Warehouse material storage location distributing and sorting system based on genetic algorithm

Abstract

The invention relates to the technical field of logistics management, in particular to a warehouse material storage location distributing and sorting system based on a genetic algorithm, and the system comprises a data access and analysis module which is connected with a main database of a warehouse management system through a security protocol, synchronously updates the information of materials, orders and employees, carries out the establishment of a data index, and transmits the data index to a server; according to the invention, through the design of the sorting system and the combination of simulated annealing and a genetic algorithm, the intelligent configuration of the material storage position is effectively realized, the sorting path is optimized, the material carrying time and the manual error are reduced, the sorting accuracy is improved, the AGV is managed by adopting an asynchronous algorithm, the task scheduling is optimized, and the sorting efficiency is improved. The material processing speed is accelerated, the response efficiency is improved, the inventory configuration is dynamically adjusted, the market demand change is adapted, the high efficiency and the quick response capability of the inventory are kept, the automation and the intelligence of the inventory management are promoted, and the operation efficiency and the service quality of the whole warehouse are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics management, and in particular to a warehouse material storage location allocation and picking system based on genetic algorithm. Background Art

[0002] The core of the field of logistics management technology is to reduce costs, improve service quality and accelerate response speed through effective management. It aims to optimize the use efficiency of storage space, improve inventory control and speed up order processing, which is achieved through precise material storage location planning and efficient picking path design.

[0003] The main purpose of the warehouse material storage location allocation and picking system based on genetic algorithm is to arrange the storage location and picking path of materials in the warehouse in an intelligent way, so as to reduce warehouse operating costs, shorten material picking time, improve picking accuracy, and significantly improve the overall efficiency and effectiveness of warehouse operations.

[0004] Existing technologies have limitations in optimizing warehouse operating efficiency and response speed. In terms of material storage location allocation and picking route design, it is difficult to respond to changes in market demand in real time, resulting in long picking times and high error rates. When faced with a large number of order changes, warehouses cannot effectively adjust their operating strategies, resulting in reduced service quality or increased costs. Fixed storage location configurations and route planning cannot efficiently cope with sudden increases in order volumes, leading to congestion or delays, affecting overall customer satisfaction and the company's competitiveness. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a warehouse material storage location allocation and picking system based on genetic algorithm.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: A warehouse material storage location allocation and picking system based on genetic algorithm comprises: Data access and analysis module: connect to the main database of the warehouse management system through a security protocol, synchronously update material, order and employee information, establish data indexes, and obtain material database connection reports; Inventory status management module: based on the data in the material database connection report, monitor and update the inventory status of each material in real time, form inventory monitoring records, and generate inventory status reports; Warehousing strategy formulation module: Based on the inventory status report, analyze the current inventory gap and warehousing demand, arrange the specific warehousing sequence and material priority, form a warehousing operation guide, and generate a warehousing strategy guide; Genetic algorithm storage location configuration module: Based on the warehousing strategy guide, a simulated annealing-genetic algorithm hybrid model is adopted, and a genetic algorithm is applied to perform storage location optimization calculation according to material characteristics and historical inventory data to generate a storage location configuration plan; Task scheduling and AGV management module: Based on the storage configuration scheme, an asynchronous algorithm is used to arrange AGV robots to perform material handling, adjust task queues and route maps to optimize transportation routes, record task execution details, and generate task completion records; Outbound strategy formulation module: Based on the task completion records, evaluate the inventory status and immediate demand, arrange the priority and scheduling plan of outbound materials, formulate outbound operation guidelines, and generate outbound execution guidelines; Inventory dynamic adjustment module: Based on the outbound execution guide, dynamically adjust the inventory configuration, update the inventory status in real time to adapt to changes in market demand, maintain inventory efficiency and response speed, and generate adjusted inventory status.

[0007] As a further solution of the present invention, the data access and analysis module includes a database connection submodule, a data synchronization submodule and an index establishment submodule, wherein: Database connection submodule: connects to the main database of the warehouse management system through a secure protocol, verifies the connection security and stability of data transmission, ensures uninterrupted data flow, and obtains connection status details; Data synchronization submodule: based on the connection status details, synchronize material number, order progress and employee role data, update the timestamp of each data, and generate synchronized data records; Index establishment submodule: based on the synchronized data records, creates a multidimensional data index, optimizes data retrieval speed and efficiency, provides support for system query and report generation, and obtains a material database connection report; The material database connection report specifically includes material coding, order processing status and employee role allocation.

