Rapid and efficient dispatching method for intelligent supply chain logistics

Abstract

The invention discloses an intelligent supply chain logistics rapid and efficient scheduling method, which comprises the following steps: S1, establishing a data-driven intelligent decision: analyzing supply chain data by using big data and artificial intelligence technology to realize intelligent decision of logistics scheduling, and establishing a data-driven intelligent decision through a data mining and machine learning algorithm; demand prediction, transportation route optimization and problem discovery in advance; s2, real-time monitoring and feedback are carried out; s3, an intelligent scheduling algorithm; s4, application of the Internet of Things technology: real-time monitoring and management of transport vehicles and goods are realized by using the Internet of Things, visual management of the logistics transportation process is improved, and information asymmetry and uncertainty are reduced; and S5, cooperative partner collaboration. The scheduling method has high efficiency and rapidness, rapid and efficient logistics transportation arrangement is realized through an intelligent decision and scheduling algorithm, and the overall supply chain efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial supply chain, and in particular to a method for fast and efficient scheduling of intelligent supply chain logistics. Background Art

[0002] Logistics, which originally means "physical distribution" or "goods distribution", is a part of supply chain activities. It is the process of planning, implementing and controlling the efficient and low-cost flow and storage of goods, service consumption and related information from the place of production to the place of consumption in order to meet customer needs. Logistics is centered on warehousing and promotes the synchronization of production and market. Logistics is the whole process of planning, implementing and managing the transportation of raw materials, semi-finished products, finished products and related information from the place of production to the place of consumption of goods at the lowest cost through transportation, storage and distribution in order to meet customer needs. Supply chain scheduling is a global optimization decision-making problem of supply chain members at the operational level, including operation processing scheduling and transportation arrangement. It finds the scheduling plan that can optimize the efficiency of the entire supply chain by establishing a unified optimization decision-making model. Supply chain scheduling can bring significant economic benefits to the supply chain and its member companies. At present, in the replenishment ordering links of upstream and downstream suppliers in the supply chain, inventory is generally arranged based on the current inventory, future needs, arrival time, and historical data such as year-on-year and month-on-month sales.

[0003] In the process of supply chain management, due to the large variety of logistics products, high frequency of mobilization, large information differences between logistics products, scattered distribution of supply areas, and the inefficiency of conventional supply chain logistics management methods, it is impossible to achieve dynamic supervision and optimization for many logistics products, and the overall degree of intelligence is low. Therefore, a fast and efficient scheduling method for intelligent supply chain logistics is proposed. Summary of the invention

[0004] 1. Technical issues to be solved

[0005] In view of the shortcomings of the prior art, the present invention provides a fast and efficient scheduling method for intelligent supply chain logistics, which solves the following problems: in the management process of the supply chain, due to the large variety of logistics products, high mobilization frequency, large information differences between logistics products, scattered distribution of supply areas, and the low efficiency of conventional supply chain logistics management methods, dynamic supervision and optimization cannot be achieved for numerous logistics products, and the overall intelligence level is low.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a fast and efficient scheduling method for intelligent supply chain logistics, including the following methods:

[0008] S1, establish data-driven intelligent decision-making: use big data and artificial intelligence technology to analyze supply chain data, realize intelligent decision-making in logistics scheduling, and predict demand, optimize transportation routes, and discover problems in advance through data mining and machine learning algorithms;

[0009] S2, real-time monitoring and feedback: establish a real-time monitoring system to track and monitor the logistics and transportation links in real time, find problems and make adjustments in time, introduce a feedback mechanism, collect and analyze data from each link, and provide a basis for scheduling decisions;

[0010] S3, intelligent scheduling algorithm: uses advanced scheduling algorithms to optimize the scheduling of supply chain logistics and achieve fast and efficient transportation arrangements;

[0011] S4, Application of Internet of Things Technology: Using the Internet of Things to achieve real-time monitoring and management of transport vehicles and goods, improve the visual management of the logistics and transportation process, and reduce information asymmetry and uncertainty;

[0012] S5, Partner Collaboration: Establish close cooperative relationships with partners in all links of the supply chain, share information, resources and technology, and achieve collaborative optimization of the entire supply chain.

