CN112580852A - Intensive automatic stereoscopic warehouse goods space optimization method for electric power materials - Google Patents
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Abstract
An intensive automatic stereoscopic warehouse goods space optimization method for electric power goods and materials adopts a goods grid array to define an electric power goods and materials storage unit and a classified storage strategy, and electric power goods and materials are placed in a classified and orderly manner, so that frequent goods and materials transferring operation is avoided, and the storage and taking efficiency is improved. The goods location optimization targets of the multiple electric power material automatic stereoscopic warehouses are established by combining multiple goods location optimization principles, such as the principle of depending on turnover rate, the stability principle, the correlation principle, the identity principle and the like, and are more comprehensive and reasonable than a single optimization target. The electric power material intensive automatic stereoscopic warehouse multi-target goods location optimization model is established by comprehensively considering the turnover rate and the relevance of the electric power materials and the structural characteristics of the electric power material intensive automatic warehouse, and can be well combined with the actual problem of electric power material storage and retrieval. The method can optimize the distribution of the goods space of the electric power materials, help a decision maker to make a correct strategy, and has effectiveness on the traditional non-intensive warehouse and the intensive warehouse.
Description
Technical Field
The invention relates to the field of power systems, in particular to a method for optimizing the goods space of an intensive automatic stereoscopic warehouse facing to power materials.
Background
In recent years, the high-quality development of power grids puts higher and higher requirements on intelligent storage of electric power materials. The electric power materials are crucial to stable and reliable operation of an electric power system, and have the characteristics of large volume and mass, undecomposed property, large warehouse entry and exit workload, multiple varieties and types and the like, and the characteristics cause the complexity of power supply enterprises in warehouse management. In a traditional manual management warehouse, electric power materials are often stacked in a mess and are not flexibly called, so that the phenomena of low efficiency and high cost of people for finding goods are caused, further, the electric power emergency rescue is notched, and the electric power materials are aged or technically eliminated due to long-term idling. Under the large background of material resource intensification and standardization, along with the improvement of the automation level and the innovation of the informatization technology, the electric power material automatic stereoscopic warehouse gradually replaces the traditional manual management warehouse, can reduce the cost of power supply enterprises on the warehouse management, and improves the operating efficiency and the enterprise income of the warehouse management.
Although many power grid companies in provinces and cities in China have made corresponding research and design on power material storage management, for example, the power grid in Jiangsu and the power grid in Zhejiang can provide a relatively perfect storage construction system and basically meet the requirements of turnover and centralized management of material supply, most power supply enterprises still have the problems of random distribution of goods and lack of regularity when carrying out storage management on power materials, so that the storage and taking efficiency of a warehouse can be greatly influenced, and the storage management cost is increased. Therefore, scientific and reasonable goods allocation optimization for power material storage is a problem that power supply enterprises need to research and solve urgently.
Disclosure of Invention
The invention mainly solves the technical problem of adopting a cargo grid storage unit to reduce the operation of transferring electric power materials, comprehensively considering the turnover rate and the degree of association of the electric power materials and providing an intensive automatic stereoscopic warehouse cargo space optimization method for the electric power materials.
The invention adopts the following technical scheme: aiming at the characteristics of a shuttle car type intensive automatic stereoscopic warehouse, a goods grid column is adopted to define an electric power material storage unit, and a classified storage strategy is adopted to store electric power materials; comprehensively considering historical order data and inventory data of the electric power materials, and performing data processing and calculation to obtain the turnover rate of the electric power materials; historical order data of different types of electric power materials are comprehensively considered, and relevance degrees of the different types of electric power materials are provided; the goods location optimization targets of the multiple electric power material automatic stereoscopic warehouses are determined by combining multiple goods location optimization principles, such as a principle of depending on turnover rate, a stability principle, a correlation principle, an identity principle and the like; comprehensively considering the structure, storage strategy and principle of the intensive automatic stereoscopic warehouse for electric power materials, and establishing a multi-target goods space optimization model of the intensive automatic stereoscopic warehouse for the electric power materials; and solving the proposed electric power material goods space optimization model by adopting a self-adaptive genetic algorithm to obtain a goods space optimization result.
Specifically, the goods location optimization method comprises the following steps:
s10, analyzing and processing the structure and inventory of an intensive automatic stereoscopic warehouse for storing electric power materials to obtain the shelf basic data of the stereoscopic warehouse and the data of the type, quantity, quality, initial goods position coordinates and the like of the electric power materials;
s20, aiming at the characteristics of the shuttle car type intensive automatic stereoscopic warehouse, defining an electric power material storage unit by adopting a cargo grid array, and storing electric power materials by adopting a classification storage strategy;
s30, comprehensively considering historical order data and inventory data of the electric power materials, and performing data processing and calculation to obtain the turnover rate of the electric power materials;
s40, comprehensively considering historical order data of different types of electric power materials, and providing the association degrees of the different types of electric power materials;
s50, determining the goods location optimization targets of the plurality of electric power material automatic stereoscopic warehouses by combining a plurality of goods location optimization principles, such as a principle of depending on turnover rate, a stability principle, a correlation principle, an identity principle and the like;
s60, comprehensively considering the structure, storage strategy and principle of the intensive automatic stereoscopic warehouse for electric power materials, and establishing a multi-target goods space optimization model of the intensive automatic stereoscopic warehouse for electric power materials;
and S70, solving the proposed electric power material goods space optimization model by adopting a self-adaptive genetic algorithm to obtain a goods space optimization result.
