CN115456537A - Warehouse picking path planning method and system - Google Patents
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Abstract
The invention provides a warehouse picking path planning method, which comprises the steps of regarding each order number as a point, converting a one-dimensional picking path planning problem into a two-dimensional point clustering problem; clustering all points for the first time by using a rapid clustering algorithm; performing secondary clustering on the points which cannot be clustered after the primary clustering by using a minimum shelf clustering algorithm; performing multiple point exchange operations in the cluster by using a global optimization algorithm until all clusters are traversed to obtain a new cluster; the new cluster is output as a pick-up order, and the path of each cluster and the total path of the pick-up order are calculated at the same time. The invention can classify a large number of orders received by the warehouse into a plurality of sub-orders, so that the repeated path among the sub-orders is smaller, thereby completing the picking of a plurality of orders at one time, greatly reducing the total picking path and greatly improving the working efficiency.
Description
Technical Field
The invention relates to the technical field of warehouse logistics, in particular to a warehouse goods picking path planning method and a warehouse goods picking path planning system.
Background
Warehousing and picking are the most complicated links in a warehousing system and play an important role in the whole supply chain system. With the development of the e-commerce market, the warehouse throughput is larger and larger, and the warehouse pick-up demand is increased in some special time periods such as e-commerce time. Fast sorting and shorter picking paths are the focus of warehouse management, and therefore, a reasonable and efficient warehouse picking path planning method is more and more important.
At present, large-scale storage goods are generally selected by adopting a seeding type goods selection method, a system receives a certain number of orders and then packages the orders and sends the orders to goods selection personnel, and the goods selection personnel select the goods from small to large according to goods shelf codes. However, the large storage order amount is huge, the picking paths of a plurality of picking personnel are repeated in a large amount, the carrying distance during picking is increased, the transportation cost is increased, and meanwhile the working efficiency is reduced due to the collision problem.
Therefore, it is desirable to provide a warehouse picking path planning method to solve the above problems of the conventional method in picking.
Disclosure of Invention
Therefore, the technical problem to be solved by the invention is to overcome the problems in the prior art and provide a warehouse goods-picking path planning method and system, which greatly reduce the total goods-picking path and greatly improve the working efficiency.
In order to solve the technical problem, the invention provides a warehouse goods picking path planning method, which comprises the following steps:
s1, regarding each order number as a point based on the one-dimensional arrangement characteristic of a shelf, and converting a one-dimensional picking path planning problem into a two-dimensional point clustering problem;
s2, clustering all the points for the first time by using a rapid clustering algorithm to obtain a first cluster;
s3, performing secondary clustering on the points which cannot be clustered after primary clustering by using a minimum shelf clustering algorithm to obtain a second cluster;
s4, combining the first cluster and the second cluster into a total cluster, and performing multiple point exchange operations in the total cluster by using a global optimization algorithm until all the total clusters are traversed to obtain an optimized cluster;
and S5, outputting the optimized cluster group as a picking order, and simultaneously calculating the path of each cluster and the total path of the picking order.
In one embodiment of the present invention, a method for clustering all points for the first time by using a fast clustering algorithm comprises:
s21, defining a picking path interval of each order number as [ x, y ], wherein x represents the minimum shelf number of the order number, y represents the maximum shelf number of the order number, and the path length of any point is obtained;
s22, arranging all the points in descending order according to the length of the linked paths to obtain a descending order table of the points, and taking a first point in the descending order table as an initial point C of a first cluster i,j ＝C 1,1 In which C is i,j A jth point representing an ith cluster;
s23, combining the points C 1,1 Taking the corresponding path interval as a limited interval, removing the points of the descending table, which are not included in the limited interval, of the path interval to obtain a new descending table,
s24, sequentially taking points and points C from the new descending order table according to the number of the defects in the clusters 1,1 Clustering to obtain a first cluster, wherein one cluster is a picking order;
s25, storing the clustered clusters, and repeating S22-S24 for the rest points until the clusters cannot be obtained.
In one embodiment of the present invention, the points with the greater path length are preferably selected for clustering when all points are clustered for the first time, so that the points with the greater path length are all within the same pick slip.
In one embodiment of the present invention, the method for clustering points that cannot be clustered after the first clustering by using the minimum shelf clustering algorithm for the second time comprises:
s31, taking the remaining points which are not clustered, and performing ascending arrangement on all the points according to the x values of the points to obtain an ascending table;
and S32, sequentially picking points from the ascending table to form clusters according to the required order quantity in each order picking sub-order to obtain a second cluster.
