CN101968860A - Order sorting method and system - Google Patents
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Abstract
The embodiment of the invention provides order sorting method and system. The method comprises the steps of: generating a storehouse plane layout according to the putting condition of storehouse shelves; then computing the shortest path between nodes in a storehouse; finding out the positions, sizes and the weight of goods required for an order from pre-stored data; computing the shortest path and distance between any two pieces of required goods by using the shortest path between the nodes; and computing the optimal sorting path according to the capacity of sorting equipment and the required quantity of the required goods in the order so as to sort orders by the sorting equipment according to the optimal sorting path. The embodiment of the invention can effectively compute the sorting path, reduce the working cost, enhance the working efficiency, and solve the problems of inconsistent shelf arrangement, relatively small shelf height, and small expense difference caused by storing and taking the goods on different layers of shelves of the same goods position; and sorting workers mainly need to optimize order sorting problem in the horizontal movement distance for sorting work.
Description
Technical field
The present invention relates to the picking field, warehouse in the logistic storage, the particularly a kind of order picking method and system that need in a plurality of tunnels, move horizontally.
Background technology
The warehouse picking is the core of modernized logistics distribution, and the height of the up-to-dateness of logistics distribution depends on the selection technology.Many bulk storage plants or home-delivery center adopt High Level Rack formula tiered warehouse facility, between adjacent two row's shelf a tunnel is arranged, every there is a piler in the tunnel, and piler can vertically move the goods on the vertical shelf in both sides, access tunnel simultaneously with horizontal direction.As shown in Figure 1,0 is the gateway, tunnel, and a, b, c, d, e, f are the several goods points of getting on the vertical shelf, and piler once can be chosen the goods on several goods yards, turns back to 0 container of naming a person for a particular job then and is placed on the transfer system.
At present, there has been the method that much picking is optimized." based on the picking optimization problem of ant group algorithm " (system engineering theory and practice, 2009.2:, the 179-184 page or leaf) and a kind of ant group algorithm is proposed, the access sequence of getting goods point is optimized, so that the activity duration is the shortest.But this method supposition piler one-stop operation can be chosen the goods that all get goods point, does not consider the capacity limit of the container that piler is entrained.
At the problems referred to above, " automatic stereowarehouse picking path optimization Study on Problems " (system engineering theory and practice, 2007.2, the 139-143 page or leaf) a kind of genetic algorithm is proposed, under the limited situation of piler one-stop operation capacity, at a plurality of goods points of getting on the vertical shelf, arrange the several times picking to make total selection cost minimum.As shown in Figure 2, twice picking of expression among Fig. 2, primary selection is 0-a-e-f-0 in proper order, selection for the second time is 0-b-c-d-0 in proper order.Need advance to the problem of choosing in other tunnel for piler, this method can not solve, certainly, for automatic stereowarehouse, all there is a piler in general each tunnel, be responsible for the goods on this tunnel both racks of selection, the piler in a certain tunnel does not need to advance to other tunnel and chooses.
In some cases, piler of needs is chosen the goods in a plurality of tunnels, and " research of many tunnels fixed goods shelf picking optimization problem " (control and decision-making, 2008.12, the 1338-1342 pages or leaves) is studied this problem.As shown in Figure 3, piler is from 0, choose successively tunnel 1, tunnel 2 ..., the goods in the j both racks of tunnel, then turn back to 0 point, in the selection process, the container finite capacity that piler is entrained is filled one case goods and is just turned back at 0 goods is placed on the transfer system, and, just can enter next tunnel after the picking in last tunnel is all finished and carry out operation.
But, to put complexity more for shelf, and need in a plurality of tunnels, move the situation of choosing, said method can not solve.Shelf are different in size in the warehouse, differ anyhow, need the operating personnel to move these goods of selection in a plurality of tunnels.For example, the delivery operation that replenishes in large supermarket, bookstore, pharmacy, on-line shop, warehouse etc.In this class enterprise, the type of merchandize that the batch of order is less, order is related is more, need choose in a wider context, the shelf height is not high in this class warehouse, many pickings are by manually carrying out, and the operating personnel can easier take off the commodity in the shelf high level, therefore, operation distance in vertical direction can be ignored, and said method can not head it off.
Summary of the invention
The embodiment of the invention provides a kind of order picking method and system, by finding the solution best selection path after goods yard, size and the weight of searching required goods place in the order from database; Can calculate the selection path efficiently, reduce operating cost, improve operating efficiency.
For achieving the above object, the embodiment of the invention provides a kind of method of order selection, and the order that is applied to move horizontally in a plurality of tunnels is chosen, and described method comprises: according to the display case of warehouse shelf, generate the warehouse floor plan; Calculate and store the information of tunnel and node; Shortest path in calculating and the storage repository between each node; The goods yard information of storage goods, the size and the weight information of goods;
When carrying out the order selection, goods yard, size and the weight of from the data that prestore, searching required goods in the described order; According to the size of the quantity of required goods in the described order and the goods that finds and the demand that weight is calculated required goods; And the shortest path between each node of utilization storage in advance calculates the shortest path between any two required goods;
Demand according to required goods in the capacity of storting apparatus, shortest path between described any two required goods and the described order, find the solution best selection path by genetic algorithm and LK algorithm, so that described storting apparatus carries out the order selection according to described best selection path.
The embodiment of the invention also provides a kind of system of order selection, and the order that is applied to move horizontally in a plurality of tunnels is chosen, and described system comprises: server, WLAN and portable terminal; Wherein, described server comprises:
Mapping unit according to the display case of warehouse shelf, generates the warehouse floor plan; Calculate the information of tunnel and node; Calculate the shortest path between each node in the warehouse;
Storage unit, the information of goods yard, size and the weight of storage goods, and the information of described tunnel and node; Shortest path in the storage repository between each node;
Computing unit is used for searching from the data that prestore goods yard, size and the weight of required goods the described order; According to the size of the quantity of required goods in the described order and the goods that finds and the demand that weight is calculated required goods; And the shortest path between each node of utilization storage in advance calculates the shortest path between any two required goods;
Find the solution the unit, be used for capacity, the shortest path between described any two required goods and the demand of the required goods of described order according to storting apparatus, find the solution best selection path by genetic algorithm and LK algorithm, so that described storting apparatus carries out the order selection according to described best selection path.
