A vehicle route planning and scheduling method and system for garbage collection and transportation scenarios

Abstract

The application discloses a kind of vehicle route planning scheduling methods and systems for garbage collection and transportation scene, belong to wisdom environmental sanitation, intelligent scheduling, path planning and operations optimization technical field.A kind of vehicle route planning scheduling method for garbage collection and transportation scene of the application, including data acquisition, cost matrix construction, collection and transportation merchant space division, single trip collection and transportation task generation, single trip path optimization, multi-vehicle task allocation, global iteration optimization and scheduling result output.The application constructs the hierarchical scheduling mechanism of "space division-load constraint sub-trip-single trip path optimization-multi-vehicle task allocation-bottleneck vehicle global reconstruction optimization", can adapt to garbage collection and transportation vehicle multi-trip round trip, large-scale collection and transportation scene of load limited, effectively shorten the overall completion time of all collection and transportation tasks, improve vehicle work hour balance, scheduling scheme executability and engineering application efficiency.

Description

Technical Field

[0001] This invention relates to the fields of smart sanitation, intelligent scheduling, route planning and operations optimization, specifically to a vehicle route planning and scheduling method and system for waste collection and transportation scenarios, and particularly to a route planning and scheduling method and system applicable to multiple vehicles, multiple collection and transportation merchants, vehicle load-bearing capacity restrictions, and allowing vehicles to make multiple trips to and from waste disposal plants or transfer stations. Background Technology

[0002] Existing vehicle route planning technologies are mostly used in fields such as delivery, inspection, and logistics transportation. They are usually based on single-vehicle single-trip or multi-vehicle single-trip scenarios, and the objective function is usually to minimize the total travel distance, the total travel time, or the single path.

[0003] However, in the waste collection and transportation scenario, waste collection and transportation vehicles usually depart from waste disposal plants, waste transfer stations or collection and transportation centers, and visit multiple collection and transportation merchants along the route to complete the waste collection and transportation tasks. When the vehicle load reaches the maximum load limit, it needs to return to the waste disposal plant or waste transfer station to unload. After unloading, it can set off again to perform subsequent collection and transportation tasks. Therefore, it is a typical collaborative scheduling problem with multiple vehicles, multiple round trips and load limits.

[0004] Existing technologies have at least the following shortcomings: most algorithms are only applicable to single-trip path planning and are difficult to adapt to the operation mode of multiple round trips of garbage collection vehicles; most algorithms focus on minimizing total distance or total time and fail to effectively solve the problem of compressing the overall completion time of all collection merchants; for large-scale collection merchant scenarios, precise optimization methods have high solution complexity and long computation time, making it difficult to meet the needs of practical engineering applications; simple heuristic methods based on fixed areas or nearest neighbors are prone to causing uneven vehicle loads, resulting in a few garbage collection vehicles lagging behind; and there is a lack of a unified scheduling method that combines spatial partitioning, load constraint trip division, path optimization, vehicle allocation, and global iterative optimization.

[0005] Therefore, it is necessary to propose a new vehicle route planning and scheduling method and system for waste collection and transportation scenarios, so as to meet the efficient scheduling needs of large-scale, multi-vehicle, multi-trip, and load-limited scenarios. Summary of the Invention

[0006] The purpose of this invention is to provide a vehicle route planning and scheduling method and system for waste collection and transportation scenarios, so as to solve the problems of long overall completion time, uneven vehicle working hours and low efficiency of large-scale solution in the existing technology in multi-vehicle, multi-trip operation scenarios.

