CN109886478A - A kind of slotting optimization method of finished wine automatic stereowarehouse - Google Patents
A kind of slotting optimization method of finished wine automatic stereowarehouse Download PDFInfo
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Abstract
The invention discloses a kind of finished wine warehouse slotting optimization methods, first according to the seasonal characteristics of finished wine, predict the outbound frequency of optimization phase finished wine；Then rule digging is associated to History Order, obtains the degree of association between finished wine, and clustered based on the degree of association；On this basis, comprehensively consider goods yard turnover rate, shelf-stable and product relevance, construct multiple target slotting optimization model；Pareto disaggregation is finally solved, and selects optimal satisfactory solution, obtains final slotting optimization result.The present invention has fully considered the mutual restriction between multiple target, it solves the defect that the prior art does not consider finished wine seasonal characteristics and cargo type relevance, finds after actual measurement, compared with the prior art, the present invention can better adapt to the operating environment by order wave time picking, and cargo optimum results are more reasonable.
Description
Technical field
The present invention relates to logistic storage management method more particularly to a kind of slotting optimizations of finished wine automatic stereowarehouse
Method.
Background technique
In recent years, with the iterative method that China " intelligence manufacture 2025 " is planned, major brewery is one after another to finished wine warehouse
Carry out automatic updating transformation.At this stage, although most of finished wine warehouses have been realized in the automation of mechanical equipment in library
Upgrading, but warehouse still carries out goods yard management using traditional classification and orientation storage strategy, i.e., according to the experience of warehouse keeper to every
Kind finished wine designated storage area, rationed goods yard.Currently, the generally existing space utilization rate in finished wine warehouse is not high, picking
The problems such as overlong time and too low operating efficiency, limits the depot storage capacity in warehouse.
Existing slotting optimization system is widely used in the industries such as tobacco, medicine, retail, this kind of more bases of slotting optimization system
In velocity of goods circulation and shelf stabilities target, goods yard is periodically assigned for cargo type in library, such system does not account for commodity
Seasonal characteristics and cargo type relevance, velocity of goods circulation and shelf stabilities are only considered, so that reality cannot be described well
Problem.Finished wine warehouse often generates following problems of operation when using such system: first, goods yard distribution excessively relies on
Cargo outbound demand in a short time, if the fluctuation of the market demand is very big, the system often very big safe space of additional allocation,
Reduce the utilization rate of storage space；Second, in real life, the market demand of finished wine would generally be by Seasonal
It influencing, current slotting optimization system can not be directed to the seasonal characteristics of specific cargo type, finished wine goods yard is adjusted, therefore only
Higher picking efficiency can be kept in a short time；Third, finished wine warehouse is excellent as goods yard using the product system of finished wine at present
The classification standard of change, existing slotting optimization system can not excavate the planting modes on sink characteristic that goes out of commodity, and dynamic adjusts the goods yard of finished wine, heap
The single operation picking time dean of stack machine, operating efficiency are low.
Therefore, if existing slotting optimization system is applied directly in finished wine automatic stereowarehouse, in storage space
Existing defects in terms of using the adjustment of, goods yard and picking efficiency, need to improve.
Summary of the invention
Goal of the invention: of the existing technology in order to solve the problems, such as, the object of the present invention is to provide a kind of finished wine is automatic
The slotting optimization method for changing tiered warehouse facility, causes the problem that optimum results are undesirable.
Technical solution: a kind of slotting optimization method of finished wine automatic stereowarehouse includes the following steps:
(1) in the prediction optimization phase every kind of finished wine the outbound frequency；
(2) degree of association between finished wine cargo type is excavated, is clustered finished wine according to the degree of association；
(3) three-dimensional system of coordinate is constructed for finished wine warehouse, calculates in the race of finished wine distance between distance and race；
(4) the multiple target slotting optimization model under three-dimensional system of coordinate is constructed；
(5) Pareto disaggregation is sought；
(6) it is concentrated from Pareto solution and chooses optimal satisfactory solution.
Specifically, in the step (1), according to the History Order data and seasonal characteristics of cargo type in finished wine warehouse,
Utilize the outbound frequency of every kind of finished wine in the seasonal exponential smoothing method prediction optimization phase, calculation formula are as follows:
The meaning of above-mentioned formula is as follows: providing every kind of finished wine outbound frequency ytSmooth sequenceWherein, k > 0, α, β,
γ is between 0~1；atIndicate intercept, btIndicate slope, at+btK indicates trend, StFor the seasonal factor of multiplied model, s table
Show the length of seasonal periodicity, fitting effect is bestIt is worth the outbound frequency of every kind of finished wine in the optimization phase as prediction.
