CN113537640B - Goods picking frequency planning method based on package clustering and storage position recommendation - Google Patents
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Abstract
The invention discloses a picking order planning method based on package clustering and storage position recommendation, which comprises the steps of randomly extracting a package from a package pool, and initializing the order; initializing candidate parcels, adding K parcels, and calculating the concentration of the partial wave; adding another group of K packages, calculating the concentration of the partial wave again, if the concentration of the partial wave calculated again is greater than that of the current candidate package, replacing the candidate package, reserving the added K packages as the candidate packages, meanwhile, placing the previous K candidate packages in an echo secondary pool, and adding no candidate package returning to the echo secondary pool in the round; if the recalculated concentration value of the branch wave is smaller than the concentration value of the branch wave of the current candidate package, the current candidate package is reserved; if the parcel is left, returning to the previous step, and if the parcel is not left, entering an output wave sub program. The invention solves the problems of package clustering and library space assignment by putting two tasks into one problem, thereby providing a larger optimization space.
Description
Technical Field
The invention belongs to the technical field of warehouse picking, and particularly relates to a picking order planning method based on package clustering and storage position recommendation.
Background
A large amount of order information flows in real time in the storage of an e-commerce, a warehouse system picks goods according to inventory information and order requirements, a large amount of goods are stored in an e-commerce warehouse, the order is relatively small, single picking orders are very difficult, the e-commerce adopts wave-division aggregation for the current situation, a wave-order goods is distributed in each area of the warehouse, each area forms a task, picking workers in the area are responsible for picking the goods corresponding to the task, the goods picked in different areas are finally combined together and sorted into a single wave-order goods set, the single wave-order goods are finally divided into one package, finally the packing veneering sheets finally flow into a client hand through a logistics means, the picking is the most costly work in the storage logistics in all storage links, the personnel related to the picking accounts for 50% of the whole storage center, the efficiency of the whole warehouse logistics can be greatly improved by reasonably optimizing the picking tasks. Currently, the upstream wave-splitting and the downstream sorting are mostly processed separately, most of the wave-splitting process accumulates orders with a certain total amount or orders in a time window to form a wave, the downstream sorting directly traverses each area to inquire the maximum quantity of commodities required by the area to bear the wave and then allocates the commodities until all the commodity demands in the wave are allocated;
in order to fully utilize the area of the warehouse, a plurality of E-commerce warehouses begin to use random warehouse positions instead of the prior designated warehouse positions, the existing wave-dividing mode only purely accumulates orders and does not consider clustering similar orders, the current random warehouse can enable the sorting of single commodities to have a plurality of choices, the current sorting does not utilize the selectivity, if the two characteristics are fully utilized, the wave-dividing and the goods position assignment are simultaneously carried out on the orders, the workload of goods sorting can be greatly reduced, because the goods-sorting route in the current E-commerce warehouse mostly adopts an s-shaped walking route, the walking distance of the goods-sorting personnel can be disassembled into the longitudinal walking distance of the lane and the transverse walking distance of the lane, because a single sorting task is already disassembled into a single small area, the longitudinal walking distance is not concerned any more, only the transverse walking distance is concentrated on the lane length, since the length of the roadway is a fixed value, the purpose of optimizing the walking distance of picking is achieved by directly optimizing the number of the roadways through which all goods are picked, obviously, the problem that the distance of the walking required by picking is reduced because the number of the roadways through which all goods are picked is the minimum is not solved in the existing scheme, and a new planning method needs to be developed to solve the existing technical scheme.
Disclosure of Invention
The invention aims to provide a goods picking order planning method based on package clustering and storage position recommendation, which aims to solve the problem that more lanes need to be passed after all goods are picked.
In order to achieve the purpose, the invention provides the following technical scheme:
the invention discloses a picking order planning method based on package clustering and storage position recommendation, which is applied to an e-commerce warehouse using random storage positions and comprises the following steps:
step 1: randomly extracting a parcel from a parcel pool, and initializing the times;
step 2: initializing candidate packages, adding K packages, and calculating the concentration of the partial wave;
and step 3: searching K candidate packages for fusion times: adding another group of K packages, calculating the concentration of the partial wave again, if the concentration of the partial wave calculated again is greater than that of the current candidate package, replacing the candidate package, reserving the added K packages as the candidate packages, meanwhile, placing the previous K candidate packages in an echo secondary pool, and adding no candidate package returning to the echo secondary pool in the round; if the recalculated concentration value of the partial wave is smaller than the concentration value of the partial wave of the current candidate package, keeping the current candidate package, wherein K is a positive integer;
and 4, step 4: if the parcel is left, returning to the step 3, and if the parcel is not left, outputting the wave times.
