Intelligent logistics scheduling system

Abstract

An intelligent logistics scheduling system, comprising: a data collection and analysis module, which is used for collecting historical delivery data and delivery environment data, and analyzing the historical delivery data and the delivery environment data, so as to generate a delivery trend parameter; an automatic scheduling module, which is used for generating an individual delivery plan for each vehicle on the basis of the delivery trend parameter; a route optimization module, which is used for generating a delivery route and an order delivery sequence of each vehicle on the basis of the individual delivery plan of each vehicle; a real-time monitoring and adjustment module, which is used for tracking the position, the delivery progress and a road condition change of each vehicle, so as to dynamically adjust the delivery route of each vehicle; and a result export module, which is used for exporting a final scheduling scheme for all the vehicles on the basis of the delivery route of each vehicle.

Description

Intelligent Logistics Scheduling System Technical Field

[0001] This invention relates to a logistics scheduling technology, and more particularly to an intelligent logistics scheduling system. Background Technology

[0002] With the booming development of e-commerce and online shopping, the demand for logistics and delivery has surged, especially given the large fluctuations in order volume, short delivery times, and wide geographical coverage. Logistics companies face enormous challenges. Traditional logistics scheduling methods rely on manual experience and static planning, making it difficult to cope with real-time changes and unpredictable situations in logistics and delivery demand, such as traffic congestion, weather changes, and last-minute order insertions. This leads to low resource utilization, inefficient delivery, rising costs, and an inability to meet customers' expectations for fast and accurate delivery services.

[0003] While existing logistics systems possess basic automated scheduling technologies, most are only effective under static conditions and lack the ability to cope with dynamic changes. Furthermore, current systems typically fail to fully utilize real-time and historical data for intelligent analysis and prediction, and lack continuous learning and optimization mechanisms, thus limiting their ability to handle complex and ever-changing delivery scenarios. Therefore, the industry urgently needs a smart logistics scheduling system that can not only effectively utilize big data technology for automated scheduling but also achieve real-time monitoring and dynamic adjustments, and progressively optimize the scheduling model through machine learning to adapt to constantly changing delivery demands. Summary of the Invention

[0004] This invention provides an intelligent logistics scheduling system that improves delivery efficiency through data-driven intelligent scheduling, route optimization, and dynamic resource allocation, and incorporates a machine learning module for continuous optimization. This invention significantly improves resource utilization, shortens delivery time, and reduces operating costs, thereby enhancing the reliability, flexibility, and responsiveness of logistics services to meet the rapidly changing needs of the current logistics industry.

[0005] To achieve one, some, or all of the above objectives, or other objectives, an embodiment of the present invention provides an intelligent logistics scheduling system, including a data collection and analysis module, an automatic scheduling module, a route optimization module, a real-time monitoring and adjustment module, and an export result module. The automatic scheduling module is coupled to the data collection and analysis module, the route optimization module is coupled to the automatic scheduling module, the real-time monitoring and adjustment module is coupled to the route optimization module, and the export result module is coupled to the real-time monitoring and adjustment module. Furthermore, the data collection and analysis module collects historical delivery data and delivery environment data, and analyzes the historical delivery data and delivery environment data to generate delivery trend parameters; the automatic scheduling module generates an individual delivery plan for each vehicle based on the delivery trend parameters; the route optimization module generates a delivery route and order delivery sequence for each vehicle based on the individual delivery plan for each vehicle; the real-time monitoring and adjustment module tracks the location, delivery progress, and road condition changes of each vehicle to dynamically adjust the delivery route of each vehicle; and the export result module derives the final scheduling plan for all vehicles based on the delivery routes of each vehicle.

[0006] In one embodiment of the present invention, the above-mentioned intelligent logistics scheduling system further includes a database, wherein the database is coupled to a data collection and analysis module and is used to store historical delivery data and delivery environment data.

[0007] In one embodiment of the present invention, the above-mentioned intelligent logistics scheduling system further includes a user interface module, wherein the user interface module is coupled to the real-time monitoring and adjustment module and is used to display the delivery progress of each vehicle and manually adjust individual delivery plans.