[0008] As a further solution of the present invention, the inventory status management module includes an inventory monitoring submodule, a status updating submodule and a report generating submodule, wherein: Inventory monitoring submodule: based on the real-time data stream in the material database connection report, monitor the inventory dynamics of each material, identify inventory trends and abnormal changes, and obtain inventory monitoring details; Status update submodule: based on the inventory monitoring details, perform automatic update of inventory status, adjust inventory records to reflect real-time data, maintain data accuracy, and generate updated inventory records; Report generation submodule: based on the updated inventory records, integrates inventory information, generates a detailed report including inventory quantity, safety level and demand forecast, and forms an inventory status report; The inventory status report specifically includes the current inventory quantity of each material, the warning inventory level and the inventory items that are about to expire.

[0009] As a further solution of the present invention, the warehousing strategy formulation module includes an inventory analysis submodule, a time sequence arrangement submodule and a priority setting submodule, wherein: Inventory analysis submodule: Based on the inventory status report, quantitative analysis is performed on the inventory gap, and the required emergency replenishment quantity is calculated through the real-time consumption rate of materials and future order forecasts, and an inventory demand analysis report is generated; Timing scheduling submodule: using the emergency replenishment data in the inventory demand analysis report, arrange the warehousing time nodes of specific materials, optimize the order of material arrival, ensure that key materials are processed first, and generate a warehousing timing plan; Priority setting submodule: according to the order of arrival of materials in the warehousing timing plan, set the warehousing priority of materials, give priority to high-demand and high-value materials, ensure logistics efficiency, and generate warehousing strategy guidelines; The warehousing strategy guide specifically includes the sorting of warehousing materials, the scheduled warehousing schedule and the handling protocol for special materials.

[0010] As a further solution of the present invention, the genetic algorithm storage location configuration module includes a characteristic analysis submodule, an optimization calculation submodule and a strategy determination submodule, wherein: Characteristic analysis submodule: Based on the warehousing strategy guide, analyze the volume, weight and access frequency of each material, classify the materials, identify storage requirements and characteristic matching, and generate a material characteristic analysis report; Optimization calculation submodule: Based on the material characteristic analysis report, a simulated annealing-genetic algorithm hybrid model is used to perform storage optimization calculations, adjust storage allocation to meet the specific needs of different materials, and generate storage optimization results by simulating the storage and access efficiency of different configurations; Strategy determination submodule: based on the storage optimization results, formulate storage configuration strategies, specify strategy details to guide actual operations, and generate storage configuration plans; The storage location configuration plan specifically includes a recommended storage location list, the material suitability of each storage location, and the expected space utilization rate.

[0011] As a further solution of the present invention, the materials are classified and grouped according to the volume, weight and access frequency of the materials, and for access with different frequencies, the materials are identified and distinguished according to the requirements of frequent and infrequent access; The specific strategy details guide actual operations, use time series analysis to monitor material access efficiency and space utilization, set efficiency and utilization thresholds, and automatically make adjustments and reminders when data shows that it is below the standard.

[0012] As a further solution of the present invention, the task scheduling and AGV management module includes a path optimization submodule, an AGV scheduling submodule and a task recording submodule, wherein: Path optimization submodule: Based on the storage configuration plan, analyze the starting point and target storage location of each AGV, use path planning technology to reduce the travel distance, adjust each path to reduce intersections and congestion, optimize the overall transportation efficiency, and generate an optimized route map; AGV scheduling submodule: Based on the optimized route map, an asynchronous algorithm is used to schedule AGVs in real time, and tasks are dynamically allocated according to the current task load and location to ensure balanced and efficient task allocation. Each AGV performs material handling according to the adjusted route and generates an AGV task allocation table; Task record submodule: Based on the AGV task allocation table, track the task execution status of each AGV in real time, including the start and end time of each task, as well as the type and quantity of materials, record detailed operation logs, and generate task completion records; The task completion record specifically includes the AGV number, the completed task list of each AGV and the task completion time.

