CN111311003A - Component sorting method for flexible production - Google Patents
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Abstract
The invention relates to a sorting method for sorting parts for flexible production, which comprises the steps of analyzing the reasons of low sorting operation efficiency of a sorting center by using a fishbone diagram and an analytic hierarchy process (analytic hierarchy process), and analyzing and verifying the reasons by using an industrial engineering MTM (maximum transmission module) method; establishing a workload balance storage model; distributing the parts to different sorting subareas by using the workload balance model and the heuristic algorithm, and enabling the picking workload of each picker to be consistent; and establishing a storage allocation model. According to the invention, through a workload balance model among the picking subareas and solving by using a heuristic algorithm, the workload among the picking subareas is balanced, and three picking subareas with more balanced workload are obtained. Secondly, the shortest picking time is taken as a target, and aiming at the target, a storage position optimization model in the picking subarea is established, and an optimal storage position distribution scheme with the shortest path in the picking subarea is obtained by using genetic algorithm calculation. The sorting efficiency of the sorting center can be effectively improved.
Description
Technical Field
The invention relates to a part sorting method for flexible production.
Background
At present, the number of the domestic research on the picking operation is small compared with that of the foreign countries, and the research on the partition picking operation is mainly to comprehensively use a mathematical model to improve the picking efficiency through several aspects of a partition method, storage place assignment, picking strategy application and picking path design.
Establishing a path optimization model under the condition of an ABC storage strategy and solving by using a genetic algorithm to obtain an optimal picking path; under the condition of random storage, an S-type picking and return-type picking model is established in Zhujie, and the S-type picking route is better when a large number of picked goods are obtained through research; the Chen word provides a sorting path optimization algorithm based on an Ant Colony Optimization (ACO), and the effectiveness of the algorithm is verified through a simulation experiment.
The research range of the picking operation of the distribution center is very wide inside and outside, and the distribution design, the batching, the partitioning, the storage strategy and other aspects obtain obvious effects, most of the research aims at improving the efficiency of the picking operation of the distribution center, but no scholars provide clear indexes to measure the picking efficiency, and as the research on the sorting operation partition modes of the distribution center is gradually increased, more and more scholars find that the sorting partitions can improve the overall efficiency of the picking operation, but the research quantity on the workload balance problem among the sorting partitions is less, and neglects the influence of the storage space planning on the picking operation time, therefore, the paper provides the index of the vehicle service time for the first time to measure the picking operation efficiency of the distribution center, and the picking operation efficiency of the distribution center is optimized from two aspects of workload balance and storage space optimization among the picking subareas.
With the increasingly accurate automobile market positioning, the gradual expansion of customer levels and the gradually enriched market demands, the mixed-line production mode has become the main production mode of the automobile manufacturing industry. In the mixed production mode, the types and the number of the automobile parts are multiplied, and the picking operation is more and more important in the whole distribution link because the picking and the sequencing must be carried out according to the constantly changing workshop assembly requirements. The existing picking operation mode has low picking efficiency due to unbalanced picking subareas and overlong walking paths of picking personnel, and the picking operation efficiency is improved by which method, which is the key point for improving the overall efficiency of a distribution center, so that the problem of how to solve the problem is urgent.
Disclosure of Invention
The invention aims to provide a part sorting method for flexible production, aiming at the defects of the prior art.