[0007] To achieve the above objectives, this invention provides a vehicle route planning and scheduling method for waste collection and transportation scenarios. The method comprises the following steps: S1, data acquisition: acquiring the location of the central station, the locations of multiple collection and transportation merchants, the waste collection volume of each merchant, the number of waste collection vehicles, the load per vehicle, the service time of each merchant, the unloading time at the central station, and road travel time or average vehicle speed information; S2, cost matrix construction: constructing a time matrix and / or a distance matrix based on the distance information between the central station and each collection and transportation merchant, and between each merchant, or road network travel time information, to describe the travel cost between any two nodes; S3, merchant spatial division: spatially dividing the merchants based on their spatial distribution relationship with waste disposal plants or waste transfer stations, forming... The system is divided into multiple geographical sub-regions; S4, Single-trip collection task generation: Within each geographical sub-region, collection merchants are combined according to the maximum load capacity of garbage collection vehicles to generate multiple single-trip collection tasks; S5, Single-trip route optimization: The path of the access order of collection merchants in each single-trip collection task is optimized; S6, Multi-vehicle task allocation: Based on the task cost of each single-trip collection task, multiple single-trip collection tasks are allocated to multiple garbage collection vehicles for execution; S7, Global iterative optimization: With the goal of minimizing the overall completion time of all collection merchants to complete garbage collection, the initial scheduling scheme is globally iteratively optimized; S8, Scheduling result output: The operation sequence of each garbage collection vehicle, the access order of collection merchants corresponding to each trip, the estimated start and end times, the cumulative operation time of each vehicle, and the overall completion time are output to form the final scheduling result that meets the requirements of project execution.

[0008] Preferably, in step S3, the spatial partitioning method is sector partitioning based on polar angle sorting, hierarchical partitioning based on joint constraints of direction and distance, spatial grouping based on clustering algorithms, or region partitioning based on road network adjacency relationships. This step can decompose a large-scale collection and transportation merchant set into multiple relatively compact local regions, providing regional boundary data for subsequent generation of single-trip tasks based on load.

[0009] Preferably, in step S4, each single-trip collection task corresponds to a process where a vehicle departs from the central station, visits several collection merchants, and returns to the central station, and the collection merchants within the same task are kept as spatially compact as possible. Specifically, in each geographical sub-region, the collection merchants are combined according to the single vehicle load capacity to generate multiple single-trip collection tasks, including: adding collection merchants one by one to the current single-trip collection task in a preset order, and accumulating the waste collection volume of the current single-trip collection task; when adding the next collection merchant would cause the accumulated waste collection volume to exceed the single vehicle load capacity, the current single-trip collection task is terminated and a new single-trip collection task is generated; ensuring that the total waste collection volume of each single-trip collection task does not exceed the single vehicle load capacity. This step utilizes the aforementioned spatial division results and waste collection volume data to decompose the global task into multiple single-trip tasks that meet load capacity feasibility.

[0010] Preferably, in step S5, an initial access order is first generated based on the nearest neighbor principle or insertion principle. Then, it is improved using local search operators such as 2-opt (two-sided exchange local search algorithm), 3-opt (three-sided exchange local search algorithm), node exchange, node insertion, or continuous node segment relocation, and the task cost is calculated. This step takes the single-trip collection and transportation task generated in the previous stage as input and outputs the optimized path and task cost corresponding to each single-trip task.

[0011] The cost of the task includes at least one of the following: travel time between the waste disposal plant or waste transfer station and the collection and transportation merchants; travel time between the collection and transportation merchants; service time of the collection and transportation merchants; travel time to return to the waste disposal plant or waste transfer station; and time for unloading operations at the waste disposal plant or waste transfer station.

[0012] Preferably, in step S6, the tasks are first sorted from highest to lowest cost, and then the currently assigned tasks are preferentially allocated to the vehicles with the shortest cumulative work time to reduce the difference in total work time among vehicles, thus obtaining the initial scheduling scheme. This yields the work trip sequence, estimated cumulative work time, and global initial completion time for each vehicle.

[0013] Preferably, in step S7, the optimization process includes operations such as random removal and re-insertion, re-division of waste collection merchants at adjacent task boundaries, bottleneck vehicle task migration, cross-vehicle task exchange, and re-optimization of reconstructed paths. The bottleneck waste collection vehicle is the waste collection vehicle with the longest cumulative operating time. At least one single-trip collection task on the bottleneck waste collection vehicle is migrated to other waste collection vehicles, or single-trip collection tasks are exchanged between the bottleneck waste collection vehicle and other waste collection vehicles to reduce the overall completion time of all waste collection merchants. This step uses the initial scheduling scheme formed in the previous stage as input and performs targeted reconstruction optimization on the bottleneck vehicle that completes its work the latest, thereby further compressing the overall completion time. The global iterative optimization adopts an adaptive large neighborhood search mechanism, dynamically adjusting the calling probability of the corresponding reconstruction operator based on the optimization effect of different reconstruction operators in the historical iteration process.

