CN109325721B - Material racking and racking method based on intelligent analysis algorithm - Google Patents
Material racking and racking method based on intelligent analysis algorithm Download PDFInfo
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- CN109325721B CN109325721B CN201811178772.9A CN201811178772A CN109325721B CN 109325721 B CN109325721 B CN 109325721B CN 201811178772 A CN201811178772 A CN 201811178772A CN 109325721 B CN109325721 B CN 109325721B
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- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
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- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
Abstract
The invention discloses a goods and materials shelving and shelving method based on an intelligent analysis algorithm, which is based on an ant colony and a genetic algorithm, and is characterized in that pheromone updating based on path similarity is applied to an applicability function of the genetic algorithm, according to the conditions of shelving and shelving of the same goods and materials in the same batch, dumping, then ex-warehouse and estimated time in the warehouse, meanwhile, the overstock of the goods and materials is avoided, the goods and materials of the same batch and the same type are shelved preferentially in the warehouse for a longer time, a nonlinear sequence is constructed, correlation analysis is carried out, and a shelving position is recommended. The method integrates the ant colony and the genetic algorithm, and applies the 'pheromone' update based on the path similarity to the fitness function of the genetic algorithm, so that the convergence rate of clustering can be improved, the clustering accuracy can be improved, the bin recommendation accuracy is improved, and the recommendation efficiency is improved.
Description
Technical Field
The invention belongs to the field of electric power, and relates to a material shelving and shelving method, in particular to a material shelving and shelving method based on an intelligent analysis algorithm.
Background
And (3) analyzing data such as historical stock shelving and shelving records, bin turnover rate and the like by combining the characteristics of bin distribution, size, bearing and the like, and formulating a bin intelligent recommendation strategy. At present, the intelligent algorithms frequently used in the intelligent recommendation of the bin include an ant colony algorithm, a genetic algorithm, a neural network method, a particle swarm algorithm and the like. The method aims at the defects that the traditional ant colony algorithm has blindness, large search space and low efficiency in the early stage of bin recommendation, the genetic algorithm has strong capability in the aspect of global search, but the heuristic information is not sufficiently utilized in the later stage of search, and the like.
Disclosure of Invention
The invention aims to provide a material shelving and shelving method based on an intelligent analysis algorithm, which integrates an ant colony algorithm and a genetic algorithm, and applies 'pheromone' updating based on path similarity to an applicability function of the genetic algorithm, so that the convergence rate of clustering can be improved, the accuracy of clustering can also be improved, the accuracy of bin recommendation is improved, and the recommendation efficiency is improved.
The purpose of the invention is realized by the following technical scheme:
a material shelving and shelving method based on an intelligent analysis algorithm is characterized in that: on the basis of ant colony and genetic algorithm, pheromone updating based on path similarity is applied to an applicability function of the genetic algorithm, the situation that the same material is put on and taken off shelves, dumped, taken out of the warehouse and predicted in-warehouse time is taken into consideration, meanwhile, material overstock is avoided, the same batch of the same type of material is put on shelves preferentially when the warehouse time is longer, a nonlinear sequence is constructed, correlation analysis is carried out, and the position of a storehouse on shelves is recommended.
The method comprises the following specific steps:
the method comprises the following steps: basic data processing
Aiming at the position data of the goods and materials on the upper rack and the lower rack in the past year, the upper rack carries out position recommendation according to five strategies of position utilization rate, same batch and same depth, uniform distribution of a roadway, first-in and later-layer and screening of an empty tray roadway, the lower rack carries out position recommendation according to a first-in first-out strategy or a first-in first-out strategy, the positions recommended by all the strategies are recorded, and the step II is carried out;
step two: bin recommendation analysis
Based on the bin positions obtained in the step one, carrying out bin position recommendation analysis by utilizing a genetic algorithm model, an in-out warehouse efficiency principle model and a shelf stability model, and combining the results obtained in the step one to obtain an optimal solution for recommending the bin positions of the upper shelf and the lower shelf;
step three: bin recommendation result output
After the system operates and settles, recommending optimal upper and lower rack positions and a candidate position list according to a specified format;
step four: upper and lower rack bin information collection
And collecting the bin position information of the material on and off the shelf at this time, adding the bin position information into a bin position database for material entering and exiting, and processing the bin position information as basic data of the next time of loading and unloading.
According to the invention, by analyzing the data of the position of the goods and materials in and out of the warehouse in 4-5 years in history, combining the goods and materials, the warehouse location basic information and the time of the goods and materials in the warehouse, constructing a data sequence of the goods and materials out of the warehouse and the time of the goods and materials in the warehouse, predicting the insertion variable of the goods and materials out of the warehouse by taking the time required by the goods and materials as a reference, and carrying out sequence correlation analysis, a proper combination of a bin recommendation list is provided, and the bin of the goods and materials on and off the shelf at this time is recommended, so that the accuracy of bin recommendation is improved, the recommendation efficiency is improved, and the warehousing.
Drawings
FIG. 1 is a flow chart of the present invention.
FIG. 2 is a diagram of a genetic algorithm model according to the present invention.
Detailed Description
A goods and materials shelving and shelving method based on an intelligent analysis algorithm is characterized in that on the basis of an ant colony algorithm and a genetic algorithm, pheromone updating based on path similarity is applied to an applicability function of the genetic algorithm, according to the situation that the same goods and materials are shelved and dumped historically, then are taken out of a warehouse and are predicted to be in the warehouse, meanwhile, the overstock of the goods and materials is avoided, the goods and materials of the same type in the same batch are shelved preferentially in the warehouse for a longer time, a nonlinear sequence is constructed, correlation analysis is carried out, and a shelving position is recommended.