A sorting method for sorting parts in flexible production comprises the following steps:
A. analyzing the reasons of low sorting operation efficiency of the sorting center by using a fishbone diagram and an analytic hierarchy process (analytic hierarchy process), and analyzing and verifying two reasons of unbalanced workload of a sorter and overlong sorting walking path by using an industrial engineering MTM method;
B. establishing a workload balance storage model
B1, assuming that each type of goods is only distributed on a certain storage position, the condition of goods missing does not exist in the picking process, and the picking of each picker is standard operation, namely the picking time of each goods is equal, namely the whole operation time of an order i is the total time of the picker picking all the goods in the order and putting the goods into a picking appliance, and the picking time is in direct proportion to the type of the goods to be picked;
b2, establishing a model, setting M to represent the number of the sorting partitions and n generationsAmount of orders, q stands for number of items, NabRepresenting the number of times that the items a and b are simultaneously present on the order, Nab＝Nba＝Σ1≤i≤nxiaxib；
Where yah and ybh do not take 1 at the same time when Nab is of a large value (> n/2), i.e., item a and item b are not in the same sortation zone, the mathematical model is as follows:
minZ＝∑1≤a≤q∑1≤b≤q∑l≤h≤mNabyabybh(1)
∑1≤h≤myah＝1，1≤a≤q (2)
Nab≥0 (4)
B3, partitioning the goods by using a heuristic algorithm: collecting goods order history data, calculating Nab of all goods according to the ordered quantity of the goods on the order, and arranging the Nab according to the ascending order, wherein the Nab quantity of q goods is assumed to have
The number of the main components is one,
Na1b1≤Na2b2≤…≤Nafbf(5)
after ordering the Nab of the goods, partitioning the goods:
b4, use UiIndex to evaluate whether sorting partition is optimized and improved
With Zj representing the items in order i in area j, pick the quantity
QieRepresenting the number of items e picked in order i, let di＝max1≤j≤mdijWhen the time for picking a goods by the picker is t, the utilization rate of the personnel and the equipment when picking the order i in the j area is t
Average utilization rate
When U is turnediThe closer to 1, the higher the system utilization, UiWhen the sorting partition reaches more than 90%, the sorting partition works are considered to be basically balanced;
C. distributing the parts to different sorting subareas by using the workload balance model and the heuristic algorithm, and enabling the picking workload of each picker to be consistent;
D. establishing a storage allocation model
D1, aiming at the shortest picking time, sequentially picking left and right parts of a picking channel according to the fact that employees of the sorting center pick in the picking process, measuring the established walking distance by a right-angle distance, and determining the row and column distribution, the storage position and the part type of the picking subarea;
d2, determination of goods turnover rate: 1 hour was selected as the study range, and f was usedxIt is shown that the smaller fx value of the article indicates a faster turnover, the more it should be placed near the doorway, wherein,
d3, establishing a model, taking the minimum picking time as an objective function, and taking f asxPlacing the goods with the lowest value on the storage position closest to the gateway, and so on, and counting the width of the channel into the height of the storage position, wherein the objective function is as follows:
wherein a represents the length of the storage location, b represents the height of the storage location, p represents the total column number of the storage location, q represents the total layer number of the storage location, x represents the column number of the storage location where the goods are located, z represents the layer number of the storage location where the goods are located, and v represents the average speed of the picker;
d4, encoding and decoding the sorting scheme by ROV encoding rule, and determining the continuous positions X of the particlesi(0)＝[xi,1,...,xi,j,...,xi,n]Is converted into a storage location for the item,
thereby calculating a target value of the reservoir adjustment scheme corresponding to the particle;
d5, solving the storage position optimization configuration in the sorting partition by using a genetic algorithm;
further, step B3, the goods is partitioned according to the following steps:
b31, finding two kinds of goods a1 and B1 with the minimum Nab value, and distributing the goods to a first subarea Z1;
b32, two goods a2, B2 with the second smallest Nab value are found, and a2, B2 are never allocated, and a2, B2 are allocated to Zi zone;
b33, if i < m above, go to step 2, otherwise go to step B34;
b34, sequentially finding out Nasbs with smaller Nab value, and when more than two partitions have the same Sj, at least one of as and bs is not partitioned*Or Tj**When it is, then atAnd btAssigning to a picking zone having a smaller number of items; if the quantity of the goods of each subarea is the same, the goods can be divided into any one;
b35, when all the items are allocated to the picking zone, stopping; otherwise, go to step B34.
Further, step D1, the picking area is premised on the location store, and the picking zones are distributed in a rectangle.
Further, step D2, fxCan be counted from the data collection order.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, through a workload balance model among the picking subareas and solving by using a heuristic algorithm, the workload among the picking subareas is balanced, and three picking subareas with more balanced workload are obtained. Secondly, the shortest picking time is taken as a target, and aiming at the target, a storage position optimization model in the picking subarea is established, and an optimal storage position distribution scheme with the shortest path in the picking subarea is obtained by using genetic algorithm calculation. The sorting efficiency of the sorting center can be effectively improved.
