Truck-drone collaborative delivery method with drone flexible access to delivery service

Abstract

The application discloses a truck-UAV collaborative distribution method with flexible access of UAV with goods taking and delivering service, comprising the following steps: 1. constructing a target function with the minimum cost as the target based on a distribution scene; 2. constructing a carbon emission constraint; 3. constructing a path constraint; 4. constructing a space constraint; 5. constructing a time constraint; 6. constructing a load constraint; and 7. solving the distribution model by using a gurobi solver to obtain a final distribution scheme. The application constructs a more comprehensive truck-UAV collaborative distribution model and generates an optimal collaborative distribution scheme, so that the rationality and efficiency of distribution are greatly improved, the application is closer to the customer demand and the inseparable distribution in the reality, and the shortage of the existing collaborative scheme is made up.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of operational research and path planning, and in particular to a truck-unmanned aerial vehicle (UAV) collaborative distribution method with flexible access of UAVs with pick-up and delivery services based on mathematical modeling considerations. BACKGROUND

[0002] With the rapid development of e-commerce, consumers have higher requirements for logistics distribution services. The traditional truck-based distribution mode is limited by traffic congestion, complex distribution paths and other factors, and is inefficient and difficult to meet the demand for transportation speed and distribution efficiency. Unmanned aerial vehicles (UAVs) have gradually become an important technical means in the field of logistics distribution due to their efficiency, speed and flexibility. In particular, the truck-UAV collaborative distribution mode, as a new emerging logistics solution, can fully utilize the large-capacity transportation advantage of trucks and the fast and flexible characteristics of UAVs to jointly complete distribution tasks and achieve optimal allocation of resources to improve distribution efficiency.

[0003] Currently, most research on truck-UAV collaborative distribution focuses on the VRPD aspect, i.e., one truck is equipped with one UAV, and the UAV can only return to the truck from which it was launched. However, the problem of UAV endurance sufficient to continue visiting the next customer during the distribution process has not been fully considered. The truck-UAV distribution model established by existing methods is idealized and cannot well handle the case of maximizing resource utilization in practice. This leads to the fact that the distribution scheme generated by the existing model is not reasonable.

[0004] In summary, the existing truck-UAV collaborative distribution method has the following shortcomings:

[0005] 1. The environmental pollution constraint is not considered, and the generated distribution scheme is not reasonable.

[0006] 2. The model is too idealized and cannot be directly applied to actual distribution tasks. SUMMARY

[0007] The present application is to solve the above-mentioned deficiencies in the prior art, and proposes a truck-UAV collaborative distribution method with flexible access of UAVs with pick-up and delivery services, in order to effectively integrate green travel and path planning, generate an optimal or near-optimal collaborative distribution scheme, and meet the spatial constraints of vehicles, consider time constraints and vehicle load limits, thereby improving the rationality and efficiency of distribution.

[0008] To achieve the above-mentioned application purposes, the present application adopts the following technical solutions:

[0009] The truck-UAV collaborative distribution method with flexible access of UAVs with pick-up and delivery services according to the present application is characterized in that it is applied to a customer point set , the start point of the warehouse 0, the end point of the warehouse n+1 and the truck set The delivery scenario is composed of a set of drones carried by each truck ; the set of customer points and the start point of the warehouse 0 form the set of nodes from which the drones can be launched , the set of customer points and the end point of the warehouse n+1 form the set of nodes from which the drones can be recovered , the vehicle path planning method is as follows:

[0010] Step one, use formula (1) to construct the objective function F of the truck-drones collaborative delivery model considering the minimization of transportation distance as the goal for multiple visits of drones and taking and delivering services to customer points:

[0011] (1)

[0012] In formula (1), indicates whether any truck passes through the node from which the drone can be launched to the node from which the drone can be recovered , if yes, let , otherwise, let , indicates whether the drone passes through the node from which the drone can be launched to the node from which the drone can be recovered , if yes, let , otherwise, let ; indicates the distance from the node from which the drone can be launched to the node from which the drone can be recovered for any truck , indicates the distance from the node from which the drone can be launched to the node from which the drone can be recovered for any drone ; indicates the path between any node from which the drone can be launched to the node from which the drone can be recovered , and E represents the set of paths;

[0013] Step two, construct the carbon emission constraint of the truck-drones collaborative delivery model;

[0014] Step three, construct the path constraint of the truck-drones collaborative delivery model, including: truck path constraint, drone flight path constraint, truck-drones collaborative path constraint;

[0015] Step four, constructing the space constraint of the truck-UAV collaborative distribution model;

[0016] Step five, constructing the time constraint of the truck-UAV collaborative distribution model;

[0017] Step six, constructing the load constraint of the truck-UAV collaborative distribution model;

[0018] Step seven, using gurobi solver to solve the truck-UAV collaborative distribution model to obtain the truck-UAV collaborative distribution scheme, including: the number of trucks and UAVs used, the distribution path of each used truck and each used UAV.