[0008] In one embodiment of the present invention, the above-mentioned intelligent logistics scheduling system further includes a machine learning module, wherein the machine learning module is coupled to the user interface module and is used to train a machine learning model based on historical delivery data and individual delivery plans.

[0009] In one embodiment of the present invention, the above-mentioned machine learning model is a long short-term memory model or a gated loop unit.

[0010] In one embodiment of the present invention, the above-mentioned intelligent logistics scheduling system further includes a communication module, wherein the communication module is coupled to the data collection and analysis module and is used to connect to the Internet to receive delivery environment data.

[0011] In one embodiment of the present invention, the aforementioned historical delivery data includes data on each vehicle's individual delivery plan, delivery route, order delivery sequence, location, and delivery progress.

[0012] In one embodiment of the present invention, the aforementioned delivery environment data includes data on road conditions, weather, and traffic.

[0013] This invention improves logistics and distribution efficiency through data analysis, route optimization, and dynamic adjustment functions. It integrates functions such as data collection, intelligent scheduling, route optimization, real-time monitoring, and machine learning, thus providing dynamic and automated scheduling solutions during the logistics and distribution process.

[0014] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described in detail below with reference to the accompanying drawings. Attached Figure Description

[0015] Figure 1 is a system block diagram of an intelligent logistics scheduling system provided in an embodiment of the present invention;

[0016] Figure 2 is a system block diagram of the intelligent logistics scheduling system provided in another embodiment of the present invention. Detailed Implementation

[0017] This invention is described in particular by way of the following examples, which are merely illustrative. Various modifications and refinements can be made by those skilled in the art without departing from the spirit and scope of this disclosure. Therefore, the scope of protection of this disclosure is determined by the appended claims. Throughout this specification and claims, unless explicitly stated otherwise, the words “a” and “described” include a description comprising “a or at least one” element or component. Furthermore, as used herein, the singular article also includes a description of multiple elements or components unless clearly excluded from the specific context. Moreover, when applied in this description and all the following claims, unless explicitly stated otherwise, “in which” may include both “in which” and “therein”. The terms used throughout this specification and claims, unless otherwise specified, generally have their ordinary meaning in the art, in the disclosure, and in the specific context. Certain terms used to describe this invention will be discussed below or elsewhere in this specification to provide additional guidance to those skilled in the art in describing this invention. Examples found anywhere in this specification, including examples of any terms used in the discussion herein, are merely illustrative and do not limit the scope or meaning of the invention or any of the illustrative terms. Similarly, the invention is not limited to the various embodiments set forth in this specification.

[0018] Furthermore, the use of the terms "coupled" or "connected" herein includes any direct and indirect electrical connection. For example, if the text describes a first device being electrically coupled to a second device, it means that the first device can be directly connected to the second device, or indirectly connected to the second device through other devices or connection means. Additionally, regarding the description of the transmission or provision of electrical signals, those skilled in the art will understand that attenuation or other non-ideal variations may occur during the transmission of electrical signals, but unless otherwise specified, the source and receiver of the transmitted or provided electrical signal should be considered substantially the same signal. For example, if an electrical signal S is transmitted (or provided) from terminal A of an electronic circuit to terminal B of the same electronic circuit, a voltage drop may occur across the source and drain terminals of a transistor switch and / or possible stray capacitances. However, unless the purpose of this design is to intentionally utilize the attenuation or other non-ideal variations that occur during transmission (or provision) to achieve certain specific technical effects, the electrical signal S at terminals A and B of the electronic circuit should be considered substantially the same signal.

[0019] It is understood that terms such as "comprising," "including," "having," "containing," and "involving," as used herein, are open-ended, meaning they include but are not limited to. Furthermore, no embodiment or claim of this invention is required to achieve all the objects, advantages, or features disclosed in this invention. In addition, the abstract and headings are merely for assisting in patent document searching and are not intended to limit the claims of this invention.