[0014] This invention also provides a vehicle route planning and scheduling system for waste collection and transportation scenarios, characterized by comprising: a data acquisition module for acquiring the location of waste disposal plants or waste transfer stations, the location of waste collection merchants, the waste collection volume of collection merchants, the number of waste collection vehicles, and the load capacity of each vehicle; a spatial division module for spatially dividing the waste collection merchants according to the spatial distribution relationship between the waste disposal plants or waste transfer stations, generating multiple geographical sub-regions; and a single-trip task generation module for combining the waste collection merchants within each geographical sub-region according to the load capacity of each vehicle, generating multiple single-trip collection tasks. The system includes: a path optimization module for optimizing the access order of collection merchants in each single collection task to obtain the task cost of each single collection task; a vehicle allocation module for allocating multiple single collection tasks to multiple garbage collection vehicles based on the task cost of each single collection task, generating an initial scheduling scheme; a global optimization module for iteratively optimizing the initial scheduling scheme with the goal of minimizing the overall completion time of all collection merchants; and a scheduling output module for outputting the operation trip number, collection merchant access order, and final scheduling result for each garbage collection vehicle.

[0015] This invention constructs a hierarchical scheduling framework of "spatial partitioning - load constraint trip allocation - single trip route optimization - multi-vehicle task allocation - global reconstruction optimization". With the goal of minimizing the overall completion time of all waste collection and transportation merchants, it can adapt to large-scale collection and transportation scenarios with multiple round trips and load constraints for waste collection and transportation vehicles, effectively shorten the overall completion time of all collection and transportation tasks, and improve the balance of vehicle working hours, the feasibility of scheduling schemes, and the efficiency of engineering applications.

[0016] Compared with existing technologies, the key technical solution to be protected in this invention is not a single local path optimization step, but a hierarchical scheduling technical route established for waste collection and transportation scenarios with multiple vehicles, multiple round trips, and load-limited operations. Firstly, using waste disposal plants or waste transfer stations as unified central stations, the area is first divided according to the spatial distribution of collection and transportation merchants. Then, using single-vehicle load constraints, each area is broken down into multiple single-trip collection and transportation tasks, thereby ensuring that the task decomposition process simultaneously satisfies spatial compactness and load-bearing feasibility.

[0017] Secondly, after a single collection and transportation task is formed, the optimization does not end immediately. Instead, the path optimization is first performed on the access order within each single task, and then the multiple single tasks are balanced and distributed among multiple garbage collection and transportation vehicles to form an initial scheduling scheme. This hierarchical structure of "local optimization first, then global allocation" is conducive to incorporating the quality of single-trip paths and the coordination balance of multiple vehicles into the solution process simultaneously.

[0018] Third, this invention does not use the shortest total travel distance as the sole optimization objective, but rather focuses on minimizing the overall completion time for all waste collection and transportation businesses to complete the collection process. This is closer to the actual need in waste collection and transportation operations to "complete collection as early as possible." By allocating tasks and refactoring them around this objective, the problem of extended overall completion time caused by individual vehicles trailing behind can be effectively alleviated.

[0019] Fourth, this invention further introduces a global iterative reconstruction mechanism for bottleneck vehicles. By identifying the garbage collection vehicle with the longest cumulative operating time and performing operations such as migration, exchange, boundary re-division, re-insertion, and path re-optimization on its related tasks, the end-of-line operation time can be compressed in a targeted manner without compromising overall executability. The above innovations can either form protection features individually or be used for overall protection in a combined scheme of "spatial division + load constraint trip allocation + single trip path optimization + multi-vehicle allocation + global reconstruction of bottleneck vehicles".

