A method and system for optimizing cargo space in automated warehouses for packaging enterprises

Abstract

The present invention belongs to the field of automated stereoscopic warehouse storage technology, and discloses a method and system for optimizing cargo locations in automated stereoscopic warehouses for packaging enterprises, based on historical data in the WMS system. According to the historical data in the system, the coordinates of various types of goods in the past three years are counted and the average values of the corresponding quarterly cargo coordinates are calculated. The products of the packaging enterprise are classified. According to the motion characteristics of the stacker, an optimal model for the stacker path is established to minimize the time for picking up and placing goods. According to the principle of shelf stability, a mathematical model for shelf stability is established to make the shelves horizontal and vertical. According to shelf stability, cargo correlation, and the shortest pickup path, a multi-objective optimization model is established to obtain the final cargo optimization model. This solves the problem that packaging enterprises currently have a wide variety of goods, few shelf stability parameters, and only consider the cargo turnover rate without linking the related cargo correlation factors, which cannot effectively describe the actual problem, resulting in unsatisfactory optimization effects and long pickup times.

Description

Technical Field

[0001] The present invention belongs to the technical field of automated stereoscopic warehouse storage, and in particular relates to a method and system for optimizing cargo locations in automated stereoscopic warehouses for packaging enterprises. Background Art

[0002] Automated high-bay warehouses generally consist of stacking machines, steel-structured racks, electrical control systems, warehouse management systems (WMS), and warehouse scheduling systems (WCS). The storage of goods in automated high-bay warehouses is limited by the layout of the shelves. Currently, many manufacturers initially design high-bay warehouses with static layouts based on the size, quality, location, and pallet dimensions of the goods. Static warehouse layouts are directly related to the turnover efficiency and space utilization of goods within the warehouse. The efficiency of goods storage and retrieval operations is one of the primary considerations in automated high-bay warehouses. Using WCS and related optimization algorithms can improve warehouse storage and operational efficiency, a research hotspot for major companies and universities in recent years.

[0003] Automated high-bay warehouses (AHWs) have become an essential component of the manufacturing industry, particularly the packaging sector. Manufacturing enterprises employing AHWs to store products effectively improve product management, save labor, reduce production and transportation costs, and maximize storage space within a given space. The packaging industry, through AHWs, connects products with production lines, achieving internal production integration. This shortens the time it takes for a machine to come off the assembly line and allows for timely monitoring of spare parts availability, enabling timely adjustments to production plans. This effectively manages and controls costs, maximizing production efficiency and profitability. The deployment of AHWs in packaging companies is also an effective measure to increase profitability.

[0004] The packaging industry is characterized by a wide variety of products and consumables, such as unpackers, packers, balers, and palletizers. Each machine has distinct structures and functions, requiring minimal correlation between sheet metal, purchased parts, machined components, and injection molded parts. This leads to a complex assortment of products. Furthermore, each piece of equipment requires supporting consumables such as stretch film, strapping tape, and adhesive tape, all of which have varying specifications and are highly dependent on the equipment. Therefore, packaging companies can introduce automated warehouses, which can optimize current quarter shipments based on previous quarter data, reduce manufacturing costs, and improve storage efficiency. This is crucial for achieving enterprise informatization and intelligentization.

[0005] Automated warehouses used by packaging companies require not only the establishment of shelves, pallets, and bins tailored to the company's needs, but also an optimization model tailored to the industry. The core of this optimization model lies in the rational development of a multi-objective optimization mathematical model and the design of a corresponding solution algorithm. The establishment of this mathematical model depends on the structural process of the product, the degree of inter-product dependencies, analysis of historical data within the automated warehouse, and preliminary classification of goods based on inbound and outbound frequency. Products in the packaging industry are heavy, so the impact of weight on shelf deformation must be considered. Furthermore, the operating speed of the stacker crane also impacts inbound and outbound efficiency during cargo location optimization. As production plans adjust, adjustments will be made based on the number of standard parts in the warehouse, the type of stretch film, and the type of strapping tape.

