Dynamic scheduling method for concrete transportation tank truck

Abstract

The invention discloses a dynamic scheduling method for a concrete transportation tank truck, and relates to the technical field of intelligent scheduling. According to the method, the position and state information of the concrete transportation tank truck is acquired in real time, the demand data of each construction site is combined, the clustering algorithm and the priority algorithm are utilized to generate the efficient demand list and the scheduling plan, and the scheduling efficiency problem under the condition that multiple construction sites are required at the same time is solved; the optimal driving path is determined through a Dijkstra algorithm and real-time traffic data, congestion is effectively avoided, fuel consumption and vehicle loss are reduced, and therefore the transportation cost is reduced; besides, a multi-objective optimization model and a genetic algorithm are adopted, fuel consumption, vehicle loss and an unloaded driving rate are comprehensively considered, a task allocation scheme is dynamically adjusted, the flexibility and robustness of the scheduling method are improved, the timeliness and reliability of concrete supply are ensured under variable traffic and demand conditions, and the scheduling efficiency is improved. And finally, optimization of the concrete transportation process is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent scheduling, and in particular to a dynamic scheduling method for concrete transport tank trucks. Background Art

[0002] During the scheduling of concrete transport tank trucks, when multiple construction sites simultaneously issue concrete demands, the scheduling system needs to obtain real-time information such as the location, status, and remaining capacity of each tank truck, and generate a preliminary task allocation plan based on factors such as the urgency of the demand, concrete usage, and distance at each construction site. However, in this process, dynamic changes in traffic conditions will directly affect the transportation efficiency of the tank trucks. For example, sudden congestion on a certain section of road may cause delays in the original route, thereby affecting the construction progress of the construction site. At the same time, cost factors such as the tank truck's fuel consumption and vehicle wear and tear also need to be considered in scheduling decisions to avoid increased costs due to frequent lane changes or detours. In addition, the time windows for concrete demand at different construction sites may overlap or conflict. The scheduling system needs to minimize the empty driving rate and waiting time of the tank trucks while meeting the construction site's time requirements. Summary of the Invention

[0003] The purpose of the present invention is to provide a dynamic scheduling method for concrete transport tank trucks. Through real-time monitoring, path optimization and multi-objective optimization model, the efficiency, cost and adaptability problems in the scheduling process of concrete transport tank trucks are solved, the overall transportation efficiency is improved and the operating costs are reduced.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] This application provides a method for dynamic scheduling of concrete transport tank trucks, comprising the following steps:

[0006] Obtain real-time location information and status data of all tank trucks, including remaining capacity and current task completion status, to form a tank truck resource pool. Then, extract the concrete demand, demand time window, and geographic location information of each construction site from the construction site order system to generate a demand list.

[0007] Based on the tanker resource pool and demand list, the estimated travel time for each tanker to each construction site is calculated, and the Dijkstra algorithm combined with real-time traffic data is used to determine the optimal route;

[0008] By establishing a multi-objective optimization model and combining real-time traffic data and vehicle status to dynamically adjust task allocation, we can obtain the optimization results.

[0009] Among them, when the demand time window of a construction site conflicts with the assigned tasks, the availability of other tank trucks is re-evaluated and the task allocation plan is adjusted.

[0010] Furthermore, the real-time location information and status data of all tank trucks, including remaining capacity and current task completion status, are obtained to form a tank truck resource pool, specifically including:

[0011] Install positioning equipment in the acquisition system to obtain the tanker's location information in real time, monitor the tanker's remaining capacity through sensors, and transmit the data to the data processing module;

[0012] In the data processing module, the location information and remaining capacity are associated and stored, the task status information of the tanker is obtained, and updated to the task progress field in the data processing module; when the task status is completed, the vehicle status is updated to available, otherwise it is marked as working;

[0013] In the resource pool, a list of available tank trucks is generated based on location information, remaining capacity, and vehicle status. A clustering algorithm is used to group the available tank trucks to optimize resource allocation efficiency.

[0014] Furthermore, the concrete demand, demand time window, and geographic location information of each construction site are extracted from the construction site order system to generate a demand list, specifically including:

[0015] The preset data interface is used to extract the concrete demand quantity, demand time window and geographical location information from the construction site order system to obtain the original demand data. Based on the geographical location information in the original demand data, the distance between each construction site and the concrete supply point is calculated to generate a distance list.

[0016] Combined with the demand time window and distance value, a priority algorithm is used to sort the demand list and determine the scheduling priority. When the demand time window is approaching, the status code of the corresponding order is marked as urgent, and an urgent order list is generated;

[0017] According to the urgent order list and scheduling priority, a clustering algorithm is used to group the orders and generate a scheduling plan.