[0020] Compared with existing technologies, the present invention has at least the following beneficial effects: it can adapt to the actual operation mode of multiple round trips between garbage collection and transportation vehicles and garbage disposal plants or garbage transfer stations; it can shorten the time required for all collection and transportation businesses to complete garbage collection and transportation with the goal of minimizing the overall completion time; it can take into account the compactness of the route, the feasibility of the load, and the balance of working hours between vehicles; it can still quickly generate executable and high-quality scheduling schemes in the scenario of large-scale collection and transportation businesses; by focusing on the reconstruction and optimization of bottleneck garbage collection and transportation vehicles, it can effectively reduce the tailing phenomenon at the end; it has good engineering feasibility and scalability, and can be applied to various scenarios such as garbage collection and transportation, urban sanitation, and recycling transfer.

[0021] Specifically, the spatial partitioning step can reduce the complexity of large-scale global solutions and improve the efficiency of subsequent task generation and path optimization; the load constraint trip step can ensure that the generated single trip tasks meet the single vehicle loading limit and reduce invalid round trips; the single trip path optimization step can shorten the cost of a single operation route; the multi-vehicle task allocation step can improve the balance of vehicle working hours; and the bottleneck vehicle global reconstruction optimization step can specifically compress the operation time of the last completed vehicle, thereby shortening the overall completion time of all tasks. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating the overall process of the vehicle route planning and scheduling method for waste collection and transportation scenarios according to the present invention.

[0023] Figure 2 This is a module structure diagram of the vehicle route planning and scheduling system for waste collection and transportation scenarios according to the present invention.

[0024] Figure 3 This is a schematic diagram illustrating the spatial division of collection and transportation merchants and the generation of single collection and transportation tasks in this invention.

[0025] Figure 4 This is a schematic diagram illustrating the multi-vehicle task allocation and bottleneck vehicle optimization of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the following embodiments are for illustrative purposes only and are not intended to limit the scope of protection of this invention.

[0027] like Figure 1 As shown, the overall method of this invention includes: S1, data acquisition, acquiring parameters of waste disposal plants or waste transfer stations, locations of waste collection and transportation merchants, waste collection volume, and vehicle parameters; S2, cost matrix construction, constructing a time matrix and / or a distance matrix; S3, spatial division of waste collection and transportation merchants, dividing the space of waste collection and transportation merchants according to spatial distribution relationships, generating multiple geographical sub-regions; S4, single-trip collection and transportation task generation, generating multiple single-trip collection and transportation tasks in each geographical sub-region according to single-vehicle load constraints; S5, single-trip path optimization, optimizing the path of each single-trip collection and transportation task, and calculating the task cost; S6, multi-vehicle task allocation, allocating multiple single-trip collection and transportation tasks to multiple waste collection and transportation vehicles according to the task cost, generating an initial scheduling scheme; S7, global iterative optimization, performing global iterative optimization of the initial scheduling scheme with the goal of minimizing the overall completion time; S8, scheduling result output, outputting the number of operation trips, access order, estimated operation time, and final scheduling result for each waste collection and transportation vehicle.

[0028] In step S1, the location data of the waste disposal plant or waste transfer station (referred to as the central site), the location data of multiple waste collection and transportation companies, the waste collection and transportation volume data corresponding to each company, the number of waste collection and transportation vehicles, and the load capacity of each vehicle are first acquired. Optionally, the service time of the waste collection and transportation companies, the unloading time of the waste disposal plant or waste transfer station, road travel time, or average vehicle speed information are also acquired. Based on the acquired location data, business data, and vehicle constraint data, the basic input set required for subsequent scheduling solutions is constructed. Among them, the location data can be represented by latitude and longitude, planar coordinates, or road network coordinates; the waste collection and transportation volume data can be represented by tons, kilograms, or other uniform units. The output of this step is the basic business dataset and the vehicle constraint dataset.

[0029] In step S2, a time matrix and / or distance matrix are constructed based on the central station location data, merchant location data, and road network information obtained in step S1. If road network data is obtained, a travel time matrix between nodes can be generated based on the shortest path algorithm, road network engine, or historical travel times. The time matrix and / or distance matrix output in step S2 will serve as the cost basis shared by step S3 spatial partitioning, step S5 path optimization, and step S6 task allocation.

