Multi-order intelligent scheduling system for warehouse operation in building industry

Abstract

The invention relates to an intelligent warehouse multi-order scheduling management system in the building industry, and belongs to the field of intelligent scheduling and resource optimization. The system comprises a work group configuration module, a work time configuration module, an order pool module, an order priority evaluation and setting module, an initialization and dynamic scheduling algorithm module, a man-hour recording detail interface module and the like, is intercommunicated with an API (Application Program Interface) through a modular distributed architecture, and is combined with an XGBoost machine learning model to realize order operation time prediction and automatic priority evaluation. And manual intervention and necessary constraint setting are supported. An Apache Spark parallel computing, rollback and increment optimization mechanism and a FastAPI micro-service architecture are adopted, event driving is realized through Redis Streams, order dynamic change and second-level scheduling response are supported, the allocation efficiency and the resource utilization rate are improved, and the method is suitable for a building warehouse scene with frequent orders and high dynamics.

Description

Technical Field

[0001] This invention relates to the field of intelligent scheduling and resource optimization technology, specifically to a multi-order intelligent scheduling system for warehouse operations in the construction industry, applicable to large-scale, multi-scenario warehousing and distribution operation management of building materials. Background Technology

[0002] Order allocation is a core component of warehouse operations. Taking a large international construction company as an example, it processes hundreds to thousands of customer orders daily, involving diverse building materials such as steel bars, cement, and gypsum board. Dedicated personnel estimate processing time and allocate these orders to different work groups based on information such as order weight, product type, picking route, and customer work scenario (e.g., high-rise construction or ground leveling). Traditional manual allocation heavily relies on the dispatcher's personal experience, making it susceptible to subjective judgment and leading to uneven workloads among work groups, delays in some orders, and low resource utilization. Especially during peak order periods or in cases of sudden order surges (such as emergency material replenishment at construction sites), manual adjustments are time-consuming and slow to respond, with an average reassignment taking 10-30 minutes, severely hindering overall warehouse throughput efficiency and project schedule assurance.

[0003] In existing technologies, some systems have attempted to introduce rule engines or simple heuristic algorithms to achieve semi-automatic allocation, such as fixed rule matching based on order weight or priority. However, these methods do not fully consider the dynamic prediction of the actual working time of building material orders (such as complex processes like rebar cutting, packing, and loading sequence), and lack accurate modeling of the real-time working status of work groups. This makes it difficult to cope with scenarios involving frequent changes in the order pool (such as temporary changes in product specifications) or adjustments in work group attendance (such as temporary staff absences). Furthermore, existing scheduling algorithms are mostly static batch processing, unable to achieve second-level response times, and thus cannot meet the demands of modern construction supply chains for flexible and real-time scheduling. Summary of the Invention

[0004] The purpose of this invention is to address the problems of uneven workload, order delays, and low resource utilization caused by the heavy reliance on the personal experience of dispatchers in traditional manual scheduling systems. Therefore, this invention proposes a multi-order intelligent scheduling system for warehouse operations in the construction industry.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A multi-order intelligent scheduling system for warehouse operations in the construction industry includes a work group configuration module, a work start time configuration module, an order pool module, an order priority evaluation and setting module, an initialization scheduling algorithm module, a queue-jumping / dynamic scheduling algorithm module, and a work hour record details interface module.

[0007] The modules are interconnected through a distributed architecture and API interfaces, supporting cloud deployment and real-time data synchronization;

[0008] The workgroup configuration module is used to store and manage workgroup information;

[0009] The work start time configuration module is synchronized in real time with the work group configuration module and is used to set specific work start times for work groups in the work start status, including start time, end time and duration; the order pool module is responsible for the comprehensive storage and management of orders;

[0010] The order priority assessment and setting module is used to generate priority scores based on order placement time, delivery time, order duration, order type, and characteristic product category;

[0011] The initialization scheduling algorithm module is used to automatically initialize and allocate orders based on the workgroup status and order priority;

[0012] The queue-jumping / dynamic scheduling algorithm module is used to perform real-time dynamic allocation when a new order is received or the work group status changes;

[0013] The time record details interface module is used to record and query the total number of allocated order times, actual order completion time, remaining allocated time, and actual remaining working hours for each work group on the corresponding date.