[0013] As a further solution of the present invention, the asynchronous algorithm is according to the formula: , in: For the The total allocation score of the AGVs in the genetic algorithm framework, , , , , is the weight coefficient, Indicates The distance from an AGV to its next target task location, is the remaining processing time of the current task of AGV, To represent The current load level of each AGV, Indicates the energy efficiency of AGV, It is the speed priority of AGV.

[0014] As a further solution of the present invention, the delivery strategy formulation module includes an inventory assessment submodule, a priority arrangement submodule and a delivery plan submodule, wherein: Inventory assessment submodule: Based on the task completion records, conduct an instant inventory assessment, analyze the material usage frequency and remaining quantity, determine which materials need to be shipped out first to avoid inventory backlogs, and generate an instant inventory assessment report; Priority arrangement submodule: Based on the inventory instant assessment report, assess the outbound priority of each material, set a high priority for urgent or high-demand materials, formulate a detailed outbound priority strategy, and generate an outbound priority table; Outbound planning submodule: Based on the outbound priority table, arrange the specific execution plan of the outbound task, adjust the outbound sequence and time, ensure the smooth and timely outbound operation, and generate an outbound execution guide; The outbound delivery execution guide specifically includes the order of material outbound delivery, the estimated outbound delivery time point and the list of emergency outbound materials.

