A dynamic scheduling method for concrete transport tankers

Abstract

The application discloses a kind of concrete transport tank car dynamic scheduling method, it is related to intelligent scheduling technical field.The application obtains the position and state information of concrete transport tank car in real time, in combination with the demand data of each construction site, generates efficient demand list and scheduling plan using clustering algorithm and priority algorithm, solves the scheduling efficiency problem under the condition of simultaneous demand of multiple construction sites;The optimal driving path is determined by Dijkstra algorithm and real-time traffic data, effectively avoids congestion, reduces fuel consumption and vehicle wear and tear, thereby reducing transportation cost;In addition, a multi-objective optimization model and genetic algorithm are used, fuel consumption, vehicle wear and tear and empty running rate are considered comprehensively, the 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 the condition of variable traffic and demand, and finally the optimization of concrete transportation process is realized.

Description

Technical Field

[0001] This invention relates to the field of intelligent scheduling technology, specifically a dynamic scheduling method for concrete transport tank trucks. Background Technology

[0002] During the dispatching of concrete mixer trucks, when multiple construction sites simultaneously issue concrete requests, the dispatching system needs to obtain real-time information such as the location, status, and remaining capacity of each truck. It then combines this information with factors such as the urgency of each site's needs, concrete usage, and distance to generate a preliminary task allocation plan. However, dynamic changes in traffic conditions directly impact the truck's transport efficiency. For example, a sudden traffic jam on a certain road segment may cause delays in the original route, thus affecting the construction progress of the site. Simultaneously, fuel consumption and vehicle wear and tear costs also need to be considered in dispatching decisions to avoid increased costs due to frequent lane changes or detours. Furthermore, the time windows for concrete demand from different construction sites may overlap or conflict. The dispatching system needs to minimize the empty running rate and waiting time of the trucks while meeting the time requirements of the construction sites. Summary of the Invention

[0003] The purpose of this invention is to provide a dynamic scheduling method for concrete transport tank trucks. Through real-time monitoring, route optimization, and a multi-objective optimization model, this method solves the efficiency, cost, and adaptability issues in the scheduling process of concrete transport tank trucks, thereby improving overall transportation efficiency and reducing operating costs.

[0004] The objective of this invention can be achieved through the following technical solutions:

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

[0006] Obtain the 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 geographical location information of each construction site from the construction site order system to generate a demand list.

[0007] Based on the tanker truck resource pool and demand list, the estimated travel time of each tanker truck to each construction site is calculated, and the optimal route is determined by combining Dijkstra's algorithm with real-time traffic data.

[0008] By establishing a multi-objective optimization model and dynamically adjusting task allocation based on real-time traffic data and vehicle status, the optimization results are obtained.

[0009] If a construction site's demand window conflicts with an already assigned task, the availability of other tank trucks will be reassessed, and the task allocation plan will be adjusted accordingly.

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

[0011] Positioning devices are installed in the data acquisition system to obtain the location information of the tanker truck in real time. The remaining capacity of the tanker truck is monitored by sensors, and the data is transmitted 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 the task progress field in the data processing module is updated; 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 then used to group the available tank trucks to optimize resource allocation efficiency.

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

[0015] The system uses a pre-defined data interface to extract concrete demand, demand time window, and geographic location information from the construction site order system to obtain raw demand data. Based on the geographic location information in the raw demand data, the system calculates the distance between each construction site and the concrete supply point and generates a distance list.

[0016] Combining 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] Based on the list of urgent orders and scheduling priorities, 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 geographical location information of each construction site from the construction site order system to generate a demand list, the process also includes: predicting future concrete demand using a machine learning model to optimize the task allocation plan. Specific steps include:

[0019] Historical order data, construction progress information, and seasonal factors are collected as input features. A prediction model is then built based on these input features using a deep learning model.

[0020] By establishing a predictive model, the demand for concrete within a future time window can be predicted, resulting in the concrete demand and its temporal distribution for each construction site within the next week. Combined with real-time order data, the task allocation plan can be adjusted in advance.