[0014] The system integrates a high-precision job time prediction model with a dynamic scheduling algorithm to form a closed-loop allocation system, thereby enabling intelligent scheduling of construction warehouse orders.

[0015] Preferably, the order pool module integrates an external prediction model interface, based on the XGBoost machine learning model, with input features including weight, product type, picking path and customer scenario, and outputs the estimated processing time;

[0016] Furthermore, the order pool module uses MongoDB to store order data, supports high-concurrency queries, and processes 3,000 orders in just 20 seconds, which is 95% faster than manual review.

[0017] Preferably, the order priority assessment and setting module uses AI intelligent reasoning to predict the priority of input orders based on features such as order weight, product type, picking path, and customer operation scenario. It also supports manual adjustment of priority scores and allows specifying workgroup constraints that must be processed for specific orders. Furthermore, the module sets priority levels 1-3, supports manual adjustment of priority scores, and sets the priority of urgent construction site orders to 1.

[0018] Preferably, the initialization scheduling algorithm module reads data in batches from the order pool and automatically performs the initial allocation according to the algorithm. The algorithm adopts the basic logic of a dynamic knapsack problem variant combined with a greedy strategy: first, the orders are sorted in descending order of priority, and then each order is allocated to the work group with the fewest remaining working hours and matching skills to maximize resource balance.

[0019] If an order is specified to be executed by a specific workgroup, the system prioritizes compliance with the constraint and inserts exception handling logic into the allocation path. Furthermore, AI heuristic search is used to reduce computational complexity (from O(n^2) to O(n logn)). Processing 2000 orders takes 8 seconds with a completion rate of 98%, which is 25% better than manual allocation.

[0020] Preferably, the initialization scheduling algorithm module sets a maximum working time limit threshold for a single workgroup each day. When the cumulative allocated working time of a workgroup approaches the upper limit threshold, the scheduling algorithm automatically reduces the probability of it being selected. Further, the maximum working time limit per day is 480 minutes, the scheduling algorithm automatically reduces the probability of it being selected, and the load balancing standard deviation is <15 minutes.

[0021] Preferably, the queue-jumping / dynamic scheduling algorithm module uses the Apache Spark parallel computing framework to process large-scale data, ensuring real-time performance. It also integrates a rollback mechanism to prevent allocation conflicts. When a sudden change in the order pool or a change in the workgroup state is detected, the secondary scheduling prioritizes the global optimum, initiates an incremental optimization mechanism, and combines simulated annealing algorithm to avoid getting trapped in local optima, thereby improving the overall load balance. Furthermore, using Apache Spark parallel computing, the processing time is 3 seconds, and rollback is supported. During peak testing (order change rate of 20%), the response time is less than 5 seconds, and the load balance is improved by 40%.

[0022] Preferably, the time record details interface module supports real-time updates and historical queries, and provides data aggregation functions. Integrated visualization tools allow users to view time utilization charts, facilitating construction company managers to analyze performance, predict bottlenecks, and optimize resource allocation.

[0023] In addition, this module supports integration with ERP systems to automatically import work hour data into the finance module; furthermore, the interface pushes updates in real time via WebSocket, supports query API, returns JSON format data, integrates Grafana visualization, and calculates an average utilization rate of 92%.

[0024] Preferably, the system develops microservices based on the FastAPI framework, with each module communicating through a RESTful API and using Redis Streams to asynchronously process event messages, achieving second-level response to sudden order changes or staff absences; furthermore, Redis Streams is configured as a message queue to process asynchronous events, supporting dynamic order changes and second-level scheduling responses.

[0025] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0026] By integrating a high-precision operation time prediction model with a dynamic scheduling algorithm to form a closed-loop allocation system, intelligent scheduling of construction warehouse orders is achieved, significantly improving the intelligence level of warehouse order allocation for construction enterprises. It features high efficiency, flexibility, and scalability, and is suitable for large-scale, multi-scenario construction material warehousing and distribution operation management. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the functional structure of a multi-order intelligent scheduling system;

[0028] Figure 2 This is a diagram of the system's asynchronous message-driven and microservice architecture.