[0015] As a further solution of the present invention, the inventory dynamic adjustment module includes an inventory status analysis submodule, an adjustment strategy design submodule and a status update submodule, wherein: Inventory status analysis submodule: Based on the delivery execution guide, real-time data monitoring technology is used to continuously track inventory changes, dynamically evaluate inventory, use trend analysis tools to identify inventory gaps and surpluses, optimize inventory replenishment plans, and generate inventory demand forecasts; Adjustment strategy design submodule: Based on the inventory demand forecast, conduct strategy simulation, use decision support system to analyze supply chain status, design inventory adjustment strategy that adapts to market fluctuations, and generate inventory adjustment strategy; Status update submodule: Based on the inventory adjustment strategy, implement the automated inventory management system to update the inventory in real time, automatically adjust the system to respond to changes in market demand, synchronously update the ERP system data, and generate the adjusted inventory status; The adjusted inventory status specifically includes the adjusted inventory distribution, improved inventory turnover rate and inventory adjustment frequency.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are: 1. In the present invention, by designing a picking system, combined with simulated annealing and genetic algorithms, the intelligent configuration of material storage locations is effectively realized, the picking path is optimized, the material handling time and manual errors are reduced, and the picking accuracy is improved; 2. In the present invention, an asynchronous algorithm is used to manage AGVs, optimize task scheduling, speed up material processing, improve response efficiency, dynamically adjust inventory configuration, adapt to changes in market demand, maintain high inventory efficiency and rapid response capabilities, promote automation and intelligence of inventory management, and greatly improve the operational efficiency and service quality of the entire warehouse. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a system flow chart of the present invention; Figure 2 Schematic diagram of the system framework of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] See also Figure 1 The present invention provides a technical solution: a warehouse material storage location allocation and picking system based on genetic algorithm includes: Data access and analysis module: connect to the main database of the warehouse management system through a security protocol, synchronously update material, order and employee information, establish data indexes, and obtain material database connection reports; Inventory status management module: Based on the data in the material database connection report, monitor and update the inventory status of each material in real time, form inventory monitoring records, and generate inventory status reports; Warehousing strategy formulation module: Based on the inventory status report, analyze the current inventory gap and warehousing demand, arrange the specific warehousing sequence and material priority, form a warehousing operation guide, and generate a warehousing strategy guide; Genetic algorithm storage location configuration module: Based on the warehousing strategy guide, the simulated annealing-genetic algorithm hybrid model is adopted, and the genetic algorithm is applied to perform storage location optimization calculation according to material characteristics and historical inventory data to generate storage location configuration plans; Task scheduling and AGV management module: Based on the storage configuration plan, the asynchronous algorithm is used to arrange AGV robots to perform material handling, adjust the task queue and route map to optimize the transportation path, record the task execution details, and generate task completion records; Outbound strategy formulation module: Based on task completion records, evaluate inventory status and immediate needs, arrange the priority and scheduling plan of outbound materials, formulate outbound operation guidelines, and generate outbound execution guidelines; Inventory dynamic adjustment module: Based on the outbound execution guide, dynamically adjust inventory configuration, update inventory status in real time to adapt to changes in market demand, maintain inventory efficiency and response speed, and generate adjusted inventory status; See also Figure 2 The data access and analysis module includes a database connection submodule, a data synchronization submodule and an index establishment submodule, wherein: Database connection submodule: connects to the main database of the warehouse management system through a secure protocol, verifies the connection security and stability of data transmission, ensures uninterrupted data flow, and obtains connection status details; Data synchronization submodule: Based on the connection status details, synchronize the material number, order progress and employee role data, update the timestamp of each data, and generate synchronized data records; Index establishment submodule: based on synchronized data records, creates multidimensional data indexes, optimizes data retrieval speed and efficiency, provides support for system query and report generation, and obtains material database connection reports; Database connection submodule: It uses the Secure Sockets Layer protocol, and the configuration includes 128-bit encryption keys and verification server certificates to connect to the main database of the warehouse management system, verify the encryption security during data transmission, use the heartbeat detection mechanism to maintain connection stability, constantly check the connection status to avoid interruptions, and finally generate a detailed connection status report; Data synchronization submodule: Use the timestamp protocol to add an exact time stamp to each material number, order progress and employee role data, use batch data processing technology for efficient synchronization, and ensure data consistency and integrity