In an embodiment of the present invention, before performing multiple point switching operations in a cluster by using the global optimization algorithm, the cluster is screened, whether the percentage of the number of repeated points included in the cluster to the total number of points is greater than or equal to a preset value is determined, if the determination result is yes, the cluster is removed from the cluster, and the remaining clusters in the cluster are subjected to point switching operations, and if the determination result is no, the clusters in the cluster are subjected to point switching operations.
In one embodiment of the present invention, a method for performing multiple point switching operations within a cluster using the global optimization algorithm comprises:
s41, starting from the cluster i, searching all adjacent clusters, recording as a cluster A, if the cluster A does not contain any cluster, i +1, and returning to the screening operation, wherein the initial value of i is 1;
s42, selecting the m point C in the cluster i i,m As the exchanged point, the initial value of m is 1;
s43, from the cluster A, taking the nth point C in the cluster j j,n As the switching points, the initial values of j and n are 1;
s44, if C j,n If n is the maximum value, j +1 returns to S43;
s45, calculating initial paths and initial paths Z of the cluster i and the cluster j 1 Swapping C within cluster i and cluster j i,m And C j,n To point C, i.e. to point C i,n And point C j,m ；
S46, calculating new paths and Z of the cluster i and the cluster j 2 If Z is 2 <Z 1 If the n is the maximum value, j +1 is carried out, and S43 is returned;
s47, if j is the maximum value, m +1, returning to S42;
s48, if m is the maximum value, i +1, and returning to the screening operation;
and S49, if i is the maximum value, finishing the global optimization.
In addition, the invention also provides a warehouse goods picking path planning system, which comprises:
the path planning conversion module is used for taking each order number as a point based on the one-dimensional arrangement characteristic of the goods shelf and converting the one-dimensional picking path planning problem into a two-dimensional point clustering problem;
the first clustering module is used for performing first clustering on all points by using a rapid clustering algorithm to obtain a first cluster;
the secondary clustering module is used for performing secondary clustering on the points which cannot be clustered after the primary clustering by using a minimum goods shelf clustering algorithm to obtain a second cluster group;
the global optimization module is used for merging the first cluster and the second cluster into a total cluster, and performing multiple point exchange operations in the total cluster by using a global optimization algorithm until all the total clusters are traversed to obtain an optimized cluster;
and the order picking output module is used for outputting the optimized cluster group as an order picking list and simultaneously calculating the path of each cluster and the total path of the order picking list.
In an embodiment of the present invention, the method for the first clustering module to perform the first clustering on all the points by using the fast clustering algorithm includes:
s21, defining a picking path interval of each order number as [ x, y ], wherein x represents the minimum shelf number of the order number, y represents the maximum shelf number of the order number, and the path length of any point is obtained;
s22, arranging all the points in descending order according to the linked path length values of the points to obtain a descending order table of the points, and taking a first point in the descending order table as an initial point C of a first cluster i,j ＝C 1,1 In which C is i,j A jth point representing an ith cluster;
s23, combining the points C 1,1 Taking the corresponding path interval as a limited interval, removing the points of the descending table, which are not included in the limited interval, of the path interval to obtain a new descending table,
s24, sequentially taking points and C from a new descending order table according to the number of the defects in the clusters 1,1 Clustering to obtain a first cluster, wherein one cluster is a picking order;
s25, storing the clustered clusters, and repeating S22-S24 for the rest points until the clusters cannot be obtained.
In an embodiment of the present invention, the method for clustering points that cannot be clustered after the first clustering by the second clustering module using the minimum shelf clustering algorithm for the second time includes:
s31, taking the remaining points which are not clustered, and performing ascending arrangement on all the points according to the x values of the points to obtain an ascending table;
and S32, sequentially picking points from the ascending table to form clusters according to the required order quantity in each order picking sub-order to obtain a second cluster.
In an embodiment of the present invention, the method for the global optimization module to perform multiple point switching operations within a cluster using the global optimization algorithm includes:
s41, searching all adjacent clusters from the cluster i, recording the cluster as a cluster A, if the cluster A does not contain any cluster, i +1, and returning to the screening operation, wherein the initial value of i is 1;
s42, taking the m-th point C in the cluster i i,m As the exchanged point, the initial value of m is 1;
s43, from the cluster A, taking the nth point C in the cluster j j,n As the switching points, the initial values of j and n are 1;
s44, if C j,n If n is the maximum value, j +1 returns to S43;
s45, calculating initial paths and Z of the cluster i and the cluster j 1 Swapping C within cluster i and cluster j i,m And C j,n To point C, i.e. to point C i,n And point C j,m ；
S46, calculating new paths and Z of the cluster i and the cluster j 2 If Z is 2 <Z 1 If the n is the maximum value, j +1 is carried out, and S43 is returned;
s47, if j is the maximum value, m +1, and returning to S42;
s48, if m is the maximum value, i +1, and returning to the screening operation;
and S49, if i is the maximum value, finishing the global optimization.