[0030] In step S3, using the waste disposal plant or waste transfer station as a reference, all collection and transportation merchants are spatially divided into multiple geographical sub-regions based on their direction and / or distance information relative to the central station. This step breaks down a large-scale collection and transportation merchant set into multiple relatively compact local regions, providing regional boundary data for subsequent generation of single-trip tasks based on load. Preferably, sector partitioning, clustering, or a combination of direction and distance partitioning can be used. Through the above spatial partitioning, the large-scale collection and transportation merchant set can be divided into multiple relatively compact local regions, reducing the complexity of subsequent task construction and optimization; simultaneously, the geographical sub-region information output in step S3 will be directly input into step S4. The spatial division of the collection and transportation merchants based on their spatial distribution relationship with the waste disposal plant or waste transfer station includes at least one of the following: sector division based on the polar angle information of each collection and transportation merchant relative to the waste disposal plant or waste transfer station; joint division based on the direction and distance information of each collection and transportation merchant relative to the waste disposal plant or waste transfer station; spatial grouping of the collection and transportation merchants based on a clustering algorithm; and regional division of the collection and transportation merchants based on road network adjacency relationships.

[0031] like Figure 3As shown, a waste treatment plant or waste transfer station serves as the central node, with multiple waste collection and transportation businesses located at different directions and distances around the central node. By dividing the area into geographical sub-regions A, B, C, and D, the waste collection and transportation businesses can be categorized according to spatial proximity. Based on this, within each geographical sub-region, businesses are grouped according to waste collection volume and vehicle load constraints to form single collection and transportation tasks T1, T2, T3, and T4. Each single collection and transportation task corresponds to a process where a vehicle departs once, visits several businesses, and returns to the central station. Figure 3 The central node, regional boundary, merchant number, and single-trip task frame in the diagram correspond to the central station, geographical sub-region, collection merchant, and single-trip collection task, respectively. Optionally, within each geographical sub-region, the collection merchants are combined according to the vehicle's load capacity to generate multiple single-trip collection tasks. This includes: adding collection merchants one by one to the current single-trip collection task in a preset order and accumulating the waste collection volume of the current single-trip collection task; when adding the next collection merchant would cause the accumulated waste collection volume to exceed the vehicle's load capacity, the current single-trip collection task is terminated and a new single-trip collection task is generated; ensuring that the total waste collection volume of each single-trip collection task does not exceed the vehicle's load capacity.

[0032] In step S4, based on the geographical sub-region information output in step S3 and the waste collection volume data and vehicle load limits of each collection merchant in step S1, the collection merchants are combined to generate multiple single-trip collection tasks. Then, within each geographical sub-region, the collection merchants are combined according to the vehicle load limits to generate multiple single-trip collection tasks. Each single-trip collection task corresponds to a vehicle departing from a waste disposal plant or waste transfer station, visiting several collection merchants, and returning to the central station. Within the same task, the collection merchants are kept as spatially compact as possible. This step utilizes the aforementioned spatial division results and waste collection volume data to decompose the global task into multiple single-trip tasks that meet load feasibility requirements. Preferably, the waste collection volume can be gradually accumulated according to the sorting order of the collection merchants within the geographical sub-region. When the accumulated volume reaches a preset threshold or approaches the vehicle load limit, a single-trip collection task is formed. Furthermore, boundary corrections can be performed on adjacent single-trip collection tasks to improve the load utilization rate and spatial continuity of single-trip tasks. The output of this step is a set of single-trip tasks that meet the load feasibility requirements.

[0033] In step S5, for each single-trip collection and transportation task generated in step S4, the access order of the collection and transportation merchants is optimized to obtain the corresponding single-trip collection and transportation path. Preferably, an initial access order for a single trip is first generated according to the nearest neighbor principle, insertion principle, or other construction rules, and then the access order is optimized using one or more local search methods such as 2-opt, 3-opt, node swapping, node insertion, and continuous node segment relocation. For each single-trip collection and transportation path, its corresponding task cost is further calculated. The task cost includes at least the travel time from the central station to the first collection and transportation merchant, the travel time between collection and transportation merchants, the service time of each collection and transportation merchant, the travel time back to the central station, and the unloading operation time. Step S5 outputs the optimized path and task cost for each single-trip task.