The steps of the wave frequency output are as follows:
step 41: confirming the fusion wave times of the candidate parcels in the wave times;
step 42: resetting the parcel pool, and removing candidate parcels which are formally merged into the wave times in the parcel pool;
step 43: if the parcel pool has no remaining parcels or the number of parcels in the wave times reaches the upper limit, outputting the wave times in the next step; if the parcel pool has residual parcels and the number of parcels in the wave number does not reach the upper limit, returning to the step 3;
step 44: outputting the wave order.
The calculation method of the above-mentioned wavelength division concentration is as follows:
calculating the probability value of each commodity passing through each roadway, summing the probability values of each roadway, summing the probability values, and dividing the sum by the number of the commodities; the method comprises the following steps:
step 21-weight of each sku of the package appearing in aisle _ set
Weight of parcel in each lane
The similarity between the first package and the second package is
The numerator selects the smaller weight of parcel 1 and parcel 2 in each lane, and then sums the weights;
the denominator is the weight sum of the parcel 1 and the parcel 2 in all the roadways, and then the numerator is subtracted;
wherein, sku is a commodity number, and package is a package set;
sum () is a sum function, which sums the terms in bracket ();
min () is a function for solving the minimum, and the minimum item is output from the bracket ();
z [ sku, aisle ] is 0/1 variable, when sku is in laneway aisle, the value of z [ sku, aisle ] is 1, otherwise, it is 0;
b, collecting all packages;
a: a set of all lanes;
Sba set of skus in parcel b;
Aba set of the number of lanes in the parcel b;
aisle _ set _ all is the set of all lanes;
aisle _ set is a set of lanes aisle in the package;
wi [ aisle ] is the weight of the lane aisle in the package i;
wi is shorthand for all lanes wi [ aisle ];
and step 22, randomly selecting one package as a seed package, adding the seed package into the wave times, adding a new package into the wave times, covering the packages with the most lanes or the packages with the most skus, calculating the similarity of other packages and the seed package, sequencing, sequentially adding the packages into the wave times until the constraint is violated, and then reselecting the seed package to continue to execute the step 2.
All commodities in the seed package are required to be covered by the wave times, and the wave time covering formula is as follows:
b is a member of B;
w is the set of all the multiples, W is a member of W;
s is the set of all SKUs, S is a member of S;
a is a member of A;
p is the set of all priorities, P is a member of P;
the picked goods are smaller than the existing inventory,
show s commodity in
In the tunnel
The number of stocks of the priority level is,
，
the number of packages or orders contained in a single wave is limited to be not more than M, and a calculation formula is as follows
the model objective function that minimizes the number of lanes traversed is,
The higher the priority of the stock is, the warehouse can be discharged out of the warehouse preferentially, and the priority of all stock goods is
The following constraint represents a commodity
If at
The number of the stocks with the priority is more than 0, the stocks with the p2 priority cannot be taken out of the warehouse, and the constraint formula is as follows:
isap1 is the total stock of commodity s in roadway a with priority p 1;
if Xbap 1 < Isap1, then fsp2 would be 0, i.e., the p1 priority inventory would not have been taken, and the p2 priority inventory would not have been taken;
the calculation formula of the goods picked in the channel by the channel is as follows:
a method of assigning the package to a wave number:
calculating the roadway probability of each commodity, and summing the roadway probabilities of a plurality of commodities to obtain the parcel probability of each roadway selected by parcels;
when the commodities are distributed, the storage positions are assigned to all the commodities, so that the number of the roadways passed by when all the commodities are picked is minimum:
the quantity of items to be picked equal to demand is calculated as follows:
wherein,
indicating merchandise
In the first place
A roadway, the first
The number of inventories picked by each priority;
representing a commodity
The required amount of (a) to be used,
the calculation formula for the number of items picked to be less than the inventory number is:
the calculation formula of the second-time picking goods passing through the lane after the goods are picked is as follows
The objective function at the completion of picking all goods to minimize the number of lanes passed is
ZaThe variable of whether to pass the lane a is 1, and 0 is not.
The invention has the technical effects and advantages that: compared with the prior scheme, the method for planning the picking order based on the package clustering and the storage space recommendation has the advantages that one package is given as a seed, the packages are added inwards in a greedy mode, the path is optimized simultaneously, the picking path of one order is shortest, but the prior scheme has strong local optimization, the difference between the local optimization and the optimal solution is much larger than that provided by the scheme, the solution quality is not controllable, in addition, the scheduling problem with large space is changed into an assignment problem to solve, and two tasks of wave separation and storage space assignment are placed in one problem to solve, so that the larger optimization space is provided, and the method has better practical application effect.