Compared with the prior art, the technical scheme of the invention has the following advantages:
according to the warehouse goods picking path planning method and system, a large number of orders received by a warehouse can be classified into a plurality of sub-orders, repeated paths among the sub-orders are small, so that goods picking of the plurality of orders is completed at one time, the total goods picking path is greatly reduced, and the working efficiency is greatly improved.
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In order that the present invention may be more readily and clearly understood, reference will now be made in detail to the present invention, examples of which are illustrated in the accompanying drawings.
Fig. 1 is a schematic flow chart of a warehouse picking path planning method according to an embodiment of the present invention.
Detailed Description
The present invention is further described below in conjunction with the following figures and specific examples so that those skilled in the art may better understand the present invention and practice it, but the examples are not intended to limit the present invention.
Referring to fig. 1, an embodiment of the invention provides a method for planning a picking path of a warehouse, including:
s1, regarding each order number as a point based on the one-dimensional arrangement characteristic of a shelf, converting a one-dimensional picking path planning problem into a two-dimensional point clustering problem, and defining a picking path interval of each order number as [ x, y ], wherein x represents the minimum shelf number of the order number, and y represents the maximum shelf number of the order number;
s2, clustering all the points for the first time by using a rapid clustering algorithm to obtain a first cluster;
s3, performing secondary clustering on the points which cannot be clustered after primary clustering by using a minimum shelf clustering algorithm to obtain a second cluster group;
s4, combining the first cluster and the second cluster into a total cluster, and performing multiple point exchange operations in the total cluster by using a global optimization algorithm until all the total clusters are traversed to obtain an optimized cluster;
and S5, outputting the optimized cluster group as a picking order, and simultaneously calculating the path of each cluster and the total path of the picking order.
In S1, each order number contains a plurality of goods, i.e. one order number requires a picker to pick up goods from a plurality of shelf numbers. Based on the characteristic of one-dimensional arrangement of the shelves, the picking personnel picks the order from the minimum shelf number of the order, sequentially passes through the shelf number of the goods and finishes the process from the maximum shelf number. Based on this characteristic, the single order number picking path interval and path length are independent of the number of orders contained, and only depend on the minimum and maximum container numbers. At this time, the data dimension of all order numbers is the same, namely only two shelf numbers are reserved, and one order can be treated as one point. Thus, the problem of order classification is transformed into a clustering problem of geometric points, which is logically easier to handle.
The path length and corresponding point for each order are expressed as follows:
Z＝max(X)-min(X)
P＝(min(X),max(X))
S＝[min(X),max(X)]
wherein X = { X 1 ,x 2 ,…,x m H, m represents the number of orders contained in the same order number, x m The item with the representative serial number m is located on the shelf number, and X is the shelf number set contained in the order number. Z is the path length of the order. P is a point coordinate, wherein x is the minimum shelf number and y is the maximum shelf number. And S is the path section of the point.
Preferably, the software can be operated on a windows platform, an excel l form containing a document is placed in an entry where the software is located, and an order can be imported by inputting a document name, wherein the document contains 2000 orders and 5406 goods. Each order contains one to a plurality of goods, and each goods corresponds to one shelf number. The distribution center needs to combine every 20 orders into a pick order, 100 in total. Each order retains only the minimum shelf number and the maximum shelf number, which are equal if an order has only one item. Regarding an order as a point, the minimum shelf number of the order is used as the x value of the point, the maximum shelf number is used as the y value of the point, and thus 2000 orders are converted into 2000 points, namely 20 orders in the original problem are grouped into 20 point clusters, and the one-dimensional picking problem is converted into the two-dimensional clustering problem.
In S2, the method for clustering all points for the first time by using the fast clustering algorithm includes:
s21, each order corresponds to 2 values, the minimum shelf number corresponds to the maximum shelf number, the path length of all the points is calculated, and the path length is obtained by subtracting the minimum shelf number from the maximum shelf number.
S22, arranging all the points in descending order according to the path length to obtain a descending order table, wherein the first point in the descending order table is the point with the longest path length, and the first point in the descending order table is taken as the initial point C of the first cluster 1,1 。
S23, taking point C 1,1 Taking the corresponding path interval as a limited interval, removing points which are not included in the limited interval in the path interval in the descending table to obtain a new descending table, after obtaining the updated descending table, reducing the number of points in the table, and still arranging the points in the descending table, wherein 19 points are taken from the descending table, namely C 1,2 -C 1,20 And finishing clustering the first cluster.