[0029] Figure 3 This is a structural diagram of the work hour allocation and dynamic load balancing module.

[0030] Legend: A. Order Pool Module; B. Order Priority Evaluation and Setting Module; C. Initialization Scheduling Algorithm Module; D. Queue Jump / Dynamic Scheduling Algorithm Module; E. Work Group Configuration Module; F. Work Hour Record Details Interface Module; G. Job Time Prediction (XGBoost Model); S. FastAPI Microservice Architecture; T. Functional Modules; U. Redis Streams Event-Driven Processing; V. Dynamic Scheduling Algorithm Module; X. Work Group 1; Y. Work Group 2; Z. Work Group N; AA. Scheduling Algorithm; BB. Real-time Work Hour Status Update; CC. Performance Analysis and Strategy Adjustment. Detailed Implementation

[0031] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described below in conjunction with the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0032] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways than those described herein, and therefore the invention is not limited to the specific embodiments disclosed in the following specification.

[0033] Example 1:

[0034] like Figure 1 "Functional Structure Diagram of Multi-Order Intelligent Scheduling System" Figure 2 The "System Asynchronous Message-Driven and Microservice Architecture Diagram" Figure 3 As shown in the "Work Hour Allocation and Dynamic Load Balancing Module Structure Diagram", this invention proposes a novel system architecture and implementation scheme suitable for multi-order scheduling management of intelligent warehouses in the construction industry, which has the characteristics of high dynamism, multi-dimensional intelligent scheduling, automatic optimization allocation and scalability.

[0035] The following will provide a detailed description of the system's overall structure, core functional modules, key algorithms and parameters, system deployment details, data interaction and interfaces, operation process, performance assurance measures, manual intervention strategies, working hours and load balancing management, historical data aggregation and strategy closed-loop optimization, etc. The content covers software and hardware implementation, core process logic, key configuration parameters, machine learning model input feature design, scheduling algorithm details, microservice and asynchronous driver integration methods, etc., to ensure that those skilled in the art can fully implement the present invention based on this description.

[0036] I. System Overall Structure and Distributed Deployment

[0037] The multi-order scheduling system of this invention adopts a modular distributed architecture. Each functional module is interconnected via a RESTful API, supporting independent or collaborative deployment on local data centers, private clouds, or public clouds. The overall system structure and the collaborative relationships between functional modules can be found in the "Functional Structure Diagram of Multi-Order Intelligent Scheduling System," and its main components include:

[0038] 1. Order Pool Module: Used to receive, store and manage pending building material orders, supporting high-concurrency order access, status marking (pending allocation, allocated, processing, exception, etc.), multi-dimensional indexing of order features, etc.

[0039] 2. Order Priority Assessment and Setting Module: Embedded AI inference subsystem (using XGBoost model) performs feature engineering on order information (such as weight, product type, sorting path, customer application scenario, etc.), outputs priority score (such as 0-100 points), and supports manual adjustment and work group binding constraints (such as must be processed by a designated work group).

[0040] 3. Initialization scheduling algorithm module: The automatic initial allocation process based on optimization theory (such as mixed integer programming, greedy heuristic, etc.) is adopted. When the order pool is initially filled or a batch of new orders are added to the pool, the allocation is carried out according to the predicted job duration, priority and work group constraints.

[0041] 4. External Model for Job Time Prediction (XGBoost): Input the core features of the order (weight, type, sorting path length, customer urgency, etc.) into the model, and dynamically output standardized job time prediction results. This is used by the scheduling algorithm.

[0042] 5. Queue insertion / dynamic scheduling algorithm module: It can communicate bidirectionally with the order pool and workgroup. When a sudden change in the status of the order pool is detected (such as high-priority order insertion, staff absence, order cancellation, etc.), it can start incremental optimization and rollback mechanism in real time (e.g., based on simulated annealing algorithm, local reallocation).

[0043] 6. Workgroup Configuration Module: Used to set information for each workgroup (number of personnel, skill level, maximum working hours, work capacity level, historical efficiency, etc.), supports dynamic adjustment and bidirectional interaction with the scheduling algorithm.

