CN115829469A - Warehouse logistics transportation method - Google Patents
Warehouse logistics transportation method Download PDFInfo
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- CN115829469A CN115829469A CN202211556042.4A CN202211556042A CN115829469A CN 115829469 A CN115829469 A CN 115829469A CN 202211556042 A CN202211556042 A CN 202211556042A CN 115829469 A CN115829469 A CN 115829469A
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- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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Abstract
The invention discloses a warehouse logistics transportation method, which comprises the following steps: according to the distance between the goods shelf and the exit position, carrying out region division on the warehouse; analyzing and mining historical warehouse-out data through a random forest algorithm, and carrying out smooth sales arrangement on warehouse articles; judging whether the warehouse-out inventory period is in, if so, skipping to the next step, otherwise, skipping to the last step; optimizing the layout of warehouse articles according to region division and smoothness sorting; the diversion order is generated by a genetic algorithm. According to the invention, the random forest is used for mining and analyzing the historical data of the warehouse, so that a basis can be provided for the storage and layout of the articles in the warehouse, and the decision is more reliable; the generation of the transfer order is optimized through a genetic algorithm, and the transport capacity of the transfer trolley can be fully used, so that the ex-warehouse efficiency can be obviously improved, and the ex-warehouse cost can be reduced.
Description
Technical Field
The invention belongs to the technical field of warehousing planning, and relates to a warehouse storage ex-warehouse transfer method for distributing transport capacity based on random forest judgment and a genetic algorithm, in particular to an ex-warehouse transfer scheme for efficiently using transport capacity of a transfer trolley by placing positions of commodities which are easy to be ex-warehouse during a logistics ex-warehouse peak period.
Background
The development of the material storage industry is promoted by the prosperity of industrialization. The efficiency, quality and cost of warehousing are of great importance to industry, and new types of automated warehousing systems are continuously being introduced into real life. With the continuous development of economy, the flourishing of various industries promotes the rapid development of warehouse logistics. The order quantity is continuously increased, the types of orders are various, the batches are small, the order fragmentation is obvious, the demand of sorting after removing the order is increased, the order requirement timeliness is short, and higher requirements are provided for the order processing capacity of the storage system. The product orderers pay more attention to the logistics timeliness and service experience, the phenomena of untimely order processing, missed and mistaken delivery of goods and the like can reduce the satisfaction degree of the orderers, and the sales volume and the credit of enterprises are seriously influenced. Logistics plays an important role in e-commerce, being an important link connecting producers, intermediate supplies and outlets and downstream consumers, while warehousing is an essential link in the logistics supply chain. In order to ensure the quality of distribution service, enterprises pay more and more attention to the automatic and intelligent upgrading of warehouse logistics systems, and the construction investment on logistics warehouse facility projects is continuously increased. In the face of a large number of orders, enterprises expect that the warehousing system has the higher efficiency of processing orders as well as the better. But the existing ex-warehouse scheme generally has the problems of low efficiency, low cost and the like.
Disclosure of Invention
The invention aims to provide a warehouse commodity transportation method aiming at the problems in the prior art.
The technical solution for realizing the purpose of the invention is as follows: a warehouse logistics transportation method, the method comprising the steps of:
step 1, performing area division on a warehouse according to the distance between a shelf and an exit position;
step 2, analyzing and mining the historical warehouse-out data of the warehouse, and arranging warehouse articles according to the popularity;
step 3, judging whether the warehouse-out inventory period is in, if so, skipping to step 4, otherwise, skipping to step 5;
step 4, optimizing the layout of warehouse articles according to the region division in the step 1 and the smoothness sorting in the step 2;
and 5, generating a transfer order.