In order to improve the accuracy of bin recommendation and the recommendation efficiency, a corresponding information processing function is established according to a method for researching bin recommendation of upper and lower shelves, wherein the bin recommendation function comprises basic data processing, bin recommendation analysis and bin recommendation result output, and the method specifically comprises the following steps:
the method comprises the following steps: basic data processing
Aiming at the position data of the goods and materials on the upper rack and the lower rack in the past year, the upper rack carries out position recommendation according to five strategies of position utilization rate, same batch and same depth, uniform distribution of a roadway, first-in and later-layer and screening of empty tray roadway, the lower rack carries out position recommendation according to a first-in first-out strategy or a first-in first-out strategy, the positions recommended by all strategies are recorded, and the step II is carried out.
Step two: bin recommendation analysis
Based on the bin obtained in the step one, carrying out bin recommendation analysis by utilizing a genetic algorithm model, an in-out warehouse efficiency principle model and a shelf stability model, and combining the results obtained in the step one to obtain an optimal solution for bin recommendation of an upper shelf and a lower shelf, wherein the specific analysis method comprises the following steps:
1) performing pairwise intersection solution on the bin positions recommended by each strategy according to the upper and lower shelf types;
2) arranging the bins in the intersection according to the positive sequence of the occurrence times;
3) carrying out goods allocation through a genetic algorithm according to the materials on the upper shelf and the lower shelf, and carrying out global search and parallelization processing to obtain 10 recommended bin positions;
4) calculating 10 recommended positions according to the principle model of warehouse entry and exit efficiency of the materials on the upper and lower shelves;
5) calculating 10 recommended bin positions according to upper and lower shelf materials through a shelf stability principle model;
6) performing intersection solution on the results calculated in 3), 4) and 5) and the calculation result in 2) according to the sequence;
7) performing intersection solving again on the intersection solving in the step 6), and taking the obtained bin as a final recommended bin;
8) if 7) the obtained solution is empty, sequentially reducing the number of the solutions participating in intersection solving according to the sequence of 5), 4) and 3) until the solution is obtained;
9) and if 8) the solution is empty, carrying out bin position recommendation according to the sequence of 2).
Step three: bin recommendation result output
And after the system operates and settles, recommending optimal upper and lower rack positions and a candidate position list according to a specified format.
Step four: upper and lower rack bin information collection
And collecting the bin position information of the material on and off the shelf at this time, adding the bin position information into a bin position database for material entering and exiting, and processing the bin position information as basic data of the next time of loading and unloading.
Claims (1)
1. A material shelving and shelving method based on an intelligent analysis algorithm is characterized in that: on the basis of ant colony and genetic algorithm, pheromone updating based on path similarity is applied to an applicability function of the genetic algorithm, the situation that the same material is put on and taken off shelves and dumped and then taken out of the warehouse and the warehouse-in time is estimated according to the history of the same material, meanwhile, the overstock of the material is avoided, the material with the same batch and the same type of material in the warehouse is put on shelves preferentially due to longer warehouse-in time, a nonlinear sequence is constructed, correlation analysis is carried out, and the warehouse-on position is recommended;
the method comprises the following specific steps:
the method comprises the following steps: basic data processing
Aiming at the position data of the goods and materials on the upper rack and the lower rack in the past year, the upper rack carries out position recommendation according to five strategies of position utilization rate, same batch and same depth, uniform distribution of a roadway, first-in and later-layer and screening of an empty tray roadway, the lower rack carries out position recommendation according to a first-in first-out strategy or a first-in first-out strategy, the positions recommended by all the strategies are recorded, and the step II is carried out;
step two: bin recommendation analysis
Based on the bin positions obtained in the step one, carrying out bin position recommendation analysis by utilizing a genetic algorithm model, an in-out warehouse efficiency principle model and a shelf stability model, and combining the results obtained in the step one to obtain an optimal solution for recommending the bin positions of the upper shelf and the lower shelf;
step three: bin recommendation result output
After the system operates and settles, recommending optimal upper and lower rack positions and a candidate position list according to a specified format;
step four: upper and lower rack bin information collection
Collecting the bin information of the material on and off shelves, adding the bin information into a bin database for material entering and exiting, and processing the bin information as basic data of the next time of loading and unloading shelves;
in the second step, the bin recommendation analysis method is as follows:
1) performing pairwise intersection solution on the bin positions recommended by each strategy according to the upper and lower shelf types;
2) arranging the bins in the intersection according to the positive sequence of the occurrence times;
3) carrying out goods allocation through a genetic algorithm according to the materials on the upper shelf and the lower shelf, and carrying out global search and parallelization processing to obtain 10 recommended bin positions;
4) calculating 10 recommended positions according to the principle model of warehouse entry and exit efficiency of the materials on the upper and lower shelves;
5) calculating 10 recommended bin positions according to upper and lower shelf materials through a shelf stability principle model;
6) performing intersection solution on the results calculated in 3), 4) and 5) and the calculation result in 2) according to the sequence;
7) performing intersection solving again on the intersection solving in the step 6), and taking the obtained bin as a final recommended bin;
8) if 7) the obtained solution is empty, sequentially reducing the number of the solutions participating in intersection solving according to the sequence of 5), 4) and 3) until the solution is obtained;
and if 8) the solution is empty, carrying out bin position recommendation according to the sequence of 2).
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