And S24, repeating the step S23 until a certain cluster cannot be clustered. The failure to cluster means that there are no 19 points in the updated descending list after the cluster has selected the initial point again, i.e. there are a sufficient number of points. At this time, after the point in the descending table and the initial point of the cluster are marked, the point does not participate in the rapid clustering algorithm. It should be noted that the inability of the cluster to cluster does not mean that the fast clustering algorithm is unable to continue clustering, e.g., cluster C 78 Not clustered, but cluster C 79 ，C 80 ，C 81 Clustering can still occur. This is because, after the points that cannot be clustered are excluded, the remaining points continue to participate in step S22, and after the initial point is selected again, the limit section of the initial point is changed, so the number of points in the sort table is also changed, and when the number of points in the table is 20 or more, clustering is still possible.
And S25, when no point participates in the rapid clustering algorithm, the algorithm is ended. About 90% of the dots have clustered at this time, resulting in a first cluster, which now contains 92 clusters.
Based on the path characteristics of the order picking sub-sheets, when the rapid clustering algorithm is clustered, the initial point in each cluster is the point with the maximum path length in the order picking sub-sheet, and the path sections of the remaining points are all contained in the limited section of the initial point. When the cluster is formed, the path of the whole picking order sheet can be determined after the initial point is selected, and the path property of the cluster cannot be influenced when other points are added into the cluster. This may allow for clusters of large initial point paths to accommodate as many points as possible of larger other paths, thereby reducing the pick path as a whole. The points with larger paths are prevented from being mixed in the clusters with smaller paths, so that the paths of the clusters are multiplied.
And sorting the sorted order sub-orders by a rapid clustering algorithm based on the arrangement characteristics of the storage shelves, wherein the total path of each order sub-order is determined by the order with the largest path length contained in the order sub-order. After obtaining the data of all the points, the points with larger path length are preferably selected to be clustered, so that the points with larger path length are all in the same order picking order. The reason for this is to avoid that one order with a large path length is mixed into other order picking sub-orders with a smaller path, so that the path of the order picking sub-order with a short path is suddenly lengthened. When all the points are clustered for the first time, the points with the larger path length values are preferentially selected to be clustered, so that the points with the larger path length values are all in the same order picking sheet.
After the first clustering, based on the limited interval characteristic in the rapid clustering algorithm, the rapid clustering algorithm can pack and classify about 90% of the total order amount into order picking sub-orders for the system to pick. The rest orders cannot be classified by the fast clustering algorithm, but the shelf numbers of the orders are generally close, and various clustering algorithms in actual test have little influence on the final total path. Considering the situation that warehousing throughput needs to be increased in a surge time period, a clustering algorithm with a high speed and a small path is adopted for processing, so that the invention adopts a minimum shelf clustering algorithm to perform secondary clustering on the non-clustered points.
In S3, the method for clustering the points that cannot be clustered after the first clustering by using the minimum shelf clustering algorithm for the second time includes: and taking the remaining points which are not clustered, and performing ascending arrangement on all the points according to the x values of the points to obtain an ascending table, namely, the remaining orders are arranged in ascending order according to the minimum rack number of each order. At the moment, according to the order quantity required in each picking order, namely the number of points required to be contained in the cluster, the points are sequentially taken from the ascending sequence table to form the cluster, and a second cluster group is obtained and contains 8 clusters.
Due to the greedy characteristics of the rapid clustering algorithm and the minimum shelf clustering algorithm, only suboptimal solutions close to optimal solutions can be obtained, and the total path of a cluster still has a further reduced space, so that the invention provides a global optimization algorithm based on a point exchange idea. The idea of point exchange is: in a cluster group formed into clusters, a point in any one cluster is denoted as C i,m Find a certain point C in other clusters j,n If the sum of the paths of the two clusters becomes shorter after the two points are exchanged in the two clusters, the two clusters are recorded as one effective point exchange.
Before the global optimization algorithm is used for carrying out multiple point exchange operations in the cluster, the cluster is screened, whether the percentage of the repeated points in the cluster to the total points is larger than or equal to a preset value or not is judged, if the judgment result is yes, the cluster is removed from the cluster, the point exchange operation is carried out on the rest clusters in the cluster, and if the judgment result is not, the point exchange operation is carried out on the clusters in the cluster. Preferably, the preset value of the present embodiment is 25%.