The beneficial effect of the embodiment of the invention is, by finding the solution best selection path after goods yard, size and the weight of searching required goods place in the order from database; Can calculate the selection path efficiently, reduce operating cost, improve operating efficiency; Solution put at shelf differ, the shelf height is less relatively, used cost difference is little during to the goods access on the shelf of same goods yard different layers, picking need be optimized mainly is that the picking personnel move horizontally the selection problem that the situation of distance places an order.
Description of drawings
Accompanying drawing described herein is used to provide further understanding of the present invention, constitutes the application's a part, does not constitute limitation of the invention.In the accompanying drawings:
Fig. 1 is the access synoptic diagram of lane stacker on vertical shelf in the prior art;
Fig. 2 is the twice access operation synoptic diagram of lane stacker on vertical shelf in the prior art;
Fig. 3 is the selection synoptic diagram of piler in many tunnels in the prior art;
Fig. 4 is the process flow diagram of the order picking method of the embodiment of the invention 1;
Fig. 5 is the process flow diagram of the order picking method of the embodiment of the invention 2;
Fig. 6 is the synoptic diagram in the warehouse of the embodiment of the invention 2;
Fig. 7 is the process flow diagram of the optimizing process of the embodiment of the invention 2;
Fig. 8 is the process flow diagram of the interlace operation of the embodiment of the invention 2;
Fig. 9 is the process flow diagram of the mutation operation of the embodiment of the invention 2;
Figure 10 is the iteration synoptic diagram in the optimum individual process of obtaining of the embodiment of the invention 2;
Figure 11 is the selection path synoptic diagram that the optimum individual of the embodiment of the invention 2 is represented;
Figure 12 is the pie graph of the order radio frequency of the embodiment of the invention 3
Figure 13 is the formation synoptic diagram of the server of the embodiment of the invention 3;
Figure 14 is the formation synoptic diagram of the path acquiring unit of the embodiment of the invention 4.
Embodiment
For making the purpose, technical solutions and advantages of the present invention clearer, the embodiment of the invention is described in further detail below in conjunction with accompanying drawing.At this, illustrative examples of the present invention and explanation thereof are used to explain the present invention, but not as a limitation of the invention.
The embodiment of the invention provides a kind of method of order selection, is applied to comprise the order of multiple required goods, and required cargo storage is on the goods yard in warehouse, and vertically tunnel and horizontal tunnel form node.
As shown in Figure 4, described method comprises:
In the present embodiment, order can comprise the quantity of multiple required goods and every kind of required goods.The information such as goods yard, size and weight at every kind of required goods place can be stored in the database in advance.
In the present embodiment, the required cargo storage in the order on shelf, with a kind of cargo storage in same goods yard.Shelf put differ, the shelf height is less relatively, used cost difference is little during to the goods access on the shelf of same position different layers, the picking personnel need utilize storting apparatus to move horizontally in a plurality of tunnels order is chosen.
By the foregoing description as can be known, by finding the solution best selection path after goods yard, size and the weight of from database, searching required goods place in the order; Can calculate the selection path efficiently, reduce operating cost, improve operating efficiency.
Embodiment 2
The embodiment of the invention provides the method for a kind of order selection, is applied to comprise the order of multiple required goods, below on the basis of embodiment 1 said method is elaborated, and the content identical with embodiment 1 repeats no more.
As shown in Figure 5, the method for the above order selection comprises:
In one embodiment, the access site of required goods is in the tunnel, and vertically tunnel and horizontal tunnel form node.For example, as shown in Figure 6, the tunnel is the passage for picking personnel walking of access goods, supposes and has only two kinds of straight line tunnels of vertical and horizontal.Node is the point of crossing in vertical tunnel and horizontal tunnel.The goods yard is the unit of storage goods, must be arranged in the goods in this goods yard of ad-hoc location ability access in tunnel, the goods yard access site that has is in its tunnel, the left side, must go on its tunnel, right side during the goods yard access that has, the goods yard access site that has is in the tunnel of its next-door neighbour's top, and the access site that has is in the tunnel of its next-door neighbour's bottom.
In one embodiment, in order to generate and store the information of these elements, the plane, warehouse is divided into some row and columns, the ranks crossover location is a warehouse cell, this warehouse cell can be that unit, a tunnel also can be the unit, goods yard, therefore, can design " warehouse cell " a kind of like this data structure and write down the information of each warehouse cell; Can adopt the form of matrix, the element value of the capable n row of the m of matrix is 1, and the cell of the capable n row of the m in expression warehouse is a goods yard, and gets the goods yard and put tunnel up; Value is 2 to be expressed as the goods yard, get the goods yard put below the tunnel; Value is 3 to be expressed as the goods yard, gets the goods yard and puts the on the left side tunnel; Value is 4 to be expressed as the goods yard, gets the goods yard and puts tunnel on the right; Value is that 5 expression this element lattice are unit, a tunnel; Value is 6 to be expressed as a barrier unit." warehouse cell " matrix has been noted the information of each unit, warehouse, and matrix can calculate all goods yards, tunnel and node thus.
In one embodiment, can also design " tunnel " this data structure, this data structure also can be a matrix.For example, tunnel of a line display of matrix, matrix has four row.The tunnel type is shown in first tabulation, is 1, is expressed as horizontal tunnel, is the vertical tunnel of 0 expression.Secondary series is represented the row number at this place, tunnel or row number, if horizontal tunnel, and this row this tunnel of storage row in " warehouse cell " matrix number; If vertical tunnel, this row this tunnel of storage row in " warehouse cell " matrix number.The reference position in tunnel is shown in the 3rd tabulation, laterally the reference position in tunnel is meant the row of this Far Left unit, tunnel in " warehouse cell " matrix number, and vertically the reference position in tunnel is meant this tunnel unit row in " warehouse cell " matrix number topmost.The end position in tunnel is shown in the 4th tabulation, laterally the end position in tunnel is meant the row of this tunnel rightmost element in " warehouse cell " matrix number, and vertically the end position in tunnel is meant this tunnel row of unit in " warehouse cell " matrix number bottom.