Further, the electric power material storage unit is defined by adopting a cargo grid column, and the method specifically comprises the following steps:
in order to overcome the defects of the traditional non-intensive warehouse, the shelves of the intensive automatic stereoscopic warehouse are designed and arranged tightly, the existing occupied space can be utilized to the maximum extent, and the space utilization rate can be improved by more than 30%. The shuttle type intensive automatic stereoscopic warehouse has the advantages that the number of the lanes is greatly reduced, and the shuttle type mother vehicles can only carry out storing and taking operation at the front end of the goods space of each row of goods shelves close to the lanes. For a certain row of goods shelves, other lifting equipment is not arranged in the middle of the goods shelves, and the goods space operation along the depth direction of the goods shelves needs to be carried out on electric power materials by means of shuttle vehicles.
Therefore, when the electric materials are stored in and out of the storage space according to the order, the electric materials placed outside may block the traveling route of the shuttle car. At this time, the outer electric power materials need to be moved to other goods outlets, and then the inner electric power materials are taken out, namely, the materials are transferred. If the electric power materials are not classified and orderly placed in groups, the condition of transferring the materials is easy to frequently occur, the working distance and time of the shuttle primary vehicle and the shuttle secondary vehicle can be greatly increased, the storing and taking efficiency is reduced, and the complexity of a management and control system in designing a shuttle primary and secondary vehicle dispatching route can be increased. Based on the above analysis, a column consisting of all the cargo spaces in the depth direction at the exit of each layer of the shelves is defined as a cargo grid column;
the classified storage strategy is adopted to store electric power materials, and the classified storage strategy specifically comprises the following steps:
the electric power material storage strategy is a classification storage strategy, the electric power materials are effectively classified according to the attributes of the electric power materials, and the electric power materials of the same type are placed on the goods positions of the same goods grid row.
Further, the electric power material turnover rate is calculated by comprehensively considering the historical order data and the inventory data of the electric power material, and the method specifically comprises the following steps:
the turnover rate of the electric power materials can be obtained by carrying out data processing and calculation on the historical orders of the electric power materials in a certain time period, and the calculation formula is as follows:
in the formula: piThe turnover rate of the ith electric power material, T is the length (day) of the calculation time period, and Y isiThe number of the i-th electric power material discharged in the period of time,
and
the initial and final inventory of the ith kind of electric power material in the period are respectively.
Further, the historical order data of different types of electric power materials is comprehensively considered to provide the association degrees of the different types of electric power materials, and the method specifically comprises the following steps:
order matrix IC for defining ith kind of electric power materialiComprises the following steps:
ICi＝(O1,i,O2,i,...,Oq,i,...,OQ,i)
in the formula: IC (integrated circuit)i0-1 matrix of 1 × Q, Q being the total number of selected historical orders, Oq,i0/1 respectively indicate that the order q contains no/no i-th power supply.
Defining a calculation formula of the association degree of different types of electric power materials:
in the formula: ri,sThe value range of the correlation degree of the ith electric power material and the s-th electric power material is 0-1. And defining the relevance of the same electric power material as 1.
Further, the principle of relying on turnover rate is combined to establish a goods space optimization target for maximizing the electric power material access efficiency, and the method specifically comprises the following steps:
under the premise that the operation speeds of the primary shuttle vehicle and the secondary shuttle vehicle are considered to be consistent and known, the access time of the electric power materials can be positioned in a goods space (x) by the shuttle vehiclei,j，yi,j，zi,j) Electric power material Ei,jTravel distance L for transportation to loading/unloading porti,jExpressed, its expression is:
Li,j＝xi,jLl+yi,jLw+zi,jLh
the turnover rate is an important consideration factor in the optimization of the goods yard of the electric power materials, and the higher the turnover rate is, the higher the frequency of the electric power materials entering and exiting the warehouse is. Therefore, for electric power materials with high turnover rate, the materials are required to be placed near the warehouse entrance and exit, so that the materials can be stored and taken conveniently, and the warehouse entrance and exit efficiency is improved; the electric power goods and materials with low turnover rate are placed at the inner position, so that the goods space of the goods and materials with high turnover rate is not occupied, the route of the shuttle primary-secondary vehicle is prevented from being shielded when the goods and materials with high turnover rate are stored and taken as far as possible, and redundant goods and materials transferring operation is carried out.
The turnover rates of different types of electric power materials are different greatly, for example, power grid infrastructure materials such as transformers and distribution boxes are materials required in the construction/reconstruction project of a power grid, and the turnover rates are lower; and accident spare parts such as electric wires, fuses and the like are often needed in electric power overhaul, and the turnover rate is high.