[0019] The truck-UAV collaborative distribution method with flexible access of UAVs with goods taking and delivering service has the characteristics that in step two, formula (2) is used to construct the carbon emission constraint:

[0020] (2)

[0021] In formula (2), represents the weighted average emission of each unit of vehicle, represents the carbon emission of each unit of UAV, represents the total carbon emission in distribution.

[0022] Further, the step three includes:

[0023] Step 3.1, constructing the truck path constraint by using formula (3)-(6):

[0024] (3)

[0025] (4)

[0026] (5)

[0027] (6)

[0028] In formula (3)-(6), represents the truck whether the starting point 0 of the warehouse is reached by the truck to reach the node that can be recycled by the UAV , if yes, let , otherwise, let ; represents the truck whether the node that can be launched by the UAV is reached by the truck to reach the end point n+1 of the warehouse, if yes, let , otherwise, let ; denotes a truck whether to start from a customer point arrive at a node where the drone can be recycled , if yes, let , otherwise, let ; denotes a truck whether to start from a node where the drone can be launched arrive at a customer point , if yes, let , otherwise, let ;

[0029] Step 3.2, construct the drone path constraints using equations (7)-(9):

[0030] (7)

[0031] (8)

[0032] (9)

[0033] In equations (7)-(9), denotes a drone whether to start from the warehouse starting point 0 to arrive at a node where the drone can be recycled , if yes, let , otherwise, let ; denotes a drone whether to start from a node where the drone can be launched arrive at the warehouse ending point n+1, if yes, let , otherwise, let ; denotes a drone whether to start from the warehouse starting point 0 on truck k to arrive at a node where the drone can be recycled , if yes, let , otherwise, let ; denotes a drone whether to start from a node where the drone can be launched on truck k arrive at the warehouse ending point n+1, if yes, let , otherwise, let ; denotes a drone whether to start from a node where the drone can be launched arrive at a customer point , if yes, let , otherwise, let ; indicates the UAV whether the customer point is passed arriving at a node where the UAV can be recovered , then let , otherwise let ; indicates the UAV whether the node where the UAV can be launched is on the truck k arriving at the customer point , if yes, then let , otherwise, let ; indicates the UAV whether the customer point is on the truck k arriving at a node where the UAV can be recovered , if yes, then let , otherwise, let ;

[0034] Step 3.3, constructing the truck-UAV collaborative path constraint by using formula (10):

[0035] (10).

[0036] Further, the step 4 comprises:

[0037] Step 4.1, constructing the spatial constraints of the truck and the UAV by using formula (11)-(14):

[0038] (11)

[0039] (12)

[0040] (13)

[0041] (14)

[0042] In formula (11)-(14), indicates the truck whether the UAV is launched or recovered at the customer point , if yes, then let , otherwise, let 0;

[0043] Further, the step 5 comprises:

[0044] Step 5.1, constructing the time constraints of the truck from the node where the UAV can be launched to the node where the UAV can be recovered by using formula (15)-(16):

[0045] (15)

[0046] (16)

[0047] in formulas (15)-(16), denotes the truck arriving at the drone-retrievable node , denotes the truck arriving at the drone-launchable node , denotes the drone d arriving at the drone-launchable node , denotes the truck traveling from the drone-launchable node to , X denotes an integer;

[0048] Step 5.2, constructing the time constraints for the drone from the drone- launchable node to the drone-retrievable node by using formulas (17)-(18):

[0049] (17)

[0050] (18)

[0051] in formulas (17)-(18), denotes the drone arriving at the node , denotes the drone arriving at the drone-launchable node , denotes the drone traveling from the drone-launchable node to the drone-retrievable node ;

[0052] Step 5.3, constructing the working time constraints for the truck and the drone by using formulas (19)-(20):

[0053] (19)

[0054] (20)

[0055] in formulas (19)-(20), denotes whether the drone d departs from the drone-launchable node on the truck k to arrive at the drone-retrievable node , if yes, let , otherwise, let L represents an integer;

[0056] Step 5.4: Construct the flight endurance constraints of the UAV using equations (21)-(25):

[0057] (twenty one)

[0058] (twenty two)

[0059] (twenty three)

[0060] (twenty four)

[0061] (25)

[0062] In equations (21)-(25), Indicates drone From truck takeoff to customer point Cumulative flight time, Indicates drone Since the last takeoff from the customer's location Cumulative flight time, Indicates drone From truck takeoff to drone reusable node Cumulative flight time, Indicates drone Take off from the truck and leave the drone reusable node Cumulative flight time, This indicates the flight time of each drone.