[0006] Rationally utilizing historical data and designing precise control algorithms using heuristics or reinforcement learning are crucial prerequisites for achieving storage location optimization. Conventional storage location optimization algorithms are based on shelf stability, combining shortest time, shortest pickup path, and maximum turnover to create a generalized storage location optimization model. However, these models have limitations and fail to consider the connections between goods, severely impacting optimization effectiveness, affecting warehouse operational efficiency, and increasing management costs. Summary of the Invention

[0007] The purpose of the present invention is to provide a method and system for optimizing cargo space in automated three-dimensional warehouses for packaging enterprises, so as to solve the problems in the prior art raised in the above background technology.

[0008] To achieve the above object, the present invention adopts the following technical solutions:

[0009] A method for optimizing cargo space in an automated three-dimensional warehouse for packaging enterprises comprises the following steps:

[0010] S1. Establish the optimization goal of the automated warehouse storage space;

[0011] S2. Based on the data in the WMS system, calculate the frequency of entry and exit of different types of goods in the quarter over the past three years;

[0012] S3. Classify various components used in different products according to product type and obtain the correlation coefficient;

[0013] S4, dividing the shelves in the automated warehouse into regions and calibrating them within the regions;

[0014] S5. Based on the historical data of the past three years, calculate the average value of the storage locations (x, y, z) of the relevant goods in the area and use it as the initial coordinates, where x, y, and z represent the number of columns, layers, and rows of the automated warehouse, respectively;

[0015] S6. Determine the shortest time required for the stacker to pick up and store goods based on the initial coordinate values;

[0016] S7. Calculate the center of gravity of the goods and the shelf as a whole based on the weight of the goods when they enter the warehouse;

[0017] S8. Based on the principle that the weight of goods on adjacent shelves is basically the same, calculate the square difference of the weight of goods on the two shelves;

[0018] S9. Establish a multi-objective optimization model, shelf stability, shortest path, and lowest center of gravity in accordance with the "light on top and heavy on the bottom" principle. Solve the model to obtain the coordinates of the cargo position after solution, obtain preliminary optimization results, determine the degree of correlation between the cargo in the automated warehouse and the cargo turnover rate, and take the stability between the shelves as the optimization goal.

[0019] Furthermore, the frequency of goods entering and leaving the warehouse and the correlation coefficient are calculated based on historical data. The calculation formula is:

[0020]

[0021] Where, P ij is the frequency of entry and exit of the i-th type and j-th category of goods, Y ij is the total number of items of type i and category j in and out of the warehouse in a certain quarter of a year, and S is the total number of goods produced in that quarter. Since the statistics are based on the data of the quarters of the past three years, n≤3.

[0022] Furthermore, the classification results are used to calculate the various types of products in the packaging equipment, and the correlation factors between the products are obtained according to the degree of correlation between the products. The packaging enterprise products are divided into n types, of which the nth type has k categories;

[0023] The correlation factor between each type of drug is calculated as R ijef It is expressed as the number of orders for the i-th type j product that also includes items e and f. Q is the total number of orders in the quarter, which is the sum of the number of orders in each quarter of the past three years. Therefore, the formula is

[0024] Where R ijef Expressed as correlation factor, the value range of i,j is i,e,f∈1,2,3…i…n,j∈1,2,3…j…k;

[0025] According to historical data, the correlation factor matrix between the packaging company and the two goods e is calculated as follows:

[0026]

[0027] Calculate the initial coordinates of the i-th type j-th item. The initial coordinates are: Where 0≤X ij ≤A,0≤Y ij ≤B,0≤Z ij ≤C, where A is the maximum number of rack columns, B is the maximum number of rack layers, and C is the maximum number of rack rows;