Drawings
FIG. 1 single cycle picking time comparison;
FIG. 2 workload balancing assignment results;
FIG. 3 is a schematic diagram of sorting partitions;
FIG. 4 is a parameter entry interface diagram;
FIG. 5 is a graph of adaptation value changes;
FIG. 6 is a part bin distribution map;
FIG. 7 adjusted single cycle picking time;
FIG. 8 is a comparison chart of the adjusted working hours;
FIG. 9 is a chart of picking job assignments for the sorting center;
FIG. 10 is a flow chart of reservoir optimization;
FIG. 11 is a fishbone analysis chart illustrating the efficiency of the picking operation;
FIG. 12 is a hierarchical efficiency analysis diagram for a picking operation.
Detailed Description
1. Existing picking work research of company distribution center
Through analysis of the working hours and the personnel proportion of the existing four links of unloading, loading, picking and transporting of each distribution center of a company, the picking operation is found to be a key link influencing the overall operation efficiency of the distribution center of the company G, and the working hour proportion of the picking operation in each distribution center of the company is highest and reaches 41 percent by using a sorting center; the proportion of the picking personnel to the total number of people is the highest and is 61 percent. In order to further measure the efficiency of the picking operation, the method proposes that the picking operation is measured by taking the number of working hours as an index. The calculation shows that the vehicle number working hour of the sorting center is 6.7 hours/vehicle, which is the highest area of the vehicle number working hours of each distribution center of a company and indicates that the picking efficiency is the lowest, so the optimization problem of the sorting operation of the sorting center is mainly researched.
For the operation of selecting of accurate analysis sequencing center, this application has used fishbone picture and analytic hierarchy process to select the reason that the operating efficiency is low to the sequencing center and has analyzed, reachs two main reasons: 1) workload imbalance for picking zones; 2) the picker takes too long a picking path. Meanwhile, the workload and the walking time of each sorting zone are subjected to data analysis by using an industrial engineering MTM method, the highest and lowest sorting quantity difference of the three sorting zones is 32 percent, the waiting time for on-line transportation is 2.63min, and the walking man-hour accounts for 51 percent of the whole man-hour; it was further verified that the main reasons for the lower picking efficiency in the sorting center were the unbalanced workload of the picking zones and the too long distance traveled by the pickers.
2. Corporate sort center zone picking job optimization
Aiming at two main reasons that sorting efficiency of a sorting center is low, sorting operation is optimized from the aspects of sorting partition workload balancing and storage optimization respectively.
Firstly, in order to balance the workload among the sorting subareas and reduce the invalid waiting time, the invention establishes a workload balance model among the sorting subareas, uses a heuristic algorithm to solve, and finally distributes the parts to 3 sorting subareas of a sorting center sorting area according to the workload, so that the difference of the highest and lowest workloads among the sorting subareas is reduced to 11%, the waiting time for on-line transportation is reduced to 1.19min, and three sorting subareas with more balanced workload are obtained.
Secondly, after the workload of each partition of the sorting center is balanced, the method takes the shortest sorting time as a target, establishes an optimal storage position allocation model in the sorting partition aiming at the target, calculates by using a genetic algorithm to obtain an optimal storage position allocation scheme with the shortest path in the sorting partition, and reduces the picking walking man-hour to 41% of the whole man-hour by MTM calculation and the adjusted storage position.
3. Effect verification
The method and the steps provided by the application are applied to the sequencing center, the implementation result is verified again through the vehicle part working hour index, finally the vehicle part working hour of the sequencing center is reduced to 4.97 hours per vehicle, and the result proves that the optimization method provided by the application effectively improves the sorting operation efficiency of the sequencing center.