[0034] In step S6, based on the cost of each single-trip collection task output in step S5, multiple single-trip collection tasks are assigned to multiple garbage collection vehicles for execution, resulting in an initial scheduling scheme. Preferably, the single-trip collection tasks are first sorted from highest to lowest cost, and then the currently unassigned single-trip collection task is preferentially assigned to the garbage collection vehicle with the shortest cumulative operating time to reduce the difference in total operating time among garbage collection vehicles, thus obtaining the initial scheduling scheme. After the allocation is completed, the sequence of operation trips and the corresponding total operating time for each garbage collection vehicle can be obtained, and this result will be used as the input for step S7.

[0035] Figure 4 In the diagram, the vehicle frames, task blocks, migration arrows, and bottleneck markers correspond to waste collection vehicles, single-trip collection tasks, task redistribution directions, and bottleneck vehicle optimization objects, respectively. Different vehicle frames represent different waste collection vehicles, and the task blocks within each vehicle frame represent the sequence of single-trip collection tasks assigned to that vehicle. For example... Figure 4 As shown, multiple single-trip collection tasks are first assigned to multiple garbage collection vehicles V1, V2, and V3, forming a sequence of operation trips for each vehicle. The bottleneck vehicle, specially marked in the diagram, represents the garbage collection vehicle with the longest cumulative operation time. For this bottleneck vehicle, operations such as single-trip task migration, cross-vehicle task exchange, boundary merchant re-division, or re-insertion can be performed, and the affected paths can be further optimized, resulting in a more balanced operation time for each vehicle and a shorter overall completion time.

[0036] In step S7, with the goal of minimizing the overall completion time of all waste collection merchants, a global iterative optimization is performed on the initial scheduling scheme to further reduce the overall completion time of all waste collection merchants. The global iterative optimization preferably includes one or more of the following operations: randomly removing some collection merchants and re-inserting them into other single-trip collection tasks; extracting boundary collection merchants from two or more geographically adjacent single-trip collection tasks and re-dividing them; removing one or more single-trip collection tasks from the bottleneck waste collection vehicle with the longest current cumulative operation time and migrating them to other waste collection vehicles; exchanging single-trip collection tasks between different waste collection vehicles; re-optimizing the paths of the reconstructed single-trip collection tasks; and making an acceptance judgment on the global solution and retaining the better solution. Preferably, the global iterative optimization adopts an adaptive large neighborhood search mechanism, dynamically adjusting the probability of using different reconstruction operators based on historical optimization results to improve solution efficiency and solution quality. This step uses the initial scheduling scheme formed in the previous stage as input to perform targeted reconstruction optimization on the bottleneck vehicle that completes its work last, thereby further compressing the overall completion time.

[0037] In step S8, the final scheduling plan is output, including the sequence of operation trips for each waste collection vehicle, the order in which the waste collection merchants are accessed for each trip, the estimated start and end times for each trip, the cumulative operation time for each waste collection vehicle, and the overall completion time for all waste collection merchants to complete waste collection. This output can be directly used for vehicle scheduling execution, operation plan display, and business analysis.

[0038] like Figure 2 As shown, this invention also provides a vehicle route planning and scheduling system for waste collection and transportation scenarios, including a data acquisition module, a preprocessing module, a spatial partitioning module, a single-trip task generation module, a route optimization module, a vehicle allocation module, a global optimization module, and a scheduling output module. The data acquisition module collects raw business data; the preprocessing module constructs a time matrix and / or a distance matrix; the spatial partitioning module outputs geographical sub-region information based on the spatial relationship between the central station and the collection and transportation merchants; the single-trip task generation module generates single-trip collection and transportation tasks based on regional information and load constraints; the route optimization module outputs the task cost; the vehicle allocation module generates an initial scheduling scheme; the global optimization module reconstructs and optimizes the initial scheduling scheme; and the scheduling output module outputs the final scheduling result. Figure 2 Each module box and connecting arrow in the diagram corresponds to a system functional module and its data transfer relationship.