[0028] The stacker crane picks the goods according to the outgoing goods information, and the product of the total outgoing time and the frequency of ingoing and outgoing goods is minimized. Where L x ,L y Respectively represent the length and width of a single cargo space, L Z It is expressed as the distance between two adjacent rows of shelves. Since the stacker crane can operate in the X and Y directions at the same time, the time it takes for the stacker crane to store and retrieve goods in a certain row of warehouses is calculated based on the maximum of the two directions. The delivery of goods in the Z direction is achieved by conveyor belts. x ,V y Respectively represent the stacker crane's running speed in the X and Y directions, V z Indicates the speed of the conveyor belt. The time taken by the stacker's fork to store and retrieve goods is the same. For the convenience of calculation, this part of the time is ignored.

[0029] Furthermore, according to the initial coordinates of the goods on the shelf The mass of goods stored on It is expressed as the mass of the jth type of goods of type i, and the maximum mass of the goods stored in each cargo space is 1500kg. The lowest center of gravity needs to make the sum of the mass of the goods on the cargo space and the distance in the Y direction of the shelf as the minimum, which is

[0030] According to the principle of center of gravity of the shelf, the center of gravity of the goods at both ends of the shelf is close to This makes the shelf more stable in the horizontal direction, which is consistent with the vertical stability.

[0031]

[0032] According to the principle of minimum storage distance between related goods, the Euclidean distance is introduced to calculate the product of the distance between the target location and similar goods and the correlation factor. The shortest storage distance means the closest connection and the most reasonable placement. The mathematical model can be expressed as:

[0033]

[0034] Where (x i ,y i ,z i ) is each row of shelves, and the shelf center coordinates are calculated by the example.

[0035] Furthermore, based on the characteristics of packaging companies with multiple types of goods and strong correlation, we introduced the correlation factors between goods of the same and different types, the frequency of goods entering and leaving the warehouse, the center distance of the shelves among the goods, the stability of the shelves, and other factors to construct a mathematical model:

[0036]

[0037] According to the actual requirements of the packaging industry, the constraints of the mathematical model are:

[0038]

[0039] Furthermore, the established multi-objective optimization function is integrated to establish the fitness function

[0040] Genetic algorithm is used to solve the multi-objective optimization model and obtain the most reasonable cargo location coordinates.

[0041] Furthermore, when solving, the following is included:

[0042] Integer coding is used to encode the types of products produced by packaging companies in automated warehouses, the consumables and various parts they need. A matrix with a population of 200 is used, and each column represents a chromosome, corresponding to a feasible solution. The feasible solution also contains the global optimal solution. The maximum number of iterations is set to 1000. The cross-infection probability, mutation probability, and reverse transcription parameters are also calculated.

[0043] The coordinates of the relevant goods are initialized using historical data, and the average value of the data from the past three years is calculated. Then, the Mersenne rotation method is used to assign the initial value of the population. The multi-objective optimization value is further calculated, and the fitness value and congestion degree are calculated to sort them. The population selection and iterative assignment operation are performed based on the sorting results.

[0044] Use simulated annealing and hill climbing algorithms to screen the initial population and then form a new population;

[0045] The simulated binary crossover operator and polynomial mutation operator are used to introduce mutation parameters for mutation operation to generate offspring population.

[0046] Furthermore, the chromosomes in the population are reverse transcribed and integrated into the original population to form a new individual population; the parent population and the offspring population are merged to form a new population, and then a non-dominated operation is performed to select a new parent population; on this basis, the next generation of reverse transcription, crossover, and mutation operations are performed to form a new offspring population. If the number of iterations exceeds the maximum number of iterations, the program is stopped and the optimal Pareto solution set is selected from the historical iterations.

[0047] The present invention also proposes a cargo location optimization system for automated warehouses of packaging enterprises, including a memory, a terminal server, and a WCS system responsible for scheduling in the automated warehouse and running related programs. The processor executes the computer program to achieve cargo location optimization in the automated warehouse of the packaging industry.