[0018] Furthermore, after extracting the concrete demand, demand time window, and geographic location information of each construction site from the construction site order system and generating a demand list, the system also includes: predicting future concrete demand through a machine learning model to optimize the task allocation plan. The specific steps include:

[0019] Collect historical order data, construction progress information and seasonal factors as input features, and use deep learning models to build a prediction model for the input features.

[0020] By establishing a forecasting model, we can predict the concrete demand within the future time window, and obtain the concrete demand and its time distribution at each construction site in the next week. Then, combined with real-time order data, we can adjust the task allocation plan in advance.

[0021] Furthermore, based on the tanker resource pool and demand list, the estimated travel time for each tanker to each construction site is calculated. The Dijkstra algorithm combined with real-time traffic data is used to determine the optimal route, including:

[0022] Real-time data on road length, speed limits, and congestion is collected and abstracted into a weighted directed graph model. Nodes represent intersections or key locations, edges represent road segments, and edge weights represent estimated travel times. Actual travel speeds are dynamically adjusted based on real-time traffic data to reflect current road conditions.

[0023] For each tank truck, the current location is set as the starting point and the target construction site location is set as the end point. In the graph model, the distance from the starting point to itself is initialized to 0, and the distances of all other nodes are initialized to infinity. At the same time, a minimum heap is used to create a priority queue to store unvisited nodes and their current shortest distance estimates. The priority queue is sorted from smallest to largest distance, and the node with the smallest current distance is extracted.

[0024] The node with the shortest current distance is taken from the priority queue, and all its adjacent nodes are traversed. The estimated time to reach the adjacent node through the current node is calculated through relaxation operations. If the new path is shorter, the estimated shortest distance value of the adjacent node is updated and the adjacent node is added back to the priority queue. In each relaxation operation, the edge weight is dynamically adjusted according to real-time traffic data. When the target node is visited or the priority queue is empty, the algorithm ends and the shortest path and estimated travel time from the starting point to the end point are obtained.

[0025] After the Dijkstra algorithm is completed, the shortest path from the current position of the tanker to the target construction site and its estimated travel time are extracted from the graph model, and the complete path planning information is generated in combination with the estimated travel time.

[0026] Furthermore, the multi-objective optimization model uses demand urgency, concrete quantity, distance, and traffic conditions as constraints, and fuel consumption, vehicle wear and tear, and idle rate as optimization objectives, and then adopts a genetic algorithm to solve it. The specific steps include: randomly generating a set of solutions as the initial population, calculating the fitness value of each individual, and evaluating its quality based on the objective function; generating a new population through tournament selection, crossover, and mutation operations; and sorting according to the non-dominated relationship of individuals to select the optimal solution.

[0027] Furthermore, by establishing a multi-objective optimization model, we can dynamically adjust task allocation by combining real-time traffic data and vehicle status, including:

[0028] Obtain real-time traffic data and vehicle status information, use a multi-objective optimization model to calculate the initial task allocation plan, and determine whether there is a time conflict with the assigned tasks for the specific time window required by the construction site;

[0029] When there is a time conflict, the real-time status data of all tank trucks is extracted to conduct an availability assessment and analysis. Based on the availability analysis results, a tank truck combination that meets the time window requirements and has the best transportation efficiency is selected;

[0030] Recalculate the task allocation plan, generate new transportation routes and schedules, and match and verify the updated task allocation plan with real-time traffic data.

[0031] Furthermore, after dynamically adjusting task allocation, the process also includes: using a multi-scenario optimization method, combining real-time traffic data, weather forecasts, and vehicle status information, building multiple operating scenarios based on historical data and prediction models, and using robust optimization technology to adjust the multi-objective optimization model for each scenario. The specific steps include:

[0032] Generate multiple operating scenarios through Monte Carlo simulation or probability distribution-based methods. Each scenario is represented as a state vector that contains traffic data, weather forecast, and vehicle status information.

[0033] Introducing scenario variables S into the multi-objective optimization model i , the optimization objective of scheduling scheme X is expanded to the comprehensive performance under different scenarios, and the optimization objective is expressed as:

[0034] Where N is the number of scenes, w i is the scene variable S i The weight of f(X, S i ) is the scheduling scheme X in the scenario variable S i The objective function value under , represents the performance index;

[0035] During the optimization process, robustness constraints are introduced to ensure that the scheduling scheme can be effectively executed in different scenarios and to limit the fluctuation range of key performance indicators:

[0036] Among them S avg is the average scenario, ∈ is the maximum allowed fluctuation range, f(X, S avg ) represents the average performance index.

[0037] A preliminary dispatch plan is generated based on the optimization results, and the task assignment information is sent to the corresponding tanker's onboard terminal. During the task execution, the tanker's location and traffic conditions are continuously monitored. When a major delay is detected, a dynamic rescheduling mechanism is triggered.