[0020] In today's global economy, logistics and supply chain management have become key elements for the successful operation of various industries. With the rapid development of e-commerce, the demand for logistics and delivery is expanding rapidly at an average annual growth rate of nearly 10%, and the pandemic has further accelerated this growth, with more consumers relying on online shopping. Against this backdrop, the market demand for intelligent logistics scheduling systems is rapidly increasing. According to market research reports, the global intelligent logistics market is expected to continue to climb in the coming years, exceeding $100 billion by 2030. This growth indicates that intelligent logistics scheduling systems have broad market prospects and enormous commercial potential.

[0021] Intelligent logistics scheduling systems are not only applicable to traditional retail and logistics companies, but are also gradually expanding to diversified industries such as manufacturing, healthcare, and cold chain food. As companies increase their demands for logistics efficiency, resource optimization, and environmental protection, the need for dynamic scheduling, real-time monitoring, and intelligent scheduling is growing. Specific market demands are mainly reflected in the following aspects: 1. Improved delivery efficiency: Efficient logistics scheduling can shorten order processing and delivery times, helping to reduce operating costs for logistics companies and improve customer satisfaction. 2. Optimized resource utilization: Intelligent scheduling can achieve optimal allocation of manpower, vehicles, and fuel while meeting delivery needs, thereby reducing operating costs and resource waste. 3. Coping with dynamic changes: Logistics operations are frequently affected by unforeseen factors (such as weather, road conditions, and ad-hoc orders), making dynamic adjustment and real-time monitoring necessary to solve these problems. 4. Environmental protection and sustainable development needs: Optimizing routes and reducing empty vehicle rates through intelligent scheduling reduces carbon emissions and aligns with the trend of sustainable development.

[0022] Currently, some preliminary intelligent logistics solutions exist on the market, but most systems focus on static scheduling and lack real-time response and dynamic optimization capabilities. Therefore, companies that can provide intelligent logistics scheduling systems with real-time monitoring, dynamic adjustment, and machine learning-driven capabilities will have a competitive advantage. Furthermore, the market competition landscape for intelligent logistics scheduling systems is mainly composed of the following types of companies: 1. Traditional logistics companies: Some large logistics companies have begun to introduce big data and AI technologies into logistics management and are trying to develop their own intelligent scheduling systems, but most are still in the initial application stage. 2. Technology innovation companies: Emerging technology companies possess strong R&D capabilities in intelligent logistics scheduling. Their intelligent technologies and innovation capabilities inject new momentum into the logistics industry, becoming the main drivers of competition. 3. Logistics management software companies: Existing logistics management system companies are upgrading their products, adding intelligent scheduling and real-time monitoring modules to meet the demands of the intelligent logistics market.

[0023] With technological advancements and increasing logistics demands, the application of intelligent logistics scheduling systems will continue to deepen. The system's potential future application markets cover diverse industry needs, especially: 1. E-commerce: The rapid growth of e-commerce has driven last-mile delivery demand, making intelligent logistics scheduling systems highly promising in improving efficiency and meeting changing needs. 2. Cold chain logistics: Cold chain logistics requires high precision in scheduling due to its temperature control requirements, while intelligent scheduling can ensure delivery speed and stability to guarantee the quality of fresh and medical products. 3. Manufacturing: As manufacturing transforms into smart factories, its supply chain management and internal logistics scheduling will increasingly rely on intelligent technologies. 4. Medical supply chain: The medical industry's demand for timely and accurate logistics is growing, and intelligent scheduling can play a crucial role in the distribution of emergency medical supplies.

[0024] The deployment of intelligent logistics scheduling systems is expected to bring significant economic benefits to enterprises, including improved delivery speed, reduced operating costs, increased resource utilization efficiency, and enhanced logistics management flexibility. With the continuous advancement of artificial intelligence and IoT technologies, the intelligence level of logistics scheduling systems will be further improved, providing a competitive advantage in logistics efficiency, cost control, and customer satisfaction. In other words, the market demand for intelligent logistics scheduling systems is broad and growing rapidly. This system not only meets existing needs for logistics efficiency and cost optimization but also, through technological innovation and intelligent management, will occupy an important position in the future logistics market, bringing considerable economic and social benefits to enterprises.