by comparing the data status before and after. After the synchronization operation is completed, a synchronization data record containing all operation details is generated; Index establishment submodule: Use hash index technology to optimize fast access to material numbers and order information in synchronized data records, set up multi-level cache strategies to reduce data retrieval time, apply index maintenance algorithms to regularly update index status to maintain maximum efficiency, and obtain material database connection reports that include multi-dimensional data indexes and support fast queries; Among them, the material database connection report specifically includes material codes, order processing status, and employee role assignments; See also Figure 2 The inventory status management module includes an inventory monitoring submodule, a status update submodule and a report generation submodule, wherein: Inventory monitoring submodule: Based on the real-time data stream in the material database connection report, monitor the inventory dynamics of each material, identify inventory trends and abnormal changes, and obtain inventory monitoring details; Status Update Submodule: Based on inventory monitoring details, perform automatic updates of inventory status, adjust inventory records to reflect real-time data, maintain data accuracy, and generate updated inventory records; Report generation submodule: Based on updating inventory records, integrating inventory information, generating detailed reports including inventory quantity, safety level and demand forecast, forming inventory status reports; Inventory monitoring submodule: Using data flow analysis technology, based on the real-time data flow in the material database connection report, adopting time series analysis methods, especially the autoregressive moving average model, the specific configuration includes the number of autoregressive items is 5 and the number of moving average items is 5, to monitor the inventory dynamics of each material, through the outlier detection algorithm, the parameter settings include the number of trees is 100 and the number of samples is 256, to identify the trend and abnormal changes in the inventory, obtain detailed data of inventory monitoring in real time, and generate inventory monitoring details; Status update submodule: adopts event trigger mechanism, configures trigger condition as inventory quantity change exceeding the preset threshold of 5%, performs automatic update of inventory status based on inventory monitoring details, uses database triggers to directly adjust inventory records to reflect real-time data, maintains data consistency and accuracy, and finally generates updated inventory records; Report generation submodule: Using report generation technology, based on updating inventory records and applying data aggregation functions, the inventory information is integrated to generate detailed reports including inventory quantity, safety level and demand forecast to form inventory status reports; The inventory status report specifically includes the current inventory quantity of each material, the warning inventory level, and the inventory items that are about to expire; See also Figure 2 The warehousing strategy formulation module includes the inventory analysis submodule, the time scheduling submodule and the priority setting submodule, among which: Inventory analysis submodule: Based on the inventory status report, quantitative analysis of inventory gaps is performed, and the required emergency replenishment quantity is calculated through the real-time consumption rate of materials and future order forecasts, and an inventory demand analysis report is generated; Timing scheduling submodule: Use the emergency replenishment data in the inventory demand analysis report to arrange the storage time nodes of specific materials, optimize the order of material arrival, ensure that key materials are processed first, and generate a storage timing plan; Priority setting submodule: Set the material entry priority according to the material arrival order in the entry timing plan, give priority to high-demand and high-value materials, ensure logistics efficiency, and generate entry strategy guidelines; Inventory analysis submodule: Based on the inventory status report, the linear regression analysis method is used. The parameter settings include independent variables such as time and historical order quantity, and dependent variable such as inventory quantity. The inventory gap is quantitatively analyzed. The exponential smoothing method is used to calculate the real-time consumption rate of materials and the future order forecast based on time series. The required emergency replenishment quantity is calculated through the analysis process, and an inventory demand analysis report is generated. Timing scheduling submodule: using the emergency replenishment data in the inventory demand analysis report, the shortest processing time priority rule is adopted to sort the storage time nodes of specific materials, optimize the order of material arrival, and ensure that key materials are processed first through the sorting algorithm, so as to generate a storage timing plan; Priority setting submodule: Based on the arrival order of materials in the warehousing timing plan, the priority queue algorithm is used to set the warehousing priority of each material. The parameters include the urgency of the material demand and the unit value. Materials with high demand and unit value are processed first, ensuring the optimization of logistics efficiency and generating a warehousing strategy guide. Among them, the warehousing strategy guidelines specifically include the sorting of incoming materials, the scheduled warehousing schedule, and the handling protocols for special materials; See also Figure 2 The genetic algorithm storage configuration module includes a characteristic analysis submodule, an optimization calculation submodule and a strategy determination submodule, wherein: Characteristic analysis submodule: Based on the warehousing strategy guide, it analyzes the volume, weight and access frequency of each material, classifies the