CN109886478B - Goods space optimization method for finished wine automatic stereoscopic warehouse - Google Patents
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Abstract
The invention discloses a finished product wine warehouse goods space optimization method, which comprises the steps of predicting the delivery frequency of finished product wine in an optimization period according to the seasonal characteristics of the finished product wine; then, mining association rules of the historical orders to obtain association degrees among the finished wine products, and clustering based on the association degrees; on the basis, comprehensively considering the goods space turnover rate, shelf stability and product relevance, and constructing a multi-target goods space optimization model; and finally solving a pareto solution set, and selecting an optimal satisfactory solution to obtain a final goods position optimization result. The invention fully considers the mutual restriction among multiple targets, overcomes the defect that the seasonality characteristic and the commodity relevance of finished wine are not considered in the prior art, and finds that compared with the prior art, the invention can better adapt to the operation environment of picking commodities according to orders and has more reasonable commodity optimization results through actual measurement.
Description
Technical Field
The invention relates to a logistics storage management method, in particular to a goods position optimization method of a finished wine automatic stereoscopic warehouse.
Background
In recent years, with the gradual progress of the 'intelligent manufacturing 2025' plan in China, finished wine warehouses are upgraded and modified automatically in various large wineries. At present, although most finished wine warehouses have realized the automatic upgrade of mechanical equipment in the warehouses, the warehouses still adopt the traditional classification and positioning storage strategy to carry out goods position management, namely, a storage area is assigned to each kind of finished wine according to the experience of a warehouse manager, and the goods positions are quantitatively distributed. At present, the problems of low space utilization rate, overlong picking time, overlow working efficiency and the like commonly exist in a finished wine warehouse, and the warehousing capacity of the warehouse is limited.
The existing goods space optimizing system is widely applied to industries such as tobacco, medicine, retail and the like, the goods space optimizing system is mainly based on goods turnover rate and shelf stability targets and periodically assigns goods spaces for goods types in a warehouse, seasonal characteristics and goods type relevance of the goods are not considered in the system, and only goods turnover rate and shelf stability are considered, so that actual problems cannot be well described. When the finished wine warehouse uses the system, the following operation problems are often generated: firstly, the allocation of goods positions excessively depends on the goods delivery requirement in a short period, and if the fluctuation of market requirements is large, the system often additionally allocates a large safety space, so that the utilization rate of storage space is reduced; secondly, in real life, market demand of finished wine is usually influenced by seasonal factors, and the current goods space optimizing system cannot adjust the goods space of the finished wine according to the seasonal characteristics of specific goods, so that high goods picking efficiency can be kept only in a short term; thirdly, the prior finished wine warehouse takes the product system of the finished wine as the classification standard of the goods position optimization, the prior goods position optimization system can not mine the ex-warehouse characteristics of the goods and dynamically adjust the goods position of the finished wine, the single operation of the stacker has long time for picking the goods, and the operation efficiency is low.
Therefore, if the existing goods space optimizing system is directly applied to the finished wine automatic stereoscopic warehouse, the defects exist in the aspects of storage space utilization, goods space adjustment and goods picking efficiency, and improvement is needed.
Disclosure of Invention
The invention aims to: in order to solve the problems in the prior art, the invention aims to provide a goods space optimization method for a finished wine automatic stereoscopic warehouse, which causes the problem of non-ideal optimization results.
The technical scheme is as follows: a goods space optimization method for a finished wine automatic stereoscopic warehouse comprises the following steps:
(1) Predicting the ex-warehouse frequency of each finished wine in the optimization period;
(2) Mining the association degree among the finished wine and goods, and clustering the finished wine according to the association degree;
(3) Constructing a three-dimensional coordinate system for a finished wine warehouse, and calculating the intra-family distance and the inter-family distance of the finished wine;
(4) Constructing a multi-target goods location optimization model under a three-dimensional coordinate system;
(5) Solving a pareto solution set;
(6) And selecting the optimal satisfactory solution from the pareto solution set.
Specifically, in the step (1), according to the historical order data and seasonal characteristics of the seeds in the finished wine warehouse, the ex-warehouse frequency of each kind of finished wine in the optimization period is predicted by using a seasonal index smoothing method, and the calculation formula is as follows:
the meaning of the above formula is as follows: giving out the ex-warehouse frequency y of each finished wine t Is smoothed by
Wherein k is>0, alpha, beta and gamma are all between 0 and 1; a is t Represents the intercept, b t Represents the slope, a t +b t k represents a trend, S t For the seasonal factor of the multiplicative model, s represents the length of the seasonal period, the best fit will be made
The value is used as the ex-warehouse frequency of each finished wine in the predicted optimization period.