[0063] Furthermore, step 6 includes:

[0064] Step 6.1: Construct the truck delivery load constraints at the nodes using equations (26)-(27):

[0065] (26)

[0066] (27)

[0067] In equations (26)-(27), Indicates truck Reaching the drone reusable node Delivery load, Indicates truck Arrival at customer point Delivery load, Indicates drone At the customer point delivered load after transshipment, the delivery demand at the customer point ;

[0068] Step 6.2, build the pickup load constraints of the truck at the nodes using Equations (28)-(29):

[0069] (28)

[0070] (29)

[0071] In Equations (28)-(29), denotes the pickup load of the truck to the drone recyclable node , denotes the pickup load of the truck to the customer point , denotes the pickup load of the drone after transshipment at the customer point , denotes the pickup demand at the customer point ;

[0072] Step 6.3, build the delivery and pickup load constraints of the truck at the warehouse departure using Equations (30)-(31):

[0073] (30)

[0074] (31)

[0075] In Equations (30)-(31), denotes the delivery load of the truck to the drone recyclable node , denotes the pickup load of the truck to the drone recyclable node , denotes the delivery load of the truck at the warehouse starting point 0, denotes the pickup load of the truck at the warehouse starting point 0;

[0076] Step 6.4, build the maximum load constraints of the truck using Equation (32):

[0077] (32)

[0078] In Equation (32), denotes the delivery load of the truck to the customer point Delivery load, Indicates truck Arrival at customer point Pickup load, Indicates truck Maximum load capacity;

[0079] Step 6.5: Construct delivery and pickup load constraints for the drone to reach the drone recyclable node after completing its service at the customer point using equations (33) and (34):

[0080] (33)

[0081] (34)

[0082] In equations (33)-(34), Indicates drone Reaching the drone reusable node Delivery load, Indicates drone At the customer point Delivery load after transshipment Indicates drone Reaching the drone reusable node The load of goods to be picked up, Indicates drone At the node The load of goods after transshipment;

[0083] Step 6.6: Using equations (35)-(36), construct the constraints that all tasks are completed and all packages have been unloaded before the drone returns to the truck and begins a new visit:

[0084] (35)

[0085] (36)

[0086] In equations (35)-(36), Indicates truck Is it at the drone recyclable node? Launch or recover the drone; if so, then... Otherwise, let ; This indicates that the drone is at the drone reusable node. Loading capacity after transshipment Indicates truck Is it at the drone recyclable node? Launch or recover the drone; if so, then... Otherwise, let ;

[0087] Step 6.7, constructing the unmanned aerial vehicle with formula (37)-(40) to package no transport constraints when flying alone:

[0088] (37)

[0089] (38)

[0090] (39)

[0091] (40)

[0092] In formula (37)-(40), denotes the unmanned aerial vehicle arriving at the customer point the delivery load, denotes the unmanned aerial vehicle arriving at the customer point the pick-up load;

[0093] Step 6.8, constructing the maximum load constraint of the unmanned aerial vehicle with formula (41):

[0094] (41)

[0095] In formula (41), denotes the maximum load of the unmanned aerial vehicle d.

[0096] The electronic device comprises a memory and a processor, and the memory is used to store a program supporting the processor to execute the planning method, and the processor is configured to execute the program stored in the memory.

[0097] The computer readable storage medium stores a computer program, and when the computer program is run by a processor, the steps of any of the planning methods are executed.

[0098] Compared with the prior art, the beneficial effects of the present application are:

[0099] 1、The present application can reasonably plan the distribution route of the truck and the unmanned aerial vehicle by constructing a more comprehensive truck-unmanned aerial vehicle cooperative distribution model, which reduces the invalid distribution time and distance, thereby greatly improving the distribution efficiency.

[0100] 2、The present application considers carbon emission constraints, and now the environmental problem has become a serious problem faced by most companies, considering green distribution, which can provide better service for customers and meet the concept of green travel.