Further, in the step (2), using the History Order data of finished wine, using association rule mining method
The degree of association between finished wine cargo type is excavated, and is clustered finished wine according to the degree of association, specific steps include:
(2.1) finished wine order data is standardized, is converted to the format for being appropriate for association rule mining；
(2.2) Apriori algorithm for utilizing association rule mining, is arranged suitable support and confidence level, excavates strong
Correlation rule；
(2.3) according to formula " the product item degree of association=support * confidence level ", the degree of association between finished wine product item is calculated；
(2.4) according to formula " clustering distance=1- degree of correlation ", the clustering distance between finished wine product item is calculated；
(2.5) finished wine is clustered using minimum distance clustering, and is clustered finished wine for R according to pedigree chart
Class.
Further, the step (3) specifically includes:
(3.1) using the row of finished wine automatic stereowarehouse, column, layer as three-dimensional, coordinate system is constructed；
(3.2) the coordinate center of kth class product is calculated:
(3.3) the total coordinate center of R class product is calculated:
(3.4) distance between distance and race in the race of finished wine is calculated:
Wherein, d is R class goods
Distance in the supertribe of object；D distance between the supertribe of R class cargo.
Further, the step (4) specifically includes:
(4.1) relevant parameter is set；
(4.2) comprehensively consider three velocity of goods circulation, shelf stabilities and product relevance target building objective functions:
(4.3) i-th kind of cargo transports to the time t of outbound platform in modeli, calculation formula is as follows
Consider following constraint:
It is above-mentioned, xi,yi,ziThe goods yard coordinate for indicating i-th kind of cargo is xthiArrange yiArrange ziLayer, is denoted as (xi,yi,zi)；nxyz
Indicate goods yard (xi,yi,zi) on the finished wine case number that stores；A, B, C respectively indicate (xi,yi,zi) maximum number value；L is single
The side length of first goods lattice；H is the distance between every row's shelf；M is the quality of every case cargo；C be each goods lattice most multipotency store at
The case number sampled wine；VxFor the travel speed of conveyer；Vy,VzThe respectively horizontal and vertical speed of piler；niFor i-th kind of goods
The quantity of object；piFor the frequency of access of i-th kind of cargo；tiThe time of outbound platform is transported to for i-th kind of cargo；D is the total of R class cargo
Distance in race；D distance between the supertribe of R class cargo.
Further, it in the step (5), utilizes nondominated sorting genetic algorithm II (NSGAII)
Pareto disaggregation is found out, is specifically included:
(5.1) the goods yard position in finished wine warehouse is encoded by the way of real coding, it is random to generate initially
Population；
(5.2) population quantity, maximum number of iterations, crossover probability and mutation probability are set, by multiple target slotting optimization number
The inverse of objective function in model is learned as fitness function, calculates ideal adaptation angle value；
(5.3) quick non-dominated ranking is carried out to population in iterative process and crowding distance calculates；
(5.4) league matches selection strategy is used, i.e., preferentially selection sequence is worth small individual, preferentially selects if sequence value is identical crowded
Apart from big individual, intersection and mutation operation are carried out using simulation binary system crossover operator and multinomial mutation operator respectively, produced
Raw progeny population；
(5.5) parent population and progeny population are merged into an interim population, iteration carries out non-dominated ranking, it is crowded away from
From calculating, league matches selection intersects, mutation operation, new progeny population is formed, repeatedly iteration, if current iteration number is big
In maximum evolutionary generation, then stop evolving, iteration finally generates one group of stable Pareto forward position disaggregation.
Further, in the step (6), optimal satisfaction is chosen from Pareto forward position disaggregation based on Fuzzy Set Theory
Solution, specific steps include:
(6.1) it is solved according to maximum satisfaction criterion from Pareto forward position and concentrates selection optimal solution, calculate i-th of solution in jth
Satisfaction in a optimization aim
In formula,For minimum value of all solutions on j-th of objective function, i.e. optimal value, taking the functional value that is dominant is 1；For maximum value of all solutions on j-th of objective function, taking the functional value that is dominant is 0；
(6.2) function mu that is dominant is definediFor i-th of solution synthesis specific gravity shared in all targets, wjFor j-th target
Weight, be dominant functional value μiWith target weight wjIt is the number between 0-1, its calculation formula is:
(6.3) according to target significance level, satisfactory solution is selected based on maximum satisfaction criterion, is made
For final slotting optimization scheme.