Drawings
FIG. 1 is a flow chart of the wave-splitting algorithm of the present invention;
FIG. 2 is a flow chart of the add-on package of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a picking wave number planning method based on package clustering and storage position recommendation, which is applied to an e-commerce warehouse using random storage positions, the package clustering is firstly carried out, then the picking wave number planning method based on the storage position recommendation is carried out, the package clustering comprises two cycles, the next-level cycle searches K package adding waves, and the last-level cycle searches a plurality of K package adding waves until the wave number requirements are met; the package clustering wave order method comprises the following steps:
step 1: randomly extracting a parcel from a parcel pool, and initializing the times;
and 2, step: initializing candidate packages, adding K packages, and calculating concentration; the value of K is larger, the calculation speed of the package clustering is higher, but the clustering effect is poorer; in the step 2, the method for calculating the concentration of the partial wave comprises the following steps: calculating the probability of each commodity passing through each possible tunnel, summing the probability values of each tunnel, summing the probability values, and dividing the sum by the number of the commodities; the method comprises the following specific steps: step 21. the weight of aisle _ set where each sku of the package may appear is z [ sku, aisle ] =1/len (aisle _ set); the weight wrapped in each lane is w [ aisle ] = sum (z [ sku, aisle ] for aisle _ set _ all for skin in package); the similarity between the first and second parcels is sum (min (w1[ aille ], w2[ aille ]) for ai in ai _ set)/(sum (w1) + sum (w2) -sum (min (w1[ aille ], w2[ aille ]) for ai in ai _ set));
wherein, sku is a commodity number, and package is a package set;
when sku has a value of 1 in lane aisle, z sku, aisle, otherwise 0;
aisle _ set _ all is the set of all lanes;
w [ aisle ] is the weight of the roadway aisle in each parcel, and in this embodiment, w1[ aisle ] and w2[ aisle ] are the weights of the roadway aisles of two parcels respectively
Step 22, selecting seed packages, wherein the packages covering the roadway most or the packages covering sku most are selected, calculating the similarity between other packages and the seed packages, sequencing and adding the packages into the wave times in sequence until the constraints are violated, and then reselecting the seed packages to continue to execute the step 2;
and 3, step 3: searching K candidate parcel fusion times: trying to add another group of K packages, calculating the concentration again, if the concentration calculated again is higher than that of the current candidate package, replacing the candidate package, reserving the K packages added with the other group as the candidate package, meanwhile, putting the previous K candidate packages into an echo secondary pool, and no longer adding the candidate package returning to the echo secondary pool in the round, and if the concentration is not higher than that of the current candidate package, reserving the current candidate package;
and 4, step 4: if the parcels are left, returning to the step 3, and if no parcels are left, outputting the waves;
the wave frequency output step comprises the following steps:
step 41: merging candidate parcel confirmations in the wave order into the wave order;
step 42: resetting the parcel pool, and removing candidate parcels which are formally merged into the wave times in the parcel pool;
step 43: if the number of the packages which are not packaged in the package pool for the rest or the times reaches the upper limit, the next step is carried out; if the parcel pool has the surplus parcels and the number of parcels in the wave number does not reach the upper limit, returning to the step 3;
step 44: outputting the wave times;
by accumulating a batch of orders, the system needs to sort all orders and assign goods of all times to stock for picking, so that the number of roadways needed by picking all the goods is the minimum, and the distance needed by picking is reduced;
in this embodiment, commodities stored in a warehouse
Order of sorting
Set of lanes
Since the warehouse will have its own operation strategy such as clearing up the commodities with higher warehouse age, all the inventory commodities have their own priorities
For the stock with higher priority, the warehouse can be taken out of the warehouse preferentially, and when the warehouse is divided, the same order or package needs to be packaged for facilitating the subsequent sub-packaging
The commodities are divided into the same wave number;
therefore, the model needs to satisfy the requirement that once the package is taken, all the goods in the package need to be covered by the wave and the required quantity needs to be taken.