[0101] 3. This invention extends the problem of truck-drone collaboration, allowing a truck to carry multiple drones and a drone to provide pick-up and delivery services to multiple customers in a single flight. It fully considers the realities of the situation, makes better use of the advantages of drones, and avoids wasting drone flight time or making multiple trips to the same customer point, thereby improving the utilization efficiency and delivery efficiency of drones.

[0102] 4. This invention constructs a mixed integer programming model and solves it using the gurobi solver to obtain a high-efficiency delivery solution. This solution makes full use of the usage of drones, improves the quality of delivery services and customer satisfaction, and at the same time takes into account the development concept of green travel and reduces environmental pollution. Attached Figure Description

[0103] Figure 1 This is a schematic diagram of a truck-drone collaborative delivery network with flexible access for pickup and delivery services, as described in this invention.

[0104] Figure 2 This is a schematic diagram of the routes for the truck and the drone in this invention;

[0105] Figure 3 This is a flowchart of the method of the present invention. Detailed Implementation

[0106] In this embodiment, a truck-drone collaborative delivery method with flexible access for pickup and delivery services is proposed. It is based on a delivery scenario, establishing an objective function to minimize transportation distance and constructing carbon emission constraints. Then, path constraints are constructed, including truck path constraints, drone flight path constraints, and truck-drone collaborative path constraints, to ensure the rationality of the delivery path. Next, spatial constraints are constructed, including rules for truck and drone arrival and departure from nodes. Then, relevant constraints for delivery time calculation are established, calculating the arrival time of trucks and drones and the drone's endurance. Next, load constraints for trucks and drones are established, including truck delivery and pickup load constraints at nodes, drone delivery and pickup load constraints after completing a node service and starting a new visit, and maximum load constraints for trucks and drones, to ensure the rationality of package delivery. Finally, the model is solved using the Gurobi solver to obtain the final delivery scheme. Specifically, as shown below... Figure 3 As shown, the method includes the following steps:

[0107] Step 1: Constructing the Scenario: In this embodiment, it is applied to a scenario consisting of a set of customer points. The warehouse starts at point 0, ends at point n+1, and consists of the set of trucks. In the resulting delivery scenario, each truck carries a collection of drones. The set of customer points and the starting point 0 of the warehouse constitute the set of nodes that the UAV can launch from The set of customer points and the ending point n+1 of the warehouse constitute the set of nodes that the UAV can recover from :

[0108] The warehouse has k trucks of the same model in the starting point 0 Each truck carries a UAV of the same model The distance from any truck to the UAV launchable node to the UAV recoverable node is denoted as , and the time is denoted as The distance from any UAV to the UAV launchable node to the UAV recoverable node is denoted as , and the time is denoted as The endurance time of each UAV is denoted as The maximum load of each truck and the maximum load of each UAV are denoted as ;

[0109] Please refer to Figure 1 , Figure 1 0 represents the warehouse, and 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12 represent customer points 1-12, respectively. There are three types of customers: only delivery customers, only pickup customers, and customers who need both delivery and pickup. The path of truck is 0→1→3→5→4→0, the path of truck is 0→7→10→12→0, and the flight path of the UAV carried by truck is 1→2→3, 3→6→7, the flight path of the UAV carried by truck is 7→8→9→10, 10→11→1.

[0110] Step 2, extract the distribution information: according to the distribution network, obtain the distribution information of the customer nodes to be served, including node location, customer point distance, customer point demand, truck driving speed, UAV flight speed, endurance time of any UAV, and maximum load of truck and UAV.

[0111] Step 3, use formula (1) to construct the objective function F of the truck-UAV collaborative distribution model considering the minimization of transportation distance as the target of multiple visits of UAVs to customer points and pickup and delivery services:

[0112] (1)

[0113] denotes any one truck whether it passes through a node that can be launched by a UAV whether it reaches a node that can be recycled by a UAV if yes, let if not, let , , denotes any one UAV whether it passes through a node that can be launched by a UAV whether it reaches a node that can be recycled by a UAV if yes, let if not, let ; denotes any one truck distance from a node that can be launched by a UAV to a node that can be recycled by a UAV , denotes any one UAV distance from a node that can be launched by a UAV to a node that can be recycled by a UAV ; denotes the path between any node that can be launched by a UAV and a node that can be recycled by a UAV , E denotes the set of paths.

[0114] Step four, build the carbon emission constraint of the truck-UAV collaborative distribution model by formula (2):

[0115] (2)

[0116] In formula (2), denotes the weighted average emission per unit of vehicle, denotes the carbon emission per unit of UAV, denotes the total carbon emission in distribution.

