A multi-source data-driven intelligent scheduling system for the entire apparel logistics and warehousing chain.

Abstract

This invention discloses a multi-source data-driven intelligent scheduling system for the entire supply chain of apparel logistics warehousing, relating to the fields of logistics informatization and intelligent warehousing technology. The system collects order data, SKU attribute data, warehouse structure data, and personnel and equipment operation data, and fuses multi-source heterogeneous information to construct a multi-dimensional feature model characterizing warehousing operation characteristics. Based on this, it performs value assessment and classification of SKUs and optimizes warehouse allocation by combining warehouse spatial structure and operational cost characteristics. Simultaneously, by constructing a scheduling model that integrates time and space costs, it collaboratively optimizes the operation sequence, resource allocation, and execution process. Furthermore, the system achieves dynamic correction and adaptive optimization of scheduling parameters through real-time monitoring and feedback of operation execution results, thus forming a full-chain intelligent scheduling system covering data collection, analysis and decision-making, execution control, and feedback optimization.

Description

Technical Field

[0001] This invention relates to the field of logistics informatization and intelligent warehousing technology, and more specifically, to a multi-source data-driven intelligent scheduling system for the entire chain of apparel logistics warehousing. Background Technology

[0002] With the rapid development of e-commerce, intelligent manufacturing, and new retail models, logistics and warehousing systems are evolving from traditional manual operations to highly automated, information-based, and intelligent systems. Warehousing systems not only need to perform the functions of storing and handling goods, but also undertake multiple tasks such as rapid order response, precise inventory management, and logistics efficiency optimization. Their operational efficiency has become a crucial factor affecting the efficiency of the entire supply chain.

[0003] Existing warehouse management systems typically rely on fixed rules or human experience for operation scheduling and warehouse allocation, such as first-in-first-out (FIFO) principles, fixed storage locations, or simple statistical rules for location planning. While these methods may meet basic needs when order volumes are small and product types are limited, they often reveal significant shortcomings when facing multi-category, high-frequency, and highly volatile business scenarios. For example, different SKUs may have significantly different outbound frequencies, but traditional systems struggle to differentiate them precisely, leading to the mixing of high-frequency and low-frequency items, increasing picking path length and operation time. Furthermore, during promotional periods or holidays, order fluctuations are dramatic, and traditional rules struggle to respond promptly, easily resulting in picking congestion, idle equipment, and wasted manpower.

[0004] While some existing warehouse management systems incorporate data analysis or simple predictive models, most remain at the level of single-dimensional statistical analysis, such as sorting based solely on historical outbound volumes, lacking comprehensive consideration of time characteristics, seasonal variations, and multi-dimensional resource constraints. Furthermore, most systems are fragmented in their allocation of storage space, scheduling of operations, and monitoring of execution, preventing data from forming a closed-loop feedback loop. This makes it difficult for scheduling strategies to adaptively adjust based on actual performance, resulting in limited overall intelligence.

[0005] In terms of warehouse location management, existing technologies often overlook the impact of differences in the internal structure of the warehouse on operational efficiency. For example, there are differences in walking distances, equipment scheduling costs, and labor loads corresponding to different rows, columns, and levels. Warehouse location allocation relies heavily on empirical rules or static parameters, making it difficult to dynamically optimize based on real-time business changes. As a result, high-value goods are not prioritized for placement in high-efficiency areas, affecting overall picking efficiency.

[0006] At the job scheduling level, existing systems typically treat order scheduling, personnel allocation, and equipment scheduling as independent processes, lacking unified optimization goals and collaborative mechanisms. When order volume increases rapidly or structural changes occur, the system often can only be adjusted through manual intervention, failing to form an automated and intelligent collaborative scheduling strategy, thus affecting order fulfillment efficiency and system stability.

[0007] Furthermore, existing technologies generally lack adaptive optimization capabilities based on execution feedback. Most systems only record historical data for statistical analysis, failing to use real-time execution deviations to update model parameters. This results in scheduling strategies becoming rigid and unable to continuously adapt to changes in business structure. This scheduling approach, lacking a closed-loop feedback mechanism, causes the system's efficiency to gradually decline over long-term operation.

[0008] Therefore, this application designs and provides a multi-source data-driven intelligent scheduling system for the entire chain of apparel logistics and warehousing to solve the above problems. Summary of the Invention

[0009] The purpose of this invention is to solve the technical problems mentioned in the background section and to provide a multi-source data-driven intelligent scheduling system for the entire chain of apparel logistics and warehousing.