Further, in step 1, the warehouse is divided into areas according to the distance between the shelf and the exit position, specifically:
step 1-1, establishing a rectangular coordinate system for a warehouse, and acquiring coordinate positions of each shelf and an outlet of the warehouse;
step 1-2, calculating the distance between each shelf and a warehouse exit according to the coordinate position obtained in the step 1-1, dividing the shelf with the distance less than r1 into an area A, dividing the shelf with the distance more than r1 and less than r2 into an area B, and dividing the shelf with the distance more than r2 into an area C; the articles are sequentially placed in the area A, the area B and the area C according to the sequence from high to low of the popularity.
Further, the step 2 of analyzing and mining the historical warehouse-out data of the warehouse and arranging the warehouse items in good sale degree includes the following specific processes:
step 2-1, collecting historical warehouse-out data of a warehouse, wherein the historical warehouse-out data comprises types of articles, sales time and sales quantity of the articles;
2-2, calculating the popularity of the article according to the sale time and the sale quantity of the article, and constructing a sample set by taking the popularity as a label of the article;
step 2-3, dividing the sample set into a training set and a testing set;
2-4, training the random forest model by using a training set;
and 2-5, acquiring the popularity of each article in the current warehouse based on the trained random forest model, and performing descending order arrangement on the articles according to the popularity.
Further, step 4 specifically includes:
and (3) according to the clearance sorting in the step (2), placing the articles in the area A, the area B and the area C in sequence according to the clearance descending sorting in the step (2), and placing the articles in the positions from the near to the far away from the warehouse exit in each area according to the clearance descending sorting in the step (2).
Further, the generating of the diversion order in step 5 is specifically to generate the diversion order through a genetic algorithm, and includes:
step 5-1, scribing the ex-warehouse orders according to the number of the transfer vehicles, optimizing each transfer vehicle in each wafer, and executing the following processes;
and 5-2, randomly selecting articles from the corresponding sheets by the transfer trolley to generate a plurality of feasible solutions, wherein each feasible solution is expressed as gene 1 ,…,gene i ,…,gene n ],gene i Indicating whether item No. i is selected, n indicating the number of items in the sheet;
step 5-3, eliminating feasible solutions which do not meet preset constraint conditions;
step 5-4, calculating a fitness value ans for each remaining feasible solution:
wherein v is i Represents the volume of item No. i;
5-5, performing crossover, variation and duplication operations on the remaining feasible solutions;
and 5-6, judging whether a termination condition is met, if so, outputting the feasible solution with the maximum fitness value as the optimal solution, and terminating iteration, otherwise, returning to the step 5-2.
Further, the preset conditions in step 5-3 include:
constraint 1: the sum of the weights of the articles in the order does not exceed the limit weight of the transfer vehicle
w i '＝gene i *w i
Wherein, w i The weight of the No. i article is shown, and W is the limited weight of the transfer trolley;
constraint 2: the sum of the volumes of the items on the order cannot exceed the trolley limit, X, Y, Z are used to indicate the size of the item i, and X, Y, Z are used to indicate the size of the trolley that can provide the trolley.
Wherein x is i 、y i 、z i The length, the width and the height of the No. i article are respectively, and the length, the width and the height of the transfer trolley are respectively X, Y and Z;
constraint condition 3: the size of any article on the order can not exceed the size of the transfer vehicle
sorted([x i ,y i ,z i ])≤sorted([X,Y,Z])
Wherein sorted ([ x ] i ,y i ,z i ]) Indicates the size of item No. i, sorted ([ X, Y, Z)]) Indicating the size of the transfer vehicle;
if any one constraint condition is not met, the preset constraint condition is not met.
Compared with the prior art, the invention has the following remarkable advantages: the random forest is used for mining and analyzing the historical data of the warehouse, so that a basis can be provided for the storage and layout of the articles in the warehouse, and the decision is more reliable; the generation of the transfer order is optimized through a genetic algorithm, and the transport capacity of the transfer trolley can be fully used, so that the ex-warehouse efficiency can be obviously improved, and the ex-warehouse cost can be reduced.
The present invention is described in further detail below with reference to the attached drawing figures.
Drawings
Fig. 1 is a flow chart of the warehouse logistics transfer method of the invention.