[0025] Please refer to Figure 1, which is a system block diagram of an intelligent logistics scheduling system provided in an embodiment of the present invention. The intelligent logistics scheduling system 1 provided in this embodiment includes a data collection and analysis module 2, an automatic scheduling module 3, a route optimization module 4, a real-time monitoring and adjustment module 5, an export result module 6, a database 7, and a communication module 8. The automatic scheduling module 3 is coupled to the data collection and analysis module 2, the route optimization module 4 is coupled to the automatic scheduling module 3, the real-time monitoring and adjustment module 5 is coupled to the route optimization module 4, the export result module 6 is coupled to the real-time monitoring and adjustment module 5, the database 7 is coupled to the data collection and analysis module 2, and the communication module 8 is coupled to the data collection and analysis module 2.

[0026] First, the intelligent logistics scheduling system 1 is started, and the connection to the database 7 and the initialization of module parameters, such as data checking and loading, are completed. It is also ready to receive real-time data and update scheduling requirements to ensure that the intelligent logistics scheduling system 1 is ready to run at any time. The database 7 is used to store historical delivery data and delivery environment data, and the communication module 8 is used to connect to the Internet to receive the delivery environment data.

[0027] The data collection and analysis module 2 collects historical delivery data and delivery environment data, and analyzes this data to generate delivery trend parameters. Specifically, the module collects historical delivery data from database 7, including individual delivery plans, routes, order delivery order, location, delivery progress, delivery time, order volume, route preferences, etc., for each vehicle. It then combines this data with external delivery environment data, including road conditions, weather, and traffic data. The analysis of this historical and environmental data generates delivery trend parameters, such as peak hours, vehicle utilization, and route congestion rates, providing precise parameter support for the automatic scheduling module 3. More specifically, the data collection and analysis module 2 collects various types of data required for the operation of the intelligent logistics scheduling system 1, primarily including historical delivery data (such as order information, route records, vehicle resources, etc.) and external delivery environment data (such as traffic conditions, weather information, etc.). Historical delivery data helps accurately assess resource needs during scheduling, while external delivery environment data improves the accuracy of real-time dispatching. Next, data preprocessing, such as cleaning, deduplication, and formatting, is performed on the collected data before it enters the automatic scheduling module 3 to remove noisy data and ensure data consistency. The data collection and analysis module 2 also performs feature engineering to extract features that have an important impact on scheduling (such as route length, traffic congestion level, etc.), providing a high-quality data foundation for subsequent intelligent analysis and / or model training.

[0028] The automatic scheduling module 3 generates individual delivery plans for each vehicle based on delivery trend parameters. In other words, the automatic scheduling module 3 generates a preliminary schedule based on data output from the data collection and analysis module 2, covering the delivery time window and priority of each order, dynamically allocating available vehicles and arranging delivery tasks, and generating individual delivery plans for each vehicle to ensure timeliness and maximize resources. Specifically, the automatic scheduling module 3 performs optimal order allocation and resource configuration based on current order demand, delivery geographic range, vehicle availability, and other factors. The intelligent logistics scheduling system 1 uses heuristic algorithms and linear programming to optimize resources, ensuring that delivery needs are met while maximizing vehicle utilization and route efficiency. It also sets priorities based on factors such as order timeliness and urgency to ensure that high-priority orders are completed in the shortest possible time, avoiding delays caused by insufficient resource allocation.