[0013] As a further preferred embodiment of the present invention, in step S1, data-driven intelligent decision-making is used in supply chain logistics, combined with big data and artificial intelligence technology, and the following specific methods are adopted to realize intelligent decision-making of logistics scheduling:

[0014] Demand forecasting: Time series analysis, using historical sales data and other relevant factors, using time series analysis methods including ARIMA and Exponential Smoothing to predict future demand trends;

[0015] Machine learning models: Modeling multidimensional data using regression analysis, random forests, or neural networks to predict demand for different products over different time periods;

[0016] Transportation route optimization: using genetic algorithms or ant colony algorithm metaheuristic algorithms to optimize transportation routes to minimize total transportation cost or time;

[0017] Constrained optimization: Considering constraints such as vehicle capacity and time window, using optimization algorithms, integer programming, and linear programming to find the best transportation route;

[0018] Problem warning and anomaly detection: Anomaly detection model: Use anomaly detection algorithms, including isolation forest and LOF algorithms, to monitor abnormal situations in the logistics process, such as delays and damages, so as to take timely measures;

[0019] Early warning system: Establish an early warning system that combines rule engines and machine learning models to monitor data in real time and issue alerts to detect potential problems in advance;

[0020] Real-time scheduling: Use streaming data processing technology to process and analyze data collected in real time to support real-time scheduling decisions;

[0021] Reinforcement learning: With the help of reinforcement learning algorithms, decision optimization is performed in real-time scheduling, and decision strategies are dynamically adjusted according to environmental feedback to achieve more efficient logistics scheduling.

[0022] As a further preferred embodiment of the present invention, in step S2, the real-time monitoring system includes a cloud platform module, a sensor module, a data acquisition module, a visualization chart module, and a threshold warning module.

[0023] The sensor module deploys sensors and IoT devices at key nodes such as trucks, warehouses, and loading and unloading equipment to monitor location, temperature, humidity, vehicle speed, and status in real time, including GPS sensors, temperature sensors, humidity sensors, and acceleration sensors;

[0024] The data collection module sets the data collection frequency and ensures the real-time transmission of data to the cloud platform module, using 5G to achieve rapid data transmission and real-time update;

[0025] The threshold warning module sets the threshold and alarm mechanism. When the data is abnormal or exceeds the set range, the system can automatically trigger an alarm notification.

[0026] As a further preferred embodiment of the present invention, in step S3, the scheduling algorithm includes the following: initializing the population: randomly generating a set of initial solutions, i.e., different delivery route plans, each of which can be regarded as a chromosome; fitness function: defining a fitness function to evaluate the pros and cons of each chromosome delivery route plan, usually based on indicators such as cost and time; selection: based on the evaluation result of the fitness function, selecting chromosomes with higher fitness as parents, performing crossover and mutation operations to generate new offspring; crossover and mutation: through crossover and mutation operations, combining and changing the chromosome information of the parents to generate new chromosomes, representing new delivery route plans. Repeated evolution: repeating the selection, crossover and mutation operations until the stopping condition is reached.

[0027] As a further preferred embodiment of the present invention, in step S4, all cargo and vehicle information is stored in warehouse management using the Internet of Things to achieve comprehensive information sharing and management.

[0028] As a further preferred embodiment of the present invention, step S5 also includes determining key partners in the supply chain, including suppliers, manufacturers, logistics service providers, and retailers, and establishing an information sharing mechanism: formulating information sharing policies and mechanisms to ensure timely and accurate sharing of key information, including inventory data, order information, and transportation status, ensuring the interoperability of data, collaborative decision-making and planning, jointly formulating logistics scheduling plans, formulating risk response plans, and ensuring the stable operation of the supply chain.

[0029] (III) Beneficial effects

[0030] The present invention provides a fast and efficient scheduling method for intelligent supply chain logistics. It has the following beneficial effects:

[0031] The scheduling method of the present invention is efficient and rapid. Through intelligent decision-making and scheduling algorithms, fast and efficient logistics and transportation arrangements can be achieved, and the overall supply chain efficiency can be improved. Through the real-time monitoring system, problems can be discovered and adjusted in a timely manner, thereby reducing potential transportation delays and problems. Big data and machine learning technologies are used to predict demand and optimize transportation routes, improve the accuracy and efficiency of decision-making, monitor cargo and vehicle information in real time, reduce information asymmetry and uncertainty, improve the level of visual management of the transportation process, and establish close cooperative relationships and information sharing mechanisms to promote collaborative decision-making and planning among partners, thereby ensuring the stable operation and optimization of the supply chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0033] Figure 2 It is a schematic diagram of the principle framework of the real-time monitoring system of the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] See also Figure 1-2 , the embodiment of the present invention provides a technical solution:

[0036] A fast and efficient scheduling method for intelligent supply chain logistics, including the following methods:

[0037] S1, establish data-driven intelligent decision-making: use big data and artificial intelligence technology to analyze supply chain data, realize intelligent decision-making in logistics scheduling, and predict demand, optimize transportation routes, and discover problems in advance through data mining and machine learning algorithms;