[0038] Through historical data and real-time information, traffic condition prediction models are updated, or more accurate route time estimates are obtained. Fuel consumption, vehicle losses, and task completion status during each dispatch process are recorded to optimize model parameter adjustments and performance evaluation.

[0039] The beneficial effects of the present invention are:

[0040] The present invention forms a tanker resource pool by acquiring the real-time location information and status data of all tankers, including remaining capacity and current task completion status. It then extracts the concrete demand, demand time window, and geographic location information of each construction site from the construction site order system to generate a demand list. This step ensures that the scheduling method can grasp tanker resources and construction site needs in real time, thereby quickly generating a preliminary task allocation plan and effectively solving transportation efficiency issues caused by dynamic changes in traffic conditions. A clustering algorithm is used to group available tankers to optimize resource allocation efficiency, further ensuring the timely supply of concrete to meet the construction site's demand time window.

[0041] The Dijkstra algorithm, combined with real-time traffic data, determines the optimal route. This not only optimizes the tanker's route but also dynamically adjusts its speed to reflect current road conditions, thereby reducing fuel consumption and vehicle wear and tear caused by detours or idling. This effectively lowers transportation costs, improves efficiency, and minimizes environmental impact.

[0042] By establishing a multi-objective optimization model and using a genetic algorithm to solve it, multiple constraints such as demand urgency, concrete quantity, distance, traffic conditions, as well as optimization objectives such as fuel consumption, vehicle wear and tear, and empty driving rate are taken into consideration. In addition, a multi-scenario optimization method and robustness constraints are introduced to enable the scheduling plan to be effectively executed in different scenarios, limiting the fluctuation range of key performance indicators. This significantly improves the intelligence level and adaptability of the concrete transportation scheduling system, and can respond to changes in traffic conditions and fluctuations in construction site demand in real time, thereby improving the on-time rate of concrete delivery and reducing the risk of construction site shutdowns, bringing higher operational efficiency and customer satisfaction to the concrete transportation industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] For better understanding and implementation, the technical solution of the present application is described in detail below with reference to the accompanying drawings.

[0044] Figure 1 A schematic flow chart of a dynamic scheduling method for concrete transport tank trucks provided in Example 1 of the present application;

[0045] Figure 2 A schematic diagram of a flow chart of a dynamic scheduling method for concrete transport tank trucks to form a tank truck resource pool provided in Example 1 of the present application;

[0046] Figure 3 A schematic diagram of a flow chart for determining an optimal path for a dynamic scheduling method for concrete transport tank trucks provided in Example 1 of the present application;

[0047] Figure 4 A schematic flow chart of a dynamic adjustment of task allocation in a dynamic scheduling method for concrete transport tank trucks provided in Example 1 of the present application. DETAILED DESCRIPTION

[0048] To further illustrate the technical means and effectiveness of the present invention in achieving its intended purpose, exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present application. Rather, they are merely examples of methods and systems consistent with certain aspects of the present application, as detailed in the appended claims.

[0049] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. As used in this application and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0050] The following describes in detail the specific implementation methods, features and effects of the present invention in conjunction with the accompanying drawings and preferred embodiments.

[0051] Example 1

[0052] See also Figures 1-4 This embodiment provides a method for dynamic scheduling of concrete transport tank trucks, comprising the following steps:

[0053] S1. Obtain the real-time location and status data of all tank trucks, including remaining capacity and current task completion status, to form a tank truck resource pool. Then, extract the concrete demand, demand time window, and geographic location information of each construction site from the construction site order system to generate a demand list.

[0054] Furthermore, the real-time location information and status data of all tank trucks, including remaining capacity and current task completion status, are obtained to form a tank truck resource pool, specifically including:

[0055] S11. Install a positioning device in the acquisition system to obtain the location information of the tank truck in real time, monitor the remaining capacity of the tank truck through sensors, and transmit the data to the data processing module;

[0056] S12. In the data processing module, the location information and the remaining capacity are associated and stored, the task status information of the tanker is obtained, and the task status information is updated to the task progress field in the data processing module; if the task status is completed, the vehicle status is updated to available; otherwise, it is marked as working;

[0057] S13. In the resource pool, a list of available tank trucks is generated based on location information, remaining capacity, and vehicle status. A clustering algorithm is used to group the available tank trucks to optimize resource allocation efficiency.

[0058] The basis for clustering includes the tanker's current location, remaining capacity, and distance from the construction site, so that the vehicle is assigned to different task areas.