materials, identifies the storage requirements and characteristic matching, and generates a material characteristic analysis report; Optimization calculation submodule: Based on the material characteristics analysis report, the simulated annealing-genetic algorithm hybrid model is used to perform storage optimization calculations, adjust storage allocation to meet the specific needs of different materials, and generate storage optimization results by simulating the storage and access efficiency of different configurations; Strategy determination submodule: Based on the storage optimization results, formulate storage configuration strategies, specify strategy details to guide actual operations, and generate storage configuration plans; Characteristic analysis submodule: Based on the warehousing strategy guide, multi-dimensional characteristic analysis technology is used to quantitatively evaluate the volume, weight and access frequency of each material. Cluster analysis methods, especially K-means algorithm, are used. Parameter settings include the number of clusters as 5 and the number of iterations as 300. Storage requirements and characteristic matching are identified, materials are effectively classified, and material characteristic analysis reports are generated; Optimization calculation submodule: Based on the material characteristics analysis report, a simulated annealing-genetic algorithm hybrid model is used. The initial temperature of the simulated annealing is set to 1000 degrees, the cooling rate is set to 0.95, the crossover rate in the genetic algorithm is set to 0.7, and the mutation rate is set to 0.1. Storage optimization calculation is performed to adjust the storage allocation to meet the specific needs of different materials. By simulating the storage and access efficiency of different configurations, storage optimization results are generated; Strategy determination submodule: Based on the storage optimization results, the decision support system framework is adopted. The parameters include optimization algorithm score and material handling priority. The linear programming method is used to specify the strategy details, including the optimal storage allocation and accurate calculation of space utilization for each material, and the storage location is adjusted to maximize the warehouse space efficiency and material access speed, and finally a detailed and executable storage configuration plan is generated; The storage location configuration plan specifically includes a list of recommended storage locations, the material suitability of each storage location, and the expected space utilization rate; Classify materials and group them according to their volume, weight and access frequency. For materials with different access frequencies, identify and differentiate between frequent and infrequently accessed materials; Specific strategy details guide actual operations, use time series analysis to monitor material access efficiency and space utilization, set efficiency and utilization thresholds, and automatically adjust and remind when data shows below the standard; See also Figure 2The task scheduling and AGV management module includes a path optimization submodule, an AGV scheduling submodule, and a task recording submodule, among which: Path optimization submodule: Based on the storage configuration plan, analyze the starting point and target storage location of each AGV, use path planning technology to reduce the travel distance, adjust each path to reduce intersections and congestion, optimize the overall transportation efficiency, and generate an optimized route map; AGV scheduling submodule: Based on the optimized route map, the asynchronous algorithm is used to schedule AGVs in real time, dynamically assign tasks according to the current task load and location, and ensure balanced and efficient task allocation. Each AGV performs material handling according to the adjusted route and generates an AGV task allocation table; Task record submodule: Based on the AGV task allocation table, it tracks the task execution status of each AGV in real time, including the start and end time of each task, as well as the type and quantity of materials, records detailed operation logs, and generates task completion records; Path optimization submodule: Based on the storage configuration plan, the specific parameters include the coordinates of the starting point and the target storage location, calculate the shortest path of the AGV to reduce the travel distance, apply the obstacle avoidance algorithm, the parameters include the size and steering angle of the AGV, adjust each path to reduce intersections and congestion, and generate an optimized route map; AGV scheduling submodule: Based on the optimized route map, the asynchronous communication algorithm is used to dynamically allocate tasks according to the current task load and the geographical location of the AGV. The load balancing technology is applied. The parameter settings include the maximum load and running speed of each AGV to ensure balanced and efficient task allocation. Each AGV performs material handling according to the adjusted route and generates an AGV task allocation table. Task recording submodule: Based on the AGV task allocation table, it adopts an event-driven architecture to record the task execution status of each AGV, including the start and end time of each task, as well as the type and quantity of materials. It uses database triggers, and specific settings include monitoring of data writing and update operations, recording detailed operation logs, and generating task completion records. Among them, the task completion record specifically includes the AGV number, the list of completed tasks of each AGV and the task completion time; Asynchronous algorithm, according to the formula: , in: For the The total allocation score of the AGVs in the genetic algorithm framework, , , , , is the weight coefficient, Indicates The distance from an AGV to its next target task location, is the remaining processing time of the current task of AGV, To represent The current load level of each AGV, Indicates the energy efficiency of AGV, It is the speed priority of AGV.