Based on this, in S4, the method for performing multiple point switching operations within a cluster using the global optimization algorithm includes:
s41, searching all adjacent clusters from the cluster i, recording the cluster as a cluster A, if the cluster A does not contain any cluster, i +1, and returning to the screening operation, wherein the initial value of i is 1;
s42, selecting the m point C in the cluster i i,m As the exchanged point, the initial value of m is 1;
s43, from the cluster A, selecting the nth point C in the cluster j j,n As the switching points, the initial values of j and n are 1;
s44, if C j,n Within cluster j is a switching point, n +1, if n is already at its maximum value, thenj +1, return to S43;
s45, calculating initial paths and initial paths Z of the cluster i and the cluster j 1 Swapping C within cluster i and cluster j i,m And C j,n To point C, i.e. to point C i,n And point C j,m ；
S46, calculating new paths and Z of the cluster i and the cluster j 2 If Z is 2 <Z 1 If the point exchange is carried out in the two clusters, if the point exchange is not carried out, the point exchange is cancelled, n +1 is carried out, if n is the maximum value, j +1 is carried out, and the step S43 is returned;
s47, if j is the maximum value, m +1, returning to S42;
s48, if m is the maximum value, i +1, and returning to the screening operation;
and S49, if i is the maximum value, finishing the global optimization.
The idea of the global optimization algorithm is that effective exchange points may exist in retrieval between two clusters, and the essential is that traversal retrieval is performed on the points, and the algorithm is long in time consumption, so that cluster groups need to be screened, and clusters with low possibility of existence of exchange points are removed, so that the time of algorithm search is shortened. Since the fast picking algorithm will preferentially sort the points with close distance into a cluster, and since many orders contain the same goods, i.e. the shelf numbers are the same, the situation that multiple repeat points exist in a cluster can occur. More repeating points means that the cluster is more compact in real physical space, and the probability of the existence of a switching point is lower. Therefore, a small part of the switching points can be properly abandoned to be changed into a faster running speed. The truth test also verifies this guess and typically the switch points are all contained within clusters where there are no duplication points or few. The 25% screening conditions set by the algorithm were derived from multiple experiments. Compared with no screening, the algorithm is improved by nearly 3 times at an extremely low performance cost after screening. The screening condition can be reset according to the actual situation, for example, the hardware platform is sufficient in computing power, and the screening can be set to be not.
Two clusters are adjacent, where the path intervals interpreted as two clusters intersect. Whereas a swap point may only exist in two intersecting clusters. This is because if there is no intersection between the two clusters of paths, the two clusters are not adjacent in physical space, and the exchange of any two points in the two clusters will result in the change of the path interval of each cluster, which lengthens the total path, so there is no exchange point in the non-adjacent clusters. After a cluster is selected, the operation of finding the switching point is only carried out in the adjacent cluster, so that the search range can be greatly reduced, and the running speed of the algorithm is accelerated. The path segment of a cluster has the same meaning as the path segment of a point, i.e., S = [ min (X), max (X) ], where X is the set of all shelf numbers contained within the cluster.
Generally speaking, most point switching operations have no effect on the path change of the two clusters or result in an increase of the total path, and the effective point switching operations account for a very small part of the total switching operations. Frequent switching points within the cluster increase the algorithm run time and program redundancy. Therefore, during programming, actual point operations and calculations should be performed on a partial copy of the cluster, without affecting the original cluster, and only when a valid point exchange operation occurs, the operation is passed to the cluster for execution. From the whole program, the cluster optimization is carried out in real time along with the program operation. The path length of a cluster is synonymous with the path length of a point, i.e., Z = max (X) -min (X), where X is the set of all shelf numbers contained within the cluster.
The one-time global optimization algorithm approximately executes point operation of 1.5 times of cluster number, and further approaches to the theoretical optimal solution on the basis of greedy thought. The execution of one optimization operation needs to consume about 5 times of the time of the rapid goods picking path algorithm, and the specific optimized path is related to the order number, the cluster number and the number of points in the cluster. From a real scenario, it is worth to exchange more computing power and partial time for a shorter total path, so that further optimization of clusters is necessary. In practice, global optimization may be performed multiple times. This is because the point-switch operation performed each time is not necessarily the best switch operation to shorten the path, which is unpredictable by the algorithm. Meanwhile, the operation of the algorithm on the cluster is essentially the traversal point, and the already exchanged points still have the potential of being exchanged again, which cannot be realized by one traversal. Therefore, the total path of the cluster after one global optimization still has an optimized space. However, in practical tests, if multiple optimizations are performed, the path which can be shortened from the second optimization is exponentially reduced, and the running time is greatly prolonged, which is not suitable for the fast picking requirement of large warehouse. Thus, in the present invention, global optimization is performed only once by default.
For example, taking an example that the first cluster includes 92 clusters and the second cluster includes 8 clusters, the first cluster and the second cluster are combined to obtain a total cluster, and global optimization is performed on the total cluster. In this data, since the data should be divided into 100 clusters and 20 dots per cluster, the maximum value of i, j in the following steps is 100,m, n is 20. The method comprises the following specific steps:
step one, screening a total cluster, and if the number of repeated points contained in a certain cluster is more than 5, removing the cluster from the cluster. There are 76 remaining clusters.