[0048] Technical effects and advantages of the present invention: The present invention proposes a method and system for optimizing the storage space of automated three-dimensional warehouses for packaging enterprises, which has the following advantages over the prior art:

[0049] 1. By confirming the coordinates of the goods in the three-dimensional warehouse as the optimization target, the order data of the product parts and necessary consumables of the automated three-dimensional warehouse are used to calculate the in-and-out frequency and relationship factors of the related items, and the goods in the warehouse are classified according to the relationship factors. The starting coordinates of the related goods are initialized based on the historical data of three years. A multi-objective optimization mathematical model is established to ensure the shortest distance between the stacker and the in-and-out goods, the highest correlation, the best shelf stability, and the shortest storage distance. Then, the objective function is solved to obtain the optimal warehouse coordinates and the shelf stability value. This solves the problem that packaging companies currently have a wide variety of goods, few shelf stability parameters, and only consider the goods turnover rate without linking the related goods correlation factors, which cannot effectively describe the actual problem, resulting in unsatisfactory optimization results and long pickup time.

[0050] 2. The established objective function takes into account the problem of large variety of parts and components in the manufacturing industry and unreasonable classification, and makes certain optimizations especially for the problem of large variety of consumables in the packaging industry, so that the optimization efficiency of the automated three-dimensional warehouse in the packaging industry is high, the storage cost is low, the shelf stability is high, and the management and operation costs of the warehouse are reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a schematic diagram of a steel structure shelf in an automated three-dimensional warehouse for a packaging enterprise according to an embodiment of the present invention;

[0052] Figure 2 This is a flow chart of a method for optimizing cargo space in an automated three-dimensional warehouse for a packaging enterprise according to an embodiment of the present invention;

[0053] Figure 3 is a flow chart of solving a mathematical model established by a genetic algorithm in an embodiment of the present invention;

[0054] Figure 4 Schematic diagram of goods distribution before optimization of cargo locations in an automated three-dimensional warehouse of a packaging enterprise in an embodiment of the present invention;

[0055] Figure 5 This is a schematic diagram of the distribution of goods after the storage locations of the automated three-dimensional warehouse of a packaging enterprise are optimized in an embodiment of the present invention.

[0056] In the figure: 1. RGV outbound transport line; 2. Outbound platform; 3. Shelves; 4. Stacker operation aisle; 5. Inbound platform; 6. Inbound conveyor line. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. The specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0058] An embodiment of the present invention provides a cargo location optimization system for automated warehouses of packaging enterprises. The system includes a memory, a terminal server, and a WCS system responsible for scheduling in the automated warehouse and a server system that runs related programs. The processor executes the computer program to implement a cargo location optimization method for automated warehouses in the packaging industry.

[0059] like Figure 1 The racking in the automated high-bay warehouse shown in the figure specifically includes an outbound transport line (1), outbound platforms (2), racks (3), a stacker crane lane (4), an inbound platform (5), and an inbound conveyor line (6). Goods are first transported via lane (6) to inbound platform (5). The stacker crane then receives an inbound instruction from the WCS and moves to inbound platform (5). The stacker crane then travels to the designated location and places the goods on shelf (3), completing the inbound operation. During outbound operations, the stacker crane receives the outbound instruction, travels to the designated location, retrieves the goods from the location, and then travels to outbound platform (2). The goods are then transported via lane (1) to the loading area.

[0060] Based on the above optimization system and shelves, when optimizing the storage space in the automated warehouse, Figure 2-Figure 5 As shown, the following steps are included:

[0061] Step S101: Analyze the cargo type, quality, related cargo, quantity, quarterly total volume, turnover rate, and other data for the past three years based on historical data in the WMS system.

[0062] Step S102: Based on the historical data in the system, the coordinates of various types of goods in the past three years are counted and the average coordinates of the corresponding quarters are calculated.