Examples
1. Analyzing the reasons of low sorting operation efficiency of the sorting center by using a fishbone diagram and an analytic hierarchy process (analytic hierarchy process), and analyzing and verifying two reasons of unbalanced workload of a sorter and overlong sorting walking path by using an industrial engineering MTM method;
TABLE 1 MTM time value measuring table for a certain station of sorting partition
TABLE 1 (continuation)
According to the measurement results of the industrial engineering MTM time measurement method, the time for picking one appliance in the first picking zone is 5.61 minutes. In this way, three picking zones are measured for sorting center picking zone one, as shown in FIG. 1:
as can be seen from the figure, the single cycle picking times of the pickers sorting the three picking zones in the center have the following characteristics:
the shortest picking time is the first picking zone, the average picking time is 5.61min, the longest picking time is the second picking zone, the average picking time is 8.24min, the difference with the first picking zone is 32%, and the difference reaches 2.63min, so that the waiting time of on-line transportation of 2.63min is once for each cycle of picking zones, the whole order operation time is increased, and the whole labor efficiency is reduced.
Through the analysis, the difference of the workload of the sorting employees in the sorting center, namely the inconsistent time of sorting completion in the subareas, leads to the prolonging of the whole operation time, and the sorting efficiency index is related to the number of the operation time, so that the sorting efficiency of the sorting center is low.
2. Establishing a workload balance storage model
1) Preconditions and assumptions
On the premise of determining the picking operation system, the invention mainly studies the order rule of the central parts to be sorted, so that the working time between the picking sub-areas is approximately consistent, and unnecessary waiting is reduced.
To build a mathematical model, and to facilitate the analysis of the problem, the following assumptions are made for the picking system: each kind of goods is distributed on a certain storage position only; there is no item missing during the picking process; picking by each picker is a standard operation, i.e., equal time for each item to be picked.
As can be seen from the above assumed conditions, the overall operation time of an order i is the total time for the picker to pick all the items in the order and put them into the picking device, and the picking time is in direct proportion to the types of the items to be picked.
2) Workload balancing model
(1) Code
M represents the number of picking partitions; n represents the amount of orders; q represents the number of items.
NabRepresenting the number of times that the items a and b are simultaneously present on the order, Nab＝Nba＝∑1≤i≤nxiaxib。
(2) Model building
To substantially equalize the workload between the various sorting divisions, if NabThe values of (a) are large, the two items should be assigned to two different sortation zones, and the mathematical model is as follows:
minZ＝∑1≤a≤q∑1≤b≤q∑1≤h≤mNabyabybh(1)
∑1≤h≤myah＝1，1≤a≤q (2)
Nab≥0 (4)
When N is presentabWhen the value of (a) is large, y is the optimum solutionahAnd ybhIt is not possible to fetch 1 at the same time, i.e., the item a and the item b cannot be in the same picking bay. Condition (2) ensures that an item can only be stored in the picking zone. One point to be specifically noted is that in the conventional algorithm of reserve allocation, items that are normally ordered simultaneously should be allocated to the same area, but this is the case while ensuring the integrity of the overall order. The aim of the research is to balance the workload of the parallel picking zones, and order segmentation strategies need to be matched, so that goods must be distributed to different picking zones to balance the workload.
(3) Heuristic algorithm
Collecting goods order history data, calculating Nab of all goods according to the ordered quantity of the goods on the order, and arranging the Nab according to the ascending order, wherein the Nab quantity of q goods is assumed to have
And (4) respectively.
Na1b1≤Na2b2≤…≤Nafbf(5)
After ordering the Nab of the goods, partitioning the goods according to the following steps:
step 1: finding out two kinds of goods a with the minimum Nab value1、b1To the first partition Z1;
step (ii) of2: find two items a with the second smallest Nab value2、b2And a is2、b2From unassigned, will a2、b2Is distributed to ZiA zone;
and step 3: if i is less than m, then go to step 2, otherwise go to step 4;
and 4, step 4: sequentially finding out Na with smaller Nab valuesbsAnd a issAnd bsAt least one is not partitioned;
1)bsis allocated ofsIs not distributed, is provided with
J is not less than 1 and not more than m, represents asAnd the sum of the similarity coefficient of each goods e in the Zj area takes the value Sj*＝min1≤j≤mSjThen the goods a is putsIs assigned to j*In a partition.