Fig. 2 is a schematic diagram of a warehouse partition.
FIG. 3 is a flow chart of a genetic algorithm generating a diversion order.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
It should be noted that, if the description of "first", "second", etc. is provided in the embodiments of the present invention, the description of "first", "second", etc. is only for descriptive purposes and is not to be construed as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.
In one embodiment, in conjunction with fig. 1, there is provided a warehouse logistics transportation method, the method comprising the steps of:
step 1, performing area division on a warehouse according to the distance between a shelf and an exit position; with reference to fig. 2, specifically:
step 1-1, establishing a rectangular coordinate system for a warehouse, and acquiring coordinate positions of each shelf and an outlet of the warehouse;
step 1-2, calculating the distance between each shelf and a warehouse exit according to the coordinate position obtained in the step 1-1, dividing the shelf with the distance less than r1 into an area A, dividing the shelf with the distance more than r1 and less than r2 into an area B, and dividing the shelf with the distance more than r2 into an area C; the articles are sequentially placed in the area A, the area B and the area C according to the sequence from high to low of the popularity.
Step 2, analyzing and mining the historical warehouse-out data of the warehouse, and arranging warehouse articles according to the popularity; the specific process comprises the following steps:
step 2-1, collecting historical warehouse-out data including types of articles, sale time and sale quantity of the articles;
2-2, calculating the popularity of the article according to the sale time and the sale quantity of the article, and constructing a sample set by taking the popularity as a label of the article;
step 2-3, dividing the sample set into a training set and a testing set;
2-4, training the random forest model by using a training set;
and 2-5, acquiring the popularity of each article in the current warehouse based on the trained random forest model, and performing descending order arrangement on the articles according to the popularity.
And 3, judging whether the warehouse-out inventory period is in, if so, skipping to the step 4, and otherwise, skipping to the step 5.
Step 4, optimizing the layout of the warehouse goods according to the region division in the step 1 and the smoothness sorting in the step 2; the method specifically comprises the following steps:
and (3) according to the clearance sorting in the step (2), placing the articles in the area A, the area B and the area C in sequence according to the clearance descending sorting in the step (2), and placing the articles in the positions from the near to the far away from the warehouse exit in each area according to the clearance descending sorting in the step (2).
And 5, generating a transfer order through a genetic algorithm, wherein the method comprises the following steps:
step 5-1, scribing the ex-warehouse orders according to the number of the transfer vehicles, optimizing each transfer vehicle in each wafer, and executing the following processes;
and 5-2, randomly selecting articles from the corresponding sheets by the transfer trolley to generate a plurality of feasible solutions, wherein each feasible solution is expressed as gene 1 ,…,gene i ,…,gene n ],gene i Indicating whether item No. i is selected, n indicating the number of items in the sheet;
step 5-3, eliminating feasible solutions which do not meet preset constraint conditions;
the preset conditions include:
constraint condition 1: the sum of the weights of the articles in the order does not exceed the limit weight of the transfer vehicle
w i '＝gene i *w i
Wherein, w i The weight of the No. i article is shown, and W is the limited weight of the transfer trolley;
constraint 2: the sum of the volumes of items on the order must not exceed the limit of the trolley, X, Y, Z being used to indicate the size of the item i, and X, Y, Z being the size of the trolley that can provide the trolley.
Wherein x is i 、y i 、z i The length, the width and the height of the No. i article are respectively, and the length, the width and the height of the transfer trolley are respectively X, Y and Z;
constraint condition 3: the size of any article on the order can not exceed the size of the transfer vehicle
sorted([x i ,y i ,z i ])≤sorted([X,Y,Z])
Wherein sorted ([ x ] i ,y i ,z i ]) Indicates the size of item No. i, sorted ([ X, Y, Z)]) Indicating the size of the transfer vehicle;
if any constraint condition is not met, the preset constraint condition is not met;
step 5-4, calculating a fitness value ans for each remaining feasible solution:
wherein v is i Represents the volume of item No. i;
5-5, performing crossover, variation and duplication operations on the remaining feasible solutions;
and 5-6, judging whether a termination condition is met, if so, outputting the feasible solution with the maximum fitness value as the optimal solution, and terminating iteration, otherwise, returning to the step 5-2.
