CN119990940B - A shared car passenger and cargo transportation method based on vehicle-mounted cargo drone technology - Google Patents
A shared car passenger and cargo transportation method based on vehicle-mounted cargo drone technologyInfo
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Abstract
本发明涉及一种基于车载货运无人机技术下的共享汽车客货共运方法，该方法结合无人机和共享汽车进行客货共运，其中，无人机用于处理交付时间敏感的轻量级包裹，共享汽车在完成运送乘客的同时辅助无人机完成包裹配送任务，包括：依据无人机‑共享汽车客货共运方式，构建无人机‑共享汽车客货共运路径优化目标函数和约束，并选定决策变量；基于目标函数和约束进行共享汽车路径规划，并根据路径规划的结果生成无人机‑共享汽车客货共运方案。与现有技术相比，本发明提供了一种共享汽车‑无人机客货共运的物流形式，在保障客运服务效率和水平的前提下，实现货运网络流量扩张成本最小化。
This invention relates to a shared car passenger and cargo transport method based on vehicle-mounted cargo drone technology. This method combines drones and shared cars for passenger and cargo transport. The drones are used to handle lightweight packages with time-sensitive delivery times, while the shared cars assist the drones in delivering packages while transporting passengers. The method includes: constructing a drone-shared car passenger and cargo transport path optimization objective function and constraints, and selecting decision variables based on the drone-shared car passenger and cargo transport model; performing shared car path planning based on the objective function and constraints, and generating a drone-shared car passenger and cargo transport plan based on the path planning results. Compared to existing technologies, this invention provides a shared car-drone passenger and cargo transport logistics model that minimizes the cost of freight network traffic expansion while ensuring the efficiency and quality of passenger transport services.
Description
Technical Field
The invention relates to the field of shared automobile dispatching and logistics distribution, in particular to a shared automobile passenger-cargo co-transportation method based on an on-board freight unmanned plane technology.
Background
Passenger-cargo co-transportation has gained widespread attention in recent years as a new transportation model. This concept has great potential in urban transportation systems by commonly using transportation means to connect people and cargo mobility. Based on shared automobile resources, the passenger transport demand is realized, and meanwhile, the freight transport demand is completed, so that the transport efficiency is improved, and the transport cost is reduced. Because the integration of passenger and cargo flows into a single system, the number of shared vehicles traveling in a city can be reduced and passenger and cargo flows can be better synchronized. The passenger-cargo co-transportation mode truly brings a solution idea for more and more express demands, but on the other hand, the popularization of the mode is hindered due to the problems of shared automobile detours, contradiction between passenger and cargo space allocation and the like caused by inconsistent passenger transportation and cargo demand points. Therefore, students turn the eyes to the distribution direction of the logistics tail end responsible by the unmanned aerial vehicle, hope to add the unmanned aerial vehicle into the passenger-cargo co-transportation system, improve the flexibility of the distribution system, solve the problem of order delay caused by the bypassing of the shared automobile, and realize the construction of the urban passenger-cargo co-transportation mode. From the perspective of the way that unmanned aerial vehicles are integrated into logistics transportation systems, several common ways exist, such as the way that unmanned aerial vehicles form freight networks independently, the way that unmanned aerial vehicles are combined with large trucks and the way that unmanned aerial vehicles are combined with public transportation lines, and the research of the three ways is focused on carrying out path planning by improving the delivery rate of goods. For example, the Chinese application patent CN113139678A is a combination of an unmanned aerial vehicle and a large truck, provides a method for jointly distributing the unmanned aerial vehicle and a shared automobile, distributes customer points to the unmanned aerial vehicle as much as possible, distributes a plurality of packages once under the limitation of load and flight distance, distributes the shared automobile with the unmanned aerial vehicle, jointly cooperates with the unmanned aerial vehicle to jointly complete distribution tasks, improves the efficiency of goods distribution and reduces the length of the total distribution total path, and the Chinese application patent CN113359821A provides a path planning method and a path planning system based on the collaborative operation of the shared automobile and the unmanned aerial vehicle, solves the problem that the unmanned aerial vehicle and the shared automobile cannot cooperatively perform path planning in the prior art, and can not realize efficient freight of the unmanned aerial vehicle and the shared automobile, but can not realize common distribution only, namely, can not realize the quantity requirement and flexibility of express delivery simultaneously in the collaborative process of the unmanned aerial vehicle and the shared automobile.
Therefore, it is a technical problem to provide a method that can satisfy both the demand for the quantity of express delivery and the flexibility.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a shared automobile passenger-cargo co-transportation method based on the technology of a vehicle-mounted freight unmanned aerial vehicle, the combination of the unmanned aerial vehicle and a shared automobile (such as a network bus, a shared automatic driving automobile and the like) provides an innovative solution for a passenger-cargo co-transportation network, the flexibility and the quick response capability of the unmanned aerial vehicle enable the unmanned aerial vehicle to timely deliver time-sensitive lightweight packages, the shared automobile can participate in processing freight delivery tasks while mainly completing passenger delivery tasks, and the unmanned aerial vehicle and the shared automobile can independently execute respective transportation tasks and can also be carried by the shared automobile together, so that the complementarity remarkably improves the efficiency of a mixed transportation frame, and particularly in an area with dense transportation requirements.