[0117] Step five, build the path constraint of the truck-UAV collaborative distribution model, including: truck path constraint, UAV flight path constraint, truck-UAV collaborative path constraint, including:

[0118] Step 5.1, build the truck path constraint by formula (3)-(6):

[0119] (3)

[0120] (4)

[0121] (5)

[0122] (6)

[0123] In formula (3) to formula (6), denotes whether the truck arrives at the node where the drone can be recovered from the start point 0 of the warehouse , if yes, let , otherwise, let ; denotes whether the truck arrives at the end point n+1 of the warehouse from the node where the drone can be launched from , if yes, let , otherwise, let ; denotes whether the truck arrives at the node where the drone can be recovered from the customer point , if yes, let , otherwise, let ; denotes whether the truck arrives at the customer point from the node where the drone can be launched from , if yes, let , otherwise, let . Step 5.2, construct the drone path constraints using formula (7) to formula (9):

[0124]

[0125] (7)

[0126] (8)

[0127] (9)

[0128] In formula (7) to formula (9), denotes whether the drone arrives at the node where the drone can be recovered from the start point 0 of the warehouse , if yes, let , otherwise, let ; denotes whether the drone arrives at the end point n+1 of the warehouse from the node where the drone can be launched from , if yes, let , otherwise, let ; denotes whether the drone arrives at the node where the drone can be recovered from the start point 0 of the warehouse on the truck k ​​, if yes, let , otherwise, let ; denotes whether the drone arrives at the end point n+1 of the warehouse from the drone launchable node on truck k, if yes, let , otherwise, let ; denotes whether the drone arrives at the customer node from the drone launchable node , if yes, let , otherwise, let ; denotes whether the drone passes the customer node to arrive at the drone recyclable node , let , otherwise let ; denotes whether the drone arrives at the customer node from the drone launchable node on truck k, if yes, let , otherwise, let ; denotes whether the drone arrives at the drone recyclable node from the customer node on truck k, if yes, let , otherwise, let .

[0129] Step 5.3, construct the truck-drone collaborative path constraint by using formula (10):

[0130] (10)

[0131] Step six, construct the spatial constraints of the truck-drone collaborative distribution model, including:

[0132] Step 6.1, formula (11)-(14) construct the spatial constraints of the truck and the drone:

[0133] (11)

[0134] (12)

[0135] (13)

[0136] (14)

[0137] In equations (11)-(14), Indicates truck At the customer's location Launch or recover a drone; if so, then... Otherwise, let 0.

[0138] Step 7: Construct the time constraints for the truck-drone collaborative delivery model, including:

[0139] Step 7.1: Construct the time constraints for the truck from the UAV launch node to the UAV recovery node using equations (15)-(16):

[0140] (15)

[0141] (16)

[0142] In equations (15)-(16), Indicates truck Reaching the drone reusable node Time, Indicates truck Reach the drone launch node Time, This indicates that drone d has reached the drone launch node. Time, Indicates truck From the drone launch node Drive to The time, where X represents an integer.

[0143] Step 7.2: Construct the time constraints for the UAV from the launch node to the recovery node using equations (17) and (18):

[0144] (17)

[0145] (18)

[0146] In equations (17)-(18), Indicates drone arrive node Time, Indicates drone Reach the drone launch node Time, Indicates drone From the drone launch node Drive to the drone recycling node The time.

[0147] Step 7.3: Construct the working time constraints for trucks and drones using equations (19)-(20):

[0148] (19)

[0149] (20)

[0150] In equations (19)-(20), Indicates whether drone d is on truck k from a node that drones can launch from. Departure to the drone reusable node If so, then let Otherwise, let L represents an integer.

[0151] Step 7.4: Construct the flight endurance constraints of the UAV using equations (21)-(25):

[0152] (twenty one)

[0153] (twenty two)

[0154] (twenty three)

[0155] (twenty four)

[0156] (25)

[0157] In equations (21)-(25), Indicates drone From the last takeoff to the customer's location Cumulative flight time, Indicates drone Since the last takeoff from the customer's location Cumulative flight time, Indicates drone From the last takeoff to the drone's reusable node Cumulative flight time, Indicates drone Since the last takeoff from the drone's reusable node Cumulative flight time, This indicates the flight time of each drone.