Therefore, according to the principle of depending on the turnover rate, in order to maximize the access efficiency of the stereoscopic warehouse for electric materials, the total running distance of the shuttle car for accessing the electric materials and the turnover rate of the electric materials need to be comprehensively considered. Generally speaking, the electric power materials with higher turnover rate should be placed on the goods space close to the picking area; on the contrary, place the lower electric power goods and materials of turnover rate in the position of comparatively leaning on the lining, can effectively save shuttle car operating distance, promote warehouse entry efficiency.
Therefore, the maximization of the access efficiency of the stereoscopic warehouse for electric materials can be represented as:
in the formula: i is the number of electric power material categories, kiIs the number of the ith kind of electric power material, Ll、LwAnd LhRespectively length, width and height (m) of each cargo space.
Further, the stability principle is combined to establish a goods position optimization target of minimizing the gravity center of the electric power goods and materials shelf, and the method specifically comprises the following steps:
different electric power materials often can be deposited on same goods shelves, because its quality is different, the stability of goods shelves can be influenced with highly to the position of putting. Stability is of paramount importance for three-dimensional shelving. Generally speaking, the principle of "light top and heavy bottom" is followed when electric power material is put, namely, the electric power material (such as transformer) that the quality is great is placed in the lower layer of goods shelves as far as possible, and the electric power material (such as gold utensil) that the quality is little is placed in the upper layer of goods shelves, can effectively improve the security and the stability of three-dimensional goods shelves, avoid goods shelves to overturn and electric power material impaired.
Follow the stability principle of weight under the goods shelves when placing according to electric power material, the quality that will satisfy electric power material is minimum with the height product of its goods level, and whole goods shelves focus minimizing can be expressed as:
in the formula: miThe quality of the ith material.
Further, the cargo space optimization target of the nearby storage of the same kind of electric power materials is determined by combining the identity principle, and specifically comprises the following steps:
if the same type of electric power materials are stored in a plurality of positions in the warehouse in a scattered manner, when a large amount of warehouse entry and exit operations are required to be performed on the type of electric power materials, redundant material transferring operations are likely to occur. Not only can cause the access operation inconvenience of the shuttle-passing mother vehicle, influence the daily transportation and management of managers, but also the allocation of goods space in the whole warehouse can be disorderly. The principle of identity is that the same kind of electric power materials are stored in the same area, so that the storage positions of the various kinds of electric power materials are easily remembered by operators, and the shuttling primary-secondary vehicle is convenient to store and take during operation.
According to the principle of identity, the same kind of electric power materials are placed nearby, and firstly, the coordinates of the central goods space of the ith kind of electric power materials are defined as follows:
the distance from each material in the ith kind of electric power material to the central goods space thereof is as follows:
therefore, the same kind of electric power materials are stored nearby, that is, the sum of the storage distances from all kinds of electric power materials to the central goods space of each kind of materials is the minimum, which can be expressed as:
further, a goods space optimization target of electric power materials with large relevance stored nearby is established by combining a relevance principle, and the method specifically comprises the following steps:
due to the particularity of the power industry, the power materials are various, and comprise large-scale equipment such as a transformer, a distribution box, a cable branch box and the like, and small devices such as hardware fittings, strain clamps, fuses and the like. From historical order analysis, it can be found that a plurality of materials often appear in the same order, and the materials are indicated to have certain relevance, such as overhead conductors and lightning arresters.
Through analysis and processing of historical order data, the relevance of different electric power materials can be reasonably calculated, and therefore the materials are grouped. The electric power material that the degree of association is great should consider placing nearby when goods yard is optimized for the warehouse can effectively compress when carrying out the warehouse entry and exit operation of electric power material according to actual order content, selects the route, improves the operating efficiency, also can effectively alleviate the work load of the perpendicular transport of female car of shuttling, promotes access efficiency.
According to the coordinate axis setting and the cargo grid column definition, the coordinate of the cargo grid column of the xth layer of the xth column is defined as (x, z) and the decision variable d is defined according to the view from the roadway to the depth direction of the goods shelfx,z,iWhen the ith material is stored in the lattice column (x, z), dx,z,iGet 1, otherwise get 0.
Then the total relevance of the multiple materials in the grid with coordinates (x, z) is:
therefore, the maximum degree of material association in all the columns can be expressed as:
further, comprehensively considering the structure, storage strategy and principle of the intensive automatic stereoscopic warehouse for electric power materials, establishing a multi-target goods space optimization model of the intensive automatic stereoscopic warehouse for electric power materials, specifically:
the constraint conditions of the goods space optimization model are as follows:
(1) cargo space accommodation degree constraint
Only one electric power material can be placed in one goods position, and the goods positions of two different electric power materials cannot be completely the same.
|xi,j-xs,t|+|yi,j-ys,t|+|zi,j-zs,t|＞0
1≤i,s≤I；1≤j,t≤ki；i≠s∪j≠t
(2) Cargo space resource constraints
The goods position coordinates of the electric power materials on the goods shelf cannot exceed the parameter limit of the goods shelf.
(3) Lattice resource constraints
The coordinates of the columns of the goods shelf cannot exceed the parameter limits of the goods shelf.