[0020] Execution process: First initialize the weight coefficient of each AGV , , , and , obtained through historical data and simulation optimization, to ensure the reasonable influence of each factor in the evaluation, and then collect real-time data to calculate the distance from the current position of each AGV to the next task point , the remaining time of the task , Current load , Energy efficiency and operating speed , and then substitute it into the formula to get the task adaptation score of each AGV , dynamically allocate material storage locations and adjust the priority of picking tasks based on the scores, and finally continuously adjust the weight coefficients after multiple iterations to adapt to the changing environment and task requirements in warehouse operations, improve overall picking efficiency and reduce operating costs.

[0021] See also Figure 2 The outbound strategy formulation module includes the inventory assessment submodule, the priority arrangement submodule and the outbound planning submodule, among which: Inventory Assessment Submodule: Based on the task completion records, it conducts real-time inventory assessment, analyzes the material usage frequency and remaining quantity, determines which materials need to be shipped out first to avoid inventory backlog, and generates real-time inventory assessment reports; Priority Arrangement Submodule: Based on the real-time inventory assessment report, evaluate the outbound priority of each material, set high priority for urgent or high-demand materials, formulate detailed outbound priority strategies, and generate an outbound priority table; Outbound planning submodule: Based on the outbound priority table, arrange the specific execution plan of the outbound task, adjust the outbound sequence and time, ensure the smooth and timely outbound operation, and generate an outbound execution guide; Inventory assessment submodule: Based on task completion records, it uses real-time analysis technology with parameter configurations including a window size of 5 minutes and a sliding interval of 30 seconds to conduct instant inventory assessment. It uses a frequency analysis algorithm with specific settings including a minimum frequency threshold of 0.05 and a maximum frequency threshold of 0.95 to analyze the frequency of use and remaining quantity of materials, determine which materials need to be shipped out first to avoid inventory backlogs, and generate an instant inventory assessment report. Priority Arrangement Submodule: Based on the real-time inventory assessment report, a priority sorting algorithm is used. The parameters include priority allocation rules. Based on the urgency and value of the material demand, high priority is set for urgent or high-demand materials. Dynamic adjustment technology is used. The parameter settings include adjustment frequency once an hour, detailed outbound priority strategy is formulated, and an outbound priority table is generated; Outbound planning submodule: Based on the outbound priority table, the scheduling algorithm is adopted. The parameter settings include a time slice of 10 minutes, arranging the specific execution plan of the outbound task, adjusting the outbound sequence and time, ensuring the smooth and timely outbound operation, and generating an outbound execution guide; Among them, the outbound execution guide specifically includes the order of material outbound shipment, the estimated outbound shipment time point, and the list of emergency outbound materials; See also Figure 2 The inventory dynamic adjustment module includes an inventory status analysis submodule, an adjustment strategy design submodule and a status update submodule, among which: Inventory status analysis submodule: Based on the outbound execution guide, it uses real-time data monitoring technology to continuously track inventory changes, dynamically evaluate inventory, use trend analysis tools to identify inventory gaps and surpluses, optimize inventory replenishment plans, and generate inventory demand forecasts; Adjustment strategy design submodule: Based on inventory demand forecast, conduct strategy simulation, use decision support system to analyze supply chain status, design inventory adjustment strategy to adapt to market fluctuations, and generate inventory adjustment strategy; Status update submodule: Based on the inventory adjustment strategy, implement the automated inventory management system to update the inventory in real time, automatically adjust the system to respond to changes in market demand, synchronously update the ERP system data, and generate the adjusted inventory status; Inventory status analysis submodule: Based on the outbound execution guide, it uses real-time data monitoring technology. The parameter configuration includes a time window of 1 minute and event timestamp processing. It continuously tracks inventory changes and uses trend analysis tools. Specifically, it uses a sliding average algorithm. The parameter settings include a window size of 7 days to identify inventory gaps and surpluses, optimize inventory replenishment plans, and generate inventory demand forecasts. Adjustment strategy design submodule: Based on inventory demand forecast, strategy simulation is performed. The parameters include supply chain status input and market demand variables. Inventory adjustment strategies that adapt to market fluctuations are designed and generated. Status update submodule: Based on the inventory adjustment strategy, an automated inventory management system is implemented, with automatic triggers. Parameter settings include trigger conditions when the inventory level is below the safety threshold, automatically adjusting the system to respond to changes in market demand, and synchronously updating the ERP system data. Using API calls, parameters include data synchronization frequency of once every 30 seconds to generate adjusted inventory status; Among them, the adjusted inventory status specifically includes the adjusted inventory distribution, improved inventory turnover rate and inventory adjustment frequency; The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A warehouse material storage location allocation and picking system based on genetic algorithm, characterized by: The system comprises: Data access and analysis module: connect to the main database of the warehouse management system through a security protocol, synchronously update material, order and employee information, establish data indexes, and obtain material database connection reports; Inventory status management module: based on the data in the material database connection report, monitor and update the inventory status of each material in real time, form inventory monitoring records, and generate inventory status reports; Warehousing strategy formulation module: Based on the inventory status report, analyze the current inventory gap and warehousing demand, arrange the specific warehousing sequence and material priority, form a warehousing operation guide, and generate a warehousing strategy guide; Genetic algorithm storage location configuration module: Based on the warehousing strategy guide, a simulated annealing-genetic algorithm hybrid model is adopted, and a genetic algorithm is applied to perform storage location optimization calculation according to material characteristics and historical inventory data to generate a storage location configuration plan; Task scheduling and AGV management module: Based on the storage configuration scheme, an asynchronous algorithm is used to arrange AGV robots to perform material handling, adjust task queues and route maps to optimize transportation routes, record task execution details, and generate task completion records; Outbound strategy formulation module: Based on the task completion records, evaluate the inventory status and immediate demand, arrange the priority and scheduling plan of outbound materials, formulate outbound operation guidelines, and generate outbound execution guidelines; Inventory dynamic adjustment module: Based on the outbound execution guide, dynamically adjust the inventory configuration, update the inventory status in real time to adapt to changes in market demand, maintain inventory efficiency and response speed, and generate adjusted inventory status.