And step two, starting from the cluster i, searching all adjacent clusters thereof and recording as a cluster A. The initial value of i is 1.
Step three, if A does not contain any cluster, i +1, returning to step one
Step four, taking the mth point C in the cluster i i,m At the point of being swapped, m is initially 1.
Step five, from the cluster A, the nth point C in the cluster j is taken j,n As the swap point, the initial values of j and n are 1.
Step six, if C j,n If the cluster j is a switching point, n +1; and if n =20, j +1, and returning to the step five.
Step seven, calculating the initial path sum Z of the cluster i and the cluster j 1 。
Step eight, exchanging C in the cluster i and the cluster j i,m And C j,n To point C i,n And point C j,m . The path length of the original cluster may be changed after the switching point.
Step nine, calculating new paths and Z of the cluster i and the cluster j 2 。
Step ten, if Z 2 <Z 1 If n =20, j +1, and then the step five is returned.
Step eleven, if j =100, then m +1, and returning to step four.
Step twelve, if m =100, i +1, and return to step one.
Step thirteen, if i =100, the global optimization is completed, and a total of 133 point exchanges are performed.
Without loss of generality, cluster C is selected 26 The operation of finding the switching point is demonstrated:
cluster C 26 Has a path interval of [213,1033 ]]Cluster C 28 Has a path interval of [263,1034]The intersection of the two clusters of intervals is [263,1033 ]]And then are adjacent.
Further, cluster C 26 Has a path length of 820, cluster C 28 771, respectively. The current two clusters total path length is 1591.
Further, taking clusters C 26 First point C in 26,1 As a switched point, and sequentially pairs the clusters C 28 The first switching point C is used for switching operation of 26 points 28,1 。
Further, the positions of the two points in the original cluster are exchanged. At this time cluster C 26 Has a path interval of [234,1034]Path length is 800; cluster C 28 Has a path interval of [213,1034],821. The new path length is 1621, which is larger than the original path length and is therefore not an effective point switch.
Further, point C 26,1 And point C 28,2 Exchanging position, finding out that the path length is not reduced after calculation, and continuing to go from the next point C 28,3 Switching until the last point C of the cluster 28,26 No valid switching point has yet been found.
Further, get cluster C 26 Second point C in 26,2 As a switched point, and is newly in the cluster C 28 The possible switching points are retrieved internally. If not, then cluster C is selected 26 Inner third point C 26,3 The switched point is made until a valid switching point is retrieved.
Further, when taking point C 26,17 As a switched point, get point C 28,2 When it is used as a switching point. Cluster C 26 Has a path interval of [213,103 ]4]Path length 821; cluster C 28 Has a path interval of [243,1033 ]],790. The new path length is 1611, which is 10 times smaller than the original path length, and is therefore an efficient point exchange, which is performed in the original cluster.
The above example is merely to further explain the point exchange operation. An active point switch reduces the path to a minimum of 1 and a maximum of 164. It is noted that it is also common for multiple point swaps to occur within two clusters, which can significantly degrade the path sum of the two clusters. For the document data, 133 point exchanges occur in total, the total path is finally reduced by 2501, which is equivalent to the total path sum of 3 to 4 general clusters, and the total running time of the global optimization algorithm is about 4s, which is effective performance improvement.
Wherein, in S5, the total cluster group includes 100 clusters, and each cluster includes 20 points. The total cluster and the pickups are identical in nature, each pickups comprises 100 pickups, and one pickups corresponds to one cluster; a pick order contains 20 points, one for each unique order. In contrast, a unique order may include multiple repeated orders corresponding to multiple shelf numbers, which are hidden during the conversion process into points, and the hidden shelf numbers need to be displayed on the pick-up order in comparison with the original document when the warehouse outputs the pick-up order. Due to the fact that each unique order contains different numbers of repeated orders, the number difference of the shelf numbers contained in each picking order can be large, but the shelf numbers are on the same path and the same as the corresponding cluster path.
Further, the distribution center may calculate the total path distance. The total path distance is the sum of the path distances of all the pick sheets. The path distance for each pick order is equal to the maximum shelf number minus the minimum shelf number of all shelf numbers in the order. The system generates a trend of large-to-small distance of each order picking sub-path, and the situation that the distance of each order picking sub-path is extremely large may occur, so that the total distance of the path is reduced. The invention has effectiveness through real data test.
Further, the delivery center can perform order picking scheduling. The order picking sheet generated by the system has small repeated path among the order picking sub-sheets, can reasonably use the existing order picking equipment or personnel, and prevents blockage, wrong picking and the like caused by path overlapping.
Furthermore, the parameters of the system can be changed to adapt to different warehousing types and different time periods, and the portability is good.