Represents the package
Is/are as follows
Whether the goods are arranged at
Is wave-order sorted and is on the second
A roadway, the first
The number of inventories picked by each priority. This number equals the presence or absence of the parcel
Order sorting
By including in the package
Number of commodities
The formula of the wave coverage is as follows:
the formula for the calculation that each parcel must be covered by and only one order is as follows:
the calculation formula for the picked goods that must be smaller than the existing inventory is,
to represent
Goods are on
In the tunnel
The amount of inventory of the priority level,
for convenience of downstream wave separation, the calculation formula for limiting the number of packages or orders contained in a single wave number to be less than M is as follows:
all inventory items themselves have priority in order to satisfy operational policies such as clearing away higher inventory items
For the higher priority inventory, the warehouse will be out of the warehouse preferentially, and the following constraints represent the goods
If the stock with the p1 priority is not taken out, the stock with the p2 priority cannot be taken out, and the constraint formula is as follows:
the order picks up the goods in the lane, and the order picker must pass through the lane according to the calculation formula,
the objective function of the entire model is the model objective function of minimizing the number of lanes that need to be traversed
Because the problem scale cannot be directly solved, the original wave division and stock location assignment are divided into two stages during solving, and the two stages are respectively solved;
the main role of the wave splitting is to assign the package to the wave times, assuming that package b has 3 items (s1, s2, s3), and the possible lanes of s1 are (t1, t2, t3), then s1 has probability (1/3, 1/3, 1/3) of selecting lanes (t1, t2, t3) (average probability), and similarly, the probability (t 3, t 4) of s2 (1/2 ) and the probability (t 4, t 5) of s3 are (1/2 ), and the lane probabilities of multiple items are summed to obtain the probability that package p selects each lane: (t1, t2, t3, t4, t 5) is (1/3, 1/3, 1/3+1/2, 1/2+1/2, 1/2), and the maximum probability of each lane being selected is 1. Randomly selecting a parcel p as a seed parcel, adding the seed parcel into the wave times, and assuming that the roadway probability is (0.1, 0.2, 0.3, 0.5 and 0.9) and the total required quantity is 5, the initial roadway selection probability of the wave times is (0.1, 0.2, 0.3, 0.5 and 0.9) and the commodity quantity of the wave times is 5. There is a new parcel P2 with a lane probability of (0.4, 0.1, 0.8, 0.4, 1) and a total demand of 10. Adding the new parcel P2 into the wave times, wherein the concentration calculation formula of the wave times is as follows: molecule = max (lane probability of the wave order, lane probability of the new parcel P2) = (max (0.1, 0.4), max (0.2, 0.1), max (0.3, 0.8), max (0.5, 0.4), max (0.9, 1)) = (0.4, 0.2, 0.8, 0.5, 1). Numerator = sum of probabilities of respective lanes of numerator = 0.4 + 0.2 + 0.8 + 0.5 +1 = 2.9, denominator = number of commodity of wave + number of commodity of new parcel P2 = 5 + 10 = 15, concentration = numerator/denominator = 2.9/15 = 0.192, traverse all parcels, select one parcel with the highest concentration to be added to wave, assuming that parcel P2 is added to wave, then updated lane probability of wave = (0.4, 0.2, 0.8, 0.5, 1), commodity number 15, repeat the above process until the wave-splitting condition is satisfied, generally the parcel number reaches the upper limit of wave-time constraint, this method and jaccard clustering are named jaccard-variant in the coverage-like problem;
the main function of the stock space recommendation is to assign the stock space to all the commodities in one wave, so that the number of lanes for picking up all the commodities is the minimum, and because the packages are assigned to the wave when the waves are separated, the model does not pay attention to the package dimension any more, and the priority can be processed in the model through pretreatment, such as that the required quantity of s1 in the wave is 10, 5 with the priority of 9, 4 with the priority of 8 and 6 with the priority of 7. Then the preprocessing would take priority to all the s1 inventory with priority 8 and priority 9, then the lanes where the inventory with priority 8 and priority 9 would be are the lanes that must pass through, only the inventory with priority 7 would need the model to decide which lane to take one,
the quantity of items picked, which must be less than the inventory quantity, is calculated by the formula,
when goods are picked up in the lane, the person picking the goods with the wave order must pass through the lane according to the calculation formula,
the objective function at the completion of picking all items that minimizes the number of lanes passed is,
in this embodiment, the random storage location refers to the location of each item assigned to be stored, which is generated through a random process and is changed frequently, that is, any item can be stored in any available location, if the computer can be used to assist the memory management of random storage, and the location of each item in the storage is recorded by the computer, the computer can be used to allocate the location space for stocking;
in this embodiment, the designated storage space means that each storage object has its fixed storage space, and the storage spaces cannot be used in a mixed manner, so that the storage space capacity of each storage object should not be smaller than the possible storage space capacity.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments or portions thereof without departing from the spirit and scope of the invention.