The aim of the invention can be achieved by the following technical scheme:
The invention provides a shared automobile passenger and cargo sharing method based on a vehicle-mounted freight unmanned aerial vehicle technology, which combines an unmanned aerial vehicle and a shared automobile to carry out passenger and cargo sharing, wherein the unmanned aerial vehicle is used for processing a delivery time-sensitive lightweight package, and the shared automobile assists the unmanned aerial vehicle to complete a package delivery task while completing passenger transportation, and comprises the following steps:
According to an unmanned plane-shared automobile passenger-cargo co-transportation mode, constructing an unmanned plane-shared automobile passenger-cargo co-transportation path optimization objective function and constraint, and selecting decision variables, wherein the objective function comprises a minimized operation cost function and a minimized transportation time function;
And carrying out shared automobile path planning based on the objective function and the constraint, and generating an unmanned plane-shared automobile passenger-cargo sharing scheme according to the path planning result.
As an optimal technical scheme, the unmanned aerial vehicle-shared automobile passenger-cargo co-transportation mode comprises a single-vehicle single-machine mapping mode, namely, the shared automobile and the unmanned aerial vehicle have a one-to-one fixed pairing relationship, and the shared automobile only provides cargoes for the unmanned aerial vehicle and does not fulfill delivery responsibilities.
As a preferred technical solution, in the single-vehicle single-machine mapping mode, the constraint includes:
sharing automotive demand matching limit constraints: wherein, K represents a total shared automobile, K represents a kth shared automobile, j represents an end point of the passenger demand, and P represents a total passenger demand point set; Indicating whether the kth shared car is from the starting point i to the destination j, if so If not, then
Freight demand point service times constraint: Wherein T represents a total unmanned aerial vehicle, T represents a T-th unmanned aerial vehicle, j 'represents an end point of a freight demand, and P' represents a total freight demand point set; indicating whether the t unmanned aerial vehicle is from the starting point i 'to the destination j', if so, then If it is otherwise
Sharing car parking lot starting sequence constraint: And Wherein h represents any node in the road network node set V; the method comprises the steps of representing a starting yard to a node h, wherein g represents any node g in a road network node set V, and 2n+1 represents an ending yard; indicating whether the shared automobile k goes from the node g to the terminal parking lot or not, if so, then If it is otherwise
Road network node flow balance constraint: Wherein, the Representing the path distance from node h to node g of the kth shared automobile; indicating whether the kth shared automobile has a path distance from the node g to the node h, if so, then If it is otherwise
Sharing automotive path time constraints: Wherein, the D g is the service time of the shared automobile for transmitting the unmanned aerial vehicle at the node g; Representing the time from g to h of the shared automobile k in the road network; M represents an infinite constant;
Unmanned aerial vehicle path time constraint: Wherein, the Is the time for the drone t to reach g, d' g is the service time for the drone to be launched at node g,The time from g to h of the unmanned aerial vehicle T in the road network is represented by T;
Sharing the car arrival time constraint: Wherein, the Indicating the time to the origin i of the passenger demand; Indicating the time to reach the passenger demand endpoint j; The method comprises the steps of representing the time of arrival of a shared automobile at a point w of a meeting of an unmanned aerial vehicle, wherein I is a demand start point set, J is a demand end point set;
Unmanned aerial vehicle arrival time constraint: Wherein, the Representing the time from the unmanned aerial vehicle to the freight demand starting point i'; representing the time from the unmanned aerial vehicle to the freight demand meeting point w; the time from the unmanned aerial vehicle to the freight demand terminal j' is represented;
Freight demand processing time constraints: Wherein, the The time when the unmanned aerial vehicle reaches the actual freight demand starting point i' 1 is represented; representing the time when the shared automobile arrives at the virtual freight demand start i' 2; the time when the unmanned aerial vehicle reaches the actual freight demand end point j' 1 is represented; representing the time when the shared automobile arrives at the virtual freight demand start j' 2; Wherein, the The latest service time representing the freight demand; Representing the latest delivery time of the freight demand;
Sharing automotive capacity constraints: And Wherein, the Representing the number of passengers in the shared car k after leaving the demand origin i; representing the number of passengers in the shared automobile after the shared automobile k leaves any node v, p ij representing the number of passengers required, and the passenger traffic of the shared automobile on any node in the road network is less than or equal to the capacity of the shared automobile;
unmanned aerial vehicle capacity constraint: And Wherein, the Representing cargo hold capacity after the unmanned aerial vehicle t leaves the demand start point i'; The cargo capacity of the unmanned aerial vehicle t after leaving the node v is represented, q i′j′ represents the required cargo quantity, and the cargo capacity of the unmanned aerial vehicle on any node in the road network is less than or equal to the unmanned aerial vehicle capacity;
unmanned aerial vehicle duration constraint: Wherein, the Representing the path distance between the actual demand start point i '1 and the virtual demand start point i' 2 of the unmanned aerial vehicle; And B represents the continuous flight mileage of the unmanned aerial vehicle.