[0063] Step S103: Classify the packaging company's products and calculate the correlation factor based on the product and accessory category.

[0064] Step S104: Calculate the goods turnover rate of each product type based on historical data.

[0065] Step S105: Establish an optimal model for the stacker crane path based on the stacker crane's motion characteristics to minimize the time required to pick up and place goods.

[0066] Step S106: According to the shelf stability principle, a shelf stability mathematical model is established to ensure that the shelf is horizontally and vertically stable.

[0067] Step S107: Based on the principle of shortest storage distance of related goods, Euclidean distance is introduced to establish a mathematical model between storage distance and correlation factor.

[0068] Step S108: Establish a multi-objective optimization model based on shelf stability, cargo relevance, and the shortest pickup path to obtain a final cargo optimization model.

[0069] Specifically, the frequency of goods entering and leaving the warehouse and the correlation coefficient are calculated based on historical data. The calculation formula is:

[0070]

[0071] Where, P ij is the frequency of entry and exit of the i-th type and j-th category of goods, Y ij is the total number of items of type i and category j in and out of the warehouse in a certain quarter of a year, and S is the total number of goods produced in that quarter. Since the statistics are based on the data of the quarters of the past three years, n≤3.

[0072] Furthermore, various types of products in the packaging equipment are classified according to the classification results, and the correlation factors between the products are obtained according to the correlation degree between the products.

[0073] The packaging company's products are divided into n categories, where the nth category has k types; the correlation factor between each type of medicine is calculated as R ijef It is expressed as the number of orders for the i-th product of the j-th category that also includes items e and f. Q is the total number of orders in the quarter. The statistics are the sum of the number of orders in each quarter of the past three years, so the formula is Where R ijef Expressed as correlation factor, the value range of i,j is i,e,f∈1,2,3…i…n,j∈1,2,3…j…k.

[0074] According to the historical data, the correlation factor matrix between the packaging company and the two goods e is calculated as follows:

[0075]

[0076] Calculate the initial coordinates of the i-th type j-th item. The initial coordinates are Where 0≤X ij ≤A,0≤Y ij ≤B,0≤Z ij≤C, where A is the maximum number of rack columns, B is the maximum number of rack layers, and C is the maximum number of rack rows.

[0077] The stacker crane picks the goods according to the outgoing goods information, and the product of the total outgoing time and the frequency of ingoing and outgoing goods is minimized. Where L x ,L y Respectively represent the length and width of a single cargo space, L Z It is expressed as the distance between two adjacent rows of shelves. Since the stacker crane can operate in the X and Y directions at the same time, the time it takes for the stacker crane to store and retrieve goods in a certain row of warehouses is calculated based on the maximum of the two directions. The delivery of goods in the Z direction is achieved by conveyor belts. x ,V y Respectively represent the stacker crane's running speed in the X and Y directions, V z Indicates the speed of the conveyor belt. The time taken by the stacker's fork to store and retrieve goods is the same. For the convenience of calculation, this part of the time is ignored.

[0078] The mechanical equipment in the packaging industry is heavy, and shelf stability is also one of the important factors that need to be considered. Goods should be placed reasonably according to the actual bearing capacity of the shelf to achieve the best shelf stability.

[0079] According to the initial coordinates of the goods on the shelf The mass of goods stored on It is expressed as the mass of the i-th type j-th category goods, and the maximum mass of each storage location is 1500kg. The lowest center of gravity needs to make the sum of the mass of the goods on the storage location and the distance in the Y direction of the shelf as the minimum, which is

[0080] According to the principle of center of gravity of the shelf, the center of gravity of the goods at both ends of the shelf is close to This makes the shelf more stable in the lateral direction, which is consistent with the longitudinal stability.

[0081]

[0082] Due to the unique nature of packaging machinery, the packaging industry utilizes a wide variety of non-standard components and frame parts, including rollers, guide wheels, slewing support components, positioning components, locking components, and labeling components. These various products are closely interconnected, and storing them in the same area significantly increases the frequency of goods entering and leaving the warehouse. Historical order history reveals that some items are shipped in the same order, indicating a close relationship between the two, such as rollers and stretch film.