In one embodiment, there is provided a warehouse logistics transportation system, the system comprising:
the first module is used for dividing the warehouse into areas according to the distance between the goods shelf and the exit position;
the second module is used for analyzing and mining the historical warehouse-out data of the warehouse and carrying out smooth sales arrangement on warehouse articles;
the third module is used for judging whether the warehouse-out inventory period is reached, if so, the fourth module is executed by skipping, and otherwise, the fifth module is executed by skipping;
the fourth module is used for optimizing the layout of warehouse articles according to the region division and the clearance sorting;
a fifth module for generating a diversion order.
For specific limitations of the warehouse logistics transfer system, reference may be made to the above limitations of the warehouse logistics transfer method, which are not described herein. The modules in the warehouse logistics transfer system can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, there is provided a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
step 1, performing area division on a warehouse according to the distance between a shelf and an exit position;
step 2, analyzing and mining the historical warehouse-out data of the warehouse through a random forest algorithm, and carrying out smooth sales arrangement on warehouse articles;
step 3, judging whether the warehouse-out inventory period is in, if so, skipping to step 4, otherwise, skipping to step 5;
step 4, optimizing the layout of warehouse articles according to the region division in the step 1 and the smoothness sorting in the step 2;
and 5, generating a transfer order through a genetic algorithm.
For the specific definition of each step, reference may be made to the above definition of the warehouse logistics transfer method, which is not described herein again.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
step 1, performing area division on a warehouse according to the distance between a shelf and an exit position;
step 2, analyzing and mining the historical warehouse-out data of the warehouse through a random forest algorithm, and carrying out smooth sales arrangement on warehouse articles;
step 3, judging whether the warehouse-out inventory period is in, if so, skipping to step 4, otherwise, skipping to step 5;
step 4, optimizing the layout of warehouse articles according to the region division in the step 1 and the smoothness sorting in the step 2;
and 5, generating a transfer order through a genetic algorithm.
For the specific definition of each step, reference may be made to the above definition of the warehouse logistics transfer method, which is not described herein again.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and the embodiments and descriptions are only illustrative of the principles of the invention, and any modifications, equivalent substitutions, improvements and the like within the spirit and principle of the invention should be included within the scope of the invention without departing from the spirit and scope of the invention.
Claims (9)
1. A warehouse logistics transportation method, characterized in that the method comprises the steps of:
step 1, performing area division on a warehouse according to the distance between a goods shelf and an exit position;
step 2, analyzing and mining the historical warehouse-out data of the warehouse, and arranging warehouse articles according to the popularity;
step 3, judging whether the warehouse-out inventory period is in, if so, skipping to step 4, otherwise, skipping to step 5;
step 4, optimizing the layout of warehouse articles according to the region division in the step 1 and the smoothness sorting in the step 2;
and 5, generating a transfer order.
2. The warehouse logistics transfer method according to claim 1, wherein the warehouse is divided into areas according to the distance between the shelf and the exit position in step 1, specifically:
step 1-1, establishing a rectangular coordinate system for a warehouse, and acquiring coordinate positions of each shelf and an outlet of the warehouse;
step 1-2, calculating the distance between each shelf and a warehouse exit according to the coordinate position obtained in the step 1-1, dividing the shelf with the distance less than r1 into an area A, dividing the shelf with the distance more than r1 and less than r2 into an area B, and dividing the shelf with the distance more than r2 into an area C; the articles are sequentially placed in the area A, the area B and the area C according to the sequence from high to low of the popularity.