[0044] 7. Work Hour Record Details Interface Module: As the core of work hour and performance data collection, it collects and aggregates work hour consumption, order type and quantity, and job completion time of each work group in real time to support backend database analysis.

[0045] 8. Microservice architecture and message-driven modules: such as Figure 2 As shown in the "System Asynchronous Message-Driven and Microservice Architecture Diagram", each module is encapsulated as an independent service based on FastAPI, interacts with each other through RESTful interfaces, and uses RedisStreams as an event queue to achieve asynchronous event-driven and decoupling.

[0046] II. Core Functions and Implementation Details

[0047] 1. Order lifecycle management

[0048] The order pool module manages the entire order lifecycle. Order entry can be automated through manual web page operations, batch file uploads, or API integration with external ERP / WMS systems. The status of each order is managed using a finite state machine, with the core state transitions as follows:

[0049] [Pending Assignment] → [Assigned (with Assignment Result)] → [Processing] → [Completed] / [Exception] / [Cancelled]. State transitions are handled through internal module services and API specifications. All state change records are archived for scheduling closed-loop optimization and backtracking.

[0050] The order pool is supported by highly available distributed storage, such as a MySQL+Redis master-slave cluster, to ensure high-concurrency read and write operations.

[0051] 2. Automatic order priority assessment and manual correction

[0052] Priority prediction in this system is primarily handled by an AI model. Detailed steps include:

[0053] (1) Feature extraction: including order weight Material type Sorting path length Customer priority level urgency Expected completion time wait.

[0054] (2) Feature engineering: Feature standardization / normalization processing, such as One-hot encoding is used for enumerated variables.

[0055] (3) AI Modeling: Using historical order data as the training set, an XGBoost-based priority prediction model was trained. The pseudocode of the model is as follows:

[0056] string

[0057] P=XGB.predict([W,C_{type},L_{route},P_{customer},E_{urgency},T_{ddl}])

[0058] The priority score P is distributed between 0 and 100. The system sorts the results according to the algorithm output. The final priority result can be adjusted manually on the front-end page, and workgroup allocation constraints can be set for special orders. .

[0059] 3. High-precision dynamic prediction of order processing time

[0060] Job duration prediction utilizes an external XGBoost regression model, with input features including:

[0061] ●Order weight

[0062] ●Product Type

[0063] ●Types of sorting tools

[0064] ●Path length

[0065] ●Customer urgency wait

[0066] Model output predicts job time It can be expressed by a mathematical formula as follows:

[0067] string

[0068] T_{pred}=XGB_{time}.predict([W,C_{type},F_{type},L_{route},E_{urgency}])

[0069] Whenever a new order or order feature is adjusted, the system automatically invokes this prediction to achieve dynamic scheduling and resource allocation throughout the process.

[0070] 4. Initialization of the scheduling algorithm—oriented towards load balancing and time limits

[0071] The initialization scheduling algorithm employs capacity-constrained optimization scheduling. The core idea is to automatically allocate each order to the most suitable workgroup under multi-dimensional constraints, avoiding inefficient allocation caused by human experience.

[0072] Assume there is One order, There are 1 working group, and the daily working hours limit for each working group is... Each order The predicted task time is .

[0073] Objective function:

[0074] string

[0075] \min\sum_{j=1}^{M}\sum_{k=1}^{N}Assign_{j,k}\cdot Cost_{j,k}

[0076] in This is a binary variable representing an order. Assign to a workgroup .

[0077] Constraints:

[0078] string

[0079] \sum_{k=1}^{N}Assign_{j,k}\cdot T_{pred}^k \leq H_{max}^j, \forall j

[0080] \sum_{j=1}^{M}Assign_{j,k} = 1, \forall k

[0081] For some orders, if a workgroup is manually set, the relevant... Equality constraints can be added.

[0082] This problem can be approximated using algorithms such as heuristic greedy algorithms, hybrid linear programming, or ant colony / genetic simulated annealing. After the initial allocation, the allocation table is pushed to each working group.