[0021] Furthermore, based on the tanker truck resource pool and demand list, the estimated travel time of each tanker truck to each construction site is calculated. The optimal route is determined using Dijkstra's algorithm combined with real-time traffic data, specifically including:

[0022] The system acquires real-time information on road length, speed limits, and congestion, and abstracts this information into a weighted directed graph model. Nodes represent road intersections or key locations, edges represent road segments, and the weight of each edge represents the estimated travel time. Actual travel speed is dynamically adjusted based on real-time traffic data to reflect current road conditions.

[0023] For each tanker truck, the current location is set as the starting point and the target construction site location is set as the ending 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 min-heap is used to create a priority queue to store unvisited nodes and their current shortest distance estimates. The priority queue is sorted by distance from smallest to largest, and the node with the smallest current distance is extracted.

[0024] The algorithm retrieves the node with the shortest current distance from the priority queue, iterates through all its adjacent nodes, and calculates the estimated time to reach the adjacent nodes through the current node using relaxation operations. If a new path is shorter, the estimated shortest distance of the adjacent nodes is updated, and the adjacent nodes are added back to the priority queue. In each relaxation operation, the edge weights are dynamically adjusted based on real-time traffic data. The algorithm ends when the target node is visited or the priority queue is empty, yielding the shortest path from the origin to the destination and the estimated travel time.

[0025] After Dijkstra's algorithm is 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, and the complete path planning information is generated by combining the estimated travel time.

[0026] Furthermore, the multi-objective optimization model uses the urgency of demand, concrete quantity, distance, and traffic conditions as constraints, and fuel consumption, vehicle wear and tear, and empty-running 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 an 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; sorting the individuals according to their non-dominant relationships, and selecting the optimal solution.

[0027] Furthermore, by establishing a multi-objective optimization model and combining real-time traffic data and vehicle status, the task allocation is dynamically adjusted, specifically including:

[0028] Acquire real-time traffic data and vehicle status information, use a multi-objective optimization model to calculate the initial task allocation scheme, and determine whether there is a time conflict between the specific time window of the construction site's needs and the already allocated tasks.

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

[0030] The task allocation scheme is recalculated to generate new transportation routes and schedules. The updated task allocation scheme is then matched and verified against 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, constructing multiple operational scenarios based on historical data and prediction models, and adjusting the multi-objective optimization model for each scenario using robust optimization techniques. Specific steps include:

[0032] Multiple operational scenarios are generated through Monte Carlo simulation or probability distribution-based methods. Each scenario is represented as a state vector, which includes traffic data, weather forecasts, and vehicle status information.

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

[0034] Where N is the number of scenes, w i It is the scene variable S i The weights, f(X, S) i ) is the scheduling scheme X in the scenario variable S i The objective function value 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, limiting the fluctuation range of key performance indicators.

[0036] Where S avg This is the average scenario, ∈ is the maximum allowable fluctuation range, f(X, S) avg () indicates the average performance index.

[0037] Based on the optimization results, a preliminary scheduling plan is generated, and the task allocation information is sent to the on-board terminal of the corresponding tanker truck. During the task execution, the location of the tanker truck and traffic conditions are continuously monitored. When a major delay is detected, a dynamic rescheduling mechanism is triggered.

[0038] By using historical data and real-time information, traffic condition prediction models can be updated, or more accurate path time estimates can be made. Fuel consumption, vehicle wear and tear, and task completion status during each dispatch process can be recorded to optimize model parameter adjustments and performance evaluation.

[0039] The beneficial effects of this invention are as follows:

[0040] This invention forms a tanker resource pool by acquiring the real-time location and status data of all tanker trucks, including remaining capacity and current task completion status; then, it extracts the concrete demand, demand time window, and geographical 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 the tanker resources and construction site needs in real time, thereby quickly generating a preliminary task allocation plan and effectively solving the transportation efficiency problem caused by dynamic changes in traffic conditions. By grouping available tanker trucks using a clustering algorithm, the resource allocation efficiency is optimized, further ensuring the timely supply of concrete and meeting the demand time window of the construction site.

[0041] By using Dijkstra's algorithm in conjunction with real-time traffic data to determine the optimal route, this method not only optimizes the tanker truck's travel path but also dynamically adjusts the travel speed to reflect the current road conditions. This reduces fuel consumption and vehicle wear caused by detours or idling, effectively lowers transportation costs, improves transportation efficiency, and also reduces the impact on the environment.