[0039] Figure 3This diagram illustrates the spatial division of collection and transportation merchants and the generation of single-trip collection and transportation tasks according to the present invention. The central node represents a waste disposal plant or waste transfer station, and the surrounding scattered points H1 to H12 represent different collection and transportation merchants. Dashed lines or boundary lines represent the geographical sub-regions A, B, C, and D formed by the spatial division. The task boxes T1 to T4 on the right represent single-trip collection and transportation tasks generated based on single-vehicle load constraints. Each task box lists a merchant combination corresponding to the operation process of a vehicle departing once, visiting several merchants, and returning to the central station. The example path in the diagram further illustrates the internal access order and return-to-station relationship of a single-trip task. Single-trip collection and transportation tasks are generated according to load constraints, where: merchant combinations for single-trip task T1 are: H1, H2, H3; merchant combinations for single-trip task T2 are: H4, H5, H6; merchant combinations for single-trip task T3 are: H7, H8, H9; and merchant combinations for single-trip task T4 are: H10, H11, H12.

[0040] In one specific application embodiment, approximately 2,000 waste collection and transportation companies serve as service points, and 20 waste collection and transportation vehicles serve as execution units, with a maximum load capacity of 5 tons per vehicle. The central station is a waste treatment plant. After obtaining the locations of the waste treatment plant and each waste collection and transportation company, the daily waste collection volume of each company, and vehicle parameters, the system first constructs a time matrix. Then, it divides all waste collection and transportation companies into multiple geographical sub-regions. Within each geographical sub-region, it generates multiple single-trip collection and transportation tasks based on load constraints. After optimizing the path for each single-trip collection and transportation task, it assigns it to the 20 waste collection and transportation vehicles for execution. Finally, it uses global iterative optimization to compress the total operation time of the last-to-finish vehicle and outputs the final collection and transportation routes for each waste collection and transportation vehicle.

[0041] Compared to scheduling schemes based on fixed area division or simple nearest neighbor methods, this invention can significantly reduce the overall time required for all waste collection and transportation businesses to complete waste collection and transportation, and improve the working time balance among different waste collection and transportation vehicles, thus having high engineering application value.

[0042] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. This invention provides a vehicle route planning and scheduling method for waste collection and transportation scenarios, characterized in that... The vehicle route planning and scheduling method for waste collection and transportation scenarios includes the following steps: S1. Data acquisition: Acquire the location of the central station, the location of multiple collection and transportation merchants, the amount of garbage collected by each collection and transportation merchant, the number of garbage collection and transportation vehicles, the load of a single vehicle, the service time of the collection and transportation merchant, the unloading time of the central station, and road travel time or average vehicle speed information. S2. Cost matrix construction: Based on the distance information between the central station and each collection and transportation merchant, as well as the road network travel time information between each collection and transportation merchant, a time matrix and / or distance matrix are constructed to describe the travel cost between any two nodes. S3. Spatial division of collection and transportation merchants: Based on the spatial distribution relationship between collection and transportation merchants and waste disposal plants or waste transfer stations, the collection and transportation merchants are spatially divided into multiple geographical sub-regions. S4. Single-trip collection task generation: In each geographical sub-region, the collection merchants are combined according to the maximum load capacity of the garbage collection vehicles to generate multiple single-trip collection tasks. S5. Single-trip route optimization: Optimize the access order of collection and transportation merchants in each single-trip collection and transportation task. S6. Multi-vehicle task allocation: Based on the task cost of each single-trip collection and transportation task, multiple single-trip collection and transportation tasks are allocated to multiple garbage collection and transportation vehicles for execution. S7. Global Iterative Optimization: With the goal of minimizing the overall completion time for all waste collection and transportation merchants to complete waste collection and transportation, the initial scheduling scheme is globally iteratively optimized. S8. Output the scheduling results, including the sequence of each garbage collection vehicle's trips, the order of access to the collection merchants for each trip, the estimated start and end times, the cumulative working time of each vehicle, and the overall completion time, forming the final scheduling results that meet the requirements of the project execution.

2. The vehicle route planning and scheduling method for waste collection and transportation scenarios according to claim 1, characterized in that, In step S3, the spatial partitioning method is sector partitioning based on polar angle sorting, hierarchical partitioning based on joint constraints of direction and distance, spatial grouping based on clustering algorithm, or region partitioning based on road network adjacency relationship.