In one embodiment, can from the first row first row beginning of " warehouse cell " matrix line by line the value of searching be 5 element, i.e. unit, tunnel, find unit, a tunnel after, note its row number and row number, suppose that its row number is R, being listed as number is C.
In one embodiment, also can find the horizontal tunnel at place, unit, tunnel and be saved in " tunnel " matrix.Embodiment can be: traversal " tunnel " matrix, searching capable number is the horizontal tunnel of R, if C is between between the initial row in this tunnel number and the end column number, illustrate that this unit, tunnel is positioned on this horizontal tunnel, and the horizontal tunnel at its place is found and has been kept in " tunnel " matrix.Otherwise, continue to look for to be the horizontal tunnel of R next capable number, judge whether this unit, tunnel is positioned on this horizontal tunnel, if after having traveled through " tunnel " matrix, there is not corresponding laterally tunnel, illustrates that then the horizontal tunnel at this place, unit, tunnel also is not recorded in " tunnel " matrix.At this moment, from the capable C row of the R of " warehouse cell " matrix beginning traversal to the right, the value of searching is 5 element.If R is capable, C+1 is listed as, C+2 is listed as ..., C+n row element value be that 5, the C+n+1 column element values are not 5, then found a horizontal tunnel, this tunnel number of being expert at is R, and reference position is C, end position is C+n, and delegation's information in this tunnel is inserted in " tunnel " matrix.In like manner, can find vertical tunnel at this place, unit, tunnel and be saved in " tunnel " matrix.
In one embodiment, can continue to search the tunnel at place, unit, next tunnel, up to having traveled through all unit, tunnel.Unit, a tunnel is positioned on the tunnel at least.So far, write down all tunnel information in " tunnel " matrix, according to " tunnel " matrix, can find out all nodes then, node is the point of crossing in vertical tunnel and horizontal tunnel.Define " node " matrix and note the row number of all nodes, row number, the numbering in this horizontal tunnel, node place, the numbering in this vertical tunnel, node place.Two adjacent nodes on tunnel are adjacent node.
In one embodiment, can calculate internodal shortest path of all of its neighbor and distance earlier.Adjacent node refers to two adjacent nodes on the same tunnel.For any two adjacent nodes each other, if they are positioned on the vertical tunnel, then difference of its row number is its bee-line, if they are positioned on same the horizontal tunnel, then the difference of its row number is its bee-line.Shortest path between them is a straight-line segment that connects these two nodes along its tunnel, place.
In one embodiment, can be saved in the bee-line of any two adjacent nodes in " nodal distance " matrix, the position No. of this matrix is represented node serial number, and the value of element is two bee-lines between the node.Go out all internodal bee-line and shortest paths by Freud Floyd algorithm computation then.Freud's algorithm is a prior art, can repeat no more with reference to " operational research study course " (publishing house of Tsing-Hua University, 1998.6) herein.
In one embodiment because the negligible amounts of node, therefore can the bee-line between the node and shortest path calculate finish after, store on the disk.
In one embodiment, if the access site of two required goods is arranged in same tunnel, then shortest path between two access sites and distance are path and the distance along same tunnel.For example, the ranks of establishing these two access sites number be respectively (x1, y1), (x2 y2), because it is positioned on the same tunnel, therefore has x1=x2 or y1=y2, and its bee-line is
Shortest path promptly is the straight line section from an access site to another access site along this tunnel.
If two access sites are arranged in different tunnels, then can determine the nearest node of this access site of place roadway distance of access site at first respectively; According to shortest path and the distance between any two nodes that prestore, determine shortest path and distance between the nearest node; According to the shortest path between the nearest node and the distance and two access sites determine two shortest path and distances between the access site to the distance between the nearest node.
For example, suppose that these two access sites are respectively unit, tunnel A and tunnel unit B.A1, A2 are two nearest nodes of distance A on the tunnel, A place, and B1, B2 are two nearest apart from B on the tunnel, B place nodes.Path between A and the B must be via its neighbor node separately, and therefore, the bee-line of calculating between A, the B just is converted into the shortest path of asking in A-A1-B1-B, A-A2-B1-B, this four paths of A-A1-B2-B, A-A2-B2-B.Bee-line between any two nodes is tried to achieve in step 502, and distance is easy to try to achieve between A and A1, the A2, because they are positioned on the same straight line tunnel.Distance also is easy to try to achieve between B and B1, the B2.Therefore shortest path between any two goods yards and distance can in the hope of.
Step 505 is found the solution best selection path according to the demand of required goods in the capacity of storting apparatus, shortest path between any two required goods and the order, so that storting apparatus carries out the order selection according to the best selection path.
In an example, the path can (Genetic Algorithm finds the solution on basis GA) in genetic algorithm to find the solution best selection.Genetic algorithm is that professor Holland proposes the earliest, be the effective ways of finding the solution the complex combination optimization problem, typical genetic algorithm mainly comprises selection, intersection, three basic genetic operators of variation, wherein interlace operation is the main search means of genetic algorithm, it is the key factor that influences Algorithm Convergence and search efficiency, interlace operation commonly used has single-point to intersect and two point intersects, and two point intersects the more effective discrete hybrid generation of energy colony, more helps searching out optimum solution.Can repeat no more with reference to prior art herein.
In one embodiment, the crossing operation that can adopt two point to intersect.In the calculating of chromosome fitness value and in the operation of heavily inserting, can use the relevant function in the MATLAB genetic algorithm tool box of Sheffield,England (Sheffield) university.Concrete function can repeat no more with reference to " MATLAB genetic algorithm tool box and application " (publishing house of Xian Electronics Science and Technology University, 2005.4) herein.
In one embodiment, step 505 can specifically comprise the steps:
At first, determine the maximum selection number of times of order according to the demand of demand point and required goods in the capacity of storting apparatus, the order, to obtain K-1 virtual demand point; Wherein, K is the maximum selection number of times of order.