[0042] By establishing a multi-objective optimization model and solving it using a genetic algorithm, the system considers multiple constraints such as urgency of demand, concrete volume, distance, and traffic conditions, as well as optimization objectives such as fuel consumption, vehicle wear and tear, and empty-running rate. Furthermore, by introducing multi-scenario optimization methods and robust constraints, the scheduling scheme can be effectively executed in different scenarios, limiting the fluctuation range of key performance indicators. This significantly improves the intelligence and adaptability of the concrete transportation scheduling system, enabling it to respond in real time to changes in traffic conditions and fluctuations in construction site demand. This, in turn, increases the on-time delivery rate of concrete, reduces the risk of construction site downtime, and brings higher operational efficiency and customer satisfaction to the concrete transportation industry. Attached Figure Description

[0043] To better understand and implement this application, the technical solution is described in detail below with reference to the accompanying drawings.

[0044] Figure 1 A flowchart illustrating a dynamic scheduling method for concrete transport trucks provided in Embodiment 1 of this application;

[0045] Figure 2 This is a schematic diagram of the process for forming a tanker resource pool using a dynamic scheduling method for concrete transport tankers provided in Embodiment 1 of this application;

[0046] Figure 3 A flowchart illustrating the dynamic scheduling method for concrete transport trucks to determine the optimal path, as provided in Embodiment 1 of this application;

[0047] Figure 4 This is a schematic diagram illustrating the dynamic adjustment of task allocation in a dynamic scheduling method for concrete transport trucks provided in Embodiment 1 of this application. Detailed Implementation

[0048] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, exemplary embodiments will be described in detail below, examples of which are illustrated in the accompanying drawings. In the following description relating to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of methods and systems consistent with some aspects of this application as detailed in the appended claims.

[0049] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also 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 includes any or all possible combinations of one or more of the associated listed items.

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

[0051] Example 1

[0052] Please see Figures 1-4 This embodiment provides a dynamic scheduling method for concrete transport trucks, including the following steps:

[0053] S1. Obtain the 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 geographical location information of each construction site from the construction site order system to generate a demand list.

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

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

[0056] S12. 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 the task progress field in the data processing module is updated; when 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 clustering is based on factors such as the tanker's current location, remaining capacity, and distance from the construction site, thereby assigning vehicles to different task areas.

[0059] Specifically, when clustering tank trucks based on their current location, remaining capacity, and distance from various construction sites, the aim is to identify groups of tank trucks that are geographically close or have similar transportation needs, and then allocate 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 time window of demand at construction sites. 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 precision and response speed of scheduling. The application of clustering algorithms optimizes the allocation of tank truck resources, enabling vehicles to be rationally grouped and assigned to the construction sites that need them most based on geographical location and remaining capacity. This not only reduces the empty running 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 scheme, it is possible to 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 geographical location information for each construction site are extracted from the construction site order system to generate a demand list, specifically including:

[0062] The system uses a pre-defined data interface to extract concrete demand, demand time window, and geographic location information from the construction site order system to obtain raw demand data. Based on the geographic location information in the raw demand data, the system calculates the distance between each construction site and the concrete supply point and generates a distance list.

[0063] Combining 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] Based on the list of urgent orders and scheduling priorities, 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 using 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 tank truck task allocation, starting position, 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 consider the remaining capacity of the tanker, its current location, and the site requirements to minimize transportation costs and time, while ensuring that all orders can be met within the demand time window. It will allocate the most suitable tanker to each order and determine the best transportation route, thereby generating detailed scheduling instructions to guide the tanker to perform its tasks.

[0067] Simultaneously, it achieves efficient scheduling of concrete transportation, solving the problems of slow response, high cost, and resource waste in traditional scheduling. It can quickly respond to the urgent needs of construction sites. Through intelligent matching and route planning, it reduces the empty running and waiting time of tank trucks, improves transportation efficiency, and reduces fuel consumption and vehicle wear and tear by optimizing the utilization of tank truck resources, thus achieving cost savings. In addition, it enhances the adaptability to changes in traffic conditions, ensures timely concrete supply, reduces the risk of construction site shutdowns, and thereby improves the reliability and flexibility of the entire supply chain.