As an optimal technical scheme, in the single-vehicle single-machine mapping mode, the method for planning the shared automobile path solves the problem of the shared automobile path with time window and capacity constraint.
As the preferable technical scheme, the unmanned aerial vehicle-shared automobile passenger-cargo co-transportation mode also comprises a multi-automobile multi-machine mapping mode, namely the shared automobile and the unmanned aerial vehicle are in one-to-one cooperation and have no fixed pairing relation, so that the unmanned aerial vehicle is allowed to flexibly enable all shared automobiles connected into the system, the shared automobile distributes cargoes to the take-off and landing point of the unmanned aerial vehicle, the unmanned aerial vehicle can directly leave after being launched, and the unmanned aerial vehicle does not need to return to the same shared automobile.
As an preferable technical solution, in the multi-vehicle multi-machine mapping mode, the constraint includes a primary network constraint, a secondary network constraint and a linking constraint, where the primary network constraint includes:
sharing automotive demand matching limit constraints: wherein K represents a total shared automobile, K represents a kth shared automobile, j represents a starting point of a passenger demand, and P represents a total passenger demand; Indicating whether the kth shared car is from the starting point i to the destination j, if so If not, then
Sharing car parking lot starting sequence constraint: And Wherein h represents the h node in the road network node set V; G represents the g-th node in the road network node set V, 2n+1 represents the end point parking lot; Indicating the node g to the destination yard, if so, then If not, then
Road network node flow balance constraint: Wherein, the Representing the kth shared automobile from node h to node g; Representing the kth shared automobile from node g to node h, if so, then If not, then
Sharing automotive path time constraints: Wherein, the D g is the service time of the shared automobile for transmitting the unmanned aerial vehicle at the node g; Representing the time from g to h of the shared automobile k in the road network; M represents an infinite constant;
Sharing the car arrival time constraint: Wherein, the Representing the time to arrival at the passenger demand destination i; the time for reaching the passenger transport demand start point J is represented, wherein I is a demand start point set, J is a demand end point set;
Sharing automotive capacity constraints: And Wherein, the Representing the number of passengers in the shared car k after leaving the demand origin i; representing the number of passengers in the shared automobile after the shared automobile k leaves any node v, p ij representing the number of passengers required, and the passenger traffic of the shared automobile on any node in the road network is less than or equal to the capacity of the shared automobile;
the secondary network constraints include:
Unmanned aerial vehicle path time constraint: Wherein, the D' g is the service time of the unmanned aerial vehicle transmitting the unmanned aerial vehicle at the node g,The time from g to h of the unmanned aerial vehicle T in the road network is represented by T;
Unmanned aerial vehicle arrival time constraint: Wherein, the The time from the unmanned aerial vehicle to the freight demand starting point i' is represented; the time from the unmanned aerial vehicle to the freight demand terminal j' is represented;
Demand processing time constraints: Wherein, the The time when the unmanned aerial vehicle reaches the actual demand starting point i' 1 is represented; Representing the time when the shared automobile reaches the virtual demand start i' 2; The time when the unmanned aerial vehicle reaches the actual demand end point j' 1 is represented; Representing the time when the shared automobile reaches the virtual demand start j 2; Wherein, the Representing the latest service time of the demand; Representing the latest delivery time of the demand;
Unmanned aerial vehicle second grade network node balance constraint: Wherein, the Indicating whether the t-th unmanned aerial vehicle is from the node h to the node g, if so, thenIf not, thenIndicating whether the t-th unmanned aerial vehicle is from node g to node h, if so, thenIf not, then
Unmanned aerial vehicle take-off and landing point unmanned aerial vehicle quantity constraint: Wherein t g represents the unmanned aerial vehicle capacity of node g, which is a fixed constant;
unmanned aerial vehicle capacity constraint: And Wherein, the Representing cargo hold capacity after the unmanned aerial vehicle t leaves the demand start point i'; The cargo capacity of the unmanned aerial vehicle t after leaving the node v is represented, q i′j′ represents the required cargo quantity, and the cargo capacity of the unmanned aerial vehicle on any node in the road network is less than or equal to the unmanned aerial vehicle capacity;
unmanned aerial vehicle duration constraint: Wherein, the The path distance between the actual demand start point i '1 and the virtual demand start point i' 2 of the unmanned aerial vehicle is represented;
the linking constraint comprises two-stage network freight flow balance constraint, namely Wherein, the To share the amount of cargo that car k transports from node g to node h,The amount of cargo transported from node g to node h for drone t.
As an optimal technical scheme, in the multi-vehicle multi-machine mapping mode, the method for planning the shared automobile path solves the problem of double-target capacity-limited shared automobile paths with time windows.
As an optimal technical scheme, the decision variables are a 0-1 variable of whether the shared automobile passes through two nodes in the road network or not and a 0-1 variable of whether the unmanned aerial vehicle moves or not.