[0158] Step 8: Construct the load constraints for the truck-drone collaborative delivery model, including:

[0159] Step 8.1, construct the delivery load constraints of trucks at nodes with Formulas (26)-(27):

[0160] (26)

[0161] (27)

[0162] In Formulas (26)-(27), denotes the delivery load of trucks arriving at the drone-recoverable nodes, denotes the delivery load of trucks arriving at the customer nodes, denotes the delivery load of drones after transshipment at the customer nodes, denotes the delivery demand of nodes.

[0163] Step 8.2, construct the pickup load constraints of trucks at nodes with Formulas (28)-(29):

[0164] (28)

[0165] (29)

[0166] In Formulas (28)-(29), denotes the pickup load of trucks arriving at the drone-recoverable nodes, denotes the pickup load of trucks arriving at the customer nodes, denotes the pickup load of drones after transshipment at the customer nodes, denotes the pickup demand of customer nodes.

[0167] Step 8.3, construct the delivery and pickup load constraints of trucks at warehouse departure with Formulas (30)-(31):

[0168] (30)

[0169] (31)

[0170] In Formulas (30)-(31), denotes the delivery load of trucks arriving at the drone-recoverable nodes, ​​​​​​​​Delivery load, Indicates truck Reaching the drone reusable node Pickup load, Indicates truck Delivery load at warehouse starting point 0, Indicates truck The picking load at warehouse starting point 0.

[0171] Step 8.4: Construct the maximum load constraint for the truck using equation (32):

[0172] (32)

[0173] In equation (32), Indicates truck Arrival at customer point Delivery load, Indicates truck Arrival at customer point Pickup load, Indicates truck Maximum load capacity.

[0174] Step 8.5: Construct delivery and pickup load constraints for the drone to reach the drone recyclable node after completing its service at the customer point using equations (33) and (34):

[0175] (33)

[0176] (34)

[0177] In equations (33)-(34), Indicates drone Reaching the drone reusable node Delivery load, Indicates drone At the customer point Delivery load after transshipment Indicates drone Reaching the drone reusable node The load of goods to be picked up, Indicates drone At the node Loading capacity after transshipment.

[0178] Step 8.6: Using equations (35)-(36), construct the constraints that all tasks are completed and packages are unloaded before the drone returns to the truck and begins a new visit:

[0179] (35)

[0180] (36)

[0181] In formulas (35) - (36), denotes the truck whether the drone is at a drone recyclable node launches or retrieves the drone, if yes, then let , otherwise, let ; denotes the drone at a drone recyclable node after the transfer of the pickup load, denotes the truck whether the drone is at a drone recyclable node launches or retrieves the drone, if yes, then let , otherwise, let .

[0182] Step 8.7, construct the no transfer of package constraint for the drone when flying alone using formulas (37) - (40):

[0183] (37)

[0184] (38)

[0185] (39)

[0186] (40)

[0187] In formulas (37) - (40), denotes the drone arriving at the customer point with the delivery load, denotes the drone arriving at the customer point with the pickup load.

[0188] Step 8.8, construct the drone maximum load constraint using formula (41):

[0189] (41)

[0190] In formula (41), denotes the maximum load of the drone d.

[0191] Step nine, the truck drone collaborative distribution model considering multiple visits of the unmanned aerial vehicle and taking and delivering services to the customers is constructed by the objective function F and all constraint conditions, and solved by using a gurobi solver to obtain a final distribution scheme, including: the number of trucks and drones used, the specific distribution path of each used truck and each used drone, participating Figure 2 The embodiment shown: Figure 2 0 represents the warehouse starting point, 13 is the warehouse ending point, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 respectively represent customer points 1-12, there are three different types of customers, only delivery customers, only pickup customers and customers who need both pickup and delivery, the path of the truck is 0→1→3→5→4→13, the path of the truck is 0→7→10→12→13, the flight path of the drone carried by the truck is 1→2→3, 3→6→7, the flight path of the drone carried by the truck is 7→8→9→10, 10→11→12.

[0192] In this embodiment, an electronic device includes a memory and a processor, characterized in that the memory is configured to store a program supporting the processor to execute a planning method, and the processor is configured to execute the program stored in the memory.

[0193] In this embodiment, a computer readable storage medium has a computer program stored thereon, characterized in that the computer program is executed by a processor to perform the steps of the planning method.