For example, suppose that the storting apparatus capacity is Q, the related demand point number of order is n, and the demand of each demand point is not more than Q, and total demand is P, and the shortest picking number of times K in total selection path is satisfied condition:
Wherein
Expression more than or equal to
Smallest positive integral.
Proof: reduction to absurdity, suppose
Then the cargo dead-weight B of average each picking satisfies following formula:
Therefore, having the cargo dead-weight of certain picking i certainly is that B satisfies
Suppose remaining all picking cargo dead-weight all greater than Q-B, then total picking cargo dead-weight W satisfies following formula:
And total demand is P, and total selection amount Q can not be greater than P+B.Therefore, exist the cargo dead-weight of a certain picking j to be not more than Q-B certainly.Can be connected last demand point of picking i with first demand point of operation j so, merge selection, will obtain shorter selection path.
On the other hand, the number of demand point is n, and the demand of each demand point is not more than Q, and therefore, optimum selection number of times K is no more than n.And
Be obvious, must demonstrate,prove.
The capacity of supposing storting apparatus is Q, and the related demand point of certain order is n, and the aggregate demand of these demand points is P.From the above, the selection number of times of this order should not surpass
In one embodiment, can introduce K-1 virtual demand point (promptly join the goods point, its goods yard numbering can be 0).For example, suppose Q=100, n=8, the goods yard of the demand point that this order is involved is numbered " 138,4,9,330,40,30,55,149 ", and these 8 goods yards goods amount of respectively asking for is respectively: 64,34,14,29,16,27,12,31.Can calculate K=5, promptly need 4 virtual demand points.
Secondly, produce a plurality of original selection paths at random according to the goods yard of virtual demand point and required goods, each original selection path comprises (n+K-1) individual demand point; Wherein, n is the quantity of required demand point in the order.
In one embodiment, can produce N initial chromosome at random, N is a population size.Wherein, N is big more, and the optimum selection of last in theory gained path can be better, but can increase operation time.N and n, K do not have strict mathematical relation, if n+k is big more, then the dimension of problem is big more, need bigger N and more iteration optimization number of times, just can obtain a good result.
In one embodiment, can construct chromosome by the goods yard at K-1 virtual demand point and required goods place.For example, be example with the above order, the goods yard that is numbered " 138,4,9,330,40,30; 55,149 " can be mapped to 8 natural numbers of 1～8, represent this 8 goods yards with natural sequence " 1,2,3,4,5; 6,7,8 ", with 9,10,11,12 represent 4 virtual demand points.Can produce an arrangement of 1～12 these 12 integers at random, this arrangement is body one by one.If population scale is N, then produce N such individuality at random, promptly form initial population.
For example, the chromosome of " 10,1,2,8,9,6,3,12,11,5,4,7 " can represent to have carried out three pickings, and the selection route of these three pickings is respectively: " 0-138-4-149-0 ", " 0-30-9-0 " and " 0-40-330-55-0 ".When a chromosome is mapped to a plurality of picking of an order, chromosomal head and the tail are considered as respectively existing a virtual demand point, expression is joined the goods point from joining the goods point and turning back to, and in addition, two and plural continuous virtual demand point is considered as a virtual demand point.According to such scheme,, can contain all possible selection situation of this order of selection number of times from 1 to K by introducing K-1 virtual demand point.
From the above, chromosomal length can be defined as L=n+K-1, and its formed L dimension space has been contained the optimum solution space.Show by experiment, introduce more virtual point and suppose promptly that also more picking number of times only can enlarge the dimension of problem, increase useless search volume, be unfavorable for improving the quality and the speed of convergence of separating, this is consistent with theoretical analysis.
According to above-mentioned definite coding method, utilize the shortest path between any two required goods, by genetic algorithm and LK algorithm initial population is carried out repeatedly iteration optimization, and determine best selection path the population after optimizing.
In one embodiment, can determine best selection path from the population after optimizing, storting apparatus carries out the order selection according to this best selection path.For example, if the corresponding bee-line of the individuality " 10,1,2,8,9,6,3,12,11,5,4,7 " after optimizing is 132; " 10,5,2,3,9,6,8,12,11,1,4,7 " corresponding bee-line is 156.Then can determine the selection route " 0-138-4-149-0 " that " 10,1,2,8,9,6,3,12,11,5,4,7 " are corresponding, " 0-30-9-0 " and " 0-40-330-55-0 " is best selection path, and the distance of its correspondence is 132.
In one embodiment, selection operation, interlace operation, mutation operation that can be by genetic algorithm, heavily insert operation initial population is carried out repeatedly iteration optimization.
As shown in Figure 7, to chromosomal selection operation in the population, specifically can comprise:
In one embodiment, make the genetic algorithm premature convergence for fear of more excellent individual breeding rapidly, can adopt based on functional value from small to large linear ordering calculate the method for fitness.The ideal adaptation degree of minimum function value is 2 in the population, and the ideal adaptation degree of ultimate range is 0.
For example, 3 chromosome A, B, C are arranged, their pairing paths are respectively 100,80,50, and then individual A is the longest in this 3 paths, and its ordering is 3, and promptly the Pos value is 3, and its fitness value is 2-2 * (3-1)/(3-1) be that fitness is 0; And the distance of B arranges the 2nd, and promptly the Pos value is 2, and its fitness value is 2-2 * 1/2=1; Similarly, the fitness value of C is 2.
In one embodiment, when picking has surpassed the capacity of storting apparatus, go back penalty coefficient of definable and come the situation of violating the storting apparatus capacity-constrained is punished.Add this penalty value on the basis of its path distance, make its corresponding functional value become big, fitness diminishes.
For example, be example with individual " 10,1,2,8,9,6,3,12,11,5,4,7 ", the selection route of its expression is respectively: " 0-138-4-149-0 ", " 0-30-9-0 " and " 0-40-330-55-0 ".This three pickings selection amount separately is respectively: 64+34+31=129,27+14=41 and 16+29+12=57, can see, because the capacity Q=100 of storting apparatus, first picking has surpassed the capacity of storting apparatus, penalty coefficient Pe=100 of definable comes the situation of violating the storting apparatus capacity-constrained is punished that total travel distance of supposing these three pickings is D that then this chromosomal functional value is Fv=D+ (129-100) * 100.This functional value is big more, and fitness value is more little.