[0083] According to the principle of minimum storage distance between related goods, the Euclidean distance is introduced to calculate the product of the distance between the target location and similar goods and the correlation factor. The shortest storage distance means the closest connection and the most reasonable placement. The mathematical model can be expressed as:

[0084] Where (x i ,y i ,z i ) represents each row of shelves, and the shelf center coordinates are calculated using a numerical example. This paper proposes a method for optimizing shelf placement in automated warehouses for packaging companies. The acceleration and braking times of the stacker crane in the X, Y, and Z directions are ignored. Because S-curve control is employed, ignoring these parameters facilitates model construction and calculation.

[0085] Based on the characteristics of packaging companies with multiple types of goods and strong correlation, the correlation factors between goods of the same type and different types, the frequency of goods entering and leaving the warehouse, the center distance of the shelves among the goods, the stability of the shelves and other factors are introduced to construct the mathematical model as follows:

[0086]

[0087] According to the actual requirements of the packaging industry, the constraints of the mathematical model are:

[0088]

[0089] Furthermore, the established multi-objective optimization function is integrated to establish the fitness function

[0090]

[0091] Solve the multi-objective optimization model for the packaging industry and obtain the most reasonable cargo location coordinates. Use the genetic algorithm to solve the objective function. The genetic algorithm (Genetic Algorithm) to solve the objective function includes:

[0092] Integer encoding is used to encode the product types, consumables, and various parts required by packaging companies in automated warehouses. A matrix with a population of 200 is used, with each column representing a chromosome and corresponding to a feasible solution. The feasible solution also includes the global optimal solution. The maximum number of iterations is set to 1000. The cross-infection probability, mutation probability, and reverse transcription parameters are also calculated.

[0093] The coordinates of the relevant goods are initialized using historical data, and the average value of the data from the past three years is calculated. Then, the Mersenne rotation method is used to assign the initial value of the population. The values of the multi-objective optimization are further calculated, and the fitness value and congestion are calculated to sort them. The population selection and iterative assignment operations are performed based on the sorting results.

[0094] The simulated annealing and hill climbing algorithms are used to screen the initial population and then form a new population; the simulated binary crossover operator and polynomial mutation operator are used to introduce mutation parameters to perform mutation operations and generate offspring populations; the chromosomes in the population are reverse transcribed and integrated into the original population to form a new individual population;

[0095] Merge the parent population and the offspring population to form a new population, then perform non-dominated operations to select a new parent population; on this basis, perform reverse transcription, crossover, and mutation operations on the next generation to form a new offspring population. If the number of iterations exceeds the maximum number of iterations, the program will stop running and select the optimal Pareto solution set in the historical iterations:

[0096] By establishing the optimization goal of the storage location of the automated warehouse; based on the data in the WMS system, the frequency of inbound and outbound operations of different types of goods in the quarter over the past three years is calculated; based on the product type, the various components used in different products are classified to obtain the correlation coefficient; the shelves in the automated warehouse are divided into regions and calibrated within the region; based on the historical data of the past three years, the average value of the storage location (X, Y, Z) of the relevant goods in the region is calculated and used as the initial coordinates, where X, Y, Z represent the number of columns, layers, and rows of the automated warehouse respectively; based on the initial coordinate values, the shortest time required for the stacker to pick up and store goods is determined; based on the weight of the goods at the time of entry, the center of gravity of the goods and the shelf as a whole is calculated; based on the principle that the weight of goods between adjacent shelves is basically consistent, the square difference of the mass of goods on the two shelves is calculated; based on the above model relationship, a multi-objective optimization model is established, and the shelf stability, shortest path, and lowest center of gravity meet the "light on top and heavy on the bottom" principle; the model is solved to obtain the coordinates of the goods position after solution, and the preliminary optimization results are obtained; the degree of correlation between goods in the automated warehouse and the goods turnover rate are determined, and the stability between shelves is the optimization goal.