[0068] Furthermore, after extracting the concrete demand, demand time window, and geographical location information of each construction site from the construction site order system to generate a demand list, the process also includes: predicting future concrete demand using machine learning models (such as time series analysis and deep learning models) to optimize the task allocation scheme. Specific steps include:

[0069] Historical order data, construction progress information, and seasonal factors are collected as input features. A prediction model is then built based on these input features using a deep learning model.

[0070] By establishing a predictive model, the demand for concrete within a future time window can be predicted, resulting in the concrete demand and its temporal distribution for each construction site within the next week. Combined with real-time order data, the task allocation plan can be adjusted in advance.

[0071] Specifically, it significantly improves the intelligence level and response speed of concrete transportation scheduling systems. 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. Based on the tanker truck resource pool and demand list, calculate the estimated travel time of each tanker truck to each construction site, and use Dijkstra's algorithm combined with real-time traffic data to determine the optimal route;

[0073] Furthermore, based on the tanker truck resource pool and demand list, the estimated travel time of each tanker truck to each construction site is calculated. The optimal route is determined using Dijkstra's algorithm combined with real-time traffic data, specifically including:

[0074] S21. Real-time acquisition of information such as road length, speed limits, and congestion conditions is used to abstract this information into a weighted directed graph model. Nodes represent road intersections or key locations, edges represent road segments, and the weight of each edge is the estimated travel time, calculated using the formula "Estimated 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 implemented to periodically acquire the latest information from the traffic platform and dynamically update the edge weights in the graph model, ensuring the accuracy of path planning.

[0075] S22. For each tanker truck, set the current position as the starting point and the target construction site position as the ending point. In the graph model, initialize the distance from the starting point to itself to 0, and initialize the distances of all other nodes to infinity. At the same time, use a min-heap to create a priority queue to store unvisited nodes and their current shortest distance estimates. The priority queue is sorted by distance from smallest to largest to quickly extract the node with the smallest current distance, providing efficient data structure support for the operation of Dijkstra's 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 nodes through the current node using relaxation operations. If the new path is shorter, update the estimated shortest distance of the adjacent nodes and add the adjacent nodes back to the priority queue. In each relaxation operation, dynamically adjust the edge weights based on real-time traffic data. For example, when a road segment is congested, reduce the driving speed of that road segment in real time and update the weights to dynamically adjust the optimal path. When the target node is visited or the priority queue is empty, the algorithm ends, obtaining the shortest path from the starting point to the destination and the estimated travel time.

[0077] After S24 and Dijkstra's 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 decomposed into a series of navigation instructions, such as "drive 3 kilometers north, turn left onto XX Road", and combined with the estimated travel time to generate complete path planning information.

[0078] Specifically, the route planning process based on Dijkstra's algorithm optimizes routes during concrete transportation, effectively addressing the shortcomings of traditional route planning methods in handling real-time traffic changes. This method dynamically adapts to changes in traffic conditions, such as congestion and road construction, thereby reducing transportation time and fuel consumption, and improving transportation efficiency. Furthermore, through accurate route planning and estimated travel time, dispatchers can better arrange and adjust task allocation, reducing waiting time at construction sites and improving customer satisfaction. This process also helps reduce operating costs by minimizing unnecessary empty runs and idling time, while improving the utilization rate of tanker resources. This advanced route 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 and dynamically adjusting task allocation based on real-time traffic data and vehicle status, the optimization results are obtained.

[0080] In particular, if the time window for a construction site's demand conflicts with the assigned tasks, the availability of other tank trucks will be reassessed and the task allocation plan will be adjusted.

[0081] Furthermore, the multi-objective optimization model uses the urgency of demand, concrete quantity, distance, and traffic conditions as constraints, and fuel consumption, vehicle wear and tear, and empty-running 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 an 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; sorting the individuals according to their non-dominant relationships, and selecting the optimal solution.

[0082] Specifically, in multi-objective optimization models, the reason for using genetic algorithms to solve 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 a global search in the solution space to find a set of non-dominated solutions that satisfy different objectives. This method does not require weighting of objectives and can handle conflicts between objectives more naturally, thus providing decision-makers with more options.