[0083] Dynamic scheduling and event-aware response mechanism

[0084] like Figure 2 As shown in the "System Asynchronous Message-Driven and Microservice Architecture Diagram," this system's dynamic scheduling algorithm is implemented using RedisStreams to achieve event-driven operation. Events include:

[0085] ●New order entry, priority order insertion

[0086] ● Order status rollback (e.g., processing failed, error, cancellation)

[0087] ●Workgroup staff absences / leave / temporary resource changes

[0088] ●Sudden large-volume order import

[0089] Each time an event is triggered, the system automatically resamples the affected order pool and initiates secondary scheduling:

[0090] (1) Incremental rescheduling: Only affected orders are reassigned, reducing the need for full secondary reassignment calculations.

[0091] (2) Parallel algorithm acceleration: All allocation initialization and dynamic adjustment are implemented on Apache Spark distributed jobs, ensuring second-level response under large-scale order pools.

[0092] (3) Rollback mechanism: If a new allocation causes a conflict (such as order competition or excessive working hours), the system can automatically withdraw the scheduling result of that round and restore the previous stable allocation.

[0093] The mathematical expression is as follows:

[0094] string

[0095] \arg\min_{Assign'{j,k}}\left[\sum{j,k}Assign'{j,k}\cdot Cost{j,k}+\lambda\cdot ConflictPenalty\right]

[0096] in This indicates the redistribution decision for the affected orders. The rollback triggers a penalty.

[0097] 5. Dynamic adjustment mechanism for working hours and load balancing

[0098] like Figure 3As shown in the "Work Hour Allocation and Dynamic Load Balancing Module Structure Diagram", the scheduling algorithm continuously collects the cumulative work hours of each work group during the allocation process. .

[0099] A dynamic weight allocation mechanism is used to improve the system's adaptability:

[0100] When a certain work group's working hours Approaching the threshold Automatically reduce its allocation probability:

[0101] string

[0102] P_{select}^j=1-\frac{H_{used}^j}{H_{max}^j}

[0103] Therefore, the scheduling process automatically reduces the weight of high-load groups, promotes the full utilization of idle resources, and achieves globally optimal load balancing.

[0104] Historical working time data collection, aggregation, and strategy self-evolution

[0105] The work hour record details interface continuously collects actual work data from various terminals via a service-oriented API, including:

[0106] ● Order start / end time

[0107] ●Actual working hours

[0108] ● Order category, worker number, order ID, etc.

[0109] All data is stored in a unified historical working hours and performance database, enabling multi-dimensional aggregation analysis, such as (aggregated statistics by day / week, work group, order type, etc.), for performance evaluation, experience modeling, and adaptive scheduling parameters.

[0110] (1) Human intervention and abnormal handling mechanism

[0111] To balance system intelligence with business flexibility, the system supports manual control. Dispatchers can directly adjust the AI-predicted order priorities, specify orders that cannot be automatically assigned, and specify required workgroup addresses via the front-end interface. Once manual intervention occurs, the system automatically locks the relevant allocation status, preventing it from being rewritten by subsequent AI-automated scheduling.

[0112] (2) Technical parameters and hardware environment

[0113] ●Spark node configuration: 16-core CPU / 128GB RAM / high-efficiency SSD array, ensuring high throughput and low latency.

[0114] ●Redis Streams service: Independent HA node, supports 100,000 concurrent messages per second.

[0115] ●FastAPI microservice: asynchronous non-blocking communication, API response latency <10ms.

[0116] ●XGBoost model: Supports online incremental training, improving model accuracy. .

[0117] ● Data consistency guarantee: Distributed transactions and data persistence mechanisms are used to prevent task conflicts and order loss.

[0118] Algorithm closed loop and system self-evolution

[0119] The system periodically feeds back the error between the historically allocated actual working hours and the AI ​​prediction values ​​to the AI ​​model and parameter area, continuously iterates and updates the model weights, and dynamically adjusts the kernel parameters of the scheduling algorithm to achieve intelligent self-evolution of scheduling.

[0120] Example of implementation process:

[0121] (1) Batch import of orders: The dispatcher uploads batch orders in EXCEL through the front end, and the system automatically identifies and assigns serial numbers and order characteristics.