[0097] This solves the current problem of packaging companies facing a wide variety of goods, a limited number of shelf stability parameters, and an inability to effectively describe the actual problem, resulting in suboptimal optimization results and long pickup times. The established objective function takes into account the diverse and illogical classification of manufacturing parts, specifically optimizing the large variety of consumables in the packaging industry. This results in high efficiency in optimizing the storage space in automated warehouses within the packaging industry, low storage costs, and high shelf stability, reducing warehouse management and operating costs and setting a new industry benchmark.

[0098] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing cargo space in automated warehouses for packaging enterprises, characterized in that: The steps include: S1. Establish the optimization goal of the automated warehouse storage space; S2. Based on the data in the WMS system, calculate the frequency of inbound and outbound shipments of different types of goods in each quarter over the past three years. S3. Classify various components used in different products according to product type and obtain the correlation coefficient; S4, dividing the shelves in the automated warehouse into regions and calibrating them within the regions; S5. Based on the historical data of the past three years, calculate the average value of the storage locations (x, y, z) of the relevant goods in the area and use it as the initial coordinates, where x, y, and z represent the number of columns, layers, and rows of the automated warehouse, respectively; S6. Determine the shortest time required for the stacker to pick up and store goods based on the initial coordinate values; S7. Calculate the center of gravity of the goods and the shelf as a whole based on the weight of the goods when they enter the warehouse; S8. Based on the principle that the weight of goods on adjacent shelves is basically the same, calculate the square difference of the weight of goods on the two shelves; S9. Establish a multi-objective optimization model, including shelf stability, shortest path, and lowest center of gravity in accordance with the principle of "light on top and heavy on bottom". Solve the model to obtain the coordinates of the solved goods position, obtain preliminary optimization results, determine the degree of correlation between goods in the automated warehouse and the goods turnover rate, and set the stability between shelves as the optimization goal; According to the classification results, various types of products in the packaging equipment are classified, and according to the degree of correlation between products, the correlation factors between products are obtained, and the packaging enterprise products are divided into n types, of which the nth type has k categories; The correlation factor between each type of drug is calculated as R ijef It is expressed as the number of orders for the i-th type j product that also includes items e and f. Q is the total number of orders in the quarter, which is the sum of the number of orders in each quarter of the past three years. Therefore, the formula is n≤3; Where R ijef Expressed as correlation factor, the value range of i,j is i,e,f∈1,2,3…i…n,j∈1,2,3…j…k; According to historical data, the correlation factor matrix between the packaging company and the two goods e is calculated as follows: Calculate the initial coordinates of the i-th type j-th item. The initial coordinates are: Where 0≤X ij ≤A,0≤Y ij ≤B,0≤Z ij ≤C, where A is the maximum number of rack columns, B is the maximum number of rack layers, and C is the maximum number of rack rows; The stacker crane picks the goods according to the outgoing goods information, and the product of the total outgoing time and the frequency of ingoing and outgoing goods is minimized. Where L x ,L y Respectively represent the length and width of a single cargo space, L Z It is expressed as the distance between two adjacent rows of shelves. Since the stacker crane can operate in the X and Y directions at the same time, the time it takes for the stacker crane to store and retrieve goods in a certain row of warehouses is calculated based on the maximum of the two directions. The delivery of goods in the Z direction is achieved by conveyor belts. x ,V y Respectively represent the stacker crane's running speed in the X and Y directions, V z Indicates the speed of the conveyor belt. The time taken by the stacker's fork to store and retrieve goods is the same. For the convenience of calculation, this part of the time is ignored. According to the initial coordinates of the goods on the shelf The mass of goods stored on It is expressed as the mass of the jth type of goods of type i, and the maximum mass of the goods stored in each cargo space is 1500kg. The lowest center of gravity needs to make the sum of the mass of the goods on the cargo space and the distance in the Y direction of the shelf as the minimum, which is According to the principle of center of gravity of the shelf, the center of gravity of the goods at both ends of the shelf is close to This makes the shelf more stable in the horizontal direction, which is consistent with the vertical stability. According to the principle of minimum storage distance between related goods, the Euclidean distance is introduced to calculate the product of the distance between the target location and similar goods and the correlation factor. The shortest storage distance means the closest connection and the most reasonable placement. The mathematical model can be expressed as: Where (x i ,y i ,z i ) is each row of shelves, and the shelf center coordinates are calculated by the example.