As a preferred technical solution, the minimized operation cost function is:
Wherein C e |K| represents the fixed use cost of K shared vehicles and is determined by the number of the shared vehicles, C e |T| represents the fixed use cost of the T-frame unmanned aerial vehicle and is determined by the number of the freight unmanned aerial vehicles; representing the unit running cost of a unit shared automobile, wherein g and h are any points in a road network node set V, and are determined by the number of the shared automobiles and the shared automobile paths; Representing the use cost of sharing the automobile from node g to node h; representing the path distance of the shared automobile from node g to node h; Representing the use cost of the unmanned aerial vehicle from node g to node h; The path distance from node g to node h is represented.
As a preferred embodiment, the minimized transportation time function is:
Wherein, the To share the travel time of car k from node g to h; the method comprises the steps of taking the travel time of an unmanned aerial vehicle t from a node g to a node h, wherein K represents any shared automobile in a shared automobile set K, and g and h represent any node in a road network node set V.
Compared with the prior art, the shared automobile-unmanned aerial vehicle passenger and cargo co-transportation method is provided, the operation cost minimization and the overall transportation time minimization are used as double objective functions, and appropriate constraint is constructed according to an actual operation mode to carry out path planning, so that the time cost of the shared automobile for completing passenger transportation is not increased while the logistics efficiency is improved; compared with the prior art that only the shared automobile and the unmanned aerial vehicle are allowed to carry out freight work together or only the shared automobile carries out passenger-cargo co-transportation, the method provided by the invention has the advantages that the demand and the flexibility of the quantity of express delivery are simultaneously considered, the problem that the shared automobile bypasses in the passenger-cargo co-transportation process can be avoided, the unmanned aerial vehicle is utilized to the maximum extent, and the passenger-cargo co-transportation service efficiency is improved. In the whole passenger-cargo co-transportation process, the unmanned aerial vehicle completes the final delivery mileage, so that the condition that the vehicle bypasses to complete the freight delivery task and the passenger transportation efficiency is reduced, and the whole process is more flexible and suitable for wider scenes.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of a passenger-cargo co-transportation system in a single-vehicle single-machine mapping mode according to the present invention;
FIG. 3 is a logic flow diagram of a shared automobile-drone handling cargo demand in a single-car single-machine mapping mode of the present invention;
FIG. 4 is a schematic diagram of a passenger-cargo co-transportation system in a multi-vehicle multi-machine mapping mode according to the present invention;
FIG. 5 is a logic flow diagram of a shared vehicle handling passenger and cargo demand in a multi-vehicle multi-machine mapping mode of the present invention;
fig. 6 is a logic flow diagram of an unmanned aerial vehicle processing passenger and cargo demands in a multi-vehicle multi-machine mapping mode according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the other features, objects, and advantages of the application.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is to be expressly and implicitly understood by those of ordinary skill in the art that the described embodiments of the application can be combined with other embodiments without conflict.
From the perspective of the way that unmanned aerial vehicle blends into logistics transportation system, several existing common ways, such as that unmanned aerial vehicle constitutes freight network alone, unmanned aerial vehicle combines with large-scale freight train, unmanned aerial vehicle and public transport line network, can not compromise express delivery quantity and flexibility simultaneously. The combination of unmanned aerial vehicles and shared automobiles (such as network bus, shared automatic driving automobiles and the like) provides an innovative solution for passenger-cargo co-transportation networks.
The flexibility and the quick response capability of the unmanned aerial vehicle enable the unmanned aerial vehicle to timely deliver the time-sensitive lightweight packages, and the shared automobile can process delivery tasks with longer distances along the way when completing passenger delivery tasks, the unmanned aerial vehicle and the shared automobile can independently execute respective delivery tasks and can be jointly carried by the shared automobile, and the complementarity remarkably improves the efficiency of the hybrid transportation frame, especially in areas with dense traffic and transportation demands.
Therefore, the application provides a shared automobile passenger-cargo co-transportation method based on the vehicle-mounted freight unmanned aerial vehicle technology, which combines an unmanned aerial vehicle and a shared automobile to carry out passenger-cargo co-transportation, wherein the unmanned aerial vehicle is used for processing a light-weight package with sensitive delivery time, the shared automobile assists the unmanned aerial vehicle to complete package delivery of a package delivery task while completing passenger delivery, and before carrying out path planning, planning of passenger-cargo co-transportation mode operation flow details is carried out firstly, and the method specifically comprises the following steps:
a) And determining the components of the passenger-cargo distribution scene.
The method comprises the steps of determining a main body participating in mode operation, tasks of the main body in a passenger-cargo co-transportation system and interrelationships among the main body, and particularly relates to the main body comprising a shared automobile, a vehicle-mounted freight unmanned aerial vehicle, cargoes, freight and delivery task demand points, passenger transport task demand points, unmanned aerial vehicle take-off and landing points, a delivery center and a road network.
The shared automobile can transmit and receive the vehicle-mounted freight unmanned aerial vehicle only when reaching the take-off and landing point of the unmanned aerial vehicle, the shared automobile preferentially completes a passenger order, and attribute parameters included in the shared automobile include passenger capacity, freight capacity, vehicle speed, maximum driving distance, bicycle cost, bicycle unit mileage power consumption, carbon emission and the like.