(4) Decision variable constraints
Decision variables (x)i,j,yi,j,zi,j) And dx,z,iThe following constraint relationships directly exist:
further, the proposed electric power material goods space optimization model is solved by adopting a self-adaptive genetic algorithm, and the method specifically comprises the following steps:
chromosomal coding: because the cargo space optimization problem is a multi-objective combined optimization problem in nature, an integer permutation coding scheme is adopted according to the characteristics of the problem. Wherein, each chromosome represents a goods space distribution scheme of all electric power supplies in the intensive automatic three-dimensional warehouse, and the total number of genes on the chromosome corresponds to the total number of the supplies. If N genes exist on the chromosome, N electric power materials are needed to carry out goods location optimization; the value on the gene indicates the coordinate position of the goods space, and the number corresponding to the gene indicates the number of the goods and materials.
Normalization of the objective function: of the above 4 objective functions, f1、f2、f3All take the minimum value as the optimal solution, for f4Now the following transformations are made:
due to f1，f2，f3All dimensions are in the form of rice (m), f4The method is dimensionless, and in order to avoid that when a multi-objective function is converted into a single-objective function for calculation through setting a weight, the influence of different size ranges of each function on a calculation result is greatly different, the following normalization processing is respectively carried out on the four functions:
the reason for adopting the above-mentioned transformation is that if a certain function value f is in the simple 0-1 normalization operation processiIs its minimum value minfiThen the normalized result of the function value is 0. When multiplied by the corresponding weight w, the result is still 0, the weight loses its practical meaning, and repeated iterations eventually lead to program overflow. Therefore, a coefficient of multiplication and addition is respectively added in the normalization, and the 0-1 normalization operation is carried outA mapping interval is moved, so that the operation process of the genetic algorithm is not influenced, and the program overflow can be avoided.
Constructing a fitness function: the fitness is an index for evaluating the quality degree of the individuals in the population, and the higher the fitness is, the better the individuals are, and the higher probability is passed to the next generation; conversely, individuals with smaller fitness have poorer fitness and are eliminated with greater probability. The fitness function generally has the characteristics of continuity and nonnegativity, and most of the time, the objective function is mapped to the fitness function in the form of a maximum value. To solve the multi-objective function, weight values w are given to 4 targets by setting different weight coefficients for the multi-objective functioni(i ═ 1,2,3,4), the resulting fitness function is as follows:
in the formula: w is a1+w2+w3+w4＝1。
The selection method of roulette is adopted to carry out population selection operation, and through self-adaptive cross operation and self-adaptive variation operation, an elite retention strategy is adopted to fully ensure that individuals with the highest fitness in the population are not lost.
The technical scheme provided by the invention has the beneficial effects that:
according to the method for optimizing the goods space of the intensive automatic stereoscopic warehouse for the electric power materials, the goods lattice definition electric power material storage units and the classification storage strategy are adopted, the electric power materials are placed in a classification and orderly mode, frequent material transferring operation is avoided, and the storing and taking efficiency is improved. The goods location optimization targets of the multiple electric power material automatic stereoscopic warehouses are established by combining multiple goods location optimization principles, such as the principle of depending on turnover rate, the stability principle, the correlation principle, the identity principle and the like, and are more comprehensive and reasonable than a single optimization target. The electric power material intensive automatic stereoscopic warehouse multi-target goods location optimization model is established by comprehensively considering the turnover rate and the relevance of the electric power materials and the structural characteristics of the electric power material intensive automatic warehouse, and can be well combined with the actual problem of electric power material storage and retrieval. The method can better optimize the distribution of the electric power material goods space, help a decision maker to make a correct strategy and has effectiveness on the traditional non-intensive warehouse and the intensive warehouse.
Drawings
Fig. 1 is a flowchart of an intensive automatic stereoscopic warehouse cargo space optimization method for electric power materials in the embodiment of the present invention.
Fig. 2 is a schematic diagram of a shuttle type intensive automated stereoscopic warehouse cargo grid in an embodiment of the present invention.
FIG. 3 is a two-dimensional distribution diagram of the positions of the cargo grids in the embodiment of the invention.
FIG. 4 is a block diagram of a flow chart of an improved adaptive genetic algorithm in an embodiment of the present invention.
Fig. 5 is a distribution diagram of the electric material cargo space before and after the non-intensive stereoscopic warehouse is optimized according to the embodiment of the invention.
Fig. 6 is a distribution diagram of the electric material cargo space before and after the optimization of the intensive automatic stereoscopic warehouse according to the embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the drawings in the specification.
The invention provides an electric power material-oriented intensive automatic stereoscopic warehouse multi-target goods space optimization method, which comprises the following detailed steps:
step S10, analyzing and processing the structure and inventory of an intensive automatic stereoscopic warehouse for storing electric power materials to obtain the basic data of the shelves of the stereoscopic warehouse and the data of the type, quantity, quality, initial goods position coordinates and the like of the electric power materials.
Step S20, defining the position of the storage unit of the cargo grid, defining the coordinates of the cargo grid and the decision variables thereof:
a column of all the cargo spaces in the depth direction at the exit of each shelf is defined as a cargo grid column, as shown in fig. 2.
According to the coordinate axis setting and the definition of the cargo lattice columns, the two-dimensional distribution of the positions of the cargo lattice columns is shown in fig. 3 when the goods are seen from the roadway to the depth direction of the goods shelf.