3. The warehouse logistics transfer method of claim 1, wherein the step 2 of analyzing and mining the historical warehouse-out data and ranking warehouse items on good sale comprises the following specific steps:
step 2-1, collecting historical warehouse-out data including types of articles, sale time and sale quantity of the articles;
2-2, calculating the popularity of the article according to the sale time and the sale quantity of the article, and constructing a sample set by taking the popularity as a label of the article;
step 2-3, dividing the sample set into a training set and a testing set;
2-4, training the random forest model by using a training set;
and 2-5, acquiring the smoothness of each article in the current warehouse based on the trained random forest model, and sequencing the articles in a descending order according to the smoothness.
4. The warehouse logistics transfer method according to claim 2 or 3, characterized in that step 4 specifically comprises:
and (3) according to the clearance sorting in the step (2), placing the articles in the area A, the area B and the area C in sequence according to the clearance descending sorting in the step (2), and placing the articles in the positions from the near to the far away from the warehouse exit in each area according to the clearance descending sorting in the step (2).
5. The warehouse logistics transfer method of claim 4, wherein the step 5 of generating the transfer order is specifically generating the transfer order by a genetic algorithm, and comprises the following steps:
step 5-1, scribing the ex-warehouse orders according to the number of the transfer vehicles, optimizing each transfer vehicle in each wafer, and executing the following processes;
and 5-2, randomly selecting articles from the corresponding sheets by the transfer trolley to generate a plurality of feasible solutions, wherein each feasible solution is expressed as gene 1 ,…,gene i ,…,gene n ],gene i Indicating whether item No. i is selected, n indicating the number of items in the sheet;
step 5-3, eliminating feasible solutions which do not meet preset constraint conditions;
step 5-4, calculating a fitness value ans for each remaining feasible solution:
wherein v is i Represents the volume of item No. i;
5-5, performing crossover, variation and duplication operations on the remaining feasible solutions;
and 5-6, judging whether a termination condition is met, if so, outputting the feasible solution with the maximum fitness value as the optimal solution, and terminating iteration, otherwise, returning to the step 5-2.
6. The warehouse logistics transfer method of claim 5, wherein the preset conditions in step 5-3 comprise:
constraint 1: the sum of the weights of the articles in the order does not exceed the limit weight of the transfer vehicle
w i '＝gene i *w i
Wherein, w i The weight of the No. i article is shown, and W is the limited weight of the transfer trolley;
constraint 2: the sum of the volumes of the items on the order cannot exceed the trolley limit, X, Y, Z are used to indicate the size of the item i, and X, Y, Z are used to indicate the size of the trolley that can provide the trolley.
Wherein x is i 、y i 、z i The length, the width and the height of the No. i article are respectively, and the length, the width and the height of the transfer trolley are respectively X, Y and Z;
constraint 3: the size of any article on the order can not exceed the size of the transfer vehicle
sorted([x i ,y i ,z i ])≤sorted([X,Y,Z])
Wherein sorted ([ x ] i ,y i ,z i ]) Indicates the size of item No. i, sorted ([ X, Y, Z)]) Indicating the size of the transfer vehicle;
if any constraint condition is not met, the preset constraint condition is not met.
7. The warehouse logistics transfer system according to any one of claims 1 to 6, wherein the system comprises:
the first module is used for carrying out region division on the warehouse according to the distance between the goods shelf and the exit position;
the second module is used for analyzing and mining the historical warehouse-out data of the warehouse and carrying out smooth sales arrangement on warehouse articles;
the third module is used for judging whether the warehouse-out inventory period is reached, if so, the fourth module is executed by skipping, and otherwise, the fifth module is executed by skipping;
the fourth module is used for optimizing the layout of warehouse articles according to the region division and the clearance sorting;
a fifth module for generating a diversion order.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 6 are implemented when the computer program is executed by the processor.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
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| CN116051004A (en) * | 2023-03-27 | 2023-05-02 | 深圳市宏大供应链服务有限公司 | Intelligent management method, system and medium based on big data |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116051004A (en) * | 2023-03-27 | 2023-05-02 | 深圳市宏大供应链服务有限公司 | Intelligent management method, system and medium based on big data |
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