[0010] The above-mentioned objective of the present invention is achieved through the following technical solution:

[0011] A multi-source data-driven intelligent scheduling system for the entire apparel logistics and warehousing chain includes:

[0012] Multi-source data acquisition module, data fusion and feature modeling module, demand forecasting and SKU value assessment module, intelligent optimization module for warehouse space and storage location, operation scheduling and resource collaborative scheduling module, execution monitoring and feedback optimization module, and end-to-end intelligent management platform;

[0013] The multi-source data acquisition module is used to collect order data, SKU attribute data, warehouse structure data, personnel data, equipment operation data, and logistics network data.

[0014] The data fusion and feature modeling module is used to clean and standardize the collected multi-source data and construct a multi-dimensional feature model to describe the characteristics of logistics operations.

[0015] The demand forecasting and SKU value assessment module forecasts future order demand based on historical order data and assesses the value level of SKUs.

[0016] The intelligent optimization module for warehouse space and storage locations dynamically optimizes and allocates warehouse storage locations based on SKU value levels and warehouse structure characteristics.

[0017] The job scheduling and resource collaborative scheduling module is used to jointly schedule the order job sequence, personnel configuration and equipment usage.

[0018] The execution monitoring and feedback optimization module is used to monitor the job execution status in real time and adaptively correct the scheduling model;

[0019] The full-link intelligent management platform is used to uniformly schedule and visualize the above modules, so as to realize the full-link intelligent scheduling of logistics and warehousing operations.

[0020] Furthermore, the order data collected by the multi-source data acquisition module includes at least the order generation time, SKU code, order quantity, and order priority information, and the SKU attribute data includes at least the SKU outbound frequency, outbound quantity, category attribute, and seasonal attribute information.

[0021] Furthermore, the data fusion and feature modeling module jointly models the SKU outbound quantity characteristics, outbound frequency characteristics, and seasonal characteristics to form a multi-dimensional feature vector that describes the comprehensive operational characteristics of the SKU.

[0022] Furthermore, the demand forecasting and SKU value assessment module constructs a demand forecasting model based on historical order time series data to predict the order demand in different future periods, and uses the forecast results as the input basis for subsequent warehouse space optimization and work scheduling.

[0023] Furthermore, the SKU value assessment module classifies SKUs into different categories, including at least high-frequency high-value SKUs, medium-frequency medium-value SKUs, and low-frequency low-value SKUs, based on the SKU's outbound frequency, outbound volume, and predicted demand results.

[0024] Furthermore, the intelligent optimization module for warehouse space and storage location prioritizes high-value SKUs by allocating them to efficient areas close to operational nodes based on SKU value level, warehouse location, and operational path distance. The remaining SKUs are stored in separate zones according to seasonal attributes and outbound characteristics.

[0025] Furthermore, the job scheduling and resource collaborative scheduling module comprehensively considers the urgency of orders, SKU value level, personnel skill level, and real-time equipment load status to perform multi-objective collaborative scheduling of outbound operation sequence, personnel grouping, and equipment call.

[0026] Furthermore, the job scheduling and resource coordination module prioritizes the allocation of job resources for orders corresponding to high-value SKUs during peak order periods, and dynamically adjusts personnel and equipment resources during off-peak order periods to improve overall resource utilization.

[0027] Furthermore, the execution monitoring and feedback optimization module is used to collect job completion time, personnel and equipment utilization rate and order delay information in real time, and feed the collection results back to the data fusion and feature modeling module to adaptively update the scheduling model parameters.

[0028] Furthermore, the end-to-end intelligent management platform provides functions such as warehouse status visualization, operation progress monitoring, scheduling result evaluation, and historical data backtracking to achieve centralized management and intelligent decision support for the entire logistics and warehousing chain.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] 1. The solution of this invention constructs a multi-source data-driven intelligent scheduling system for the entire chain of apparel logistics warehousing, realizing unified modeling and collaborative analysis of order data, SKU attribute data, warehouse structure data, and personnel and equipment operation data. Compared with traditional warehouse management methods that rely on experience rules or single indicators, the solution of this invention can systematically characterize the warehousing operation process under a unified data framework. By introducing multi-dimensional feature modeling and value classification mechanisms, the operational characteristics of different types of SKUs are accurately expressed, thereby effectively solving problems such as mixed storage of high-frequency and low-frequency goods, low warehouse space utilization efficiency, and extensive allocation of warehouse resources in existing technologies, and improving warehouse space utilization and overall operational efficiency.