The coordinates of the lattice column defining the xth column and the z-th layer are (x, z), and a decision variable d is definedx,z,iWhen the ith material is stored in the lattice column (x, z), dx,z,iGet 1, otherwise get 0.
Step S30, comprehensively considering the historical order data and the inventory data of the electric power materials, and calculating to obtain the turnover rate of the electric power materials:
in the formula: piThe turnover rate of the ith electric power material, T is the length (day) of the calculation time period, and Y isiThe number of the i-th electric power material discharged in the period of time,
and
the initial and final inventory of the ith kind of electric power material in the period are respectively.
Step S40, comprehensively considering historical order data of different types of electric power materials, calculating the association degree of the different types of electric power materials:
order matrix IC for defining ith kind of electric power materialiComprises the following steps:
ICi＝(O1,i,O2,i,...,Oq,i,...,OQ,i)
in the formula: IC (integrated circuit)i0-1 matrix of 1 × Q, Q being the total number of selected historical orders, Oq,i0/1 respectively indicate that the order q contains no/no i-th power supply.
Defining a calculation formula of the association degree of different types of electric power materials:
in the formula:Ri,sthe value range of the correlation degree of the ith electric power material and the s-th electric power material is 0-1. And defining the relevance of the same electric power material as 1.
Step S50, determining the goods space optimization targets of the plurality of electric power material automatic stereoscopic warehouses by combining a plurality of goods space optimization principles, such as a principle of depending on turnover rate, a stability principle, a correlation principle, an identity principle and the like, wherein specific objective functions comprise:
a. the access efficiency of the electric power materials is maximized:
in the formula: i is the number of electric power material categories, kiIs the number of the ith kind of electric power material, Ll、LwAnd LhRespectively length, width and height (m) of each cargo space.
b. Minimizing the center of gravity of the electric material shelf:
in the formula: miThe quality of the ith material.
c. The similar electric power materials are stored nearby:
d. the power material with large relevance degree is stored nearby:
the coordinates of the lattice column defining the xth column and the z-th layer are (x, z), and a decision variable d is definedx,z,iWhen the ith material is stored in the lattice column (x, z), dx,z,iGet 1, otherwise get 0.
Then the total relevance of the multiple materials in the grid with coordinates (x, z) is:
therefore, the maximum degree of material association in all the columns can be expressed as:
step S60, establishing an intensive automatic stereoscopic warehouse multi-target goods space optimization model facing the electric power materials:
the constraint conditions of the goods space optimization model are as follows:
(1) cargo space accommodation degree constraint
Only one electric power material can be placed in one goods position, and the goods positions of two different electric power materials cannot be completely the same.
|xi,j-xs,t|+|yi,j-ys,t|+|zi,j-zs,t|＞0
1≤i,s≤I；1≤j,t≤ki；i≠s∪j≠t
(2) Cargo space resource constraints
The slot coordinates of the electrical supply on the shelf cannot exceed the parameter limits of the shelf, wherein A, B, C is the parameter limit of the shelf, i.e., the size specification limit of the shelf with respect to the xyz three dimensions.
(3) Lattice resource constraints
The coordinates of the columns of the goods shelf cannot exceed the parameter limits of the goods shelf.
(4) Decision variable constraints
Decision variables (x)i,j,yi,j,zi,j) And dx,z,iThe following constraint relationships directly exist:
step S70, solving the proposed electric power material goods space optimization model by adopting a self-adaptive genetic algorithm to obtain a goods space optimization result:
FIG. 4 is a block diagram of the flow of the improved adaptive genetic algorithm employed in the present invention, which comprises the following specific steps:
a. chromosomal coding: because the cargo space optimization problem is a multi-objective combined optimization problem in nature, an integer permutation coding scheme is adopted according to the characteristics of the problem. Wherein, each chromosome represents a goods space distribution scheme of all electric power supplies in the intensive automatic three-dimensional warehouse, and the total number of genes on the chromosome corresponds to the total number of the supplies. If N genes exist on the chromosome, N electric power materials are needed to carry out goods location optimization; the value on the gene indicates the coordinate position of the goods space, and the number corresponding to the gene indicates the number of the goods and materials.
b. Normalization of the objective function: of the above 4 objective functions, f1、f2、f3All take the minimum value as the optimal solution, for f4Now the following transformations are made:
due to f1，f2，f3All dimensions are in the form of rice (m), f4The method is dimensionless, and in order to avoid that when a multi-objective function is converted into a single-objective function for calculation through setting a weight, the influence of different size ranges of each function on a calculation result is greatly different, the following normalization processing is respectively carried out on the four functions:
c. constructing a fitness function: the fitness is an index for evaluating the quality degree of the individuals in the population, and the higher the fitness is, the better the individuals are, and the higher probability is passed to the next generation; conversely, individuals with smaller fitness have poorer fitness and are eliminated with greater probability. The fitness function generally has the characteristics of continuity and nonnegativity, and most of the time, the objective function is mapped to the fitness function in the form of a maximum value. To solve the multi-objective function, weight values w are given to 4 targets by setting different weight coefficients for the multi-objective functioni(i ═ 1,2,3,4), the resulting fitness function is as follows:
in the formula: w is a1+w2+w3+w4＝1。
d. And selecting a roulette plate.
e. Adaptive crossover operation, adaptive mutation operation.
f. Elite retention strategy.
g. Setting basic parameters of simulation: population size, adaptive crossover operator, adaptive mutation operator and maximum iteration number.