[0059] Specifically, when clustering tank trucks based on their current location, remaining capacity, and distance to each construction site, the goal is to identify groups of tank trucks with similar geographical locations or similar transportation needs, and then assign them to specific task areas. This not only reduces the empty driving distance of tank trucks and improves transportation efficiency, but also ensures the timely supply of concrete to meet the construction site's demand time window; the clustering results will directly affect the generation of scheduling plans, enabling the scheduling system to manage tank truck resources more intelligently and efficiently, and optimize task allocation and route planning.

[0060] By monitoring the location and status of tank trucks in real time, accurate control of transportation resources is ensured, thereby improving the accuracy and response speed of scheduling. The application of clustering algorithms optimizes the allocation of tank truck resources, allowing vehicles to be reasonably grouped according to their geographical location and remaining capacity and assigned to the construction sites where they are most needed. This not only reduces the empty driving rate and driving distance of tank trucks, but also reduces fuel consumption and vehicle wear and tear. In addition, by dynamically adjusting the task allocation plan, it can flexibly respond to changes in construction site demand and fluctuations in traffic conditions, thereby ensuring the timeliness and reliability of concrete supply.

[0061] Furthermore, the concrete demand, demand time window, and geographic location information of each construction site are extracted from the construction site order system to generate a demand list, specifically including:

[0062] The preset data interface is used to extract the concrete demand quantity, demand time window and geographical location information from the construction site order system to obtain the original demand data. Based on the geographical location information in the original demand data, the distance between each construction site and the concrete supply point is calculated to generate a distance list.

[0063] Combined with the demand time window and distance value, a priority algorithm is used to sort the demand list and determine the scheduling priority. When the demand time window is approaching, the status code of the corresponding order is marked as urgent, and an urgent order list is generated;

[0064] According to the urgent order list and scheduling priority, a clustering algorithm is used to group the orders and generate a scheduling plan.

[0065] This also includes obtaining the remaining capacity and location information of available tank trucks for each group of orders in the scheduling plan, matching the optimal tank truck resources for each group of orders through a resource matching algorithm (such as a greedy algorithm or linear programming), and generating specific scheduling instructions based on the matching results and the scheduling plan to update the order status code and tank truck task status; the scheduling instructions include information such as the tank truck's task allocation, starting location, destination, transportation route, and task priority.

[0066] Specifically, when matching the optimal tanker resources for each group of orders, the greedy algorithm or linear programming algorithm will take into account the tanker's remaining capacity, current location, and site requirements to minimize transportation costs and time, while ensuring that all orders can be met within the demand time window. The most suitable tanker will be assigned to each order, and the optimal transportation route will be determined, thereby generating detailed scheduling instructions to guide the tanker to perform tasks.

[0067] At the same time, it achieves efficient scheduling of concrete transportation, solves the problems of slow response, high cost and waste of resources in traditional scheduling, and can quickly respond to urgent needs of construction sites. Through intelligent matching and route planning, it reduces the idle driving and waiting time of tank trucks, improves transportation efficiency, and reduces fuel consumption and vehicle loss by optimizing the utilization of tank truck resources, achieving cost savings. In addition, it enhances the ability to adapt to changes in traffic conditions, ensures the timely supply of concrete, reduces the risk of site shutdowns, and thus improves the reliability and flexibility of the entire supply chain.

[0068] Furthermore, after extracting the concrete demand, demand time window, and geographic location information of each construction site from the construction site order system and generating a demand list, the system also includes: predicting future concrete demand through machine learning models (such as time series analysis and deep learning models) to optimize the task allocation plan. The specific steps include:

[0069] Collect historical order data, construction progress information and seasonal factors as input features, and use deep learning models to build a prediction model for the input features.

[0070] By establishing a forecasting model, we can predict the concrete demand within the future time window, and obtain the concrete demand and its time distribution at each construction site in the next week. Then, combined with real-time order data, we can adjust the task allocation plan in advance.

[0071] Specifically, it significantly improves the intelligence level and response speed of the concrete transportation scheduling system. By predicting future demand, it can more proactively plan resources and allocate tasks, reducing the waste of transportation resources and the risk of construction site shutdowns caused by demand fluctuations. At the same time, this method can also help companies better understand demand patterns, optimize inventory management and production plans, thereby improving overall operational efficiency and customer satisfaction.

[0072] S2. Calculate the estimated travel time for each tanker to each construction site based on the tanker resource pool and demand list, and use the Dijkstra algorithm combined with real-time traffic data to determine the optimal route;

[0073] Furthermore, based on the tanker resource pool and demand list, the estimated travel time for each tanker to each construction site is calculated. The Dijkstra algorithm combined with real-time traffic data is used to determine the optimal route, including:

[0074] S21. Real-time data on road length, speed limits, congestion, and other information is acquired and abstracted into a weighted directed graph model, where nodes represent road intersections or key locations, edges represent road segments, and edge weights are estimated travel times, calculated using the formula "estimated travel time = road length / actual travel speed." The actual travel speed is dynamically adjusted based on real-time traffic data to reflect current road conditions. Furthermore, a data update mechanism is established to regularly obtain the latest information from the traffic platform and dynamically update the edge weights in the graph model to ensure accurate path planning.