Step 702 obtains the selecteed probability of chromosome according to fitness:
Wherein N is a population scale.
Step 703 is selected chromosome of future generation according to selecteed probability from a plurality of chromosomes.
In one embodiment, can adopt the roulette wheel back-and-forth method, select corresponding chromosome to enter the next generation according to the generation gap parameter of setting.For example, it is 0.9 that the generation gap parameter can be set, and promptly each has 10% optimum individual directly to copy among the next generation in generation.
As shown in Figure 8, a plurality of chromosomes are carried out interlace operation, specifically can comprise:
Step 801 according to default crossover probability, is selected chromosome to be intersected.
In one embodiment, can match in twos, can carry out the reorganization of two point intersection by crossover probability Pc to new individuality.
Step 802 is any two chromosome assortative mating zones to be intersected.
In one embodiment, can be at random select a mating zone in two individualities, for example two individualities and mating zone can be chosen to be: A=" 10,1,2; | 8,9,6,3,12; | 11,5,4,7 ", B=" 9; 8,3, | 11,6; 4,1,2, | 5; 10,7,12 ", wherein the part between two " | " is the mating zone.
Step 803 exchanges the chromosomal mating zone behind two assortative mating zones;
In one embodiment, the mating zone exchange of A and B for example, can be able to be obtained: A '=" 10,1,2,11,6,4,1,2, | 11,5,4,7 ", B '=" 9,8,3, | 8,9,6,3,12, | 5,10,7,12 ".
Step 804 is for the chromosome after each exchange removes the gene of repetition.
In one embodiment, the gene that removes repetition for the chromosome after each exchange specifically can comprise: find the allele in the exchange prochromosome of duplicate factor correspondence; With the duplicate factor in the chromosome after the allele replacement exchange.
With A ' is example, can search the intersection region previous section gene identical earlier, can see that gene 1 and 2 has repetition, at this moment with gene in the intersection region, find the allele 3 and 12 of gene 1 and 2 in A in the intersection region, with the duplicate factor 1 and 2 before its replacement intersection region.Obtain: A '=" 10,3,12, | 11,6; 4,1,2, | 11,5; 4,7 ", then, search the intersection region aft section gene identical with the intersection region, can find that gene 11 and gene 4 all have repetition, at this moment, find gene 11 and allele 8 and the gene 6 of gene 4 in A in the intersection region, duplicate factor 11 and gene 4 with behind these two genes replacement intersection regions obtain: A '=" 10,3; 12, | 11,6,4,1; 2, | 8,5,6,7 ".
Then, there is identical gene 6 intersection region among the intersection region of checking A ' and the A, with the gene in the A ' intersection region 6, replaces with its allele 9 in A, at last A '=" 10,3,12, | 11,9,4,1,2, | 8,5,6,7 ".Replace duplicate factor before and after the intersection region for B ' with identical method, obtain two new individualities at last and be: A '=" 10,3,12,11,9,4,1,2, | 8,5,6,7 ", B '=" 6,11,1, | 8,9,4,3,12, | 5,10,7,2 ".
As shown in Figure 9, a plurality of chromosomes are carried out mutation operation, specifically can comprise:
Step 901 according to default variation probability, is selected chromosome to be made a variation.
In one embodiment, the probability that can make a variation is set to Pm, can select chromosome to be made a variation according to Pm.
Step 902 is selected genetic fragment to be made a variation at random from chromosome to be made a variation.
In one embodiment, when individuality morphs, can select a variation fragment at random.With individual A=" 10,1,2,8,9,6,3,12,11,5,4,7 " is example, suppose A=" 10,1,2, | 8,9,6,3,12, | 11,5,4,7 ", wherein part is for being about to the fragment of variation between two " | ", genetic fragment then to be made a variation is " 8,9,6,3,12 ".
Step 903 is treated the genetic fragment of variation and is carried out turning operation, with the chromosome after obtaining making a variation.
In one embodiment, the individual A ' after this fragment of overturning obtains making a variation=" 10,1,2, | 12,3,6,9,8,11,5,4,7 ".
Further, also can a plurality of chromosomal heavy insertion operations directly be joined chromosome optimum in a plurality of chromosomes in the follow-on chromosome.For example, can restrain, 10% optimum individual in the parent directly can be copied among the next generation in order to guarantee genetic algorithm.
By above-mentioned steps, can carry out repeatedly obtaining best selection path after the iteration optimization to original chromosome.Using genetic algorithm that an order that contains 25 demand points has been carried out repeatedly experiment, even find that population size is made as 500, after the iteration 100 times, some optimum individuals represented separate in choose route and still deposit the unnecessary round situation of turning back.
Further, take place for fear of above-mentioned situation, the further quality of optimization solution can be introduced LK (Lin_Kernighan) algorithm and the optimum individual in each generation is carried out back optimize, and determines the best path of choosing according to the population after optimizing once more and shortest path and distance.Wherein, basic LK algorithm is that S.Lin and B.W.Kernighan proposed the earliest in 1973, can repeat no more with reference to prior art herein.
In one embodiment, can carry out repeatedly iteration optimization to a plurality of original chromosomes by genetic algorithm and LK algorithm, wherein an iterative process can comprise: by genetic algorithm a plurality of chromosomes are selected, intersect, make a variation, heavily inserted operation, obtain follow-on a plurality of chromosome; By the LK algorithm individuality optimum in follow-on a plurality of chromosomes is optimized.
Wherein, can use basic LK algorithm that a plurality of pickings in the optimum individual in each generation (being a plurality of hamiltonian circuits) are optimized, then recomputate this individual fitness value, and it is copied among the next generation, improve the quality of separating with this, can be this algorithm called after GA-LK algorithm.Why only the optimum individual in each generation being carried out that the back optimizes is to consider the running time of algorithm problem, is compromise between quality of separating and operational efficiency.