The utility model has the advantages that compared with prior art, the present invention has following marked improvement:.It has fully considered between each target
Mutual restriction, in conjunction with the actual operation feature in finished wine warehouse, based on to History Order data the prediction of the outbound frequency and
The degree of association is excavated, and finds out Pareto forward position disaggregation using the preferable NSGAII algorithm of robustness, and then determine final goods yard
Allocation plan；By actual measurement, the present invention can adapt to the dynamic job environment of finished wine warehouse wave time picking to the full extent；Base
Even more ideal in the cargo optimum results that this method obtains, cargo distribution is more reasonable, can greatly improve warehousing and storage activities efficiency, drops
Low warehousing operation cost.
Detailed description of the invention
Fig. 1 is the implementation flow chart of the finished wine warehouse goods yard distribution optimization method of the embodiment of the present invention；
Fig. 2 is the finished wine outbound frequency seasonal index smoothing prediction figure of the embodiment of the present invention；
Fig. 3 is the finished wine Model of Mining Rules data flow diagram of the embodiment of the present invention；
Fig. 4 is the finished wine Model of Mining Rules result schematic diagram of the embodiment of the present invention；
Fig. 5 is the finished wine degree of association cluster result pedigree chart of the embodiment of the present invention；
Fig. 6 is the finished wine automatic stereowarehouse goods yard distributed effect figure of the embodiment of the present invention；
Fig. 7 is the algorithm flow chart that the embodiment of the present invention uses NSGAII algorithm to solve slotting optimization model；
Fig. 8 is the goods yard distribution map obtained using the prior art；
Fig. 9 is the goods yard distribution map obtained using optimization method of the present invention.
Specific embodiment
Technical solution of the present invention work more comprehensively, is meticulously described below in conjunction with Figure of description and embodiment.
As shown in Figure 1, a kind of slotting optimization method of finished wine automatic stereowarehouse, first according to finished wine in warehouse
Seasonal characteristics, predict the outbound frequency of every kind of finished wine of this optimization phase；Then by being closed to warehouse historical order
Join rule digging, obtains the degree of association between various finished wines, and cluster to finished wine based on the degree of association；It is basic herein
On, comprehensively consider finished wine outbound efficiency, shelf-stable and product relevance, constructs the mathematical modulo of multiple target slotting optimization
Type；Finally selected using what NSGAII algorithm solved to one group of stable Pareto forward position disaggregation, and based on maximum satisfaction criterion
Optimal satisfactory solution is selected, final slotting optimization result is obtained.Each step is described in detail below.
Step 1: the outbound frequency p of every kind of finished wine in the prediction optimization phasei
The outbound of finished wine has apparent seasonal characteristics, the consumption habit of this and consumer are relevant.For example, at
The product white wine summer outbound frequency is lower, and winter is higher.Correspondingly, the goods yard summer of finished product white wine, should to separate out library platform closer,
And winter is farther out, and the outbound efficiency of warehouse entirety can be improved in this way.Therefore, warehouse needs one during actual operation
Season adjusts a goods yard allocation plan.Based on this, it is necessary to the outbound frequency data to the optimization phase to predict for this method, make
For given data input model.
Certain stub finished wine in actual measurement finished wine warehouse is chosen herein illustrates that the finished wine outbound frequency is predicted as example
General procedure.Data are handled using SPSS Statistics software, it is contemplated that finished wine history outbound data when
Between sequence have both tendency and seasonal data characteristics, carried out using the smooth Winters- multiplied model of seasonal index pre-
It surveys.
Calculated by software, obtain fitting effect it is more excellent when damping factor it is as follows: α=0.097；β=0.782；γ=
0.071.The predicted value for knowing four season in this kind of finished wine optimization phase is respectively 570,000 times, 500,000 times, 420,000 times and 660,000
Secondary, the goodness of fit 95.7%, effect is preferable, and fitting result chart is shown in attached drawing 2.
Step 2: excavating the degree of association between finished wine cargo type, finished wine clusters to this sentences actual measurement for R class according to the degree of association
For the main finished product wine product of 10 kinds of brewery, the process of calculation of relationship degree and clustering is illustrated.Order data is turned first
The reference format that can be identified and handled by SPSS Modeler software is turned to, is as follows:
Using SPSS Modeler software, data flow is constructed, rule is associated to order data using Apriori algorithm
It excavates, as shown in Fig. 3.The minimum support of association rule mining is set as 69%, min confidence 67%, maximum preceding paragraph
Number is 1, guarantee by preceding paragraph and it is consequent be that 1 correlation rule is all excavated, run available 90 rule of algorithm, digging
The results are shown in attached figure 4 for pick, and the degree of association calculated between 10 kinds of finished wines is as follows:
According to formula " distance coefficient=1- degree of association ", the degree of association is converted to clustering distance, using in hierarchical clustering method
Minimum range cluster to each product item of finished wine carry out same clan's division.SPSS Statistics software is entered data into, is obtained most
Small distance Cluster tendency is shown in attached drawing 5.