[0075] S22. For each tank truck, set its current location as the starting point and the target construction site location as the end point. In the graph model, initialize the distance from the starting point to itself to 0, and the distances of all other nodes to infinity. At the same time, use a minimum heap to create a priority queue, storing unvisited nodes and their current shortest distance estimates. The priority queue is sorted from smallest to largest distance, quickly extracting the node with the shortest current distance, and providing efficient data structure support for the operation of the Dijkstra algorithm.

[0076] S23. Take the node with the shortest current distance from the priority queue, traverse all its adjacent nodes, and calculate the estimated time to reach the adjacent node through the current node through a relaxation operation. If the new path is shorter, update the estimated shortest distance of the adjacent node and add the adjacent node back to the priority queue. In each relaxation operation, dynamically adjust the edge weight according to real-time traffic data. For example, when a certain road section is congested, reduce the driving speed of the road section in real time and update the weight, thereby dynamically adjusting the optimal path. When the target node is visited or the priority queue is empty, the algorithm ends, and the shortest path and estimated travel time from the starting point to the end point are obtained.

[0077] After S24 and the Dijkstra algorithm are completed, the shortest path from the tanker's current location to the target construction site and its estimated travel time are extracted from the graph model. The optimal path is broken down into a series of navigation instructions, such as "Drive north for 3 kilometers and turn left onto XX Road," and combined with the estimated travel time to generate a complete path planning information.

[0078] Specifically, the Dijkstra algorithm-based path planning process optimizes routes during concrete transportation, effectively addressing the shortcomings of traditional path planning methods in the face of real-time traffic changes. This method can dynamically adapt to changing traffic conditions, such as congestion and road construction, thereby reducing transportation time and fuel consumption and improving transportation efficiency. In addition, through precise path planning and estimated travel times, dispatchers can better arrange and adjust task allocations, reducing waiting time at construction sites and improving customer satisfaction. This process also helps reduce operating costs by reducing unnecessary idle driving and idling time while improving the utilization of tanker resources. This advanced path planning method brings a higher level of intelligence and operational efficiency to the concrete transportation industry.

[0079] S3. By establishing a multi-objective optimization model, dynamically adjust task allocation based on real-time traffic data and vehicle status to obtain the optimization results;

[0080] When the time window required by a construction site conflicts with the assigned tasks, the availability of other tank trucks is reassessed and the task allocation plan is adjusted;

[0081] Furthermore, the multi-objective optimization model uses demand urgency, concrete quantity, distance, and traffic conditions as constraints, and fuel consumption, vehicle wear and tear, and idle rate as optimization objectives, and then adopts a genetic algorithm to solve it. The specific steps include: randomly generating a set of solutions as the initial population, calculating the fitness value of each individual, and evaluating its quality based on the objective function; generating a new population through tournament selection, crossover, and mutation operations; and sorting according to the non-dominated relationship of individuals to select the optimal solution.

[0082] Specifically, in multi-objective optimization models, the reason for using genetic algorithms for solving problems is that genetic algorithms can effectively handle complex multi-objective optimization problems. By simulating natural selection and genetic mechanisms, such as tournament selection, crossover, and mutation operations, genetic algorithms can perform global searches in the solution space and find a set of non-dominated solutions that meet different objectives. This method does not require weighting of objectives and can more naturally handle conflicts between objectives, thereby providing decision makers with more options.

[0083] By solving a multi-objective optimization model using a genetic algorithm, the task allocation of concrete transport tank trucks was optimized, effectively balancing multiple objectives such as fuel consumption, vehicle wear and tear, and idle driving rate. This method improved the flexibility and efficiency of the scheduling system, enabling it to quickly adapt to changes in demand and dynamic adjustments to traffic conditions, thereby reducing transportation costs, improving service quality, and enhancing the ability to respond to emergencies.

[0084] Furthermore, by establishing a multi-objective optimization model, we can dynamically adjust task allocation by combining real-time traffic data and vehicle status, including:

[0085] S31. Obtain real-time traffic data and vehicle status information, use a multi-objective optimization model to calculate an initial task allocation plan, and determine whether there is a time conflict with the assigned tasks for the specific time window required by the construction site;

[0086] S32. When there is a time conflict, extract the real-time status data of all tank trucks, perform availability evaluation and analysis, and select a tank truck combination that meets the time window requirements and has the best transportation efficiency based on the availability analysis results;

[0087] S33. Recalculate the task allocation plan, generate a new transportation route and time schedule, match and verify the updated task allocation plan with real-time traffic data to ensure the feasibility of the plan, and synchronously update the determined task allocation plan to all relevant vehicles and construction site systems.