[0083] By solving a multi-objective optimization model using a genetic algorithm, the task allocation for concrete transport trucks was optimized, effectively balancing multiple objectives such as fuel consumption, vehicle wear and tear, and empty running rate. This method improves 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 and combining real-time traffic data and vehicle status, the task allocation is dynamically adjusted, specifically including:

[0085] S31. Obtain real-time traffic data and vehicle status information, use a multi-objective optimization model to calculate the initial task allocation scheme, and determine whether there is a time conflict between the specific time window of the construction site demand and the already allocated tasks.

[0086] S32. When there is a time conflict, extract the real-time status data of all tank trucks, perform availability assessment analysis, and select the 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 new transportation routes and schedules, 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 and adaptability of the concrete transportation scheduling system have been significantly improved. It can respond in real time to changes in traffic conditions and fluctuations in construction site demand. Through precise task allocation and route planning, transportation delays have been reduced and the on-time delivery rate of concrete has been improved. In addition, by optimizing the use of tank trucks, unnecessary empty runs and idling time have been reduced, thereby reducing fuel consumption and vehicle wear and tear, and achieving cost savings. This strategy has also improved the construction efficiency of the construction site because the concrete can arrive on time, reducing downtime and waiting time, 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, constructing multiple operational scenarios based on historical data and prediction models, and adjusting the multi-objective optimization model for each scenario using robust optimization techniques. Specific steps include:

[0090] Through Monte Carlo simulation or probability distribution-based methods, scenarios such as traffic congestion and severe weather are generated. Each scenario is represented as a state vector, which includes traffic data, weather forecasts, and vehicle status information.

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

[0092] Where N is the number of scenes, w i It is the scene variable S i The weights, f(X, S) i ) is the scheduling scheme X in the scenario variable S i The objective function value 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, limiting the fluctuation range of key performance indicators (such as task completion time and fuel consumption).

[0094] Where S avg This is the average scenario, ∈ is the maximum allowable fluctuation range, f(X, S) avg () indicates the average performance index.

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

[0096] Specifically, a multi-objective optimization model was solved using a genetic algorithm to optimize the task allocation for concrete transport trucks, effectively balancing multiple objectives such as fuel consumption, vehicle wear and tear, and empty-running rate. This method improves 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.

[0097] S4. Generate a preliminary scheduling plan based on the optimization results, send the task allocation information to the on-board terminal of the corresponding tanker truck, continuously monitor the tanker truck location and traffic conditions during task execution, and trigger the dynamic rescheduling mechanism when a major delay is detected.

[0098] S5. Update the traffic condition prediction model or make more accurate path time estimates by using historical data and real-time information, and record fuel consumption, vehicle wear and tear and task completion status during each scheduling process for optimizing model parameter adjustment and performance evaluation.

[0099] Example 2

[0100] This embodiment describes the implementation process of a dynamic scheduling method for concrete transport trucks, specifically focusing on concrete transport scheduling during peak urban construction periods. The details include:

[0101] During peak construction season in the city, a concrete transportation company faces the challenge of simultaneously supplying large quantities of concrete to multiple construction sites. First, it uses GPS positioning devices and onboard sensors to obtain real-time information on the location, remaining capacity (e.g., tanker A has 8 cubic meters remaining, tanker B has 12 cubic meters), and task status (e.g., tanker C is currently performing a task and is expected to complete it in 30 minutes). Simultaneously, it extracts the concrete demand from five construction sites (e.g., site 1 requires 20 cubic meters, site 2 requires 15 cubic meters), demand time windows (e.g., site 1's demand time is from 10:00 AM to 12:00 PM), and geographical location information (e.g., site 1 is 5 kilometers from the supply point) from the site order system. Then, using a clustering algorithm, based on the tanker's current location, remaining capacity, and distance from the construction site, the tankers are divided into three task groups (e.g., group 1 includes tankers A and B, responsible for sites 1 and 2; group 2 includes tankers C and D, responsible for sites 3 and 4), optimizing resource allocation efficiency.

[0102] Next, using Dijkstra's algorithm combined with real-time traffic data (e.g., a road segment where traffic speed drops from 60km / h to 30km / h due to congestion), the optimal route and estimated travel time for each concrete mixer truck to various construction sites are calculated (e.g., the optimal route from mixer 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 established, using the urgency of demand, concrete quantity, distance, and traffic conditions as constraints to optimize fuel consumption, vehicle wear and tear, and empty-running rate. A genetic algorithm is used to solve for the optimal task allocation scheme (e.g., mixer truck A is responsible for construction site 1, and mixer truck B is responsible for construction site 2), and a preliminary scheduling scheme is generated. The task allocation information is then sent to the onboard terminals of the corresponding mixer trucks to monitor the location of the mixer trucks and traffic conditions in real time. When a major delay is detected (e.g., a sudden accident on a road segment causes mixer truck C to be delayed by 20 minutes), the system triggers a dynamic rescheduling mechanism to reassign tasks (e.g., transferring the task of mixer truck C to mixer truck D) to ensure timely concrete supply.