[0038] S2, real-time monitoring and feedback: establish a real-time monitoring system to track and monitor the logistics and transportation links in real time, find problems and make adjustments in time, introduce a feedback mechanism, collect and analyze data from each link, and provide a basis for scheduling decisions;

[0039] S3, intelligent scheduling algorithm: uses advanced scheduling algorithms to optimize the scheduling of supply chain logistics and achieve fast and efficient transportation arrangements;

[0040] S4, Application of Internet of Things Technology: Using the Internet of Things to achieve real-time monitoring and management of transport vehicles and goods, improve the visual management of the logistics and transportation process, and reduce information asymmetry and uncertainty;

[0041] S5, Partner Collaboration: Establish close cooperative relationships with partners in all links of the supply chain, share information, resources and technology, and achieve collaborative optimization of the entire supply chain.

[0042] In step S1, data-driven intelligent decision-making is used in supply chain logistics, combined with big data and artificial intelligence technology, and the following specific methods are adopted to realize intelligent decision-making in logistics scheduling:

[0043] Demand forecasting: Time series analysis, using historical sales data and other relevant factors, using time series analysis methods including ARIMA and Exponential Smoothing to predict future demand trends;

[0044] Machine learning models: Modeling multidimensional data using regression analysis, random forests, or neural networks to predict demand for different products over different time periods;

[0045] Transportation route optimization: using genetic algorithms or ant colony algorithm metaheuristic algorithms to optimize transportation routes to minimize total transportation cost or time;

[0046] Constrained optimization: Considering constraints such as vehicle capacity and time window, using optimization algorithms, integer programming, and linear programming to find the best transportation route;

[0047] Problem warning and anomaly detection: Anomaly detection model: Use anomaly detection algorithms, including isolation forest and LOF algorithms, to monitor abnormal situations in the logistics process, such as delays and damages, so as to take timely measures;

[0048] Early warning system: Establish an early warning system that combines rule engines and machine learning models to monitor data in real time and issue alerts to detect potential problems in advance;

[0049] Real-time scheduling: Use streaming data processing technology to process and analyze data collected in real time to support real-time scheduling decisions;

[0050] Reinforcement learning: With the help of reinforcement learning algorithms, decision optimization is performed in real-time scheduling, and decision strategies are dynamically adjusted according to environmental feedback to achieve more efficient logistics scheduling.

[0051] In step S2, the real-time monitoring system includes a cloud platform module, a sensor module, a data acquisition module, a visualization chart module, and a threshold warning module.

[0052] Sensor module, deploying sensors and IoT devices at key nodes such as trucks, warehouses, loading and unloading equipment to monitor location, temperature, humidity, speed, and status in real time, including GPS sensors, temperature sensors, humidity sensors, and acceleration sensors;

[0053] The data collection module sets the data collection frequency and ensures the real-time transmission of data to the cloud platform module, using 5G to achieve fast data transmission and real-time update;

[0054] The threshold warning module sets the threshold and alarm mechanism. When the data is abnormal or exceeds the set range, the system can automatically trigger an alarm notification.

[0055] In step S3, the scheduling algorithm includes the following: Initialize the population: randomly generate a set of initial solutions, that is, different delivery route plans, each of which can be regarded as a chromosome. Fitness function: define a fitness function to evaluate the pros and cons of each chromosome delivery route plan, usually based on indicators such as cost and time. Selection: according to the evaluation result of the fitness function, select the chromosome with higher fitness as the parent generation, perform crossover and mutation operations, and generate new offspring. Crossover and mutation: through crossover and mutation operations, combine and change the chromosome information of the parent generation to generate a new chromosome, representing a new delivery route plan. Repeat evolution: repeat the selection, crossover and mutation operations until the stop condition is reached.

[0056] In step S4, all cargo and vehicle information is stored in warehouse management using the Internet of Things to achieve comprehensive information sharing and management.

[0057] Step S5 also includes identifying key partners in the supply chain, including suppliers, manufacturers, logistics service providers, and retailers, and establishing an information sharing mechanism: formulating information sharing policies and mechanisms to ensure timely and accurate sharing of key information, including inventory data, order information, and transportation status, ensuring data interoperability, collaborative decision-making and planning, jointly formulating logistics scheduling plans, formulating risk response plans, and ensuring the stable operation of the supply chain.