The vehicle-mounted freight unmanned aerial vehicle only goes back and forth between the take-off and landing point and the delivery task demand point to complete freight delivery demand, and the attribute parameters included in the vehicle-mounted freight unmanned aerial vehicle comprise freight capacity, unit load endurance, single machine cost, flying speed and the like.
The goods are the main bodies of the whole delivery service, and the properties of the goods comprise weight, volume, type and the like, and the goods in a passenger-goods co-transportation system comprise two types, namely passengers and goods, and when the demand of a certain demand point is larger than the loading capacity of a bicycle, the goods need to be transported for multiple times after being split.
The demand points are also usually location points where service objects are located, and can be regarded as nodes on corresponding roads for sharing objects that the automobile must serve in the delivery task, and in real life, each demand point generally includes a location, a demand amount, a serviceable time, a service priority level, and the like, and according to different demands, the freight demand point may be accessed multiple times, or may only need to be accessed once, while the passenger demand point default needs to be accessed only once.
The unmanned aerial vehicle take-off and landing point is a service point for sharing the automobile to launch or recycle the unmanned aerial vehicle, is also an important functional node in the road network, is a destination point for sharing the automobile to deliver the goods, and is also a starting point for the unmanned aerial vehicle to deliver the goods.
The distribution center is the starting point of each shared automobile, the traditional solution VRP (Vehicle Routing Problem) problem requires that all the shared automobiles must start from the distribution center, all cargoes are returned to the distribution center after distribution, generally, one or more distribution centers can be arranged, the attribute of each distribution center comprises a position, service starting time, service ending time, the number of the shared automobiles and the like, the distribution center in the scene is an express storage station, and although a plurality of storage stations are arranged in one city, each storage station corresponds to a fixed community service point, so that the single-yard VRP problem can be seen from the aspect of dividing regions, and in addition, all the shared automobiles which are taken by customers start from the storage stations, and return to the nearest storage station after the tasks are executed.
The road network is a carrier of the shared automobile, the shared automobile must complete distribution service through the road network, and the information contained in the road network comprises the degree of road congestion, the connection relation between nodes, the transportation distance and the like, so that the optimal distribution path can be directly determined.
B) And constructing the system constraint of the passenger-cargo co-transportation system.
The shared car and the unmanned aerial vehicle are not allowed to exceed the rated load, and the comfort level of passengers and the total time cost are considered, so that each shared car is set to only allow one passenger order to be served at a time in a passenger-cargo total mode, namely different passengers are not taken into account.
The number of the shared automobiles in the road network is enough to meet the instant travel demands of all passengers, the starting points of all the riding orders are cargo storage stations, and the speed of the vehicles in the journey is constant.
After the passenger submits the order, the passenger needs to wait at the unmanned aerial vehicle take-off and landing point, namely, the passenger demand point can only be selected from the unmanned aerial vehicle take-off and landing points.
All passengers default to agree to take the goods together, passengers are not allowed to get off the vehicle in the middle, the total time of getting on and off the vehicle is fixed, after the combination matching is successful, the goods are immediately started after being loaded in the transfer, and the loading and unloading time of the goods is the determined time.
The unmanned aerial vehicle has constant flying speed, linearly reciprocates between two points, automatically changes full cell batteries after returning to the shared automobile each time, and can complete power change before the next take-off.
C) And determining a vehicle-machine cooperation mode in the passenger-cargo distribution scene and designing an operation flow.
According to the characteristics of road network density and the like of the applicable scene, the vehicle-machine mapping mode of the passenger-cargo co-transportation scene, the corresponding passenger-cargo distribution demand processing sequence, task distribution logic, unmanned aerial vehicle distribution range division and the like can be determined, the application adopts a single vehicle-single machine mapping mode and a multi-vehicle-multi-machine mapping mode, and the shared vehicle-unmanned aerial vehicle passenger-cargo co-transportation design is carried out according to the flow shown in fig. 1, and the steps comprise:
S1, according to an unmanned plane-shared automobile passenger and cargo co-transportation mode (single automobile or multiple automobiles and multiple automobiles), an unmanned plane-shared automobile passenger and cargo co-transportation path optimization objective function and constraint are constructed, decision variables are selected, and the objective function comprises a minimized operation cost function and a minimized transportation time function.
S2, carrying out shared automobile path planning based on the objective function and the constraint, and generating an unmanned plane-shared automobile passenger-cargo co-transportation scheme according to the path planning result.
For details, the flow of the single vehicle single machine mapping mode and the multi-vehicle multi-machine mapping mode is described in example 1 and example 2.