Step 3: constructing three-dimensional system of coordinate for finished wine warehouse, facilitate distance D between distance d and race in the race for calculating finished wine
For convenience of understanding, attached drawing 6 gives finished wine automatic stereowarehouse goods yard distributed effect figure.It is automatic with finished wine
The row of change tiered warehouse facility, column, layer are used as three-dimensional, building coordinate system.The row nearest apart from outbound platform is denoted as the 1st row, distance goes out
Platform nearest column in library are denoted as the 1st column, and shelf bottom is denoted as the 1st layer, sets the coordinate of outbound platform to (0,0,0), goods yard coordinate
It is denoted as (x, y, z), indicates that xth arranges z layers of y column of goods yard.
Step 4: the multiple target slotting optimization model under building three-dimensional system of coordinate establishes simulated environment
50 kinds of finished wines in actual measurement warehouse, the data source as Case Simulation are had chosen herein.According to the method pair of step 1
The outbound frequency of 50 kinds of finished wines is predicted, using predicted value as the reference information of current season slotting optimization.According to step 2
Method is excavated and is clustered to finished wine degree of being associated, and same clan's information of the finished wine of model solution needs is obtained.50 kinds of finished products
The outbound frequency and same clan's information of wine summarize that it is as shown in the table:
Step 5: finding out Pareto forward position disaggregation using NSGAII algorithm
Model is solved using NSGAII algorithm, algorithm flow is shown in attached drawing 7, and population scale 200, crossover probability is arranged
0.8, mutation probability 0.2, iteration 1000 times, solution obtains one group of stable Pareto forward position disaggregation.This group of solution is had enough to meet the need in cargo
It is mutually restricted in three rate, shelf stabilities and cargo type relevance targets.
Step 6: choosing optimal satisfactory solution from Pareto forward position disaggregation
According to maximum satisfaction criterion, it is arranged target weight w=[0.4,0.2,0.4], concentrates and chosen most from Pareto solution
Excellent compromise solution draws the optimal compromise using Matlab and solves corresponding goods yard allocation plan such as attached drawing 8.As can be seen that cargo
Close to outbound platform, center of gravity is lower for whole placement position, and similar commodity are put relatively closely, and 3 targets are preferably met.
For the superiority for embodying this method, identical data are inputted into existing slotting optimization system here, it is available
Slotting optimization scheme such as attached drawing 9 under the prior art.
For the actual effect for comparing two kinds of goods yard allocation plans, according to actual measurement finished wine warehouse actual operation situation, at
Sample wine warehouse 1000 practical orders as data source, simulation generates the order of 100 waves time, each wave time about 8-12 item
Order.Under the periodic job environment of wave time picking, for given goods yard allocation plan, each wave time stacking is solved respectively
The average operation time of machine, the standard deviation of activity duration and operation total time it is corresponding to obtain two kinds of goods yard allocation plans
Picking operation situation is as follows:
As can be seen that under the dynamic job environment of actual measurement warehouse wave time picking, using the final optimization pass scheme of this method,
The operating efficiency of piler will be apparently higher than the prioritization scheme obtained using the prior art.
Claims (7)
1. a kind of slotting optimization method of finished wine automatic stereowarehouse, which comprises the steps of:
(1) in the prediction optimization phase every kind of finished wine the outbound frequency；
(2) degree of association between finished wine cargo type is excavated, is clustered finished wine according to the degree of association；
(3) three-dimensional system of coordinate is constructed for finished wine warehouse, calculates in the race of finished wine distance between distance and race；
(4) the multiple target slotting optimization model under three-dimensional system of coordinate is constructed；
(5) Pareto disaggregation is sought；
(6) it is concentrated from Pareto solution and chooses optimal satisfactory solution.
2. slotting optimization method according to claim 1, which is characterized in that in the step (1), according to finished wine warehouse
The History Order data and seasonal characteristics of interior cargo type utilize every kind of finished wine in the seasonal exponential smoothing method prediction optimization phase
The outbound frequency, calculation formula are as follows:
The meaning of above-mentioned formula is as follows: providing every kind of finished wine outbound frequency ytSmooth sequenceWherein, k > 0, α, β, γ
Between 0~1；atIndicate intercept, btIndicate slope, at+btK indicates trend, StFor the seasonal factor of multiplied model, s is indicated
The length of seasonal periodicity, fitting effect is bestIt is worth the outbound frequency of every kind of finished wine in the optimization phase as prediction.