2) If b iss，asAre all not distributed, are provided with
Is provided with
① if
Then a will besIs assigned to j*Zone, and pair j*Sum of region similarity coefficients
Update, order
Then b will besIs assigned to j**Zone(s)
② if
Then b will besIs assigned to j**Zone, and pair j**Sum of region similarity coefficients
Update, order
A is tosIs assigned to j*Zone(s)
And 5: stopping when all items are assigned to the picking zone; otherwise, go to step 4.
In the measurement and calculation of step 4, when more than two partitions have the same Sj*Or Tj**When it is, then atAnd btTo a picking zone with a smaller number of items. If the quantity of the goods of each subarea is the same, the subareas are divided into any one.
(4) Evaluation index of model
With Zj representing the items in order i in area j, pick the quantity
QieRepresenting the number of items e picked in order i, let di＝max1≤j≤mdijWhen the time for picking a goods by the picker is t, the utilization rate of the personnel and the equipment when picking the order i in the j area is t
The average utilization of the system can be expressed as
When U is turnediThe closer to 1, the system advantage is shownThe higher the rate, in general, UiAbove 90%, the picking zone work may be considered substantially balanced.
After partitioning the goods using heuristic algorithms, U is usediAn index to evaluate whether the culling partition is optimized for improvement.
3. Sort center sort zone model analysis
From an order perspective, some of these parts often need to be picked together, and the specific order data is shown in table 2, the parts required for the order are picked from different shelves by multiple pickers, and then the pickers transport the sorting instrument to the designated site delivery driver, all the parts are distributed into three picking divisions, i.e., m is 3. It is now desirable to allocate these parts to different picking bays and to substantially equalize the picking workload among the pickers.
TABLE 2 sorting of parts in sorting centers
Table 2 (continuation)
In table 2, the numbers represent order numbers, i.e., each row represents a sort order; the letters represent part numbers, i.e., each column represents a part. The question here is how to allocate these 24 parts into 3 culling sections.
1) According to the data in the table, the program is embedded into the Excel module through VBA programming by utilizing the workload balance model and the heuristic algorithm, and the distribution result is displayed by using the macro, so that the sorting partition result is shown in figure 2.
A first sorting area: item O, E, B, K, X, S, I, G
A second sorting area: item H, N, V, T, D, U, W, P
A third sorting area: item F, L, R, J, C, Q, M, A
According to the model calculation result, the result of the partition of the 24 parts in the sequencing center is shown in the table 3:
TABLE 3 sequencing center sort zone
As can be seen, sort center 24 parts have been assigned to three culling sections, 8 parts per culling section, by workload balancing models and heuristics.
4. Storage allocation model
The storage locations of the parts in the bays are optimized to minimize picking time by the picker.
1) Preconditions and assumptions
According to the storage allocation model researched by the invention, the shortest picking time is taken as a target, the walking distance is a very critical factor, and according to the characteristics of picking operation in the sorting center, employees generally pick left and right parts of a picking channel in sequence in the picking process, so that the walking distance established by the model is measured by a right-angle distance.
According to the current situation of a sorting center, a sorting area is premised on positioning storage, sorting partitions are distributed in a rectangular mode, 15 columns and 2 rows are formed, 30 storage positions are formed in the sorting partitions, and 30 parts are arranged in the sorting partitions. As shown in fig. 3:
2) determination of goods turnover
The goods turnover rate refers to the number of orders needed by the goods within a certain time, 1 hour is selected as a research range in the text, and f is usedxIt is specifically defined as follows:
fxthe smaller the value of the article, the faster the turnover is, the more the article should be placed near the doorway, with respect to fxCan be selected from data collection ordersAnd (6) counting.
3) Model building
With the picking time as the minimum as the objective function, f needs to be reducedxLower items are placed in the storage locations closer to the gate so that the distance traveled by the picker when picking a greater number of items is reduced to achieve the goal of minimizing picking time. In the measuring and calculating process, the width of the channel is counted into the height of the storage position for convenient calculation. The objective function is:
wherein, a represents the length (m) of the storage position, 2m is taken as the storage position, b represents the height (m) of the storage position, 3m is taken as the total number of the p storage positions, the total layer number x of the q storage positions represents the storage position column number of the goods, z represents the storage position layer number of the goods, v represents the average speed (m/s) of the picker, and v is 2.5m/s in the application
4) Encoding
In order to continue the discrete problems, the ROV coding rule is adopted by the application to carry out coding and decoding on the sorting scheme.