According to the warehouse goods picking path planning method and system, a large number of orders received by a warehouse can be classified into a plurality of sub-orders, repeated paths among the sub-orders are small, so that goods picking of the plurality of orders is completed at one time, the total goods picking path is greatly reduced, and the working efficiency is greatly improved.
In the following, a warehouse picking path planning system disclosed in the embodiment of the present invention is introduced, and a warehouse picking path planning system described below and a warehouse picking path planning method described above may be referred to correspondingly.
An embodiment of the present invention further provides a warehouse picking path planning system, including:
the path planning conversion module is used for taking each order number as a point based on the one-dimensional arrangement characteristic of the goods shelves, converting the one-dimensional picking path planning problem into a two-dimensional point clustering problem, and defining a picking path interval of each order number as [ x, y ], wherein x represents the minimum goods shelf number of the order number, and y represents the maximum goods shelf number of the order number;
the first clustering module is used for performing first clustering on all points by using a rapid clustering algorithm to obtain a first cluster;
the secondary clustering module is used for performing secondary clustering on the points which cannot be clustered after the primary clustering by using a minimum shelf clustering algorithm to obtain a second cluster;
the global optimization module is used for merging the first cluster and the second cluster into a total cluster, and performing multiple point exchange operations in the total cluster by using a global optimization algorithm until all the total clusters are traversed to obtain an optimized cluster;
and the order picking output module is used for outputting the optimized cluster groups as order picking lists and calculating the path of each cluster and the total path of the order picking lists.
The warehouse picking path planning system of the present embodiment is used for implementing the warehouse picking path planning method, and therefore, the specific implementation of the system can be seen in the foregoing embodiment sections of the warehouse picking path planning method, and therefore, the specific implementation thereof can refer to the description of the corresponding embodiments of the respective sections, and will not be further described herein.
In addition, since the warehouse picking path planning system of the embodiment is used for implementing the warehouse picking path planning method, the function of the warehouse picking path planning system corresponds to that of the method, and details are not described here.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications of the invention may be made without departing from the spirit or scope of the invention.
Claims (10)
1. A warehouse picking path planning method is characterized by comprising the following steps:
s1, regarding each order number as a point based on the one-dimensional arrangement characteristic of a shelf, and converting a one-dimensional picking path planning problem into a two-dimensional point clustering problem;
s2, clustering all the points for the first time by using a rapid clustering algorithm to obtain a first cluster;
s3, performing secondary clustering on the points which cannot be clustered after primary clustering by using a minimum shelf clustering algorithm to obtain a second cluster;
s4, combining the first cluster and the second cluster into a total cluster, and performing multiple point exchange operations in the total cluster by using a global optimization algorithm until all the total clusters are traversed to obtain an optimized cluster;
and S5, outputting the optimized cluster group as a picking order, and calculating the path of each cluster and the total path of the picking order.
2. The method of claim 1, wherein the first clustering of all points using a fast clustering algorithm comprises:
s21, defining a picking path interval of each order number as [ x, y ], wherein x represents the minimum shelf number of the order number, y represents the maximum shelf number of the order number, and the path length of any point is obtained;
s22, arranging all the points in a descending order according to the link path lengths of the points to obtain a descending order table of the points, and taking a first point in the descending order table as an initial point C of a first cluster i,j ＝C 1,1 In which C is i,j A jth point representing an ith cluster;
s23, combining the points C 1,1 Taking the corresponding path interval as a limited interval, removing points of the descending table, of which the path interval is not included in the limited interval, to obtain a new descending table,
s24, sequentially taking points and points C from the new descending order table according to the number of the defects in the clusters 1,1 Clustering to obtain a first cluster, wherein one cluster is a picking order;
s25, storing the clustered clusters, and repeating S22-S24 for the rest points until the clusters cannot be obtained.
3. The warehouse pick path planning method according to claim 2, wherein: when all points are clustered for the first time, the points with larger path length are preferentially selected to be clustered, so that the points with larger path length are all in the same order picking sheet.
4. The warehouse picking path planning method according to claim 2, wherein the method for clustering the points which cannot be clustered after the first clustering by using the minimum shelf clustering algorithm for the second time comprises:
s31, taking the remaining points which are not clustered, and performing ascending arrangement on all the points according to the x values of the points to obtain an ascending table;
and S32, sequentially picking points from the ascending table to form clusters according to the required order quantity in each order picking sub-order to obtain a second cluster.
5. The warehouse picking path planning method according to claim 1, wherein before performing multiple point switching operations in a cluster using the global optimization algorithm, the cluster is screened, whether the percentage of the number of repeated points included in the cluster to the total number of points is greater than or equal to a preset value is determined, if yes, the cluster is removed from the cluster, and the remaining clusters in the cluster are subjected to the point switching operation, and if no, the cluster in the cluster is subjected to the point switching operation.