[0122] (2) Scheduling and allocation: The system calls the AI ​​model to score the priority of each order and predict the operation time. The initial scheduling algorithm is used to automatically allocate the order for the first time, and the allocation information is pushed to each terminal working group.

[0123] (3) Dynamic queue jumping: Emergency orders are injected into the order pool through the interface, triggering the Redis message stream and automatically redistributing some orders to realize emergency queue jumping.

[0124] (4) Abnormal event response: If personnel suddenly leave their posts, the system will automatically retrieve the current allocation table, calculate the affected orders, and then start dynamic optimization allocation.

[0125] (5) Real-time work hour update: Each work group automatically reports order progress and work hour consumption. The work hour details module counts the load of each group and provides real-time feedback to the main scheduler.

[0126] The above processes can be flexibly integrated into the company's existing ERP / WMS system, and the algorithms, hardware resources, API parameters, and manual control granularity can be adjusted according to the project scale.

[0127] Effects of Example 1:

[0128] Through the aforementioned integrated automatic scheduling and manual intervention mechanism, the system significantly improves scheduling response speed, resource utilization efficiency, and global load balancing capabilities in multi-order scenarios in construction warehouses, specifically in the following aspects:

[0129] (1) Improved second-level response capability

[0130] Previously, manual scheduling or optimization using traditional rule engines resulted in latency ranging from minutes to hours. This system employs Redis Streams asynchronous event-driven scheduling and Spark parallel acceleration. All core scheduling logic (including initialization, queue insertion, and rollback optimization) completes the allocation and tuning of thousands of orders within 1-3 seconds, achieving true "second-level" response times. Assuming the system processes 5000-8000 concurrent orders daily, the average response latency... Significant decline:

[0131] string

[0132] t_{resp,orig} > 60\ s\rightarrow t_{resp,new} < 3\ s

[0133] (2) Resource utilization and load balancing optimization

[0134] AI-powered job duration prediction and time-aware probability adjustment ensure a more balanced workload across workgroups, maximizing resource utilization. By optimizing the objective function, the system can adjust the allocation structure in real time, significantly reducing resource idleness during peak periods and overload in individual groups.

[0135] Quantitative load balancing metrics :

[0136] string

[0137] B = 1-\frac{max_j(H_{used}^j)-min_j(H_{used}^j)}{\sum_j H_{used}^j / M}

[0138] A value close to 1 indicates that the load distribution of each group is approaching the optimal level.

[0139] (3) Error rate reduction and prediction accuracy improvement

[0140] The AI ​​model for work duration is continuously trained online, incorporating historical errors in work time allocation. The average time has been reduced from 10-15 minutes under the original empirical rules to 2-5 minutes, which greatly improves the scheduling accuracy and the reliability of subsequent plans.

[0141] (4) Ease of maintenance and operation

[0142] The system adopts a highly modular microservice deployment, supporting hot upgrades and elastic scaling, and automatic failover for single points of failure. All API interfaces are standardized, supporting integration with any existing ERP or WMS system, greatly reducing the difficulty of enterprise implementation and secondary development.

[0143] (5) Strong performance analysis and strategy optimization capabilities

[0144] The system automatically calculates the working hours consumed by each group, the total number of orders, priority error distribution, etc., for managers to view in real time, and proposes adjustments to the scheduling strategy to achieve closed-loop optimization.

[0145] (6) Human intervention and flexible customization guarantee

[0146] The system not only has highly intelligent allocation capabilities, but also allows front-end manual specification of key order processing / rollback operations, fully compatible with highly sensitive orders or special scenario requirements, and achieves business personalization while maintaining overall efficiency.

[0147] (7) Adaptability to high-frequency changing scenarios

[0148] Addressing the pain points of construction warehouses, such as high order volatility and frequent staffing adjustments, the system can self-organize and adaptively adjust resources in situations such as sudden surges in orders or staff shortages, significantly outperforming traditional static scheduling solutions.

[0149] (8) Data closed loop and knowledge self-evolution

[0150] Historical work hour data, strategy parameters, and AI model weights are continuously accumulated and accumulated to form a customized knowledge graph for enterprise allocation, providing a solid foundation for future project expansion and AI iteration.