[0088] Specifically, through multi-objective optimization models and dynamic adjustment strategies, the intelligence level and adaptability of the concrete transportation scheduling system have been significantly improved. It can respond to changes in traffic conditions and fluctuations in construction site demand in real time, and through precise task allocation and route planning, it reduces transportation delays and improves the on-time rate of concrete delivery. In addition, by optimizing the use of tank trucks, unnecessary empty driving and idling time are reduced, thereby reducing fuel consumption and vehicle wear and tear, and achieving cost savings. This strategy also improves construction efficiency at the construction site, because concrete can arrive on time, reducing construction site downtime and waiting time, and bringing higher operational efficiency and customer satisfaction to the concrete transportation industry.

[0089] Furthermore, after dynamically adjusting task allocation, the process also includes: using a multi-scenario optimization method, combining real-time traffic data, weather forecasts, and vehicle status information, building multiple operating scenarios based on historical data and prediction models, and using robust optimization technology to adjust the multi-objective optimization model for each scenario. The specific steps include:

[0090] Generate scenarios such as traffic congestion and severe weather through Monte Carlo simulation or probability distribution-based methods. Each scenario is represented as a state vector that contains traffic data, weather forecast, and vehicle status information.

[0091] Introducing scenario variables S into the multi-objective optimization model i , the optimization objective of scheduling scheme X is expanded to the comprehensive performance under different scenarios, and the optimization objective is expressed as:

[0092] Where N is the number of scenes, w i is the scene variable S i The weight of f(X, S i ) is the scheduling scheme X in the scenario variable S i The objective function value under , represents the performance index;

[0093] During the optimization process, robustness constraints are introduced to ensure that the scheduling scheme can be effectively executed in different scenarios and to limit the fluctuation range of key performance indicators (such as task completion time and fuel consumption).

[0094] Among them S avg is the average scenario, ∈ is the maximum allowed fluctuation range, f(X, S avg ) represents the average performance index.

[0095] in, Used to find the minimum value of the scheduling scheme X; Used to find the performance index f(X, S) of the scheduling scheme X in all scenarios i ) and the average performance index f(X, S avg )The maximum value of the deviation is determined and ensured that the maximum deviation does not exceed the allowed fluctuation range ∈.

[0096] Specifically, by solving a multi-objective optimization model using a genetic algorithm, the dispatching of concrete tank trucks was optimized, effectively balancing multiple objectives, including fuel consumption, vehicle wear and tear, and idle rate. This approach improves the flexibility and efficiency of the dispatching system, enabling rapid adaptation to changing demand and dynamic adjustments to traffic conditions, thereby reducing transportation costs, improving service quality, and enhancing responsiveness to emergencies.

[0097] S4. Generate a preliminary dispatch plan based on the optimization results and send the task assignment information to the corresponding tanker's onboard terminal. During the task execution process, continuously monitor the tanker's location and traffic conditions. If a major delay is detected, a dynamic rescheduling mechanism is triggered.

[0098] S5. Update the traffic condition prediction model or more accurate route time estimation through historical data and real-time information, and record the fuel consumption, vehicle loss and task completion status during each dispatch process to optimize the model parameter adjustment and performance evaluation.

[0099] Example 2

[0100] This embodiment illustrates the implementation of a method for dynamically scheduling concrete tanker trucks, using the scenario of concrete transportation scheduling during the peak construction period in an urban area. The specific content includes:

[0101] During the peak construction season, a concrete transportation company faced the challenge of simultaneously supplying large quantities of concrete to multiple construction sites. First, using GPS positioning devices and on-board sensors, they obtained the real-time locations, remaining capacities (e.g., Tank Truck A had a remaining capacity of 8 cubic meters, and Tank Truck B had 12 cubic meters), and task statuses (e.g., Tank Truck C was currently on a mission and expected to complete in 30 minutes) of 20 tank trucks. Simultaneously, they extracted concrete demand from the construction site order system for five construction sites (e.g., Site 1 had a demand of 20 cubic meters, and Site 2 had a demand of 15 cubic meters), time windows (e.g., Site 1 had a demand time of 10:00 AM to 12:00 PM), and geographic locations (e.g., Site 1 was 5 kilometers from the supply point). A clustering algorithm was then used to categorize the tank trucks into three task groups based on their current location, remaining capacity, and distance to the construction sites, optimizing resource allocation efficiency. Group 1 consisted of Tank Trucks A and B, responsible for Sites 1 and 2; Group 2 consisted of Tank Trucks C and D, responsible for Sites 3 and 4.