6. The warehouse pick path planning method of claim 5, wherein the method of performing multiple point switching operations within a cluster using the global optimization algorithm comprises:
s41, starting from the cluster i, searching all adjacent clusters, recording as a cluster A, if the cluster A does not contain any cluster, i +1, and returning to the screening operation, wherein the initial value of i is 1;
s42, taking the m-th point C in the cluster i i,m As the exchanged point, the initial value of m is 1;
s43, from the cluster A, selecting the nth point C in the cluster j j,n As the switching points, the initial values of j and n are 1;
s44, if C j,n If n is the maximum value, j +1 returns to S43;
s45, calculating initial paths and initial paths Z of the cluster i and the cluster j 1 Swapping C within cluster i and cluster j i,m And C j,n To point C, i.e. to point C i,n And point C j,m ；
S46, calculating new paths and Z of the cluster i and the cluster j 2 If Z is 2 <Z 1 If n is the maximum value, j +1, returning to S43；
S47, if j is the maximum value, m +1, returning to S42;
s48, if m is the maximum value, i +1, and returning to the screening operation;
and S49, if i is the maximum value, finishing the global optimization.
7. A warehouse pick path planning system, comprising:
the path planning conversion module is used for taking each order number as a point based on the one-dimensional arrangement characteristic of the goods shelf and converting the one-dimensional picking path planning problem into a two-dimensional point clustering problem;
the first clustering module is used for performing first clustering on all points by using a rapid clustering algorithm to obtain a first cluster;
the secondary clustering module is used for performing secondary clustering on the points which cannot be clustered after the primary clustering by using a minimum goods shelf clustering algorithm to obtain a second cluster group;
the global optimization module is used for merging the first cluster and the second cluster into a total cluster, and performing multiple point exchange operations in the total cluster by using a global optimization algorithm until all the total clusters are traversed to obtain an optimized cluster;
and the order picking output module is used for outputting the optimized cluster groups as order picking lists and calculating the path of each cluster and the total path of the order picking lists.
8. The warehouse pick path planning system of claim 7, wherein: the method for the first clustering module to cluster all the points for the first time by using the rapid clustering algorithm comprises the following steps:
s21, defining a picking path interval of each order number as [ x, y ], wherein x represents the minimum shelf number of the order number, y represents the maximum shelf number of the order number, and the path length of any point is obtained;
s22, all the points are linked according to the pathsThe lengths are arranged in a descending order to obtain a descending order table of points, and a first point in the descending order table is taken as an initial point C of a first cluster i,j ＝C 1,1 In which C is i,j A jth point representing an ith cluster;
s23, putting the point C 1,1 Taking the corresponding path interval as a limited interval, removing the points of the descending table, which are not included in the limited interval, of the path interval to obtain a new descending table,
s24, sequentially taking points and points C from the new descending order table according to the number of the defects in the clusters 1,1 Clustering to obtain a first cluster, wherein one cluster is a picking order;
s25, storing the clustered clusters, and repeating S22-S24 for the rest points until the clusters cannot be obtained.
9. The warehouse pick path planning system of claim 7, wherein: the method for clustering the points which cannot be clustered after the clustering for the first time by the secondary clustering module by using the minimum shelf clustering algorithm comprises the following steps:
s31, taking the remaining points which are not clustered, and performing ascending arrangement on all the points according to the x values of the points to obtain an ascending table;
and S32, sequentially picking points from the ascending table to form clusters according to the required order quantity in each order picking sub-order to obtain a second cluster.
10. The warehouse pick path planning system of claim 7, wherein: the method for the global optimization module to perform multiple point switching operations in the cluster by using the global optimization algorithm comprises the following steps:
s41, searching all adjacent clusters from the cluster i, recording the cluster as a cluster A, if the cluster A does not contain any cluster, i +1, and returning to the screening operation, wherein the initial value of i is 1;
s42, taking the m-th point C in the cluster i i,m As the exchanged point, the initial value of m is 1;
s43, from the cluster A, selecting the nth point C in the cluster j j,n As the switching points, the initial values of j and n are 1;
s44, if C j,n If n is the maximum value, j +1 returns to S43;
s45, calculating initial paths and initial paths Z of the cluster i and the cluster j 1 Swapping C within cluster i and cluster j i,m And C j,n To point C, i.e. to point C i,n And point C j,m ；
S46, calculating new paths and Z of the cluster i and the cluster j 2 If Z is 2 <Z 1 If the n is the maximum value, j +1 is carried out, and S43 is returned;
s47, if j is the maximum value, m +1, returning to S42;
s48, if m is the maximum value, i +1, and returning to the screening operation;
and S49, if i is the maximum value, finishing the global optimization.
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