By the GA-LK algorithm of such scheme, can carry out the back to the optimum individual in each generation and optimize.Rule of thumb be worth, the value of general crossover probability Pc can be 0.4～0.99, and the value of variation probability P m can be 0.0001～0.1.
In one embodiment, 3 combinations of crossover probability Pc and variation probability P m can be chosen, Pc=0.6 can be respectively, Pm=0.05; Pc=0.8, Pm=0.1; Pc=0.9, Pm=0.1; Why select bigger variation probability to be because consider that the singularity of path optimization and the specific operation of chromosomal variation are the specific chromosome segments of counter-rotating, and according to former experiment, find that there are situations such as reciprocal in some path, the counter-rotating chromosome segment is head it off to a certain extent.Each combination has been carried out repeatedly experiment at random for specific population scale and iterations, finds as crossover probability Pc=0.8, and during variation probability P m=0.1, experimental result is best.
In order to verify the validity of such scheme, produce an order (seeing 25 demand points of mark among Fig. 6) that contains 25 demand points at random, 25 goods yard numberings are respectively: 186,98,116,48,217,52,54,26,141,143,47,222,36,225,37,31,138,220,65,234,126,30,45,169,5, the demand in each goods yard is 16,19,17,18,11,18,2,2,14,3,13,20,6,11,6,7,13,17,4,8,4,9,10,15,3, and the storting apparatus capacity limit is 100.Carry out repeated experiments at random 3 times for a certain population size and the iterations set, GA-LK algorithm and the GA algorithm that does not use the LK algorithm to carry out optimizing the back are contrasted, be shown in Table 1.
Table 1
As shown in table 1, " optimum selection path " optimum selection path in the table 1 for being obtained in the repeated experiments at random 3 times, 3 the pairing selection path of optimum individual mean values " on average choosing path " and obtain for 3 times for repeated experiments, " optimal path loop number " is for repeating the picking number of times of this represented order of optimum individual in 3 experiments.
As can be seen from Table 1, in 3 repeated experiments of under identical population size and iterations, carrying out at random, the optimum individual that the GA-LK algorithm is obtained and on average choose path and all significantly be better than the GA algorithm, the GA-LK algorithm has also shown ratio speed of convergence faster, population size is set to 100, iterations is 100 can converge to optimum solution substantially, and will obtain separating of same quality, and it be 500 iteration 100 times that the GA algorithm needs population size.
Listed experiment in the table 1 is 500 at population scale, and during iteration 100 times, average 75 seconds consuming time of GA-LK algorithm, and average 40 seconds consuming time of GA algorithm are can satisfy actual needs fully.
Figure 10 and Figure 11 are numbered 4 experiment to obtain iteration situation and the represented selection path of optimum individual in the optimum individual process, this order need be decomposed into three pickings as can be seen from Figure 11, demand point next door is annotated among Figure 11 is numbered the job order of this demand point in this picking number, these are all drawn according to optimum chromosome sequence programming, carry out picking in order to the pilot operationp personnel.There is not the unnecessary round situation of turning back in each picking in the optimum as can be seen from Figure 11 selection path.
Why the concussion situation appears in the fitness value that the kind group mean is separated among Figure 10, is because the penalty coefficient that is provided with in the algorithm is 100, has the picking amount to surpass the situation of storting apparatus capacity in the population; In addition, in the search volume of algorithm, can produce the worse individuality of some quality, therefore, the GA-LK algorithm can not guarantee that kind of the group mean lattice of lifting a curfew successively decrease.
As can be seen from Figure 10, promptly during the 0th generation, in 500 individualities of Chan Shenging, the selection path total length of optimum individual correspondence is about 400 at random before iteration, is 184 through the selection path of 100 iteration optimum individual correspondences.Through repeatedly testing these data is stable basically, also promptly produce at random in 500 selection paths, optimal path length average out to 400, through optimal path length behind the execution GA-LK algorithm is about 180, shortened 55%, also promptly saved half many selection time, visual effects is very tangible.
From theoretical analysis, carry out once when the calculating in path only needs system initialization between the drafting of warehouse floor plan and any two nodes, when order is chosen, can calculate by the shortest path between the node when need calculating between two goods yards shortest path, because, must be through the node on its next-door neighbour's the same tunnel at the shortest path between two goods yards on the same tunnel, and shortest path only needs a subtraction to try to achieve between two goods yards on the same tunnel.The quantity in goods yard has thousands of, can not the shortest path between any two goods yards be stored, but, can the shortest path between any two nodes be stored, because the quantity of node is very limited, when calculating between two goods yards shortest path by node between shortest path only need 16 plus and minus calculations can obtain shortest path between these two goods yards.When drawing between two goods yards shortest path, also need draw by the node on the shortest path.
In addition, the capacity limit of above-described storting apparatus mainly contains two dimensions, and one is volume, and one is dead weight capacity.The demand of every kind of required goods also has two dimensions, and one is volume, and one is weight.And picking should be satisfied the storting apparatus capacity limitation and satisfies dead weight capacity restriction again, when calculating the maximum selection number of times of order, calculate from volume and these two aspects of dead weight capacity respectively, then get bigger that number and multiply by a experience factor again and choose number of times as maximum greater than 1.Be that the path of this chromosome correspondence adds that penalty value on these two dimensions is as this chromosomal functional value, according to this this chromosomal fitness value of functional value calculating when calculating a chromosomal functional value.In most of practical applications, characteristics according to practical problems, the restriction that only needs to pay close attention to a certain dimension gets final product, and for example only need pay close attention to the capacity limitation of storting apparatus for the lighter article of weight, only need pay close attention to the dead weight capacity restriction of storting apparatus for the little and heavier article of volume.
By the foregoing description as can be known, after goods yard, size and the weight of from database, searching required goods place in the order, find the solution best selection path; Can calculate the selection path efficiently, reduce operating cost, improve operating efficiency; Solution put at shelf differ, the shelf height is less relatively, used cost difference is little during to the goods access on the shelf of same goods yard different layers, picking need be optimized mainly is that the picking personnel move horizontally the selection problem that the situation of distance places an order.