Example 1
The embodiment provides a shared automobile-unmanned aerial vehicle passenger-cargo sharing mode of a single automobile, namely the shared automobile and the unmanned aerial vehicle have a one-to-one fixed pairing relation, and the shared automobile only provides cargoes for the unmanned aerial vehicle and does not fulfill delivery responsibilities, so that the load driving distance of the unmanned aerial vehicle is furthest reduced, and the shared automobile is regarded as a mobile warehouse of the unmanned aerial vehicle. The unmanned aerial vehicle basically moves along the shared automobile, leaves the shared automobile briefly during goods taking and delivering, independently executes tasks, brings goods back to the shared automobile after goods taking, and executes delivery tasks after the shared automobile is transported to a take-off and landing point near a goods demand point and finally returns to the shared automobile. The execution logic is shown in fig. 3, and specifically comprises:
Whether the shared automobile accepts the new passenger order is judged, the judgment is only dependent on the current state of the shared automobile, if the shared automobile is completing the passenger receiving or delivering task, the new order is not accepted, if the shared automobile is temporarily free of the passenger task, the new passenger order is accepted, and the shared automobile does not need to be used as a main body to process the freight order, namely, the shared automobile only distributes one passenger task at a time.
When there is a shipping order request, the passenger-cargo co-transportation system checks the status of the drone. Whether the unmanned aerial vehicle accepts the freight order also depends on the unmanned aerial vehicle task state only, but whether the unmanned aerial vehicle accepts the delivery task can influence the subsequent path selection of the shared automobile, and the shared automobile can select the nearest take-off and landing point to launch or recycle the unmanned aerial vehicle under the condition that the passenger order time allows.
If the unmanned aerial vehicle is in a delivery state or a pickup state, a new freight order request is refused, and the shared automobile launches the unmanned aerial vehicle in the process of executing the passenger transport task.
And if the unmanned aerial vehicle is temporarily not tasked, receiving a freight order request, and transmitting the unmanned aerial vehicle by the shared automobile in the process of executing the passenger transport task.
If the unmanned aerial vehicle gets goods, a new freight order request is refused, and the shared automobile recovers the unmanned aerial vehicle in the process of executing the passenger transport task.
The above flow ensures the order and efficiency of the unmanned aerial vehicle when executing tasks, and simultaneously ensures the response modes of the unmanned aerial vehicle in different task states.
Step S1 is performed, in which the objective function in this mode is first confirmed:
In order to explore how to introduce a novel passenger-cargo co-transportation mode operation effect of a vehicle-mounted freight unmanned aerial vehicle, both operation cost (in view of standing on an operation platform) and service level (in view of standing on social benefit and user experience) are required to be considered. The transportation mode service level takes the average time of order completion of a transportation system as a consideration, namely, the minimum total transportation time as a target, so that the objective function is a double objective function of minimizing the operation cost and minimizing the total transportation time. Specifically, the minimized operation cost function is:
Wherein C e |K| represents the fixed use cost of K shared vehicles and is determined by the number of the shared vehicles, C e |T| represents the fixed use cost of the T-frame unmanned aerial vehicle and is determined by the number of the freight unmanned aerial vehicles; representing the unit running cost of a unit shared automobile, wherein g and h are any points in a road network node set V, and are determined by the number of the shared automobiles and the shared automobile paths; Representing the use cost of sharing the automobile from node g to node h; representing the path distance of the shared automobile from node g to node h; Representing the use cost of the unmanned aerial vehicle from node g to node h; The path distance from node g to node h is represented.
The minimized transit time function is:
Wherein, the To share the travel time of car k from node g to h; the method comprises the steps of taking the travel time of an unmanned aerial vehicle t from a node g to a node h, wherein K represents any shared automobile in a shared automobile set K, and g and h represent any node in a road network node set V.
Next, decision variables in this mode were confirmed as shown in table 1.
Table 1 decision variable table in single vehicle single machine mapping mode
Finally, constructing path planning constraint under single-vehicle single-machine mode, which specifically comprises the following steps:
sharing automotive demand matching limit constraints: wherein, K represents a total shared automobile, K represents a kth shared automobile, j represents an end point of the passenger demand, and P represents a total passenger demand point set; Indicating whether the kth shared car is from the starting point i to the destination j, if so If not, then
Freight demand point service times constraint: Wherein T represents a total unmanned aerial vehicle, T represents a T-th unmanned aerial vehicle, j 'represents an end point of a freight demand, and P' represents a total freight demand point set; indicating whether the t unmanned aerial vehicle is from the starting point i 'to the destination j', if so, then If it is otherwise
Sharing car parking lot starting sequence constraint: And Wherein h represents any node in the road network node set V; the method comprises the steps of representing a starting yard to a node h, wherein g represents any node g in a road network node set V, and 2n+1 represents an ending yard; indicating whether the shared automobile k goes from the node g to the terminal parking lot or not, if so, then If it is otherwise
Road network node flow balance constraint: Wherein, the Representing the path distance from node h to node g of the kth shared automobile; indicating whether the kth shared automobile has a path distance from the node g to the node h, if so, then If it is otherwise
Sharing automotive path time constraints: Wherein, the D g is the service time of the shared automobile for transmitting the unmanned aerial vehicle at the node g; Representing the time from g to h for a shared car k in the road network, and M represents an infinite constant.
Unmanned aerial vehicle path time constraint: Wherein, the Is the time for the drone t to reach g, d' g is the service time for the drone to be launched at node g,The time from g to h of the unmanned aerial vehicle T in the road network is represented by T, and the T represents the unmanned aerial vehicle set.