Successive positions X of the particlesi(0)＝[xi,1,...,xi,j,...,xi,n]Is converted into a storage location for the item,
thereby calculating the target value of the reservoir adjustment scheme corresponding to the particle
For example, consider the storage location of 5 items, two individuals A are drawn from the population1，A2Definition of A1＝[X1，X2，X3，X4，X5]＝[0.25，0.41，0.78，0.66，0.13]，A2＝[X’1，X’2，X’3，X’4，
X’5]＝[0.17，0.34，0.08，0.29，0.48]。
According to the rules, the vectors in a1 and a2 are arranged from small to large to obtain a1 ═ X5, X1, X2, X4, X3, and a2 ═ X ' 3, X ' 1, X ' 4, X ' 2, and X ' 5, respectively.
5) Genetic algorithm design
Genetic algorithms are used to solve for bin optimization configurations within the culling partition. Genetic Algorithms (GA) are random search methodology models generated from biological evolution.
A. Population initialization
In order to ensure the excellence and diversity of the population, an initial population is generated using a random method, a genetic algorithm randomly generates an initialized population M (the value of M is 100 to 500), calculates the fitness G of each individual, and then an iterative search is started, setting the initial population M to 100.
B. Selecting
The genetic algorithm selects the next generation with high fitness based on the principle of survival of the fittest.
Giving the population scale M and the individual fitness G, the probability that the individual is selected as the next generation is
C. Crossover operation
The cross-fingers randomly select the same location of an individual to swap in order to create a new individual.
The individual a1 ═ 0.13, 0.25, 0.41, 0.66, 0.78], a2 ═ 0.08, 0.17, 0.29, 0.34, 0.48], the right two-digit crossover a1 ═ 0.13, 0.25, 0.41, 0.34, 0.48], a2 ═ 0.13, 0.25, 0.41, 0.66, 0.78], and the crossover probability P herein takes 0.8.
D. Variation of
Based on the principle of biological gene mutation, the variation probability point is consistent with the minimum variation, and the value of Pm is generally 0.0001-0.1. For example, individual a1 is [0.13, 0.25, 0.41, 0.66, 0.78 ]. When the gene at the 2 nd and 4 th positions is mutated, the gene is increased by 30%, and A' 1 is [0.17, 0.33, 0.53, 0.66 and 0.78 ]; in the genetic algorithm, mutation operation compensates the defects of the crossover operation, because when all individuals are consistent, new individuals cannot be generated by the crossover operation alone, and calculation is carried out by the mutation operation, so that the mutation increases the characteristics of the global optimization. The mutation probability of the invention is 0.05.
E. Globally optimal convergence
The invention sets the operation to be terminated when the iteration number reaches 500.
F. Sequencing center storage allocation model analysis
Through the research in the previous section of the invention, the sorting center is divided into three sorting divisions, and taking the first sorting division as an example, now the first sorting division has eight types of parts, and according to the field research on the sorting center, the eight types of parts are 30 types in total, and the storage allocation is carried out on the 30 types of parts in the division, namely, the 30 types of parts in the sorting division are allocated to the 30 storage locations in the goods area according to the target of the invention. The part characteristics are tabulated, and as within the picking zone, the inventory of items is defined as the capacity of a single packaging container.
TABLE 4 parts characteristic Table
Simulation conditions are as follows:
① the inlet and outlet of the sorting area are arranged at the same end.
② mean walking speed v of pickerx＝2.5m/s
From the above, the crossover probability pe is 0.8, the mutation probability pm is 0.05, the population size M is 100, and the number of iterations is 500.
The objective of minimum picking time is to place the goods with small FX value in the storage position which is easier to pick, so that the total carrying time of the goods is minimum, and the picking operation efficiency of the sorting center is improved.
And (4) obtaining a parameter input interface through C + + programming, and inputting various parameters as shown in the figure.
As can be seen from fig. 4, the adaptation value G is ramped up after 500 iterations, indicating that convergence has been achieved after 500 iterations.