Embodiment 3
The embodiment of the invention provides a kind of system of order selection, and the order that is applied to move horizontally in a plurality of tunnels is chosen, and as shown in figure 12, described system comprises: server, WLAN and portable terminal; Wherein,
As shown in figure 13, server comprises: mapping unit 1301, storage unit 1302, computing unit 1303, find the solution unit 1304; Wherein,
Mapping unit 1301 is used for the display case according to the warehouse shelf, generates the warehouse floor plan; Calculate the information of tunnel and node; Calculate the shortest path between each node in the warehouse;
Storage unit 1302 is used to store the information of goods yard, size and the weight of goods, and the information of described tunnel and node; Shortest path in the storage repository between each node;
Computing unit 1303 is used for searching from the data that prestore goods yard, size and the weight of required goods the described order; According to the size of the quantity of required goods in the described order and the goods that finds and the demand that weight is calculated required goods; And the shortest path between each node of utilization storage in advance calculates the shortest path between any two required goods;
Find the solution unit 1304 and be used for capacity, the shortest path between described any two required goods and the demand of the required goods of described order according to storting apparatus, find the solution best selection path by genetic algorithm and LK algorithm, so that described storting apparatus carries out the order selection according to described best selection path.
Each ingredient of the device of present embodiment is respectively applied for each step of the method that realizes previous embodiment, owing in method embodiment, each step is had been described in detail, does not repeat them here.
By the foregoing description as can be known, after goods yard, size and the weight of from database, searching required goods place in the order, find the solution best selection path; Can calculate the selection path efficiently, reduce operating cost, improve operating efficiency.
Embodiment 4
The embodiment of the invention provides the system of a kind of order selection, is applied to the order selection that need move horizontally in a plurality of tunnels, below on the basis of embodiment 3 described system is further specified, and the place identical with embodiment 3 repeats no more.
Described system comprises: server, WLAN and portable terminal; Wherein, server comprises: mapping unit 1301, storage unit 1302, computing unit 1303, find the solution unit 1304, as described in embodiment 3.
In one embodiment, can be deployed with database management system software on the server, information such as the goods number in the warehouse, title, kind, weight, size, goods yard, place all are kept in the database.During system initialization, can on server, move the drawing program of floor plan,, the floor plan in warehouse be stored according to the storage organization of warehouse floor plan.
In one embodiment, the drafting of floor plan can divide several steps: the first step, draw a grid array earlier, the line number of grid array equals horizontal goods yard number and port number sum in the warehouse, and the columns of grid array equals vertical goods yard number and port number sum in the warehouse; Second step, allow the user can a plurality of grid cells of Continuous Selection by mouse and keyboard, the type of these grid cells is set, mainly contain several types such as unit, goods yard, unit, tunnel, if the unit, goods yard, the access site that also needs the user further to indicate this goods yard is (four selections in upper and lower, left and right to be arranged) on which tunnel of its next-door neighbour.In the 3rd step, user's setting is saved in the warehouse cell matrix, and further converts file to and be saved on the disk.In the 4th step, can pass through shortest path and distance between any two nodes of Freud's algorithm computation, and be saved in disk.System initialization work only need be carried out once, only need access the file in the disk when moving for the second time to draw out the warehouse floor plan.
In one embodiment, when an order arrived, computing unit 1303 can be searched the goods yard of goods and size, the weight of goods according to the numbering of required goods in this order in database; Then, according to the size of the quantity of required goods and the goods that finds and the demand that weight is calculated required goods; And calculate shortest path and the distance between any two kinds of goods in this order.
Finding the solution unit 1304 can be according to size, the quantity of goods required in the order, and the capacity of storting apparatus, adopts embodiment 2 described GA-LK algorithms, finds the solution optimum selection path.
As shown in figure 14, finding the solution unit 1304 specifically can comprise: number of times determining unit 1401, path generation unit 1402 and path acquiring unit 1403; Wherein,
The demand that number of times determining unit 1401 is used for the capacity according to described storting apparatus, the required demand point of described order and required goods is determined the maximum selection number of times of described order, to obtain K-1 virtual demand point; Wherein, K is the maximum selection number of times of described order;
In one embodiment, can send to the optimum selection path of trying to achieve on the portable terminal by wireless network, these portable terminals are placed on the storting apparatus, and the picking personnel can handle storting apparatus according to the selection path of the best and choose.For a big order, can resolve into the situation of more picking, can be successively with the path drawing of each picking on portable terminal, after the operation personnel have finished this picking, draw the selection path of next operation again, perhaps a plurality of pickings path of this order is plotted in respectively on a plurality of picking personnel's the portable terminal to guide a plurality of operating personnels simultaneously an order to be chosen.
Each ingredient of the device of present embodiment is respectively applied for each step of the method that realizes previous embodiment, owing in method embodiment, each step is had been described in detail, does not repeat them here.
By the foregoing description as can be known, by finding the solution best selection path after goods yard, size and the weight of from database, searching required goods place in the order; Can calculate the selection path efficiently, reduce operating cost, improve operating efficiency; Solution put at shelf differ, the shelf height is less relatively, used cost difference is little during to the goods access on the shelf of same goods yard different layers, picking need be optimized mainly is that the picking personnel move horizontally the selection problem that the situation of distance places an order.
The professional can also further recognize, the unit and the algorithm steps of each example of describing in conjunction with embodiment disclosed herein, can realize with electronic hardware, computer software or the combination of the two, for the interchangeability of hardware and software clearly is described, the composition and the step of each example described prevailingly according to function in the above description.These functions still are that software mode is carried out with hardware actually, depend on the application-specific and the design constraint of technical scheme.The professional and technical personnel can use distinct methods to realize described function to each specific should being used for, but this realization should not thought and exceeds scope of the present invention.
The method of describing in conjunction with embodiment disclosed herein or the step of algorithm can use the software module of hardware, processor execution, and perhaps the combination of the two is implemented.Software module can place the storage medium of any other form known in random access memory (RAM), internal memory, ROM (read-only memory) (ROM), electrically programmable ROM, electrically erasable ROM, register, hard disk, moveable magnetic disc, CD-ROM or the technical field.