Sharing the car arrival time constraint: Wherein, the Indicating the time to the origin i of the passenger demand; Indicating the time to reach the passenger demand endpoint j; the time of the shared automobile reaching the point w of the unmanned aerial vehicle is represented, I is a demand start point set, and J is a demand end point set.
Unmanned aerial vehicle arrival time constraint: Wherein, the Representing the time from the unmanned aerial vehicle to the freight demand starting point i'; representing the time from the unmanned aerial vehicle to the freight demand meeting point w; indicating the time of the drone to the freight demand endpoint j'.
Freight demand processing time constraints: Wherein, the The time when the unmanned aerial vehicle reaches the actual freight demand starting point i' 1 is represented; representing the time when the shared automobile arrives at the virtual freight demand start i' 2; the time when the unmanned aerial vehicle reaches the actual freight demand end point j' 1 is represented; representing the time when the shared automobile arrives at the virtual freight demand start j' 2; Wherein, the The latest service time representing the freight demand; indicating the latest delivery time of the shipment demand.
Sharing automotive capacity constraints: And Wherein, the Representing the number of passengers in the shared car k after leaving the demand origin i; Indicating the number of passengers in the shared car after the shared car k leaves any node v, p ij indicating the number of passengers required, and the shared car passenger traffic at any node in the road network is less than or equal to the shared car capacity.
Unmanned aerial vehicle capacity constraint: And Wherein, the Representing cargo hold capacity after the unmanned aerial vehicle t leaves the demand start point i'; The cargo capacity of the unmanned aerial vehicle t after leaving the node v is represented, q i′j′ represents the required cargo quantity, and the cargo capacity of the unmanned aerial vehicle on any node in the road network is less than or equal to the unmanned aerial vehicle capacity.
Unmanned aerial vehicle duration constraint: Wherein, the Representing the path distance between the actual demand start point i '1 and the virtual demand start point i' 2 of the unmanned aerial vehicle; And B represents the continuous flight mileage of the unmanned aerial vehicle.
Step S2 is executed, and a method for solving the shared automobile path problem with a time window and a capacity constraint, that is, a CVRPTW problem solving method, is used to solve the problem based on the objective function, the constraint and the decision variable, and the method is a common means for those skilled in the art and is not described herein in detail.
Example 2
In this embodiment, a passenger-cargo sharing mode of a multi-vehicle multi-machine shared vehicle-unmanned aerial vehicle is provided, that is, the shared vehicle and the unmanned aerial vehicle are in one-to-one cooperative non-fixed pairing relationship, so that the unmanned aerial vehicle is allowed to flexibly enable all the shared vehicles connected to a system, the shared vehicle distributes cargoes to the take-off and landing point of the unmanned aerial vehicle, the unmanned aerial vehicle can directly leave after being launched, and the unmanned aerial vehicle does not need to return to the same shared vehicle. The mode is shown in fig. 4, the shared automobile and the unmanned aerial vehicle are in one-to-one cooperation and have no fixed pairing relation, the unmanned aerial vehicle is allowed to flexibly enable all the shared automobiles connected to the system, the shared automobiles distribute goods to the take-off and landing points of the unmanned aerial vehicle, the unmanned aerial vehicle can directly leave after being emitted, the unmanned aerial vehicle does not need to return to the same shared automobile, in the execution process, the one-time delivery task only needs to access the take-off and landing points of the unmanned aerial vehicle for at most two times, and the selection of the take-off and landing points also needs to prevent the vehicle from deviating from the shortest passenger transport path as far as possible while ensuring the shortest delivery path of the unmanned aerial vehicle. Compared with the single vehicle single machine mapping mode in the embodiment 1, the single vehicle single machine mapping mode does not need to consider the round of the shared vehicle and the unmanned aerial vehicle, the shared vehicle does not need to specially recycle the unmanned aerial vehicle after the unmanned aerial vehicle performs the delivery task, and two-level path planning is involved in the process of carrying out passenger-cargo sharing by multiple vehicles and multiple machines, wherein a primary network is used for planning a path of the shared vehicle for transporting cargoes from a storage point to each unmanned aerial vehicle take-off and landing point, and a secondary network is used for the unmanned aerial vehicle to deliver cargoes from the take-off and landing point to a specific freight demand point. Wherein, the logic executed by the primary network is shown in fig. 5, the logic executed by the secondary network is shown in fig. 6, and the logic executed by the primary network comprises:
whether the shared automobile accepts a new passenger order is judged, and the shared automobile is only dependent on the current state of the shared automobile, if the shared automobile is completing a passenger receiving or delivering task, the shared automobile does not accept the new order, if the shared automobile is temporarily free of the passenger task, the new passenger order is accepted, and meanwhile the shared automobile needs to be used as a main body to process the freight order requirement, namely, the shared automobile only distributes one passenger task at a time.
Whether the shared automobile accepts a new freight order is judged, and the state of the shared automobile is also dependent on the current state of the shared automobile, if the shared automobile is executing a freight task, the new order is not accepted, and if the shared automobile is temporarily empty of the freight task, the new passenger order is accepted, namely, the shared automobile only distributes one freight task at a time.