Minimum value minG of adaptation value G from the beginning is 1.47 × 10-3The rise after 500 iterations was 2.97X 10-3This method was shown to be effective.
The reserve of each part is obtained through a C + + genetic algorithm, which is specifically shown in FIG. 6.
According to the model and the measuring and calculating steps, all the part storage positions of three sorting subareas in one area are sorted by the sorting center and allocated.
MTM assay
Through the above-mentioned research, the parts in the first sorting zone have been uniformly distributed into the storage positions in the three sorting sections, and it is examined whether the workload and the sorting travel distance of the first sorting zone are optimized through the MTM measuring and calculating method.
According to the MTM measurement and calculation procedure described in chapter iii, the adjusted sorting partition single-appliance sorting time is measured and calculated, and the obtained result is shown in fig. 7.
By comparison, it can be seen that by adjusting the single cycle picking times of the three picking sections to be balanced, the picking time of each picking section is reduced, and the overall workload is reduced. The single-cycle picking time of the first picking zone with the shortest picking time is 5.38min, the single-cycle picking time of the second picking zone with the longest picking zone is 6.57min, the difference between the first picking zone and the second picking zone is 11%, the first picking cycle picking time of the adjusted first picking zone is 6.57min, and the transportation waiting time is reduced to 1.19 min.
And (3) checking the walking distance, namely checking the sorting work of the first sorting zone after adjustment according to the MTM measuring and calculating method, and placing the parts with larger turnover rate at a position close to the entrance after adjustment to reduce the average walking distance for sorting the parts, wherein the result is shown in figure 8: as can be seen from the figure, the walking time after the adjustment of the picking section is finished accounts for 41 percent of the total operation time, and is reduced by 10 percent compared with that before the adjustment.
The picking operation is a necessary operation link of each automobile part distribution center, so that the picking operation flow generally adopts the work hours of a vehicle as an index for measuring the efficiency. The number of working hours represents the working time required for 1 worker to produce 1 vehicle, and lower indexes represent higher efficiency. Automobile host factories generally express the work efficiency of production line workers by vehicle parts working hours; in the field of automobile logistics, the working efficiency of employees in various links of automobile part storage and distribution can be expressed by the working hours of vehicles. The calculation formula of the vehicle share time is as follows:
vehicle share man-hour (number of people x average man-hour/yield (1)
The sorting center is used for solving the problem of low sorting efficiency and better providing service for a final assembly workshop, the sorting operation is adjusted according to an optimization method of chapter IV, 24 parts of the sorting center are evenly distributed into three sorting subareas according to a workload balancing model, the workload of each sorting subarea tends to be balanced, and waiting caused by inconsistent sorting time of each subarea is reduced; and allocating the parts in the picking subarea to the storage positions according to the storage position allocation model. The number of vehicle shares in the rear sort center adjusted according to the equation (3-1) becomes as shown in fig. 9. As can be seen from fig. 9, the dispatch schedule for the sort center picking operation is reduced from 6.7 to 4.97, which is already below the average of two plants, approaching the group industry level, i.e., the efficiency of the sort center picking operation is improved due to the reduction of the overall working time and the number of pickers. Moreover, through the research of the invention and the optimization work of the sequencing center, 6 persons are optimized in the whole sequencing distribution center, and the operation cost of the distribution center is reduced.