In order to further understand the present invention, the following will explain the practical application of the present invention by taking a certain intensive automatic warehouse for storing electric power materials as an example.
Firstly, analyzing and processing a historical order of an intensive automatic stereoscopic warehouse for storing electric power materials to obtain shelf basic data of the stereoscopic warehouse and data such as types, quantity, quality and initial goods space coordinates of the electric power materials:
the intensive automated stereoscopic warehouse shelf base data are shown in table 1.
Table 1 intensive automation stereoscopic warehouse shelf base data
The data of the type, the quantity, the initial goods space coordinates, the turnover rate, the quality and the like of the electric power materials are shown in the table 2, wherein the turnover rate is calculated according to the electric power material turnover rate formula in the invention.
TABLE 2 electric power material basic data
The correlation coefficient between different types of electric power materials can be obtained by the electric power material correlation coefficient calculation formula constructed according to the invention, as shown in table 3.
TABLE 3 correlation coefficient between different electric power materials
After the basic data of the electric power materials are obtained, setting basic parameters of genetic algorithm simulation: initial population size, maximum iteration number, adaptive crossover operator, and adaptive mutation operator, as shown in table 4. In order to save the operation time, only part of the goods shelves close to the unloading area are intercepted for analysis.
TABLE 4 genetic Algorithm simulation base parameters
And finally, analyzing and comparing simulation results of the traditional non-intensive stereoscopic warehouse and the intensive automatic stereoscopic warehouse facing the electric power materials.
1. Simulation result of traditional non-intensive three-dimensional electric power material warehouse
For a traditional non-intensive electric power material stereoscopic warehouse, only the turnover rate and shelf stability of electric power materials and the principle of nearby storage of the same kind of electric power materials are considered, and the principle of relevance between the electric power materials is not considered. First, the objective function weight w is set1＝0.4，w2＝0.2，w3＝0.4，w4The optimization results obtained by MATLAB simulation analysis are shown in table 5, where the results are 0.
TABLE 5 distribution table of goods location of electric power material before and after optimization of non-intensive stereoscopic warehouse
According to the coordinates of the electric power materials before and after optimization, f is paired1，f2，f3The respective optimization efficiencies of (a) are calculated as shown in table 6.
TABLE 6 calculation of efficiency before and after optimization of non-intensive electric power material stereoscopic warehouse
The distribution diagram of the goods space of the electric power materials before and after the optimization is shown in figure 5. Through observation, the output result is the optimization result of the traditional non-intensive electric power material stereoscopic warehouse with a single-row shelf. It can be seen that the whole distribution of the electric power materials is more compact and mainly concentrated at the loading and unloading port; the whole gravity center of the electric power material is obviously moved downwards, and the stability of the goods shelf is improved; the electric power material of the same kind is placed more concentratedly, more does benefit to managers and carries out the categorised sign. Therefore, the multi-objective goods location optimization method provided by the invention can effectively improve the distribution of the goods locations of the electric materials in the traditional non-intensive warehouse.
2. Simulation result of intensive electric power material automatic stereoscopic warehouse
For the intensive electric power material automatic stereoscopic warehouse, the concept of a cargo lattice is introduced, and w is set1＝0.2，w2＝0.2，w3＝0.2，w4MATLAB simulation analysis, 0.4, gave the results of intensive shelf optimization as shown in table 7.
Table 7 distribution table of electric power goods and materials goods location before and after optimization of intensive automatic stereoscopic warehouse
According to the coordinates of the electric power materials before and after optimization, f is paired1，f2，f3，f4The respective optimization efficiencies of (a) are calculated as shown in table 8.
TABLE 8 calculation of efficiency before and after optimization of intensive electric power material automatic stereoscopic warehouse
The distribution diagram of the electric power material cargo space before and after optimization is shown in fig. 6, and the output result is the optimization result of the electric power material automatic stereoscopic warehouse with the intensive racks. Compared with the conventional non-intensive stereoscopic warehouse goods location distribution diagram shown in fig. 5, it is easy to find that although the conventional non-intensive warehouse can meet the first three basic targets, it is difficult to avoid the operation of transferring goods and materials and increase the dispatching route of the shuttle primary-secondary vehicle when picking according to the order.
After the cargo lattice columns and the principle thereof are introduced, the same cargo lattice columns can be placed with the same electric power materials or different electric power materials with larger association degree as far as possible while the first three optimization targets are met to a certain extent. Therefore, when the electric power materials are stored and taken according to the order, the shuttle car path cannot be shielded by other electric power materials when the electric power materials close to the inside are to be stored and taken, and the travelling distance increased by the operation of transferring the materials of the shuttle primary car and the shuttle secondary car can be effectively reduced.