[0058] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0059] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A fast and efficient scheduling method for intelligent supply chain logistics, characterized by: The following methods are included: S1, establish data-driven intelligent decision-making: use big data and artificial intelligence technology to analyze supply chain data, realize intelligent decision-making in logistics scheduling, and predict demand, optimize transportation routes, and discover problems in advance through data mining and machine learning algorithms; S2, real-time monitoring and feedback: establish a real-time monitoring system to track and monitor the logistics and transportation links in real time, find problems and make adjustments in time, introduce a feedback mechanism, collect and analyze data from each link, and provide a basis for scheduling decisions; S3, intelligent scheduling algorithm: uses advanced scheduling algorithms to optimize the scheduling of supply chain logistics and achieve fast and efficient transportation arrangements; S4, Application of Internet of Things Technology: Using the Internet of Things to achieve real-time monitoring and management of transport vehicles and goods, improve the visual management of the logistics and transportation process, and reduce information asymmetry and uncertainty; S5, Partner Collaboration: Establish close cooperative relationships with partners in all links of the supply chain, share information, resources and technology, and achieve collaborative optimization of the entire supply chain.

2. According to claim 1, a fast and efficient intelligent supply chain logistics scheduling method is characterized by: In step S1, data-driven intelligent decision-making is used in supply chain logistics, combined with big data and artificial intelligence technology, and the following specific methods are adopted to realize intelligent decision-making in logistics scheduling: Demand forecasting: Time series analysis, using historical sales data and other relevant factors, using time series analysis methods including ARIMA and Exponential Smoothing to predict future demand trends; Machine learning models: Modeling multidimensional data using regression analysis, random forests, or neural networks to predict demand for different products over different time periods; Transportation route optimization: using genetic algorithms or ant colony algorithm metaheuristic algorithms to optimize transportation routes to minimize total transportation cost or time; Constrained optimization: Considering constraints such as vehicle capacity and time window, using optimization algorithms, integer programming, and linear programming to find the best transportation route; Problem warning and anomaly detection: Anomaly detection model: Use anomaly detection algorithms, including isolation forest and LOF algorithms, to monitor abnormal situations in the logistics process, such as delays and damages, so as to take timely measures; Early warning system: Establish an early warning system that combines rule engines and machine learning models to monitor data in real time and issue alerts to detect potential problems in advance; Real-time scheduling: Use streaming data processing technology to process and analyze data collected in real time to support real-time scheduling decisions; Reinforcement learning: With the help of reinforcement learning algorithms, decision optimization is performed in real-time scheduling, and decision strategies are dynamically adjusted according to environmental feedback to achieve more efficient logistics scheduling.

3. According to claim 1, a fast and efficient scheduling method for intelligent supply chain logistics is characterized by: In step S2, the real-time monitoring system includes a cloud platform module, a sensor module, a data acquisition module, a visualization chart module, and a threshold warning module. The sensor module deploys sensors and IoT devices at key nodes such as trucks, warehouses, and loading and unloading equipment to monitor location, temperature, humidity, vehicle speed, and status in real time, including GPS sensors, temperature sensors, humidity sensors, and acceleration sensors; The data collection module sets the data collection frequency and ensures the real-time transmission of data to the cloud platform module, using 5G to achieve rapid data transmission and real-time update; The threshold warning module sets the threshold and alarm mechanism. When the data is abnormal or exceeds the set range, the system can automatically trigger an alarm notification.

4. According to claim 1, a fast and efficient intelligent supply chain logistics scheduling method is characterized by: In step S3, the scheduling algorithm includes the following: Initialize the population: randomly generate a set of initial solutions, that is, different delivery route plans, each plan can be regarded as a chromosome, fitness function: define a fitness function to evaluate the pros and cons of each chromosome delivery route plan, usually based on indicators such as cost and time, selection: according to the evaluation result of the fitness function, select the chromosome with higher fitness as the parent generation, perform crossover and mutation operations, and generate new offspring, crossover and mutation: through crossover and mutation operations, combine and change the chromosome information of the parent generation to generate a new chromosome, representing a new delivery route plan. Repeat evolution: repeat the selection, crossover and mutation operations until the stop condition is reached.

5. According to claim 1, a fast and efficient scheduling method for intelligent supply chain logistics is characterized by: In step S4, all cargo and vehicle information is stored in warehouse management using the Internet of Things to achieve comprehensive information sharing and management.

6. The method for fast and efficient scheduling of intelligent supply chain logistics according to claim 1 is characterized by: Step S5 also includes identifying key partners in the supply chain, including suppliers, manufacturers, logistics service providers, and retailers, and establishing an information sharing mechanism: formulating information sharing policies and mechanisms to ensure timely and accurate sharing of key information, including inventory data, order information, and transportation status, ensuring data interoperability, collaborative decision-making and planning, jointly formulating logistics scheduling plans, formulating risk response plans, and ensuring the stable operation of the supply chain.

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