3. slotting optimization method according to claim 1, which is characterized in that in the step (2), utilize going through for finished wine
History order data excavates the degree of association between finished wine cargo type using association rule mining method, and according to the degree of association by finished wine
Cluster, specific steps include:
(3.1) finished wine order data is standardized, is converted to the format for being appropriate for association rule mining；
(3.2) Apriori algorithm for utilizing association rule mining, is arranged suitable support and confidence level, excavates strong association
Rule；
(3.3) according to formula " the product item degree of association=support * confidence level ", the degree of association between finished wine product item is calculated；
(3.4) according to formula " clustering distance=1- degree of correlation ", the clustering distance between finished wine product item is calculated；
(3.5) finished wine is clustered using minimum distance clustering, and is clustered finished wine for R class according to pedigree chart.
4. slotting optimization method according to claim 1, which is characterized in that the step (3) specifically includes:
(4.1) using the row of finished wine automatic stereowarehouse, column, layer as three-dimensional, coordinate system is constructed；
(4.2) the coordinate center of kth class product is calculated:
(4.3) the total coordinate center of R class product is calculated:
(4.4) distance between distance and race in the race of finished wine is calculated, wherein d is distance in the supertribe of R class cargo；D is R class goods
Distance between the supertribe of object:
5. slotting optimization method according to claim 1, which is characterized in that the step (4) specifically includes:
(5.1) relevant parameter is set；
(5.2) comprehensively consider three velocity of goods circulation, shelf stabilities and product relevance target building objective functions:
(5.3) i-th kind of cargo transports to the time t of outbound platform in modeli, calculation formula is as follows
Consider following constraint:
It is above-mentioned, xi, yi, ziThe goods yard coordinate for indicating i-th kind of cargo is xthiArrange yiArrange ziLayer, is denoted as (xi, yi, zi)；nxyzIt indicates
Goods yard (xi, yi, zi) on the finished wine case number that stores；A, B, C respectively indicate (xi, yi, zi) maximum number value；L is unit load
The side length of lattice；H is the distance between every row's shelf；M is the quality of every case cargo；C is that each goods lattice most multipotency stores finished wine
Case number；VxFor the travel speed of conveyer；Vy, VzThe respectively horizontal and vertical speed of piler；niFor i-th kind of cargo
Quantity；piFor the frequency of access of i-th kind of cargo；tiThe time of outbound platform is transported to for i-th kind of cargo；D is in the supertribe of R class cargo
Distance；D distance between the supertribe of R class cargo.
6. slotting optimization method according to claim 1, which is characterized in that in the step (5), utilize band elitism strategy
Quick non-dominated sorted genetic algorithm find out Pareto disaggregation, specifically include:
(6.1) the goods yard position in finished wine warehouse is encoded by the way of real coding, generates initial population at random；
(6.2) population quantity, maximum number of iterations, crossover probability and mutation probability are set, by multiple target slotting optimization mathematical modulo
The inverse of objective function calculates ideal adaptation angle value as fitness function in type；
(6.3) quick non-dominated ranking is carried out to population in iterative process and crowding distance calculates；
(6.4) league matches selection strategy is used, i.e., preferentially selection sequence is worth small individual, the preferential selection crowding distance if sequence value is identical
Big individual carries out intersecting respectively and mutation operation, generation is sub using simulation binary system crossover operator and multinomial mutation operator
For population；
(6.5) parent population and progeny population are merged into an interim population, iteration carries out non-dominated ranking, crowding distance meter
It calculates, league matches selection intersects, mutation operation, new progeny population is formed, repeatedly iteration, if current iteration number is greater than most
Macroevolution algebra then stops evolving, and iteration finally generates one group of stable Pareto forward position disaggregation.
7. slotting optimization method according to claim 1, which is characterized in that reasonable based on fuzzy set in the step (6)
Optimal satisfactory solution is chosen by from Pareto forward position disaggregation, specific steps include:
(7.1) it is solved according to maximum satisfaction criterion from Pareto forward position and concentrates selection optimal solution, it is excellent at j-th to calculate i-th of solution
Change the satisfaction in target
In formula,For minimum value of all solutions on j-th of objective function, i.e. optimal value, taking the functional value that is dominant is 1；
For maximum value of all solutions on j-th of objective function, taking the functional value that is dominant is 0；
(7.2) function mu that is dominant is definediFor i-th of solution synthesis specific gravity shared in all targets, wjFor the power of j-th of target
Weight, be dominant functional value μiWith target weight wjIt is the number between 0-1, its calculation formula is:
(7.3) according to target significance level, satisfactory solution is selected based on maximum satisfaction criterion, as final slotting optimization side
Case.
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