[0029] The route optimization module 4 generates delivery routes and order delivery sequences for each vehicle based on its individual delivery plan. In other words, it optimizes each vehicle's delivery route based on real-time traffic conditions and distance to achieve the lowest possible time or shortest distance, and optimizes the delivery sequence according to the timeliness requirements of orders. Specifically, the route optimization module 4 selects the optimal delivery route based on factors such as geographic information, real-time traffic conditions, and route length. It employs path planning techniques such as Dijkstra's algorithm and A* algorithm, and considers different delivery locations and vehicle capacity to generate delivery plans. When unpredictable situations arise (such as traffic congestion, weather changes, or unexpected order insertions), the route optimization module 4 can adjust delivery routes and resource allocation in real time to ensure smooth delivery and minimize delays. It also optimizes routes based on parameters such as travel distance and fuel consumption to ensure that delivery needs are met while reducing operating costs and environmental impact, achieving fuel savings and reduced carbon emissions.

[0030] The real-time monitoring and adjustment module 5 tracks the location, delivery progress, and road condition changes of each vehicle to dynamically adjust its delivery route. In other words, the module tracks vehicle location, delivery progress, and road condition changes in real time, promptly readjusting routes when encountering congestion, malfunctions, or delays. In case of emergencies, the module dynamically adjusts the schedule. Specifically, the module tracks the real-time location and route of each delivery vehicle via GPS, continuously updating the delivery status for comprehensive monitoring. It also sets multiple anomaly monitoring indicators (such as vehicle delays, deviations from the planned route, etc.) to automatically send alarm notifications when anomalies occur, enabling rapid response and ensuring the smooth execution of the delivery plan. When real-time data shows changes in delivery status (such as traffic congestion, vehicle malfunctions, etc.), the module immediately notifies the scheduling module to re-optimize resources, ensuring timely delivery.

[0031] The Export Results Module 6 is used to export the final scheduling plan for all vehicles based on the delivery routes of each vehicle. In other words, the Export Results Module 6 exports the final scheduling plan, including specific plans such as order allocation, vehicle dispatching, optimal routes, and estimated delivery times. It also generates a complete delivery report containing daily or weekly delivery data and stores performance analysis reports such as success rate and delay analysis. The intelligent logistics scheduling system 1 can adjust based on this performance data, providing a basis for improvement in the next scheduling. Specifically, the Export Results Module 6 generates reports based on delivery results, including delivery time, cost, and delay status, facilitating managers to evaluate overall delivery efficiency and propose optimization suggestions based on the analysis results (such as improving vehicle utilization on specific road segments or increasing vehicle allocation during certain time periods), helping enterprises continuously optimize logistics resource management.

[0032] Please refer to Figure 2, which is a system block diagram of a smart logistics scheduling system provided in another embodiment of the present invention. The smart logistics scheduling system 100 provided in this embodiment includes a data collection and analysis module 2, an automatic scheduling module 3, a route optimization module 4, a real-time monitoring and adjustment module 5, an export result module 6, a database 7, a communication module 8, a user interface module 9, and a machine learning module 10. The automatic scheduling module 3 is coupled to the data collection and analysis module 2; the route optimization module 4 is coupled to the automatic scheduling module 3; the real-time monitoring and adjustment module 5 is coupled to the route optimization module 4; the export result module 6 is coupled to the real-time monitoring and adjustment module 5; the database 7 is coupled to the data collection and analysis module 2; the communication module 8 is coupled to the data collection and analysis module 2; the user interface module 9 is coupled to the real-time monitoring and adjustment module 5; and the machine learning module 10 is coupled to the user interface module 9. The difference from the smart logistics scheduling system 1 provided in the previous embodiment is that the smart logistics scheduling system 100 provided in this embodiment also includes a user interface module 9 and a machine learning module 10. The remaining modules are the same; therefore, only the user interface module 9 and the machine learning module 10 will be described in detail, while the other modules will not be described in detail.

[0033] User interface module 9 displays the delivery progress of each vehicle and allows for manual adjustments to individual delivery plans. In other words, it provides operators with an interface to view delivery progress, enabling manual intervention and displaying resource utilization, facilitating resource assessment, management, and vehicle monitoring. Specifically, user interface module 9 is a visual dashboard that displays scheduling progress, delivery status, and anomalies, allowing operators to query and monitor delivery status at any time. When anomalies occur or specific delivery requests arise, alarm notifications are automatically sent. Operators can manually adjust delivery plans and set emergency scheduling strategies to flexibly respond to special situations. It also generates detailed delivery reports and performance data to help operators analyze delivery efficiency and resource utilization for subsequent system optimization.