[0031] 2. The solution of this invention constructs a warehouse location evaluation model that integrates time and space costs, quantifies factors such as differences in warehouse internal structure, operating distance, labor costs, and management complexity, and combines it with optimization algorithms to globally solve warehouse location allocation. This allows warehouse location layout to no longer rely on manual experience or fixed rules, but to be dynamically adjusted according to changes in business load. At the same time, by combining SKU value level, seasonal characteristics, and warehouse location zoning strategies, high-value, high-frequency goods are prioritized for placement in high-efficiency operating areas, ensuring picking efficiency while reducing ineffective handling and resource waste, thereby significantly improving overall operational efficiency and responsiveness.

[0032] 3. This invention introduces an execution feedback and adaptive adjustment mechanism, feeding back information such as execution deviations and resource utilization during actual operations to the scheduling model. This allows for dynamic correction of key parameters, achieving continuous optimization and self-evolution of the scheduling strategy. This closed-loop mechanism enables the system to maintain stable and efficient operation despite changes in order structure, fluctuations in business scale, or adjustments in the operational environment, avoiding the performance degradation problems caused by fixed parameters in traditional systems. Through these technical means, this invention enables efficient collaboration, intelligent decision-making, and continuous optimization of logistics and warehousing systems in complex business scenarios, demonstrating significant practical value and promising prospects for wider application. Attached Figure Description

[0033] Figure 1 This is a system block diagram of the present invention;

[0034] Figure 2 This is a logic diagram of the system of the present invention; Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of this invention clearer, the following description is provided in conjunction with embodiments and appendices. Figures 1-2 The present invention will be further described in detail below. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0036] In this embodiment, a multi-source data-driven intelligent scheduling system for the entire chain of apparel logistics and warehousing achieves warehouse space optimization, work scheduling optimization, and closed-loop control of execution feedback by uniformly collecting and processing order data, SKU attribute data, warehouse structure data, and personnel and equipment operation data.

[0037] The order data collected by the system within the statistical period includes at least the order generation time, SKU code, quantity of a single shipment, and order priority information; SKU attribute data includes at least the cumulative shipment quantity, cumulative shipment frequency, and timestamp information of the corresponding SKU within the statistical period; warehouse structure data includes the number of warehouse rows, columns, and layers, as well as the relative position of the order merging area and the entrance / exit; personnel and equipment data includes the number of picking personnel, personnel skill levels, and the operating status of forklifts and conveyor equipment.

[0038] After deduplication, missing value completion, outlier correction, and time alignment, the aforementioned multi-source data is used to construct a unified data sample set as input for subsequent calculations and decisions. Assume there are a total of [number missing] data points within the statistical period. The SKU, the The cumulative outbound volume of each SKU during this period is recorded as follows: The cumulative number of outbound shipments is recorded as follows: The system respectively... and Sort the SKUs in descending order and calculate their cumulative percentage to complete the ABC classification based on both outbound quantity and outbound frequency. Then, through cross-analysis of the two classifications, divide the SKUs into three categories: high-frequency high-value, medium-frequency medium-value, and low-frequency low-value. The value category of each SKU is marked as ,in .

[0039] This classification result serves as an important input parameter for warehouse space allocation and operation scheduling. To characterize the seasonal outbound features of SKUs, the year is divided into four seasons: spring, summer, autumn, and winter. The statistics for the first season are then analyzed. SKU in season The cumulative outbound volume is recorded as follows: And calculate its seasonal outbound percentage:

[0040] ;

[0041] in, Indicates the first SKU in season The proportion of outbound shipments within the country This indicates that the SKU is in season The cumulative outbound volume within the specified period, with the denominator being the total outbound volume of that SKU throughout the year. The system is based on... The size of the SKU determines whether it is a single-season main seller, a double-season main seller, or has no obvious seasonal characteristics, and determines its priority seasonal zoning in the warehouse space accordingly.

[0042] During the warehouse location evaluation phase, each location in the warehouse is represented by a triplet. It means that, among them For queuing, For column number, This refers to the floor number.