[0194] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A truck-drone collaborative delivery method with flexible access of drones with pick-and-drop service, characterized by, Is applied to the distribution scene composed of customer point set , the starting point 0 of the warehouse, the terminal point n+1 of the warehouse and the truck set , wherein each truck carries a set of drones ; the set of nodes that the drones can launch is composed of the customer point set and the starting point 0 of the warehouse , the set of nodes that the drones can recycle is composed of the customer point set and the terminal point n+1 of the warehouse , and the truck-drones collaborative distribution method is as follows: Step one, using formula (1) to construct the objective function F of the truck-drone collaborative distribution model considering multiple visits of unmanned aerial vehicles to minimize transportation distance for taking and delivering goods from customer points: (1) in formula (1), denotes any one truck whether it passes through a node where a UAV can be launched arrives at a node where a UAV can be recovered , if yes, let , otherwise, let , denotes a UAV whether it passes through a node where a UAV can be launched arrives at a node where a UAV can be recovered , if yes, let , otherwise, let ; denotes any one truck travels from a node where a UAV can be launched to a node where a UAV can be recovered , denotes any one UAV travels from a node where a UAV can be launched to a node where a UAV can be recovered ; denotes a path between any node where a UAV can be launched and a node where a UAV can be recovered , and E denotes a set of paths Step two, construct the carbon emission constraint of the truck-drone collaborative distribution model; Step three, construct the path constraint of the truck-drone collaborative distribution model, including: truck path constraint, unmanned aerial vehicle flight path constraint, truck-drone collaborative path constraint; Step four, construct the space constraint of the truck-drone collaborative distribution model; Step five, construct the time constraint of the truck-drone collaborative distribution model; Step six, construct the load constraint of the truck-drone collaborative distribution model; Step seven, use gurobi solver to solve the truck-drone collaborative distribution model to get the truck-drone collaborative distribution scheme, including: the number of trucks and drones used, the distribution path of each truck and each drone used.

2. The truck-drone collaborative delivery method with drone flexible access for pick-and-place service according to claim 1, wherein, The step two is to construct the carbon emission constraint using formula (2): (2) In formula (2), represents the weighted average emission per unit of vehicle, represents the carbon emission per unit of drone, represents the total carbon emission in distribution.

3. The truck-drone collaborative delivery method with drone flexible access to pick-and-place service according to claim 2, wherein, The step three includes: Step 3.1, construct the truck path constraint using formula (3)-(6): (3) (4) (5) (6) in formulas (3) - (6), indicates whether the truck arrived at a node where the drone can pick up from the start point 0 of the warehouse , if yes, then let , otherwise, let ; indicates whether the truck arrived at a node where the drone can launch from the end point n+1 of the warehouse , if yes, then let , otherwise, let ; indicates whether the truck arrived at a node where the drone can pick up from the customer point , if yes, then let , otherwise, let ; ; indicates whether the truck arrived at a node where the drone can launch from the customer point , if yes, then let , otherwise, let ; ; Step 3.2, construct the unmanned aerial vehicle path constraint using formula (7)-(9): (7) (8) (9) In equations (7)-(9), Indicates drone Does it start from warehouse starting point 0 and reach the node where the drone can be recycled? If so, then let Otherwise, let ; Indicates drone Whether from a node that can be launched by a drone If the destination n+1 is the warehouse, then let... Otherwise, let ; Indicates drone Does the truck (k) start from warehouse origin 0 and reach the node where the drone can be recycled? If so, then let Otherwise, let ; Indicates drone Is it a node that can be launched from a drone on truck k? If the destination n+1 is the warehouse, then let... Otherwise, let ; Indicates drone Whether from a node that can be launched by a drone Departure to customer location If so, then let Otherwise, let ; Indicates drone Did it go through the customer's point? Reaching the node where the drone can be recovered Then let Otherwise ; Indicates drone Is it a node that can be launched from a drone on truck k? Departure to customer location If so, then let Otherwise, let ; Indicates drone Is it on truck K from the customer point? Departure to the node where the drone can be recovered If so, then let Otherwise, let ; Step 3.3, construct the truck-drone collaborative path constraint using formula (10): (10) In formula (10), represents whether the drone d is on the truck k from the node where the drone is launchable departure to arrival at the node where the drone is retrievable , if yes, then let , otherwise, let .

4. The truck-drone collaborative delivery method with drone flexible access to pick-and-place service according to claim 3, wherein, The step four includes: Step 4.1, construct the space constraint of the truck and the unmanned aerial vehicle using formula (11)-(14): (11) (12) (13) (14) in formulas (11) - (14), representing a truck whether at a customer point launching a drone or recovering a drone, if so, then let , otherwise, let 0.