2. The warehouse material storage location allocation and picking system based on genetic algorithm according to claim 1 is characterized in that: The material database connection report specifically includes material coding, order processing status and employee role allocation.

3. The warehouse material storage location allocation and picking system based on genetic algorithm according to claim 1 is characterized in that: The inventory status report specifically includes the current inventory quantity of each material, the warning inventory level, and inventory items that are about to expire.

4. The warehouse material storage location allocation and picking system based on genetic algorithm according to claim 1 is characterized in that: The warehousing strategy guide specifically includes the sorting of warehousing materials, the scheduled warehousing schedule and the handling protocol for special materials.

5. The warehouse material storage location allocation and picking system based on genetic algorithm according to claim 1 is characterized in that: The genetic algorithm storage location configuration module specifically includes a characteristic analysis submodule, an optimization calculation submodule and a strategy determination submodule, wherein: Characteristic analysis submodule: Based on the warehousing strategy guide, analyze the volume, weight and access frequency of each material, classify the materials, identify storage requirements and characteristic matching, and generate a material characteristic analysis report; Optimization calculation submodule: Based on the material characteristic analysis report, a simulated annealing-genetic algorithm hybrid model is used to perform storage optimization calculations, adjust storage allocation to meet the specific needs of different materials, and generate storage optimization results by simulating the storage and access efficiency of different configurations; Strategy determination submodule: based on the storage optimization results, formulate storage configuration strategies, specify strategy details to guide actual operations, and generate storage configuration plans; The storage location configuration plan includes a recommended storage location list, the material suitability of each storage location, and the expected space utilization.

6. The warehouse material storage location allocation and picking system based on genetic algorithm according to claim 5 is characterized in that: The materials are classified and grouped according to their volume, weight and access frequency, and for access with different frequencies, the materials are identified and differentiated according to the needs of frequent and infrequent access; The specific strategy details guide actual operations, use time series analysis to monitor material access efficiency and space utilization, set efficiency and utilization thresholds, and automatically make adjustments and reminders when data shows that it is below the standard.

7. The warehouse material storage location allocation and picking system based on genetic algorithm according to claim 1 is characterized in that: The task scheduling and AGV management module includes a path optimization submodule, an AGV scheduling submodule and a task recording submodule, wherein: Path optimization submodule: Based on the storage configuration plan, analyze the starting point and target storage location of each AGV, use path planning technology to reduce the travel distance, adjust each path to reduce intersections and congestion, optimize the overall transportation efficiency, and generate an optimized route map; AGV scheduling submodule: Based on the optimized route map, an asynchronous algorithm is used to schedule AGVs in real time, and tasks are dynamically allocated according to the current task load and location to ensure balanced and efficient task allocation. Each AGV performs material handling according to the adjusted route and generates an AGV task allocation table; Task record submodule: Based on the AGV task allocation table, track the task execution status of each AGV in real time, including the start and end time of each task, as well as the type and quantity of materials, record detailed operation logs, and generate task completion records; The task completion record specifically includes the AGV number, the completed task list of each AGV and the task completion time.

8. The warehouse material storage location allocation and picking system based on genetic algorithm according to claim 7 is characterized in that: The asynchronous algorithm is based on the formula: ， in: For the The total allocation score of the AGVs in the genetic algorithm framework, , , , , is the weight coefficient, Indicates The distance from an AGV to its next target task location, is the remaining processing time of the current task of AGV, To represent The current load level of each AGV, Indicates the energy efficiency of AGV, It is the speed priority of AGV.

9. The warehouse material storage location allocation and picking system based on genetic algorithm according to claim 1, characterized in that: The outbound delivery execution guide specifically includes the order of material outbound delivery, the estimated outbound delivery time point and the list of emergency outbound materials.

10. The warehouse material storage location allocation and picking system based on genetic algorithm according to claim 1, characterized in that: The adjusted inventory status specifically includes adjusted inventory distribution, improved inventory turnover rate and inventory adjustment frequency.

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