[0043] The system first calculates the lateral movement weight of the position. Its expression is:

[0044] ;

[0045] in, Indicates the column number of the position. This indicates the horizontal time weight corresponding to the column. The closer the column number is to the order merging area or the main channel, the smaller its horizontal movement time weight.

[0046] Based on the queue number and column number, the system calculates the time cost of the warehouse:

[0047] ;

[0048] in, Indicates that it is located at the th Ranked The unit time cost of listing positions. It is a time conversion factor, whose value is determined based on the weighted calculation results of round-trip time per unit distance, labor cost per unit operation, and average daily picking frequency. It is used to uniformly map spatial distance into time cost.

[0049] Simultaneously, to reflect the operational costs associated with space occupancy, a space cost function is introduced. Let the maximum column number covered by the main channel be... Then the number of primary columns and the number of non-primary columns are defined as follows:

[0050] , ;

[0051] And set a queuing threshold. This is used to distinguish between regular operating areas and remote storage areas. When the warehouse location number exceeds this threshold, a cross-location cost is introduced, which is defined as:

[0052] ;

[0053] in, This is a preset queuing threshold used to describe the additional management and scheduling costs introduced when operations exceed the normal scope.

[0054] Based on the above variables, construct the position cost function:

[0055] ;

[0056] in, The opportunity cost coefficient is the primary factor. For non-primary opportunity cost coefficients, To adjust the cost coefficient for cross-row operations, This is the daily management cost coefficient corresponding to the queuing number.

[0057] To make time cost and position cost comparable, they are normalized. Let the maximum and minimum time costs among all feasible positions be respectively... and The position efficiency score is:

[0058] ;

[0059] Similarly, let the maximum and minimum values ​​of the position cost be respectively... and The position cost score is:

[0060] ;

[0061] Further construct a comprehensive position evaluation function:

[0062] ;

[0063] in, This is a weighting coefficient for efficiency and cost, used to balance the relative importance of operational efficiency and operating costs in warehouse allocation.

[0064] In the position allocation phase, decision variables are introduced. When the first Ranked Liede Layered positions are allocated to the value category. The value is 1 for the specified SKU and 0 otherwise. The system aims to minimize the overall weighted cost, and the objective function is constructed as follows:

[0065] ;

[0066] in, This is the weighted coefficient for time cost and position cost.

[0067] The constraints include: a maximum of one SKU can be assigned to the same warehouse location; the number of SKUs assigned to each category is equal to the total number of SKUs in that category; the seasonal attributes of the SKUs must be consistent with the seasonal zone to which the warehouse location belongs; and some empty warehouse locations in high-efficiency operating areas are reserved for the dynamic pre-positioning of subsequent best-selling SKUs.

[0068] To address the aforementioned combinatorial optimization problem, the system employs a genetic algorithm to solve it, encoding the complete allocation scheme into chromosome individuals and defining a fitness function:

[0069] ;

[0070] in, This represents the objective function value corresponding to the current position allocation scheme. A higher fitness value indicates better overall performance of the position allocation scheme. The population is iteratively updated through selection, crossover, and mutation operations, gradually approaching the optimal solution while satisfying constraints, ultimately outputting the position allocation scheme.

[0071] After optimizing the warehouse layout, the system uses the warehouse locations and corresponding time costs for outbound operation scheduling. For any order... Let the set of SKUs contained in this order be . Therefore, the estimated picking time for this order is:

[0072] ;

[0073] in, Indicates order The set of SKUs included Indicates SKU The column number of the current warehouse. (Based on system data) Orders are prioritized and divided into waves based on order priority and SKU value category. During peak order periods, priority is given to ensuring the allocation of operational resources for high-value SKU orders, while during off-peak order periods, personnel and equipment resources are dynamically adjusted to improve overall utilization.

[0074] During the execution of the task, the system collects the actual completion time of the order in real time. And calculate the execution deviation:

[0075] ;

[0076] in, This represents the deviation between the actual execution time and the predicted time of the order. This execution deviation is fed back as feedback data to the data fusion and feature modeling module for dynamically adjusting the time conversion coefficient. Weight parameters And SKU classification thresholds, thus forming a closed-loop intelligent scheduling system that combines data collection, model calculation, scheduling execution and feedback correction.