In conclusion, the method can effectively optimize the distribution of the goods space of the electric power materials in the intensive automatic stereoscopic warehouse and is considered more comprehensively. After the storage units of the goods grids are introduced, the electric power material transfer operation in the intensive warehouse can be effectively reduced, and the storage efficiency is improved.
The above description is only a preferred embodiment of the present invention, and the scope of the present invention is not limited to the above embodiment, but equivalent modifications or changes made by those skilled in the art according to the present disclosure should be included in the scope of the present invention as set forth in the appended claims.
Claims (10)
1. An intensive automatic stereoscopic warehouse goods space optimization method for electric power materials is characterized by comprising the following steps: the method comprises the following steps:
s10, analyzing and processing the structure and inventory of an intensive automatic stereoscopic warehouse for storing electric materials to obtain the shelf basic data of the stereoscopic warehouse and the electric material data, wherein the electric material data include but are not limited to type, quantity, quality and initial goods position coordinates;
s20, aiming at the characteristics of the shuttle car type intensive automatic stereoscopic warehouse, the position of a cargo grid array storage unit is determined, the cargo grid array is adopted to define an electric power material storage unit, and a classification storage strategy is adopted to store electric power materials;
s30, comprehensively considering historical order data and inventory data of the electric power materials, and performing data processing and calculation to obtain the turnover rate of the electric power materials;
s40, comprehensively considering historical order data of different types of electric power materials, and providing the association degrees of the different types of electric power materials;
s50, determining the goods location optimization targets of the plurality of electric power material automatic stereoscopic warehouses by combining a plurality of goods location optimization principles, such as a principle of depending on turnover rate, a stability principle, a correlation principle and an identity principle;
s60, comprehensively considering the structure, storage strategy and principle of the intensive automatic stereoscopic warehouse for electric power materials, and establishing a multi-target goods space optimization model of the intensive automatic stereoscopic warehouse for electric power materials;
and S70, solving the proposed electric power material goods space optimization model by adopting a self-adaptive genetic algorithm to obtain a goods space optimization result.
2. The method for optimizing the goods space of the intensive automatic stereoscopic warehouse facing the electric power supplies according to claim 1, wherein the method comprises the following steps: s20, defining an electric power material storage unit by adopting a goods grid column, and particularly defining a column formed by all goods positions in the depth direction at the outlet of each layer of goods shelf of the intensive automatic stereoscopic warehouse as a goods grid column; the classified storage strategy is adopted to store the electric power materials, specifically, the electric power materials are effectively classified according to the attributes of the electric power materials, and the electric power materials of the same type are placed on the goods positions of the same goods grid row.
3. The method for optimizing the goods space of the intensive automatic stereoscopic warehouse facing the electric power supplies according to claim 1, wherein the method comprises the following steps: s30, calculating to obtain the electric power material turnover rate by comprehensively considering the historical order data and the inventory data of the electric power material, specifically:
the turnover rate of the electric power materials is obtained by carrying out data processing and calculation on the historical orders of the electric power materials in a certain time period, and the calculation formula is as follows:
in the formula: piIs the turnover rate of the ith kind of electric power material, T is the length of the calculation time period, and Y is the unit of dayiThe number of the i-th electric power material discharged in the period of time,
and
the initial and final inventory of the ith kind of electric power material in the period are respectively.
4. The method for optimizing the goods space of the intensive automatic stereoscopic warehouse facing the electric power supplies according to claim 1, wherein the method comprises the following steps: s40, the method for proposing the relevance degree of the different kinds of electric power materials by comprehensively considering the historical order data of the different kinds of electric power materials comprises the following steps:
order matrix IC for defining ith kind of electric power materialiComprises the following steps:
ICi＝(O1,i,O2,i,...,Oq,i,...,OQ,i)
in the formula: IC (integrated circuit)i0-1 matrix of 1 × Q, Q being the total number of selected historical orders, Oq,i0 indicates that the order q does not contain the ith power supply, Oq,i1 means that the order q contains the ith kind of power material;
defining a calculation formula of the association degree of different types of electric power materials:
in the formula: ri,sThe association degree of the ith electric power material and the s-th electric power material is set to be in a value range of 0-1; and defining the relevance of the same electric power material as 1.
5. The method for optimizing the goods space of the intensive automatic stereoscopic warehouse facing the electric power supplies according to claim 1, wherein the method comprises the following steps: s50, establishing a goods space optimization target for maximizing the electric power material access efficiency by combining the principle of depending on the turnover rate, wherein:
under the premise that the operation speeds of the primary shuttle vehicle and the secondary shuttle vehicle are considered to be consistent and known, the access time of the electric materials is positioned in a goods space (x) by the shuttle vehiclei,j，yi,j，zi,j) Electric power material Ei,jTravel distance L for transportation to loading/unloading porti,jExpressed, its expression is:
Li,j＝xi,jLl+yi,jLw+zi,jLh
according to the principle of depending on the turnover rate, the electric power materials with higher turnover rate are placed on the goods space close to the goods sorting area, and the electric power materials with lower turnover rate are placed at the inner position; therefore, the access efficiency of the electric material stereoscopic warehouse is maximized as follows:
in the formula: i is the number of electric power material categories, kiIs the number of the ith kind of electric power material, Ll、LwAnd LhRespectively length, width and height in meters for each cargo space.
