of table is 50
The comparing result of two kinds of algorithms when 6. order volume of table is 100
It can be concluded that according to table 4, table 5 and table 6
(1) genetic algorithm and blending heredity simulated annealing can solve dynamic and static picking routing problem,
Opposite, blending heredity simulated annealing is better than the optimum results of genetic algorithm, and picking efficiency improves 9.45%.
(2) compared to static order-picking policy, dynamic order-picking policy is all smaller on time and path total length, illustrates to use
Dynamic order-picking policy can further save the time, reduce the distance in picking path, improve picking operation efficiency 7.6%.
(3) under the same conditions, order volume is more, and the working efficiency of picking personnel is higher, and dynamic picking mode is more suitable for
Solve high-volume picking list picking task, therefore the research using blending heredity simulated annealing to dynamic picking path optimization
Method is correct.
The invention proposes a kind of dynamic order-picking policy and its implementation, propose the think of that goods yard is adjusted in picking first
Road to picking sequence carry out active path planning, and using dynamically adjust goods yard complete picking task the time it takes it is minimum as
Target establishes picking routing problem mathematical model, using blending heredity simulated annealing to routing problem mathematics
Model is solved, and optimal picking route scheme is finally obtained.The strategy is applicable not only to artificial picking, is also applied for electronics mark
Label sort.In case verification, the present invention show that static and dynamic picks using genetic algorithm and Global Genetic Simulated Annealing Algorithm respectively
Time and path length under cargo interests formula show that in high-volume order, dynamic picking mode can significantly improve picking people
The efficiency of member；Under the same conditions, the optimum results of blending heredity simulated annealing are better than genetic algorithm.Goods of the invention
Position dynamic adjustable strategies are not in the case where increasing current picking list LK algorithm, the road of the subsequent picking list of optimization of maximum possible
Diameter, and can be to avoid secondary sorting.In addition, the cargo of the quantification for pertaining only to next picking list due to adjustment, is not deposited
Adjustment in entire goods yard, so not influencing the existing WMS in warehouse.Therefore designed dynamic order-picking policy is simple and convenient, easily
In implementation, picking efficiency is further improved.The present invention has reference for practical warehouse picking operation.
Claims (9)
1. a kind of implementation method of dynamic order-picking policy, it is characterised in that: the following steps are included:
1) goods yard for recording next picking list that the path of current picking list is passed through, obtains goods yard set A；
If 2) set A in goods yard is not empty set, judge with the presence or absence of some goods yard in the set A of goods yard, and by subtracting the goods yard
The picking time of next picking list of current picking list can be made shorter, if judging result is to exist, will be made in the set A of goods yard
The picking time of next picking list of current picking list reaches shortest goods yard and is set to the goods yard for needing to adjust；
3) while completing the picking task of current picking list, the goods that will need to adjust in next picking list of current picking list
Corresponding the needed picking object shifting in position is put into warehouse entrance, when completing the picking task of next picking list of current picking list
The cargo is picked at warehouse entrance.
2. a kind of implementation method of dynamic order-picking policy according to claim 1, it is characterised in that: in the step 2), if
Goods yard set A is empty set, then goes to step 1) with next picking list of current picking list for current picking list.
3. a kind of implementation method of dynamic order-picking policy according to claim 1, it is characterised in that: in the step 2), if
Judging result is that there is no then go to step 1) with next picking list of current picking list for current picking list.
4. a kind of implementation method of dynamic order-picking policy according to claim 1, it is characterised in that: before the step 1),
According to the distribution of the goods yard of picking list each in a cycle respectively to the path of corresponding picking list according to the most short progress of picking time
Path optimization.
5. a kind of implementation method of dynamic order-picking policy according to claim 1, it is characterised in that: right in the step 2)
The path of next picking list of current picking list according to the remaining goods yard for the picking task for reducing some goods yard the picking time
Most short carry out path optimization, so that it is determined that the picking time of next picking list of current picking list is made to reach shortest goods yard, and
Update the path of the picking list；Then picking is carried out according to step 3)；It then is current with next picking list of current picking list
Picking list, goes to step 1).
6. a kind of implementation method of dynamic order-picking policy according to claim 4, it is characterised in that: before the step 1),
Using the picking time most short target as path optimization, the objective function table for the picking routing problem mathematical model established
It is shown as:
Wherein, ZrFor r-th of picking single total picking time under static order-picking policy；dijFor any two goods yard in warehouse it
Between distance；xijThe path passed through for picking；V is that picking personnel are averaged the speed of travel；trFor needed for picking list r initialization operation
Time；tuTime needed for averagely picking single goods yard for picking personnel；Sr,kThe goods yard for needing to pick for r-th of picking list
Number；K is the maximum value of the coding in the goods yard for needing to pick in picking list.
7. a kind of implementation method of dynamic order-picking policy according to claim 5, it is characterised in that: in the step 2), with
The remaining goods yard picking time most short target as path optimization, the objective function for the routing problem mathematical model established
It indicates are as follows:
I, j ≠ it is adjusted goods yard
Wherein, Z "r+1Total picking time behind s-th of goods yard being adjusted for next picking list removal of r-th of picking list；dij
For the distance between any two goods yard in warehouse；xijThe path passed through for picking；V is that picking personnel are averaged the speed of travel；tr+1
The time required to next picking list initialization operation for r-th of picking list；tuIt is averagely picked needed for single goods yard for picking personnel
Time；Sr+1,kThe goods yard number for needing to pick for next picking list of r-th of picking list；er,sExpression makes r-th of picking list
S-th of goods yard that the picking time of next picking list shortens；K is the maximum value of the coding in the goods yard for needing to pick in picking list.
8. a kind of implementation method of dynamic order-picking policy described according to claim 6 or 7, it is characterised in that: the path optimization
Problem is solved using blending heredity simulated annealing.
9. a kind of implementation method of dynamic order-picking policy according to claim 8, it is characterised in that: the blending heredity simulation
It is object element by picking nonoculture in annealing algorithm, carries out integer coding according to the goods yard picking sequence in picking path；Iteration time
Number T is 500~2000, and population scale N is 80~100, initial temperature T0It is 40000~50000, mutation probability pmValue range
For (0.1,1), crossover probability pcValue range is (0.01,1).
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