[0102] Next, the Dijkstra algorithm, combined with real-time traffic data (e.g., a road congestion causing a speed drop from 60 km / h to 30 km / h), calculates the optimal route and estimated travel time for each tanker truck to each construction site (e.g., the optimal route for tanker truck A to construction site 1 is 8 kilometers, with an estimated travel time of 15 minutes). Based on this, a multi-objective optimization model is developed to optimize fuel consumption, vehicle wear and tear, and idle rate, using demand urgency, concrete volume, distance, and traffic conditions as constraints. A genetic algorithm is used to solve the optimal task allocation (e.g., tanker truck A to construction site 1, tanker truck B to construction site 2) and generate a preliminary scheduling plan. This task allocation information is then transmitted to the onboard terminal of the corresponding tanker truck, which monitors the tanker truck's location and traffic conditions in real time. If a significant delay is detected (e.g., a sudden accident on a certain road section causes tanker truck C to be delayed by 20 minutes), the system triggers a dynamic rescheduling mechanism to reallocate tasks (e.g., transferring tanker truck C's task to tanker truck D) to ensure the timely supply of concrete.

[0103] Through historical data and real-time information, the traffic condition prediction model is continuously updated (for example, predicting that a certain road section will be congested at 5 pm), route time estimates are optimized, and fuel consumption (for example, the total fuel consumption of a certain dispatch is 50 liters), vehicle losses (for example, the mileage of a tank truck increases by 200 kilometers), and task completion status (for example, all construction site requirements are met on time) during each dispatch process are recorded. These are used to optimize the model's parameter adjustment and performance evaluation, significantly improving the efficiency and reliability of concrete transportation, reducing transportation costs and the risk of construction site downtime, and providing strong support for efficient construction during the peak period of urban construction.

[0104] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as above in terms of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make some changes or modifications to equivalent embodiments using the technical contents disclosed above. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for dynamic scheduling of concrete transport tank trucks, characterized by: The steps include: Obtain real-time location information and status data of all tank trucks, including remaining capacity and current task completion status, to form a tank truck resource pool. Then, extract the concrete demand, demand time window, and geographic location information of each construction site from the construction site order system to generate a demand list. Based on the tanker resource pool and demand list, the estimated travel time for each tanker to each construction site is calculated, and the Dijkstra algorithm combined with real-time traffic data is used to determine the optimal route; By establishing a multi-objective optimization model and combining real-time traffic data and vehicle status to dynamically adjust task allocation, we can obtain the optimization results. Among them, when the demand time window of a construction site conflicts with the assigned tasks, the availability of other tank trucks is re-evaluated and the task allocation plan is adjusted.

2. A method for dynamic scheduling of concrete transport tank trucks according to claim 1, characterized in that: Obtain real-time location information and status data of all tank trucks, including remaining capacity and current task completion status, to form a tank truck resource pool, including: Install positioning equipment in the acquisition system to obtain the tanker's location information in real time, monitor the tanker's remaining capacity through sensors, and transmit the data to the data processing module; In the data processing module, the location information and remaining capacity are associated and stored, the task status information of the tanker is obtained, and updated to the task progress field in the data processing module; when the task status is completed, the vehicle status is updated to available, otherwise it is marked as working; In the resource pool, a list of available tank trucks is generated based on location information, remaining capacity, and vehicle status. A clustering algorithm is used to group the available tank trucks to optimize resource allocation efficiency.

3. The method for dynamic scheduling of concrete transport tank trucks according to claim 1, characterized in that: Extract the concrete demand, demand time window, and geographic location information of each construction site from the construction site order system to generate a demand list, including: The preset data interface is used to extract the concrete demand quantity, demand time window and geographical location information from the construction site order system to obtain the original demand data. Based on the geographical location information in the original demand data, the distance between each construction site and the concrete supply point is calculated to generate a distance list. Combined with the demand time window and distance value, a priority algorithm is used to sort the demand list and determine the scheduling priority. When the demand time window is approaching, the status code of the corresponding order is marked as urgent, and an urgent order list is generated; According to the urgent order list and scheduling priority, a clustering algorithm is used to group the orders and generate a scheduling plan.

4. The method for dynamic scheduling of concrete transport tank trucks according to claim 1, characterized in that: After extracting the concrete demand, demand time window, and geographic location information of each construction site from the construction site order system and generating a demand list, the process also includes: using a machine learning model to predict future concrete demand to optimize the task allocation plan. The specific steps include: Collect historical order data, construction progress information and seasonal factors as input features, and use deep learning models to build a prediction model for the input features. By establishing a forecasting model, we can predict the concrete demand within the future time window, and obtain the concrete demand and its time distribution at each construction site in the next week. Then, combined with real-time order data, we can adjust the task allocation plan in advance.