5. The truck-drone collaborative delivery method with drone flexible access to pick and drop services according to claim 4, wherein, The step five includes: Step 5.1, construct the time constraint of the truck from the unmanned aerial vehicle launchable node to the unmanned aerial vehicle recyclable node using formula (15)-(16): (15) (16) in formulas (15)-(16), time of arrival of the truck at the drone-retrievable node , time of arrival of the truck at the drone-launchable node , time of arrival of the drone d at the drone-launchable node , time of departure of the truck from the drone-launchable node , X represents an integer; Step 5.2, construct the time constraint of the unmanned aerial vehicle from the unmanned aerial vehicle launchable node to the unmanned aerial vehicle recyclable node using formula (17)-(18): (17) (18) in formulas (17)-(18), representing the drone arriving at the node , representing the drone arriving at the drone launchable node , representing the drone traveling from the drone launchable node to the drone recoverable node , Step 5.3, construct the working time constraint of the truck and the unmanned aerial vehicle using formula (19)-(20): (19) (20) In formula (19)-(20), L represents an integer; Step 5.4, construct the endurance time constraint of the unmanned aerial vehicle during flight using formula (21)-(25): (21) (22) (23) (24) (25) in formulas (21)-(25), representing a drone cumulative flight time from truck takeoff to customer point , representing a drone cumulative flight time from last takeoff to leaving customer point , representing a drone cumulative flight time from truck takeoff to drone recyclable node , representing a drone cumulative flight time from truck takeoff to leaving drone recyclable node , representing the endurance time of each drone.

6. The truck-drone collaborative delivery method with drone flexible access to pick-and-place service of claim 5, wherein, The step six includes: Step 6.1, construct the delivery load constraint of the truck at the node using formula (26)-(27): (26) (27) in formula (26) - formula (27), representing a truck arriving at the drone collectible node with a delivery load, representing a truck arriving at the customer point with a delivery load, representing a drone after being transshipped at the customer point with a delivery load, representing a customer point with a delivery demand; Step 6.2, construct the pickup load constraint of the truck at the node using formula (28)-(29): (28) (29) in formulas (28) - (29), representing a truck arriving at a drone collectible node with a pickup load, representing a truck arriving at a customer node with a pickup load, representing a drone after transshipment at a customer node with a pickup load, representing a customer node with a pickup demand; Step 6.3, construct the delivery and pickup load constraint of the truck when leaving the warehouse using formula (30)-(31): (30) (31) in formula (30) - formula (31), representing a truck arriving at the drone-recyclable node with a delivery load, representing a truck arriving at the drone-recyclable node with a pick-up load, representing a truck arriving at the warehouse starting point 0 with a delivery load, representing a truck arriving at the warehouse starting point 0 with a pick-up load; Step 6.4, construct the maximum load constraint of the truck using formula (32): (32) in formula (32), representing a truck arriving at a customer point with a delivery load, representing a truck arriving at a customer point with a pick-up load, representing a truck with a maximum load capacity; Step 6.5, construct the delivery and pickup load constraint of the unmanned aerial vehicle after serving the customer point to the unmanned aerial vehicle recyclable node using formula (33)-(34): (33) (34) in formula (33) - formula (34), representing a drone arriving at a drone collectable node of a delivery load, representing a drone at a customer point after transshipment of a delivery load, representing a drone arriving at a drone collectable node of a pick-up load, representing a drone at a node after transshipment of a pick-up load; Step 6.6, construct the constraint that the unmanned aerial vehicle has completed all tasks and unloaded all packages before returning to the truck and starting a new visit using formula (35)-(36): (35) (36) in formulas (35) - (36), representing a truck whether the drone is at a drone-retrievable node launch or retrieve a drone, if so, let , else let ; representing a truck whether the drone is at a drone-retrievable node representing a truck whether the drone is at a drone-retrievable node launch or retrieve a drone, if so, let , else let ; Step 6.7, construct the package non-transshipment constraint of the unmanned aerial vehicle during solo flight using formula (37)-(40): (37) (38) (39) (40) in formulae (37) to (40), representing a drone to the customer point of the delivery load, representing a drone to the customer point of the pick-up load; Step 6.8, construct the maximum load constraint of the unmanned aerial vehicle using formula (41): (41) In formula (41), represents the maximum payload of the drone d.

7. An electronic device comprising a memory and a processor, characterized in that The memory is configured to store a program supporting the processor to perform the method of any one of claims 1-6.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that The computer program, when executed by a processor, performs the steps of the method of any one of claims 1-6.

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