Further, in the step (2), by using historical order data of the finished wine, a correlation rule mining method is adopted to mine the correlation degree between the finished wine categories, and the finished wine is clustered according to the correlation degree, and the specific steps include:
(2.1) carrying out standardized processing on the order data of the finished wine, and converting the order data into a format suitable for association rule mining;
(2.2) setting appropriate support degree and confidence coefficient by using an Apriori algorithm mined by the association rule, and mining a strong association rule;
(2.3) calculating the association degree between the finished wine items according to a formula of 'item association degree = support degree x confidence degree';
(2.4) calculating the clustering distance among the finished wine items according to a formula of clustering distance = 1-correlation degree;
and (2.5) clustering finished wine by adopting a minimum distance clustering method, and clustering the finished wine into R classes according to a pedigree diagram.
Further, the step (3) specifically includes:
(3.1) taking the rows, the columns and the layers of the finished wine automatic stereoscopic warehouse as three dimensions to construct a coordinate system;
(3.2) calculating the coordinate center of the kth product:
Further, the step (4) specifically includes:
(4.1) setting relevant parameters;
(4.2) comprehensively considering three targets of the goods turnover rate, the shelf stability and the product relevance to construct an objective function:
(4.3) time t for the ith cargo to be transported to the delivery platform in the model i The calculation formula is as follows
Consider the following constraints:
above, x i ,y i ,z i The goods space coordinate of the ith goods is represented as x i Row y i Column z i Layer, is marked as (x) i ,y i ,z i )；n xyz Indicating a cargo space (x) i ,y i ,z i ) The number of the stored finished product wine boxes is increased; A. b and C each represent (x) i ,y i ,z i ) The maximum number value of (d); l is the side length of the unit cargo grid; h is the distance between every two rows of goods shelves; m is the mass of each container of goods; c, the number of boxes which can store finished wine at most in each goods grid; v x Is the speed of travel of the conveyor; v y ,V z Respectively the horizontal and vertical speeds of the stacker; n is i The quantity of the ith goods; p is a radical of i The access frequency of the ith cargo; t is t i The time for the ith cargo to be transported to the delivery platform; d is the total intra-family distance of the R-class cargos; d is the distance between the general groups of the R-type cargos.
Further, in the step (5), the finding of the pareto solution set by using a fast non-dominated sorting genetic algorithm (NSGAII) with elite strategy specifically includes:
(5.1) coding the position of the goods position in the finished product wine warehouse by adopting a real number coding mode, and randomly generating an initial population;
(5.2) setting the population number, the maximum iteration times, the cross probability and the variation probability, taking the reciprocal of an objective function in the multi-objective goods space optimization mathematical model as a fitness function, and calculating an individual fitness value;
(5.3) performing rapid non-dominated sorting and crowding distance calculation on the population in an iterative process;
(5.4) adopting a tournament selection strategy, namely preferentially selecting individuals with small sequence values, preferentially selecting individuals with large crowding distances if the sequence values are the same, and performing crossover and mutation operations respectively by adopting a simulated binary crossover operator and a polynomial mutation operator to generate offspring populations;
and (5.5) combining the parent population and the offspring population into a temporary population, iteratively performing non-dominated sorting, congestion distance calculation, tournament selection, intersection and mutation operations to form a new offspring population, repeating iteration in such a way, stopping evolution if the current iteration number is greater than the maximum evolution algebra, and iterating to finally generate a group of stable pareto front solution sets.
Further, in the step (6), an optimal satisfactory solution is selected from the pareto frontier solution set based on a fuzzy set theory, and the specific steps include:
(6.1) selecting an optimal solution from the pareto frontier solution set according to the maximum satisfaction criterion, and calculating the satisfaction degree of the ith solution on the jth optimization target
In the formula (I), the compound is shown in the specification,
taking the dominance function value as 1 for the minimum value, namely the optimal value, of all solutions on the jth objective function;
taking the dominance function value as 0 for the maximum value of all solutions on the jth objective function;
(6.2) defining the dominance function μ i The combined weight, w, of the ith solution over all targets j For the weight of the jth target, the dominance function value μ i And a target weight w j All are numbers between 0 and 1, and the calculation formula is as follows:
Has the advantages that: compared with the prior art, the invention has the following remarkable progress: . Mutual restrictive property among all targets is fully considered, actual operation characteristics of a finished wine warehouse are combined, ex-warehouse frequency prediction and relevance mining of historical order data are based, a non-subsampled bulk density (NSGAII) algorithm with good robustness is adopted to solve a pareto frontier solution set, and a final goods allocation scheme is further determined; through actual measurement, the method can adapt to the dynamic operation environment of sorting finished wine warehouses in a wave mode to the greatest extent; the goods optimization result obtained based on the method is more ideal, the goods distribution is more reasonable, the warehousing operation efficiency can be greatly improved, and the warehousing operation cost is reduced.