2. The method for optimizing cargo space in an automated three-dimensional warehouse for packaging enterprises according to claim 1, characterized in that: The frequency of goods entering and leaving the warehouse and the correlation coefficient are calculated based on historical data. The calculation formula is: Where, P ij is the frequency of entry and exit of the i-th type and j-th category of goods, Y ij is the total number of items of type i and category j in and out of the warehouse in a certain quarter of a year, and S is the total number of goods produced in that quarter. Since the statistics are based on the data of the quarters of the past three years, n≤3.

3. The method for optimizing cargo space in an automated three-dimensional warehouse for packaging enterprises according to claim 2, characterized in that: Based on the characteristics of packaging companies with multiple types of goods and strong correlation, the correlation factors between goods of the same type and different types, the frequency of goods entering and leaving the warehouse, the center distance of the shelves among the goods, the stability of the shelves and other factors are introduced to construct the mathematical model as follows: According to the actual requirements of the packaging industry, the constraints of the mathematical model are:

4. The method for optimizing cargo space in an automated three-dimensional warehouse for packaging enterprises according to claim 3, characterized in that: Integrate the established multi-objective optimization function to establish the fitness function Genetic algorithm is used to solve the multi-objective optimization model and obtain the most reasonable cargo location coordinates.

5. The method for optimizing cargo space in an automated three-dimensional warehouse for packaging enterprises according to claim 4, characterized in that: When solving, include the following: Integer coding is used to encode the types of products produced by packaging companies in automated warehouses, the consumables and various parts they need. A matrix with a population of 200 is used, and each column represents a chromosome, corresponding to a feasible solution. The feasible solution also contains the global optimal solution. The maximum number of iterations is set to 1000. The cross-infection probability, mutation probability, and reverse transcription parameters are also calculated. The coordinates of the relevant goods are initialized using historical data, and the average value of the data from the past three years is calculated. Then, the Mersenne rotation method is used to assign the initial value of the population. The multi-objective optimization value is further calculated, and the fitness value and congestion degree are calculated to sort them. The population selection and iterative assignment operation are performed based on the sorting results. Use simulated annealing and hill climbing algorithms to screen the initial population and then form a new population; The simulated binary crossover operator and polynomial mutation operator are used, and mutation parameters are introduced to perform mutation operation to generate offspring population.

6. The method for optimizing cargo space in an automated three-dimensional warehouse for packaging enterprises according to claim 5, characterized in that: The chromosomes in the population are reverse transcribed and integrated into the original population to form a new individual population; the parent population and the offspring population are merged to form a new population, and then a non-dominated operation is performed to select a new parent population; on this basis, the next generation of reverse transcription, crossover, and mutation operations are performed to form a new offspring population. If the number of iterations exceeds the maximum number of iterations, the program is stopped and the optimal Pareto solution set is selected from the historical iterations.

7. A system for optimizing cargo space in automated warehouses for packaging enterprises, for implementing the method according to any one of claims 1 to 6, characterized in that: It includes storage, terminal servers, and a WCS system responsible for scheduling in an automated warehouse and running related programs. The processor executes computer programs to optimize the cargo location of the automated warehouse in the packaging industry.

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