[0151] (9) Reduce costs and labor dependence

[0152] By significantly reducing reliance on highly skilled dispatchers, dispatching positions can be downgraded to frontline operator support, reducing human error and time waste, and helping companies reduce costs and increase efficiency.

[0153] (10) Compliance, security and data consistency

[0154] The end-to-end transaction and rollback mechanism ensures data consistency and prevents malicious events such as order misallocation or omission, providing a safe and reliable scheduling foundation for large-scale engineering projects.

[0155] Working principle:

[0156] The working principle of this system's multi-order intelligent scheduling is to organically integrate advanced technologies such as modern artificial intelligence, parallel computing, and distributed software systems to achieve automated and efficient scheduling and intelligent load management. Its core principle can be broken down into the following levels:

[0157] (1) Distributed information flow driven framework

[0158] The entire scheduling process operates in a loop, driven by events. Key information such as order pool status, workgroup changes, and work hour statistics are propagated asynchronously via event messages. Each module operates decoupled, fully leveraging the high concurrency and high reliability flow control capabilities of message queues such as Redis Streams to achieve a highly redundant and highly available information flow architecture.

[0159] (2) AI intelligent decision-making and feature mapping

[0160] The challenge of order allocation lies in the combined effect of multiple factors—such as weight, type, sorting distance, and customer level—and the complex impact of different orders on resource consumption. This system maps all entity features to input vectors for a machine learning model, trains the model using XGBoost to learn complex nonlinear relationships, and accurately predicts order priority and processing time, thereby transforming the scheduling objective into mathematical quantities for optimization algorithms.

[0161] (3) Application of multi-objective constrained optimization theory

[0162] Automatic scheduling is essentially a combinatorial optimization problem under multidimensional constraints. The system models task allocation as a binary allocation problem constrained by an objective function (such as maximizing priority-weighted total benefit, minimizing maximum load, minimizing scheduling cost, etc.), typically expressed as follows:

[0163] string

[0164] \max \sum_{j,k}Assign_{j,k} \cdot P_{k}

[0165] Constrained by total working hours and work group-specified limitations. Metaheuristic optimization methods such as relaxation algorithms, heuristic greedy algorithms, mixed integer programming, and simulated annealing can be used to improve the global robustness and adaptability of the algorithm.

[0166] (4) Parallel and incremental optimization mechanism

[0167] Parallel computing frameworks such as Spark divide problems into independent subtasks, each of which runs in parallel on multiple cores / machines, greatly improving computing speed in scenarios with massive orders and truly achieving "second-level" allocation. Incremental optimization combined with rollback protection only requires reallocation of the changed parts, reducing unnecessary adjustments and increasing system stability and resilience.

[0168] (5) Dynamic load sensing and probabilistic load distribution adjustment

[0169] The system collects and analyzes the current load of each workgroup at any time, and adjusts the load using a dynamic time threshold formula. It adaptively adjusts the allocation probability of each group, effectively avoiding early resource depletion or the "overcrowding" effect at endpoints, and achieving continuous equilibrium.

[0170] (6) Integration of artificial intelligence and expert experience

[0171] The system introduces a human intervention channel, which on the one hand utilizes AI to make automatic decisions based on large samples, and on the other hand absorbs the experience of front-line experts and real-time human judgment. Through a hybrid mechanism, the system is both efficient and flexible, avoiding the management risks under the "black box" algorithm.

[0172] (7) Closed-loop data monitoring and system self-evolution

[0173] After each order is assigned and the task is completed, the system automatically collects the actual time taken and feeds the prediction error back into the AI ​​model; historical performance data is also used to continuously correct parameters, model hyperparameters, and scheduling weights, and the system evolves from "static programming" to "self-learning-adaptive intelligent agent".

[0174] (8) High-availability microservices and API link assurance

[0175] All modules are highly decoupled and lightweightly encapsulated as FastAPI microservices, with fully protocol-based API links for easy future feature upgrades and integration with external systems. Combined with containers and elastic computing resources, modules can be scaled up and down in seconds and automatically recover from failures.