Drawings
FIG. 1 is a flow chart of an embodiment of the method for optimizing allocation of goods space in finished wine warehouse according to the present invention;
FIG. 2 is a diagram of seasonal index smoothing prediction of the frequency of wine production from a warehouse, according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a product wine association rule mining model data flow according to an embodiment of the invention;
FIG. 4 is a schematic diagram of the result of the finished wine association rule mining model according to the embodiment of the invention;
FIG. 5 is a pedigree diagram of the clustering result of the association degree of the finished wine product according to the embodiment of the invention;
FIG. 6 is a diagram illustrating the distribution effect of the finished wine in the goods space of the automated stereoscopic warehouse according to the embodiment of the present invention;
FIG. 7 is a flowchart of an algorithm for solving the cargo space optimization model using the NSGAII algorithm according to an embodiment of the present invention;
FIG. 8 is a distribution diagram of cargo space obtained using the prior art;
FIG. 9 is a distribution diagram of cargo space obtained by the optimization method of the present invention.
Detailed Description
The technical solution of the present invention will be more fully and precisely described below with reference to the drawings and examples of the specification.
As shown in figure 1, a goods space optimization method of an automatic stereoscopic warehouse for finished wine is characterized in that firstly, the ex-warehouse frequency of each finished wine in the optimization period is predicted according to the seasonal characteristics of the finished wine in the warehouse; then, association rules are mined for historical orders of the warehouse to obtain association degrees among various finished wines, and the finished wines are clustered based on the association degrees; on the basis, the finished wine delivery efficiency, shelf stability and product relevance are comprehensively considered, and a mathematical model of multi-objective goods location optimization is constructed; and finally, solving a group of stable pareto frontier solution sets by adopting an NSGAII algorithm, and selecting an optimal satisfaction solution based on a maximum satisfaction criterion to obtain a final goods location optimization result. The steps are explained in detail below.
Step 1: predicting the ex-warehouse frequency p of each finished wine in the optimization period i
The ex-warehouse of finished wine has obvious seasonal characteristics, which are related to the consumption habits of consumers. For example, finished white spirit is low in ex-warehouse frequency in summer and high in winter. Correspondingly, the goods position of the finished product white spirit should be closer to the delivery platform in summer and farther in winter, so that the overall delivery efficiency of the warehouse can be improved. Therefore, the warehouse needs to adjust the allocation scheme of the cargo space once a quarter in the actual operation process. Based on the data, the method needs to predict the ex-warehouse frequency data of the optimization period as the known data input model.
A certain king-selling finished wine in an actually measured finished wine warehouse is taken as an example, and a rough process of predicting the frequency of finished wine delivery is explained. And processing the data by using SPSS Statistics software, considering that the time sequence of the finished wine historical ex-warehouse data has both trend and seasonal data characteristics, and predicting by using a Winters-multiplication model with seasonal index smoothness.
Through software calculation, the damping factor when the fitting effect is better is obtained as follows: α =0.097; β =0.782; γ =0.071. The predicted values of the four seasons in the optimized period of the finished wine are 57 ten thousand times, 50 ten thousand times, 42 ten thousand times and 66 ten thousand times respectively, the fitting goodness is 95.7%, the effect is good, and the fitting effect graph is shown in an attached figure 2.
Step 2: digging the correlation degree among the finished wine and goods, and clustering the finished wine into R classes according to the correlation degree
The flow of association degree calculation and cluster analysis is illustrated here by taking the actually measured 10 main finished wine products in the winery as an example. Order data is first converted to a standard format that can be recognized and processed by the SPSS Modeler software, as follows:
and (3) constructing a data stream by using SPSS Modeller software, and mining association rules of order data by adopting an Apriori algorithm, as shown in the attached figure 3. Setting the minimum support degree of mining of the association rules to be 69%, the minimum confidence degree to be 67% and the maximum antecedent number to be 1, ensuring that all association rules with antecedent and consequent being 1 are mined, operating the algorithm to obtain 90 rules, wherein the mining result is shown in figure 4, and calculating the association degrees among 10 finished wines as follows:
and (3) converting the association degree into a clustering distance according to a formula of distance coefficient = 1-association degree, and carrying out family classification on each item of the finished wine by adopting minimum distance clustering in a systematic clustering method. The data were input into the SPSS Statistics software to obtain a minimum distance clustering pedigree map as shown in FIG. 5.