6. The method for optimizing the goods space of the intensive automatic stereoscopic warehouse facing the electric power supplies according to claim 1, wherein the method comprises the following steps: s50, establishing a goods space optimization target of minimizing the gravity center of the electric power goods shelf by combining the stability principle, wherein:
follow the stability principle of weight under the goods shelves when placing according to electric power material, the quality that will satisfy electric power material is minimum with the height product of its goods level, and whole goods shelves focus minimizing is expressed as:
in the formula: miThe quality of the ith material.
7. The method for optimizing the goods space of the intensive automatic stereoscopic warehouse facing the electric power supplies according to claim 1, wherein the method comprises the following steps: s50, determining a goods space optimization target of the nearby storage of the same kind of electric power materials by combining the identity principle, wherein:
according to the principle of identity, the same kind of electric power materials are placed nearby, and firstly, the coordinates of the central goods space of the ith kind of electric power materials are defined as follows:
the distance from each material in the ith kind of electric power material to the central goods space thereof is as follows:
therefore, the same kind of electric power materials are stored nearby, that is, the sum of the storage distances from all kinds of electric power materials to the central goods space of the materials of the respective kinds is the minimum, and is expressed as:
8. the method for optimizing the goods space of the intensive automatic stereoscopic warehouse facing the electric power supplies according to claim 1, wherein the method comprises the following steps: s50, establishing a goods space optimization target of the electric power material storage nearby with large relevance degree by combining a relevance principle, wherein:
calculating the association degrees of different electric power materials by analyzing and processing historical order data, thereby grouping the materials; the power materials with larger relevance are placed nearby when the cargo space is optimized;
according to the coordinate axis setting and the goods grid column definition, when the goods grid column of the xth layer of the x-th column is seen from the roadway to the depth direction of the goods shelf, the coordinate of the goods grid column of the xth layer of the x-th column is defined as (x, z), and a decision variable d is definedx,z,iWhen the ith material is stored in the lattice column (x, z), dx,z,iTaking 1, otherwise, taking 0;
then the total relevance of the multiple materials in the grid with coordinates (x, z) is:
therefore, the maximum degree of material association in all the columns is represented as:
9. the method for optimizing the goods space of the intensive automatic stereoscopic warehouse facing the electric power supplies according to claim 1, wherein the method comprises the following steps: s60, comprehensively considering the structure, storage strategy and principle of the intensive automatic stereoscopic warehouse for electric power materials, and establishing a multi-target goods space optimization model of the intensive automatic stereoscopic warehouse for electric power materials, wherein:
the intensive automatic stereoscopic warehouse multi-target goods space optimization model for the electric power materials is expressed as follows:
the constraint conditions of the goods space optimization model are as follows:
(1) goods position accommodation degree restraint, an electric power goods and materials can only be placed to a goods position, and the goods position of two different electric power goods and materials can not the exact same:
|xi,j-xs,t|+|yi,j-ys,t|+|zi,j-zs,t|＞0
1≤i,s≤I；1≤j,t≤ki；i≠s∪j≠t
(2) the goods position resource is restricted, and the goods position coordinates of the electric power materials on the goods shelf cannot exceed the parameter limit of the goods shelf:
(3) and (3) resource constraint of the cargo lattice column, wherein the coordinates of the cargo lattice column cannot exceed the parameter limit of the goods shelf:
(4) decision variable constraint, decision variable (x)i,j,yi,j,zi,j) And dx,z,iThe following constraint relationships directly exist:
10. the method for optimizing the goods space of the intensive automatic stereoscopic warehouse facing the electric power supplies according to claim 1, wherein the method comprises the following steps: s70, solving the proposed power material goods space optimization model by adopting a self-adaptive genetic algorithm, and comprising the following steps:
s701, chromosomal coding: because the cargo space optimization problem is a multi-objective combined optimization problem in nature, an integer permutation coding scheme is adopted according to the characteristics of the problem; each chromosome represents a goods space distribution scheme of all electric power supplies in the intensive automatic three-dimensional warehouse, and the total number of genes on the chromosome corresponds to the total number of the supplies; if N genes exist on the chromosome, N electric power materials are needed to carry out goods location optimization; the value on the gene represents the coordinate position of the goods space, and the number corresponding to the gene represents the number of the goods and materials;
s702, normalization of an objective function: of 4 objective functions, f1、f2、f3All take the minimum value as the optimal solution, for f4Now the following transformations are made:
due to f1，f2，f3All dimensions are in the form of rice, f4The method is dimensionless, and in order to avoid that when a multi-objective function is converted into a single-objective function for calculation through setting a weight, the influence of different size ranges of each function on a calculation result is greatly different, the following normalization processing is respectively carried out on the four functions:
s703, constructing a fitness function: the 4 targets are given weight values w by setting different weight coefficients theretoi(i ═ 1,2,3,4), the resulting fitness function is as follows:
in the formula: w is a1+w2+w3+w4＝1；
The selection method of roulette is adopted to carry out population selection operation, and through self-adaptive cross operation and self-adaptive variation operation, an elite retention strategy is adopted to fully ensure that individuals with the highest fitness in the population are not lost.
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