[0034] The machine learning module 10 trains a machine learning model based on historical delivery data and individual delivery plans. This model uses a Long Short-Term Memory (LSTM) model or a Gated Recurrent Unit (GRU) to analyze time-series data, predict potential delivery delays, and respond proactively to improve prediction accuracy and adapt to complex scenarios. In other words, the machine learning module 10 trains the model based on historical delivery data, individual delivery plans, and user feedback, continuously improving scheduling accuracy and route selection strategies to enhance future scheduling precision. Specifically, the machine learning module 10 analyzes past delivery data, using regression analysis, random forests, and other machine learning models to predict future order demand trends and route congestion, thereby improving scheduling accuracy. It also periodically updates model parameters based on the latest delivery data and optimizes the scheduling algorithm. This allows the intelligent logistics scheduling system 100 to continuously adapt to new business needs and changes in the delivery environment, improving scheduling efficiency.

[0035] In summary, this invention improves delivery efficiency through data-driven intelligent scheduling, route optimization, and dynamic resource allocation. It integrates data collection, intelligent scheduling, route optimization, real-time monitoring, and machine learning to achieve continuous optimization. Therefore, it provides dynamic and automated scheduling solutions during the logistics delivery process, significantly improving resource utilization, shortening delivery time, and reducing operating costs. This enhances the reliability, flexibility, and responsiveness of logistics services, meeting the rapidly changing needs of the current logistics industry. Furthermore, through advanced data analysis and intelligent decision-making technologies, the system not only achieves efficient resource utilization but also provides enterprises with forward-looking optimization solutions, achieving continuous improvement.

[0036] 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 methods and techniques disclosed above without departing from the scope of the present invention to create equivalent embodiments. 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 scope of the present invention.

Claims

1. A smart logistics scheduling system, characterized in that, include: A data collection and analysis module is used to collect historical delivery data and delivery environment data, and to analyze the historical delivery data and delivery environment data to generate delivery trend parameters; An automatic scheduling module, coupled to the data collection and analysis module, is used to generate an individual delivery plan for each vehicle based on the delivery trend parameters. A route optimization module, coupled to the automatic scheduling module, is used to generate the delivery route and order delivery sequence for each vehicle based on the individual delivery plan for each vehicle. A real-time monitoring and adjustment module, coupled to the route optimization module, is used to track the location, delivery progress and road condition changes of each vehicle in order to dynamically adjust the delivery route of each vehicle. An export results module, coupled to the real-time monitoring and adjustment module, is used to export the final scheduling plan for all vehicles based on the delivery routes of each vehicle.

2. The intelligent logistics scheduling system as described in claim 1, characterized in that, Including: A database, coupled to the data collection and analysis module, is used to store the historical delivery data and the delivery environment data.

3. The intelligent logistics scheduling system as described in claim 1, characterized in that, Including: A user interface module, coupled to the real-time monitoring and adjustment module, is used to display the delivery progress of each vehicle and to manually adjust the individual delivery plan.

4. The intelligent logistics scheduling system as described in claim 3, characterized in that, Including: A machine learning module, coupled to the user interface module, is used to train a machine learning model based on the historical delivery data and the individual delivery plan.

5. The intelligent logistics scheduling system as described in claim 4, characterized in that, The machine learning model is a long short-term memory model or a gated recurrent unit.

6. The intelligent logistics scheduling system as described in claim 1, characterized in that, Including: A communication module, coupled to the data collection and analysis module, is used to connect to the Internet to receive the delivery environment data.

7. The intelligent logistics scheduling system as described in claim 1, characterized in that, The historical delivery data includes data on the individual delivery plan, delivery route, order delivery sequence, location, and delivery progress for each vehicle.

8. The intelligent logistics scheduling system as described in claim 1, characterized in that, The delivery environment data includes data on road conditions, weather, and traffic.

Images