Claims (1)
1. A picking order planning method based on package clustering and storage position recommendation is characterized in that: the method is applied to an e-commerce warehouse using random warehouse positions and comprises the following steps:
step 1: randomly extracting a parcel from a parcel pool, and initializing the times;
step 2: initializing candidate parcels, adding K parcels, and calculating the concentration of the partial wave;
and step 3: searching K candidate packages for fusion times: adding another group of K packages, calculating the concentration of the partial wave again, if the concentration of the partial wave calculated again is greater than that of the current candidate package, replacing the candidate package, reserving the added K packages as the candidate packages, meanwhile, placing the previous K candidate packages in an echo secondary pool, and adding no candidate package returning to the echo secondary pool in the round; if the recalculated concentration value of the partial wave is smaller than the concentration value of the partial wave of the current candidate package, reserving the current candidate package, wherein K is a positive integer;
and 4, step 4: if the packages are left, returning to the step 3, and if the packages are not left, outputting the wave times; the steps of wave frequency output are as follows:
step 41: confirming the fusion wave times of the candidate parcels in the wave times;
step 42: resetting the parcel pool, and removing candidate parcels which are formally merged into the wave times in the parcel pool;
step 43: if the parcel pool has no remaining parcels or the number of parcels in the wave times reaches the upper limit, outputting the wave times in the next step; if the parcel pool has the remaining parcels and the number of parcels in the wave number does not reach the upper limit, returning to the step 3;
step 44: outputting the wave times; in step 2, the calculation method of the sub-wave concentration is as follows:
calculating the probability value of each commodity passing through each roadway, summing the probability values of each roadway, summing the probability values, and dividing the sum by the number of the commodities; the method comprises the following steps:
step 21-weight of each sku of the package appearing in aisle _ set
Weight of parcel in each lane
The similarity between the first package and the second package is
The numerator selects the smaller weight of parcel 1 and parcel 2 in each lane, and then sums the weights;
the denominator is the weight sum of the parcel 1 and the parcel 2 in all the roadways, and then the numerator is subtracted;
wherein, sku is a commodity number, and package is a package set;
sum () is a sum function, which sums the terms in bracket ();
min () is the minimization function, and the minimum item is output from the bracket ();
z [ sku, aisle ] is 0/1 variable, when sku is in laneway aisle, the value of z [ sku, aisle ] is 1, otherwise, it is 0;
b, collecting all packages;
a: a set of all lanes;
Sba set of skus in parcel b;
Aba set of the number of lanes in the parcel b;
aisle _ set _ all is the set of all lanes;
aisle _ set is a set of lanes aisle in the package;
wi [ aisle ] is the weight of the lane aisle in the package i;
wi is shorthand for all lanes wi [ aisle ];
step 22, randomly selecting one package as a seed package, adding the seed package into the wave times, adding a new package into the wave times, covering the packages with the most lanes or the packages with the most skus, calculating the similarity of other packages and the seed package, sequencing and sequentially adding the packages into the wave times until the constraint is violated, and then reselecting the seed package to continue to execute the step 2; a method of assigning the package to a wave number:
calculating the roadway probability of each commodity, and summing the roadway probabilities of a plurality of commodities to obtain the wrapping probability of each roadway selected by wrapping;
when the commodities are distributed, the storage positions are assigned to all the commodities, so that the number of the roadways passed by when all the commodities are picked is minimum:
the number of items to be picked equal to the number of items demanded is calculated as follows:
wherein,
indicating merchandise
In the first place
A roadway, the first
The number of inventories picked by each priority;
indicating merchandise
The required amount of (a) to be used,
the calculation formula for the number of items picked to be less than the inventory number is:
the calculation formula of the second-time picking goods passing through the lane after the goods are picked is as follows
The objective function at the completion of picking all goods to minimize the number of lanes passed is
ZaA variable indicating whether the tunnel a passes or not is shown, wherein 1 is pass and 0 is not pass; all commodities in the seed package need to be covered by the wave timesThe formula is as follows:
b is a member of B;
w is the set of all the multiples, W is a member of W;
s is the set of all SKUs, S is a member of S;
a is a member of A;
p is the set of all priorities, P is a member of P;
the picked goods are smaller than the existing inventory,
show s commodity in
In the tunnel
The amount of inventory of the priority level,
，
the number of packages or orders contained in a single wave is limited to be not more than M, and the calculation formula is as follows
the model objective function that minimizes the number of lanes traversed is,
isap1 is the total stock of commodity s in roadway a with priority p 1;
if Xbap 1 < Isap1, then fsp2 would be 0, i.e., the p1 priority inventory would not have been taken, and the p2 priority inventory would not have been taken;
the calculation formula of the goods picked in the channel by the channel is as follows:
Priority Applications (1)
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