5. The method for dynamic scheduling of concrete transport tank trucks according to claim 1, characterized in that: Based on the tanker resource pool and demand list, the estimated travel time for each tanker to each construction site is calculated. The Dijkstra algorithm combined with real-time traffic data is used to determine the optimal route. Specifically, the following steps are performed: Real-time data on road length, speed limits, and congestion is collected and abstracted into a weighted directed graph model. Nodes represent intersections or key locations, edges represent road segments, and edge weights represent estimated travel times. Actual travel speeds are dynamically adjusted based on real-time traffic data to reflect current road conditions. For each tank truck, the current location is set as the starting point and the target construction site location is set as the end point. In the graph model, the distance from the starting point to itself is initialized to 0, and the distances of all other nodes are initialized to infinity. At the same time, a minimum heap is used to create a priority queue to store unvisited nodes and their current shortest distance estimates. The priority queue is sorted from smallest to largest distance, and the node with the smallest current distance is extracted. The node with the shortest current distance is taken from the priority queue, and all its adjacent nodes are traversed. The estimated time to reach the adjacent node through the current node is calculated through relaxation operations. If the new path is shorter, the estimated shortest distance value of the adjacent node is updated and the adjacent node is added back to the priority queue. In each relaxation operation, the edge weight is dynamically adjusted according to real-time traffic data. When the target node is visited or the priority queue is empty, the algorithm ends and the shortest path and estimated travel time from the starting point to the end point are obtained. After the Dijkstra algorithm is completed, the shortest path from the current position of the tanker to the target construction site and its estimated travel time are extracted from the graph model, and the complete path planning information is generated in combination with the estimated travel time.

6. The method for dynamic scheduling of concrete transport tank trucks according to claim 1, characterized in that: The multi-objective optimization model uses demand urgency, concrete quantity, distance, and traffic conditions as constraints, and fuel consumption, vehicle wear and tear, and idle rate as optimization objectives, and then uses a genetic algorithm to solve the problem. The specific steps include: randomly generating a set of solutions as the initial population, calculating the fitness value of each individual, and evaluating its quality based on the objective function; generating a new population through tournament selection, crossover, and mutation operations; and sorting the individuals according to their non-dominated relationships to select the optimal solution.

7. The method for dynamic scheduling of concrete transport tank trucks according to claim 1, characterized in that: By establishing a multi-objective optimization model, we can dynamically adjust task allocation by combining real-time traffic data and vehicle status, including: Obtain real-time traffic data and vehicle status information, use a multi-objective optimization model to calculate the initial task allocation plan, and determine whether there is a time conflict with the assigned tasks for the specific time window required by the construction site; When there is a time conflict, the real-time status data of all tank trucks is extracted to conduct an availability assessment and analysis. Based on the availability analysis results, a tank truck combination that meets the time window requirements and has the best transportation efficiency is selected; Recalculate the task allocation plan, generate new transportation routes and schedules, and match and verify the updated task allocation plan with real-time traffic data.

8. The method for dynamic scheduling of concrete transport tank trucks according to claim 7, characterized in that: After dynamically adjusting task allocation, the process also includes: using a multi-scenario optimization method to combine real-time traffic data, weather forecasts, and vehicle status information, building multiple operating scenarios based on historical data and prediction models, and using robust optimization technology to adjust the multi-objective optimization model for each scenario. The specific steps include: Generate multiple operating scenarios through Monte Carlo simulation or probability distribution-based methods. Each scenario is represented as a state vector that contains traffic data, weather forecast, and vehicle status information. Introducing scenario variables S into the multi-objective optimization model i , the optimization objective of scheduling scheme X is expanded to the comprehensive performance under different scenarios, and the optimization objective is expressed as: Where N is the number of scenes, w i is the scene variable S i The weight of f(X, S i ) is the scheduling scheme X in the scenario variable S i The objective function value under , represents the performance index; During the optimization process, robustness constraints are introduced to ensure that the scheduling scheme can be effectively executed in different scenarios and to limit the fluctuation range of key performance indicators: Among them S avg is the average scenario, ∈ is the maximum allowed fluctuation range, f(X,S avg ) represents the average performance index.

9. The method for dynamic scheduling of concrete transport tank trucks according to claim 1, characterized in that: Also includes: A preliminary scheduling plan is generated based on the optimization results, and the task allocation information is sent to the on-board terminal of the corresponding tank truck. During the task execution, the tank truck position and traffic conditions are continuously monitored. When a major delay is detected, the dynamic rescheduling mechanism is triggered.

10. The method for dynamic scheduling of concrete transport tank trucks according to claim 1, characterized in that: Also includes: Through historical data and real-time information, traffic condition prediction models are updated, or more accurate route time estimates are obtained. Fuel consumption, vehicle losses, and task completion status during each dispatch process are recorded to optimize model parameter adjustments and performance evaluation.

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