Above-described embodiment; purpose of the present invention, technical scheme and beneficial effect are further described; institute is understood that; the above only is the specific embodiment of the present invention; and be not intended to limit the scope of the invention; within the spirit and principles in the present invention all, any modification of being made, be equal to replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (10)
1. order picking method is applied to the order selection that need move horizontally in a plurality of tunnels, it is characterized in that described method comprises:
According to the display case of warehouse shelf, generate the warehouse floor plan; Calculate and store the information of tunnel and node; Shortest path in calculating and the storage repository between each node; The goods yard information of storage goods, the size and the weight information of goods;
When carrying out the order selection, goods yard, size and the weight of from the data that prestore, searching required goods in the described order; According to the size of the quantity of required goods in the described order and the goods that finds and the demand that weight is calculated required goods; And the shortest path between each node of utilization storage in advance calculates the shortest path between any two required goods;
Demand according to required goods in the capacity of storting apparatus, shortest path between described any two required goods and the described order, find the solution best selection path by genetic algorithm and LK algorithm, so that described storting apparatus carries out the order selection according to described best selection path.
2. method according to claim 1 is characterized in that, the shortest path between each node that described utilization is stored in advance calculates the shortest path between any two required goods, specifically comprises:
If the access site of described two required goods is arranged in same tunnel, the shortest path between then described two required goods is the path along described same tunnel;
If the access site of described two required goods is arranged in different tunnels, then determine two pairs of nearest nodes of the described access site of distance on these two tunnels respectively; Obtain the shortest path between these two pairs of nodes the shortest path between each node of storage in advance; Determine shortest path between described two required goods according to described access site to the distance between its nearest node again.
3. method according to claim 1, it is characterized in that, the demand of required goods in shortest path between described capacity according to storting apparatus, described any two the required goods and the described order is found the solution best selection path by genetic algorithm and LK algorithm, specifically comprises:
Determine the maximum selection number of times of described order according to the demand of demand point and required goods in the capacity of described storting apparatus, the described order, to obtain K-1 virtual demand point; Wherein, K is the maximum selection number of times of described order;
Goods yard according to described virtual demand point and required goods produces a plurality of initial chromosomes at random, and each initial chromosome comprises (n+K-1) individual demand point; Wherein, n is the quantity of demand point in the described order;
Utilize the shortest path between described any two required goods, described a plurality of initial chromosomes are carried out repeatedly iteration optimization, and determine described best selection path according to a plurality of chromosomes after optimizing by genetic algorithm and LK algorithm.
4. method according to claim 3 is characterized in that, by genetic algorithm and LK algorithm described a plurality of initial chromosomes is being carried out repeatedly in the iteration optimization, and one time iterative process comprises:
By genetic algorithm a plurality of chromosomes are selected, intersect, make a variation, heavily inserted operation, obtain follow-on chromosome;
By the LK algorithm follow-on optimum chromosome is optimized.
5. method according to claim 4 is characterized in that, described a plurality of chromosomes is selected, and specifically comprises:
Calculate each chromosomal fitness in described a plurality of chromosome;
Obtain the selecteed probability of described chromosome according to described fitness;
From described a plurality of chromosomes, select follow-on chromosome according to described selecteed probability.
6. method according to claim 4 is characterized in that, described a plurality of chromosomes is carried out interlace operation, specifically comprises:
According to default crossover probability, select chromosome to be intersected;
Be any two chromosome assortative mating zones to be intersected;
Exchange the chromosomal mating zone behind described two assortative mating zones;
Remove the gene of repetition for the chromosome after each exchange; Specifically comprise: find the allele in the described chromosome before the exchange of described duplicate factor correspondence; With the described duplicate factor in the chromosome after the described allele replacement exchange.
7. method according to claim 4 is characterized in that, described a plurality of chromosomes is carried out mutation operation, specifically comprises:
According to default variation probability, select chromosome to be made a variation;
From chromosome described to be made a variation, select genetic fragment to be made a variation at random;
Genetic fragment described to be made a variation is carried out turning operation, with the chromosome after obtaining making a variation.
8. method according to claim 4 is characterized in that, described a plurality of chromosomes is heavily inserted operation, specifically comprises: some chromosomes optimum in described a plurality of chromosomes are directly joined in the follow-on chromosome.
9. order radio frequency is applied to the order selection that need move horizontally in a plurality of tunnels, it is characterized in that described system comprises: server, WLAN and portable terminal; Wherein, described server comprises:
Mapping unit according to the display case of warehouse shelf, generates the warehouse floor plan; Calculate the information of tunnel and node; Calculate the shortest path between each node in the warehouse;
Storage unit, the information of goods yard, size and the weight of storage goods, and the information of described tunnel and node; Shortest path in the storage repository between each node;
Computing unit is used for searching from the data that prestore goods yard, size and the weight of required goods the described order; According to the size of the quantity of required goods in the described order and the goods that finds and the demand that weight is calculated required goods; And the shortest path between each node of utilization storage in advance calculates the shortest path between any two required goods;
Find the solution the unit, be used for capacity, the shortest path between described any two required goods and the demand of the required goods of described order according to storting apparatus, find the solution best selection path by genetic algorithm and LK algorithm, so that described storting apparatus carries out the order selection according to described best selection path.
10. system according to claim 9 is characterized in that, the described unit of finding the solution specifically comprises:
The number of times determining unit, the demand that is used for the capacity according to described storting apparatus, described order demand point and required goods is determined the maximum selection number of times of described order, to obtain K-1 virtual demand point; Wherein, K is the maximum selection number of times of described order;
The path generation unit is used for producing a plurality of initial chromosomes at random according to the goods yard of described virtual demand point and required goods, and each initial chromosome comprises (n+K-1) individual demand point; Wherein, n is the quantity of demand point in the described order;
The path acquiring unit, be used to utilize the shortest path between described any two required goods, by genetic algorithm and LK algorithm described a plurality of initial chromosomes are carried out repeatedly iteration optimization, and determine described best selection path according to a plurality of chromosomes after optimizing.
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