[0176] In summary, this system utilizes multiple technical approaches, including artificial intelligence prediction, parallel allocation strategies, dynamic weighting of work hours and workloads, asynchronous message-driven operations, and closed-loop historical data collection, to form a highly intelligent, real-time, adaptive, and scalable multi-order scheduling platform for construction and warehousing. Through a combination of theoretical and engineering approaches, it effectively solves long-standing common problems in the construction industry, such as low efficiency in multi-order allocation, slow manual response, uneven load, low resource utilization, and lack of dynamic scheduling capabilities, achieving a new breakthrough in intelligent warehouse scheduling and management methods for the industry.

[0177] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A multi-order intelligent scheduling system for warehouse operations in the construction industry, characterized by, The system comprises a work group configuration module, a work time configuration module, an order pool module, an order priority evaluation and setting module, an initialization scheduling algorithm module, a queuing / dynamic scheduling algorithm module, and a work time record details interface module; each module is interconnected through a distributed architecture and an API interface, supports cloud deployment and real-time data synchronization; The work group configuration module is configured to store and manage work group information; The work time configuration module is in real-time synchronization with the work group configuration module, and is configured to set specific work time for the work group in the work state, including start time, end time and duration; The order pool module is responsible for comprehensive storage and management of orders; The order priority evaluation and setting module is configured to generate a priority score based on order placement time, delivery time, order duration, order type and characteristic product category; The initialization scheduling algorithm module is configured to realize automatic initialization distribution of orders according to work group state and order priority; The queuing / dynamic scheduling algorithm module is configured to perform real-time dynamic allocation when a new order is received or the work group state changes; The work time record details interface module is configured to record and query the total distribution order time, actual completed order time, remaining allocatable time and actual remaining work time of each work group on the corresponding date; The system forms a closed-loop allocation system by integrating a high-precision work time prediction model and a dynamic scheduling algorithm to realize intelligent scheduling of construction warehouse orders.

2. The multi-order intelligent dispatching system of claim 1, wherein, The order pool integrates an external prediction model interface, an XGBoost-based machine learning model, which inputs features including weight, product type, picking path and customer scenario, and outputs predicted processing time.

3. The multi-order intelligent dispatching system of claim 1, wherein, The order priority evaluation and setting module predicts the priority of an input order based on features such as order weight, product type, picking path and customer work scenario, supports manual adjustment of priority score, and allows specific orders to be assigned to a specific work group constraint.

4. The multi-order intelligent dispatching system of claim 1, wherein, The initialization scheduling algorithm module reads data from the order pool in batches, and automatically performs the first distribution according to the algorithm. The algorithm uses a dynamic knapsack problem variant combined with the basic logic of a greedy strategy: first, sort the orders in descending order of priority, then assign each order to the work group with the least remaining work time and matching skills to maximize resource balance; if an order is designated to be executed by a specific work group, the system will prioritize the constraint and insert exception handling logic in the allocation path.

5. The multi-order intelligent dispatching system of claim 4, wherein, The system sets a maximum work time threshold for a single work group each day, and when the cumulative allocated work time of the work group approaches the upper limit threshold, the scheduling algorithm automatically reduces the probability of being selected.

6. The multi-order intelligent dispatch system of claim 1, wherein, The queuing / dynamic scheduling algorithm module uses the Apache Spark parallel computing framework to process large-scale data to ensure real-time performance, and integrates a rollback mechanism to prevent allocation conflicts. When detecting order pool mutations or work group state changes, the secondary scheduling prioritizes global optimization, starts the incremental optimization mechanism, and combines the simulated annealing algorithm to avoid falling into a local optimal solution, improving overall balance.

7. The multi-order intelligent dispatch system of claim 1, wherein, The man-hour record details interface module supports real-time updating and historical inquiry, provides data aggregation functions, integrates visualization tools, users can view man-hour utilization charts, and building enterprise managers can analyze performance, predict bottlenecks and optimize resource allocation; in addition, the module supports interfacing with the ERP system to realize automatic import of man-hour data into the financial module.

8. The multi-order intelligent dispatch system of claim 1, wherein, The system develops microservices based on the FastAPI framework, communicates between modules through RESTful API, and uses Redis Streams to asynchronously process event messages, achieving second-level response to sudden order changes or personnel absence events.

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