Claims (4)
1. A sorting method for sorting parts in flexible production is characterized by comprising the following steps:
A. analyzing the reasons of low sorting operation efficiency of the sorting center by using a fishbone diagram and an analytic hierarchy process (analytic hierarchy process), and analyzing and verifying two reasons of unbalanced workload of a sorter and overlong sorting walking path by using an industrial engineering MTM method;
B. establishing a workload balance storage model
B1, assuming that each type of goods is only distributed on a certain storage position, the condition of goods missing does not exist in the picking process, and the picking of each picker is standard operation, namely the picking time of each goods is equal, namely the whole operation time of an order i is the total time of the picker picking all the goods in the order and putting the goods into a picking appliance, and the picking time is in direct proportion to the type of the goods to be picked;
b2, establishing a model, setting M to represent the number of the sorting subareas, N to represent the amount of orders, q to represent the number of goods, and NabRepresenting the number of times that the items a and b are simultaneously present on the order, Nab＝Nba＝∑1≤i≤nxiaxib；
Where yah and ybh do not take 1 simultaneously when Nab has a value > n/2, i.e., item a and item b are not in the same picking zone, the mathematical model is as follows:
minZ＝∑1≤a≤q∑1≤b≤q∑l≤h≤mNabyahybh(1)
∑1≤h≤myah＝1，1≤a≤q (2)
ysh1 or 0, 1 ≤ a ≤ q, 1 ≤ h ≤ m (3)
Nab≥0 (4)
B3, partitioning the goods by using a heuristic algorithm: collecting goods order history data, calculating Nab of all goods according to the ordered quantity of the goods on the order, and arranging the Nab according to the ascending order, wherein the Nab quantity of q goods is assumed to have
The number of the main components is one,
Na1b1≤Na2b2≤…≤Nafbf(5)
after the nabs of the goods are sorted, the goods are partitioned.
B4, use UiIndex to evaluate whether sorting partition is optimized and improved
By ZjRepresenting orders i in region jThe goods are sorted by the quantity of
QieRepresenting the number of items e picked in order i, let di＝max1≤j≤mdijWhen the time for picking a goods by the picker is t, the utilization rate of the personnel and the equipment when picking the order i in the j area is t
Average utilization rate
When U is turnediThe closer to 1, the higher the system utilization, UiWhen the sorting partition reaches more than 90%, the sorting partition works are considered to be basically balanced;
C. distributing the parts to different sorting subareas by using the workload balance model and the heuristic algorithm, and enabling the picking workload of each picker to be consistent;
D. establishing a storage allocation model
D1, aiming at the shortest picking time, sequentially picking left and right parts of a picking channel according to the fact that employees of the sorting center pick in the picking process, measuring the established walking distance by a right-angle distance, and determining the row and column distribution, the storage position and the part type of the picking subarea;
d2, determination of goods turnover rate: 1 hour was selected as the study range, and f was usedxIt is shown that the smaller fx value of the article indicates a faster turnover, the more it should be placed near the doorway, wherein,
d3, establishing a model, taking the minimum picking time as an objective function, and taking f asxPlacing the goods with the lowest value on the storage position closest to the gateway, and so on, and counting the width of the channel into the height of the storage position, wherein the objective function is as follows:
wherein a represents the length of the storage location, b represents the height of the storage location, p represents the total column number of the storage location, q represents the total layer number of the storage location, x represents the column number of the storage location where the goods are located, z represents the layer number of the storage location where the goods are located, and v represents the average speed of the picker;
d4, encoding and decoding the sorting scheme by ROV encoding rule, and determining the continuous positions X of the particlesi(0)＝[xi，1，...，xi，j，...，xi，n]Is converted into a storage location for the item,
thereby calculating a target value of the reservoir adjustment scheme corresponding to the particle;
and D5, solving the reservoir position optimization configuration in the sorting partition by using a genetic algorithm.
2. The component sorting method for flexible production according to claim 1, wherein: step B3, the goods is partitioned according to the following steps:
b31, finding two kinds of goods a1 and B1 with the minimum Nab value, and distributing the goods to a first subarea Z1;
b32, two goods a2, B2 with the second smallest Nab value are found, and a2, B2 are never allocated, and a2, B2 are allocated to Zi zone;
b33, if i < m, go to step 2, otherwise go to step B34;
b34, sequentially finding out Nasbs with smaller Nab value, and when more than two partitions have the same Sj, at least one of as and bs is not partitioned*Or Tj**When it is, then atAnd btAssigning to a picking zone having a smaller number of items; if the quantity of the goods of each subarea is the same, the goods can be divided into any one;
b35, when all the items are allocated to the picking zone, stopping; otherwise, go to step B34.
3. The component sorting method for flexible production according to claim 1, wherein: step D1, pick zone is premised on location storage and pick zone is rectangularly distributed.
4. The component sorting method for flexible production according to claim 1, wherein: step D2, fxCan be counted from the data collection order.
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