[0103] By continuously updating traffic condition prediction models using historical data and real-time information (e.g., predicting congestion on a certain road segment at 5 PM), optimizing route time estimates, and recording fuel consumption (e.g., total fuel consumption of 50 liters in a certain dispatch), vehicle wear (e.g., a tanker truck's mileage increases by 200 kilometers) and task completion status (e.g., all construction site demands are met on time) during each dispatch process, these data are used to optimize model parameter adjustments and performance evaluation. This significantly improves the efficiency and reliability of concrete transportation, reduces transportation costs and the risk of construction site downtime, and provides strong support for efficient construction during peak urban construction periods.

[0104] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for dynamic scheduling of concrete delivery tankers, characterized in that: The method comprises the following steps: Obtain real-time position information and state data of all tank trucks, including residual capacity and current task completion, form a tank truck resource pool, and then extract concrete demand, demand time window and geographic location information of each construction site from the construction site order system to generate a demand list; According to the tank truck resource pool and the demand list, the estimated driving time of each tank truck to each construction site is calculated, and the Dijkstra algorithm is used to determine the optimal path in combination with real-time traffic data; It includes: real-time acquisition of road length, speed limit and congestion information data, and the information data is abstracted into a weighted directed graph model; wherein the nodes represent road intersections or key positions, and the edges represent road segments, and the weight of the edge is the estimated driving time, and the actual driving speed is dynamically adjusted according to the real-time traffic data to reflect the current road conditions; For each tank truck, the current position is set as the starting point, and the target construction site position is set as the end point. In the graph model, the distance from the starting point to itself is initialized to 0, and the distance of all other nodes is initialized to infinity. At the same time, a minimum heap is used for a priority queue to store unvisited nodes and their current shortest distance estimates, and the priority queue is sorted in ascending order of distance, and the node with the smallest distance is extracted; The node with the smallest distance is taken out from the priority queue, all adjacent nodes of the node are traversed, the estimated time of reaching the adjacent nodes through the current node is calculated through the relaxation operation, and when the new path is shorter, the shortest distance estimate of the adjacent node is updated, and the adjacent node is re-added to the priority queue. In each relaxation operation, the edge weight is dynamically adjusted according to the real-time traffic data, and when the target node is visited or the priority queue is empty, the algorithm ends, and the shortest path and the estimated driving time from the starting point to the end point are obtained. After the Dijkstra algorithm is completed, the shortest path and the estimated driving time from the current position of the tank truck to the target construction site are extracted from the graph model, and the complete path planning information is generated in combination with the estimated driving time; Through the establishment of a multi-objective optimization model, dynamic adjustment of task allocation is carried out in combination with real-time traffic data and vehicle state to obtain an optimization result. The multi-objective optimization model takes demand urgency, concrete quantity, distance and traffic conditions as constraint conditions, and takes fuel consumption, vehicle wear and tear and empty running rate as optimization objectives. When the demand time window of a construction site conflicts with the allocated task, the availability of other tank trucks is re-evaluated, and the task allocation scheme is adjusted.

2. The dynamic scheduling method of concrete transport tank vehicles according to claim 1, characterized in that: Obtain real-time position information and state data of all tank trucks, including residual capacity and current task completion, form a tank truck resource pool, specifically including: Install a positioning device in the collection system to obtain real-time position information of the tank truck, monitor the residual capacity of the tank truck through a sensor, and transmit the data to a data processing module; In the data processing module, the position information and the residual capacity are stored in association to obtain task state information of the tank truck, and the task progress field in the data processing module is updated; wherein when the task state is completed, the vehicle state is updated to available, otherwise it is marked as working. In the resource pool, according to the position information, the remaining capacity and the vehicle state, a list of available tank trucks is generated, a clustering algorithm is used to group the available tank trucks, and the resource allocation efficiency is optimized.