Under the condition that the time of the passenger order is allowed, the shared automobile finishes the tasks of taking goods from a warehouse site and sending the goods to the take-off and landing points of the unmanned aerial vehicle.
The unmanned aerial vehicle is only responsible for delivering cargoes at the take-off and landing point to the freight demand point, and when the unmanned aerial vehicle is in the delivery return process or is temporarily free of tasks, the unmanned aerial vehicle receives new delivery demands, namely, the unmanned aerial vehicle only delivers a single freight task at a time.
The above flow ensures the order and efficiency of the unmanned aerial vehicle when executing tasks, and simultaneously ensures the response modes of the unmanned aerial vehicle in different task states.
Step S1 is performed to first construct an objective function in this mode, and in this embodiment, as in embodiment 1, the operation cost minimization and the overall transportation time minimization are made as an objective function of the two-stage path planning.
Next, decision variables in this mode were confirmed as shown in table 2.
Table 2 decision variable table in multi-car multi-machine mapping mode
Finally, constructing path planning constraint in a multi-vehicle multi-machine mode, wherein the constraint comprises primary network constraint, secondary network constraint and connection constraint in the mode, and the primary network constraint comprises:
sharing automotive demand matching limit constraints: wherein K represents a total shared automobile, K represents a kth shared automobile, j represents a starting point of a passenger demand, and P represents a total passenger demand; Indicating whether the kth shared car is from the starting point i to the destination j, if so If not, then
Sharing car parking lot starting sequence constraint: And Wherein h represents the h node in the road network node set V; G represents the g-th node in the road network node set V, 2n+1 represents the end point parking lot; Indicating the node g to the destination yard, if so, then If not, then
Road network node flow balance constraint: Wherein, the Representing the kth shared automobile from node h to node g; Representing the kth shared automobile from node g to node h, if so, then If not, then
Sharing automotive path time constraints: Wherein, the D g is the service time of the shared automobile for transmitting the unmanned aerial vehicle at the node g; Representing the time from g to h for a shared car k in the road network, and M represents an infinite constant.
Sharing the car arrival time constraint: Wherein, the Representing the time to arrival at the passenger demand destination i; the time for reaching the passenger demand start point J is represented, I is a demand start point set, and J is a demand end point set.
Sharing automotive capacity constraints: And Wherein, the Representing the number of passengers in the shared car k after leaving the demand origin i; Indicating the number of passengers in the shared car after the shared car k leaves any node v, p ij indicating the number of passengers required, and the shared car passenger traffic at any node in the road network is less than or equal to the shared car capacity.
The secondary network constraints include:
Unmanned aerial vehicle path time constraint: Wherein, the D' g is the service time of the unmanned aerial vehicle transmitting the unmanned aerial vehicle at the node g,The time from g to h of the unmanned aerial vehicle T in the road network is represented by T;
Unmanned aerial vehicle arrival time constraint: Wherein, the Representing the time from the unmanned aerial vehicle to the freight demand starting point i'; the time from the unmanned aerial vehicle to the freight demand terminal j' is represented;
Demand processing time constraints: Wherein, the The time when the unmanned aerial vehicle reaches the actual demand starting point i' 1 is represented; Representing the time when the shared automobile reaches the virtual demand start i' 2; The time when the unmanned aerial vehicle reaches the actual demand end point j' 1 is represented; Representing the time when the shared automobile reaches the virtual demand start j 2; Wherein, the Representing the latest service time of the demand; Representing the latest delivery time of the demand;
Unmanned aerial vehicle second grade network node balance constraint: Wherein, the Indicating whether the t-th unmanned aerial vehicle is from the node h to the node g, if so, thenIf not, thenIndicating whether the t-th unmanned aerial vehicle is from node g to node h, if so, thenIf not, then
Unmanned aerial vehicle take-off and landing point unmanned aerial vehicle quantity constraint: Wherein t g represents the unmanned aerial vehicle capacity of node g, which is a fixed constant;
unmanned aerial vehicle capacity constraint: And Wherein, the Representing cargo hold capacity after the unmanned aerial vehicle t leaves the demand start point i'; The cargo capacity of the unmanned aerial vehicle t after leaving the node v is represented, q i′j′ represents the required cargo quantity, and the cargo capacity of the unmanned aerial vehicle on any node in the road network is less than or equal to the unmanned aerial vehicle capacity;
unmanned aerial vehicle duration constraint: Wherein, the The path distance between the actual demand start point i '1 and the virtual demand start point i' 2 of the unmanned aerial vehicle is represented;
the linking constraint comprises two-stage network freight flow balance constraint, namely Wherein, the To share the amount of cargo that car k transports from node g to node h,The amount of cargo transported from node g to node h for drone t.
Step S2 is executed, and a method for solving the double-target capacity-limited shared automobile path problem with a time window, namely a 2E-CVRPTW problem solving method is used for solving the problem based on the objective function, the constraint and the decision variable, and is a common means for a person skilled in the art and is not repeated herein.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
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