[0077] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-source data-driven intelligent scheduling system for the entire garment logistics and warehousing chain, characterized in that, include: Multi-source data acquisition module, data fusion and feature modeling module, demand forecasting and SKU value assessment module, intelligent optimization module for warehouse space and storage location, operation scheduling and resource collaborative scheduling module, execution monitoring and feedback optimization module, and end-to-end intelligent management platform; The multi-source data acquisition module is used to collect order data, SKU attribute data, warehouse structure data, personnel data, equipment operation data, and logistics network data. The data fusion and feature modeling module is used to clean and standardize the collected multi-source data and construct a multi-dimensional feature model to describe the characteristics of logistics operations. The demand forecasting and SKU value assessment module forecasts future order demand based on historical order data and assesses the value level of SKUs. The intelligent optimization module for warehouse space and storage locations dynamically optimizes and allocates warehouse storage locations based on SKU value levels and warehouse structure characteristics. The job scheduling and resource collaborative scheduling module is used to jointly schedule the order job sequence, personnel configuration and equipment usage. The execution monitoring and feedback optimization module is used to monitor the job execution status in real time and adaptively correct the scheduling model; The full-link intelligent management platform is used to uniformly schedule and visualize the above modules, so as to realize the full-link intelligent scheduling of logistics and warehousing operations.

2. The multi-source data-driven intelligent scheduling system for the entire garment logistics and warehousing chain according to claim 1, characterized in that, The order data collected by the multi-source data acquisition module includes at least the order generation time, SKU code, order quantity, and order priority information. The SKU attribute data includes at least the SKU outbound frequency, outbound quantity, category attribute, and seasonal attribute information.

3. The multi-source data-driven intelligent scheduling system for the entire garment logistics and warehousing chain according to claim 1, characterized in that, The data fusion and feature modeling module forms a multi-dimensional feature vector to describe the comprehensive operational characteristics of SKUs by jointly modeling SKU outbound volume characteristics, outbound frequency characteristics, and seasonal characteristics.

4. The multi-source data-driven intelligent scheduling system for the entire garment logistics and warehousing chain according to claim 1, characterized in that, The demand forecasting and SKU value assessment module constructs a demand forecasting model based on historical order time series data, predicts the order demand for different periods in the future, and uses the forecast results as the input basis for subsequent warehouse space optimization and work scheduling.

5. The multi-source data-driven intelligent scheduling system for the entire garment logistics and warehousing chain according to claim 4, characterized in that, The SKU value assessment module classifies SKUs into at least three categories: high-frequency high-value SKUs, medium-frequency medium-value SKUs, and low-frequency low-value SKUs, based on the SKU's outbound frequency, outbound volume, and predicted demand.

6. The multi-source data-driven intelligent scheduling system for the entire garment logistics and warehousing chain according to claim 1, characterized in that, The intelligent optimization module for warehouse space and storage location prioritizes high-value SKUs by allocating them to efficient areas close to operational nodes, based on SKU value level, warehouse location, and operational path distance. Other SKUs are stored in separate zones according to seasonal attributes and outbound characteristics.

7. The multi-source data-driven intelligent scheduling system for the entire garment logistics and warehousing chain according to claim 1, characterized in that, The job scheduling and resource collaborative scheduling module comprehensively considers the urgency of orders, the value level of SKUs, the skill level of personnel, and the real-time load status of equipment to carry out multi-objective collaborative scheduling of outbound operation sequence, personnel grouping, and equipment call.

8. The multi-source data-driven intelligent scheduling system for the entire garment logistics and warehousing chain according to claim 7, characterized in that, The job scheduling and resource coordination module prioritizes the allocation of job resources for orders corresponding to high-value SKUs during peak order periods, and dynamically adjusts personnel and equipment resources during off-peak order periods to improve overall resource utilization.

9. The multi-source data-driven intelligent scheduling system for the entire garment logistics and warehousing chain according to claim 1, characterized in that, The execution monitoring and feedback optimization module is used to collect job completion time, personnel and equipment utilization rate and order delay information in real time, and feed the collection results back to the data fusion and feature modeling module to adaptively update the scheduling model parameters.

10. The multi-source data-driven intelligent scheduling system for the entire garment logistics and warehousing chain according to claim 1, characterized in that, The end-to-end intelligent management platform provides functions such as warehouse status visualization, operation progress monitoring, scheduling result evaluation, and historical data backtracking to achieve centralized management and intelligent decision support for the entire logistics and warehousing chain.

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