3. The method of claim 1, wherein: The concrete demand, demand time window and geographic location information of each construction site are extracted from the construction order system to generate a demand list, which specifically includes: The preset data interface is used to extract the concrete demand, demand time window and geographic location information from the construction order system to obtain the demand raw data; according to the geographic location information in the demand raw data, the distance values between each construction site and the concrete supply point are calculated to generate a distance list; Combined with the demand time window and the distance value, a priority algorithm is used to sort the demand list to determine the scheduling priority, and when the demand time window is approaching, the state code of the corresponding order is marked as urgent to generate an urgent order list; According to the urgent order list and the scheduling priority, a clustering algorithm is used to group the orders to generate a scheduling plan.

4. The method of claim 1, wherein: After generating the demand list from the construction order system by extracting the concrete demand, demand time window and geographic location information of each construction site, the following steps are also included: predicting future concrete demand through a machine learning model to optimize the task allocation scheme, the specific steps including: Collecting historical order data, construction progress information and seasonal factors as input features, and establishing a prediction model for the input features through a deep learning model, Through the establishment of the prediction model, the concrete demand in the future time window is predicted to obtain the concrete demand and its time distribution of each construction site in the future week, and then combined with real-time order data, the task allocation scheme is adjusted in advance.

5. The method of claim 1, wherein: The multi-objective optimization model takes the demand urgency, concrete quantity, distance and traffic conditions as constraint conditions, and takes fuel consumption, vehicle wear and tear and empty running rate as optimization targets, and then uses a genetic algorithm to solve; the specific steps include: randomly generating a group of solutions as the initial population, calculating the fitness value of each individual, and evaluating its advantages and disadvantages based on the objective function; generate a new population through tournament selection, crossover and mutation operations; sort the individuals according to their non-dominated relationship and select the optimal solution.

6. The method of claim 1, wherein: Through the establishment of a multi-objective optimization model, dynamic adjustment of task allocation is carried out in combination with real-time traffic data and vehicle state, which specifically includes: Obtain real-time traffic data and vehicle state information, and use a multi-objective optimization model to calculate an initial task allocation scheme, and judge whether there is a time conflict with the allocated task for a specific time window of construction site demand; When there is a time conflict, extract the real-time state data of all tank trucks for availability evaluation and analysis, and select the tank truck combination that meets the time window requirements and has the optimal transportation efficiency according to the availability analysis result; Recalculate the task allocation scheme to generate a new transportation route and time schedule, and match and verify the updated task allocation scheme with real-time traffic data.

7. The method of claim 6, wherein: After dynamic adjustment of task allocation, the following steps are also included: through a multi-scenario optimization method, in combination with real-time traffic data, weather forecast and vehicle state information, a plurality of running scenarios are constructed according to historical data and prediction models, and a multi-objective optimization model is adjusted for each scenario using robust optimization technology, and the specific steps include: A variety of operating scenarios are generated by Monte Carlo simulation or probability distribution-based methods, each represented as a state vector containing traffic data, weather forecasts, and vehicle state information; Introducing scenario variables in multi-objective optimization model , the optimization objective of the scheduling scheme is extended to the comprehensive performance under different scenarios, and the optimization objective is expressed as: ; wherein, is the number of scenarios, is the weight of the scenario variable , and is the objective function value of the scheduling scheme under the scenario variable , indicating the performance index; In the optimization process, robustness constraints are introduced to make the scheduling scheme effectively executed in different scenarios, limiting the fluctuation range of key performance indicators: ; wherein is the average scenario, is the maximum allowed fluctuation range, represents the average performance indicator.

8. The method of claim 1, wherein: Also includes: A preliminary scheduling scheme is generated based on the optimization results, and task allocation information is sent to the corresponding tank truck's on-board terminal. During task execution, the tank truck's position and traffic conditions are continuously monitored. When significant delays are detected, a dynamic rescheduling mechanism is triggered.

9. The method of claim 1, wherein: Also includes: Through historical data and real-time information, the traffic condition prediction model is updated, the path time estimation is optimized, and the fuel consumption, vehicle wear and tear, and task completion during each scheduling process are recorded for parameter adjustment and performance evaluation of the optimization model.

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