3. The vehicle route planning and scheduling method for waste collection and transportation scenarios according to claim 1, characterized in that, In step S4, the process of combining collection merchants in each geographical sub-region according to the single vehicle load capacity to generate multiple single-trip collection tasks includes: adding collection merchants one by one to the current single-trip collection task in a preset order, and accumulating the garbage collection volume of the current single-trip collection task; when adding the next collection merchant will cause the accumulated garbage collection volume to exceed the single vehicle load capacity, ending the current single-trip collection task and generating a new single-trip collection task; ensuring that the total garbage collection volume of each single-trip collection task does not exceed the single vehicle load capacity.

4. The vehicle route planning and scheduling method for waste collection and transportation scenarios according to claim 1, characterized in that, In step S5, an initial access order is first generated according to the nearest neighbor principle or the insertion principle; then, the initial access order is improved using a local search method, which includes one or more of 2-opt, 3-opt, node swapping, node insertion, or continuous node segment relocation.

5. The vehicle route planning and scheduling method for waste collection and transportation scenarios according to claim 1, characterized in that, The cost of the task includes at least one of the following: travel time between the waste disposal plant or waste transfer station and the collection and transportation merchants; travel time between the collection and transportation merchants; and the service time of the collection and transportation merchants. Travel time to return to the waste disposal plant or waste transfer station; The time during which unloading operations are carried out at waste disposal plants or waste transfer stations.

6. The vehicle route planning and scheduling method for waste collection and transportation scenarios according to claim 1, characterized in that, In step S6, the tasks are first sorted from largest to smallest according to their cost, and then the tasks to be assigned are preferentially assigned to the vehicles with the shortest cumulative working time in order to reduce the difference in total working time between vehicles and obtain the initial scheduling scheme.

7. The vehicle route planning and scheduling method for waste collection and transportation scenarios according to claim 1, characterized in that, In step S7, the optimization process includes random removal and re-insertion, re-division of adjacent task boundaries for waste collection merchants, migration of bottleneck waste collection vehicles, cross-vehicle task exchange, and re-optimization of reconstructed paths. The bottleneck waste collection vehicle is the waste collection vehicle with the longest current cumulative operating time. At least one single-trip collection task on the bottleneck waste collection vehicle is migrated to other waste collection vehicles, or single-trip collection tasks are exchanged between the bottleneck waste collection vehicle and other waste collection vehicles, so as to reduce the overall completion time for all waste collection merchants to complete waste collection.

8. A vehicle route planning and scheduling system for waste collection and transportation scenarios, characterized in that, include: The data acquisition module is used to obtain the location of waste disposal plants or waste transfer stations, the location of waste collection and transportation companies, the amount of waste collected and transported by the waste collection and transportation companies, the number of waste collection and transportation vehicles, and the load capacity of each vehicle. The spatial division module is used to divide the collection and transportation merchants into multiple geographical sub-regions based on the spatial distribution relationship between the collection and transportation merchants and the waste disposal plant or waste transfer station. The single-trip task generation module is used to combine the collection and transportation merchants according to the single vehicle load in each of the geographical sub-regions to generate multiple single-trip collection and transportation tasks; the path optimization module is used to optimize the path of the access order of the collection and transportation merchants in each of the single-trip collection and transportation tasks to obtain the task cost of each of the single-trip collection and transportation tasks. The vehicle allocation module is used to allocate multiple single-trip collection and transportation tasks to multiple garbage collection and transportation vehicles for execution based on the task cost of each single-trip collection and transportation task, and generate an initial scheduling scheme. The global optimization module is used to perform global iterative optimization of the initial scheduling scheme with the goal of minimizing the overall completion time of all waste collection and transportation merchants. The scheduling output module is used to output the operation trips corresponding to each garbage collection and transportation vehicle, the access order of the collection and transportation merchants, and the final scheduling results.

9. The vehicle route planning and scheduling system for waste collection and transportation scenarios according to claim 8, characterized in that, It also includes a preprocessing module, which is used to construct a time matrix and / or a distance matrix based on the distance information or travel time information between the waste disposal plant or waste transfer station and the collection and transportation merchants, as well as between the collection and transportation merchants.

10. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the method according to any one of claims 1 to 9.

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