And step 3: a three-dimensional coordinate system is constructed for a finished wine warehouse, and the inter-family distance D and the inter-family distance D of the finished wine are conveniently calculated
For easy understanding, fig. 6 shows the distribution effect diagram of the finished wine automatic stereoscopic warehouse goods space. And (4) taking the rows, the columns and the layers of the finished wine automatic stereoscopic warehouse as three dimensions to construct a coordinate system. The row closest to the delivery table is marked as row 1, the shelf bottom layer is marked as layer 1, the coordinates of the delivery table are set to be (0, 0), the coordinates of the goods position are marked as (x, y, z), and the goods position of the layer z in the row x, the row y and the row z is shown.
And 4, step 4: establishing a multi-target goods location optimization model under a three-dimensional coordinate system, and establishing a simulation environment
The 50 finished wines of the actual measurement warehouse are selected as the data source of the example simulation. And (3) predicting the ex-warehouse frequency of 50 finished wines according to the method in the step 1, and taking the predicted value as reference information for optimizing the goods space in the quarter. And (3) performing relevance mining and clustering on the finished wine according to the method in the step (2) to obtain the family information of the finished wine required by model solution. The frequency of delivery and family information of 50 finished wines are summarized as shown in the table:
and 5: method for solving pareto frontier solution set by using NSGAII algorithm
And (3) solving the model by using an NSGAII algorithm, wherein the flow of the algorithm is shown in figure 7, the population scale is set to be 200, the cross probability is 0.8, the variation probability is 0.2, the iteration is carried out for 1000 times, and a group of stable pareto front solution sets are obtained by solving. The solutions are mutually restricted on three targets of goods turnover rate, shelf stability and goods category relevance.
Step 6: selecting optimal satisfactory solution from pareto frontier solution set
According to the maximum satisfaction criterion, setting a target weight w = [0.4,0.2,0.4], selecting an optimal compromise solution from the pareto solution set, and drawing a goods space allocation scheme corresponding to the optimal compromise solution by using Matlab as shown in fig. 8. It can be seen that the overall placement position of the goods is close to the delivery platform, the gravity center is lower, the similar goods are placed relatively close, and 3 targets are well met.
To show the advantages of the method, the same data is input into the existing cargo space optimization system, and a cargo space optimization scheme in the prior art can be obtained as shown in fig. 9.
In order to compare the actual effects of the two goods allocation schemes, 1000 actual orders of the finished wine warehouse are used as a data source according to the actual operation condition of the finished wine warehouse, and 100 orders are simulated and generated, wherein each order has about 8-12 orders. Under the periodic operation environment of picking goods by waves, aiming at a given goods location distribution scheme, the average operation time, the standard deviation of the operation time and the total operation time of each goods location stacker are respectively solved, and the picking operation conditions corresponding to the two goods location distribution schemes are obtained as follows:
it can be seen that under the dynamic operation environment of actually measuring warehouse sorting by wave, the operation efficiency of the stacker is obviously higher than that of the optimization scheme obtained by the prior art by adopting the final optimization scheme of the method.
Claims (3)
1. A goods space optimization method of an automatic stereoscopic warehouse for finished wine is characterized by comprising the following steps:
(1) Predicting the ex-warehouse frequency of each finished wine in the optimization period;
(2) Mining the relevance between the finished wine varieties, and clustering the finished wine according to the relevance;
(3) Establishing a three-dimensional coordinate system for a finished wine warehouse, and calculating the intra-family distance and the inter-family distance of finished wine;
(4) Constructing a multi-target goods space optimization model under a three-dimensional coordinate system;
(5) Solving a pareto solution set;
(6) Selecting an optimal satisfactory solution from the pareto solution set;
in the step (1), according to historical order data and seasonal characteristics of the types of goods in the finished wine warehouse, the warehouse-out frequency of each type of finished wine in the optimization period is predicted by using a seasonal index smoothing method, and the calculation formula is as follows:
the meaning of the above formula is as follows: giving out the ex-warehouse frequency y of each finished wine t Is smoothed by
Wherein k is>0, alpha, beta and gamma are all between 0 and 1; a is t Denotes intercept, b t Represents the slope, a t +b t k represents a trend, S t For the seasonal factor of the multiplicative model, s represents the length of the seasonal period, which will fit best
The value is used as the ex-warehouse frequency of each finished wine in the predicted optimization period;
in the step (2), the association degree between the finished wine categories is mined by using historical order data of the finished wine and an association rule mining method, and the finished wine is clustered according to the association degree, and the concrete steps comprise:
(2.1) carrying out standardized processing on the order data of the finished wine, and converting the order data into a format suitable for association rule mining;
(2.2) setting support degree and confidence coefficient by using an Apriori algorithm mined by association rules, and mining strong association rules;
(2.3) calculating the association degree between the finished wine items according to the formula 'item association degree = support degree × confidence degree';
(2.4) calculating the clustering distance between the finished product wine items according to a formula of 'clustering distance = 1-correlation degree';
(2.5) clustering finished wine by adopting a minimum distance clustering method, and clustering the finished wine into R classes according to a pedigree chart;
in the step (5), the pareto solution set is solved by using a fast non-dominated sorting genetic algorithm with an elite strategy, and the method specifically comprises the following steps:
(5.1) coding the position of the goods position in the finished product wine warehouse by adopting a real number coding mode, and randomly generating an initial population;
(5.2) setting the population number, the maximum iteration times, the cross probability and the variation probability, taking the reciprocal of an objective function in the multi-objective goods space optimization mathematical model as a fitness function, and calculating an individual fitness value;
(5.3) carrying out rapid non-dominated sorting and crowded distance calculation on the population in an iterative process;
(5.4) adopting a league selection strategy, namely preferentially selecting individuals with small sequence values, preferentially selecting individuals with large crowding distances if the sequence values are the same, and performing crossover and mutation operations respectively by adopting a simulated binary crossover operator and a polynomial mutation operator to generate offspring populations;
(5.5) combining the parent population and the offspring population into a temporary population, iteratively performing non-dominated sorting, congestion distance calculation, tournament selection, intersection and mutation operations to form a new offspring population, repeating iteration in such a way, stopping evolution if the current iteration number is greater than the maximum evolution algebra, and iterating to finally generate a group of stable pareto front solution sets;
in the step (6), an optimal satisfactory solution is selected from the pareto frontier solution set based on a fuzzy set theory, and the specific steps include:
(6.1) selecting an optimal solution from the pareto frontier solution set according to the maximum satisfaction criterion, and calculating the satisfaction degree of the ith solution on the jth optimization target
In the formula (I), the compound is shown in the specification,
taking the dominance function value as 1 for the minimum value, namely the optimal value, of all solutions on the jth objective function;
taking the dominance function value as 0 for the maximum value of all solutions on the jth objective function;
(6.2) defining a dominance function μ i The combined weight, w, of the ith solution over all targets j The weight of the jth target, the dominance function value mu i And a target weight w j All the numbers are between 0 and 1, and the calculation formula is as follows:
and (6.3) selecting a satisfaction solution based on the maximum satisfaction criterion according to the target importance degree to serve as a final goods space optimization scheme.
2. The cargo space optimization method according to claim 1, wherein the step (3) specifically comprises:
(3.1) taking the rows, the rows and the layers of the finished wine automatic stereoscopic warehouse as three dimensions to construct a coordinate system;
(3.2) calculating the coordinate center of the kth product:
(3.3) calculating the total coordinate center of the R type product:
(3.4) calculating the intra-family distance and the inter-family distance of the finished wine, wherein d is the total intra-family distance of the R-class cargos; d is the distance between the general groups of the R-type cargos:
3. the cargo space optimization method according to claim 1, wherein the step (4) specifically comprises:
(4.1) setting relevant parameters;
(4.2) comprehensively considering three targets of the goods turnover rate, the shelf stability and the product relevance to construct an objective function:
(4.3) time t of i-th cargo to be transported to delivery platform in model i The calculation formula is as follows
Consider the following constraints:
above, x i ,y i ,z i The coordinate of the cargo space of the ith cargo is represented as x i Row y i Column z i Layer, is marked as (x) i ,y i ,z i )；n xyz Indicating a cargo space (x) i ,y i ,z i ) The number of the stored finished wine boxes is increased; A. b and C respectively represent (x) i ,y i ,z i ) The maximum number value of (d); l is the side length of the unit goods grid; h is the distance between every two rows of goods shelves; m is the mass of each box of goods; c, the number of boxes which can store the finished wine at most for each goods grid; v x Is the speed of travel of the conveyor; v y ,V z Respectively the horizontal and vertical speeds of the stacker; n is a radical of an alkyl radical i The quantity of the ith goods; p is a radical of i The access frequency of the ith cargo; t is t i The time for the ith goods to be transported to the delivery platform; d is the total intra-family distance of the R-class cargo; d is the distance between the general groups of the R-type cargos.
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| CN108550007A (en) * | 2018-04-04 | 2018-09-18 | 中南大学 | A kind of slotting optimization method and system of pharmacy corporation automatic stereowarehouse |
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