Logistics path planning method and system based on collaborative distribution

Abstract

The invention relates to a logistics path planning method and system based on collaborative distribution, and the method comprises the steps: constructing a mixed integer programming model which is used for planning a collaborative distribution path of an electric truck and an unmanned plane, and a battery replacement path of a battery replacement vehicle; performing optimization solution on the basis of the mixed integer programming model to obtain a collaborative distribution path of the electric truck and the unmanned aerial vehicle; and on the basis of the collaborative distribution path of the electric truck and the unmanned aerial vehicle, determining a battery replacement node of the electric truck, and further obtaining a battery replacement path of the battery replacement vehicle. According to the method and the device, the electric truck with insufficient energy in the collaborative distribution process is synchronously moved to replace the battery through the battery replacing vehicle, so that the obstacles of high construction cost, difficulty in site selection and the like of a battery energy supplementing station are effectively avoided, and the distribution flexibility and efficiency are greatly improved in the cargo collaborative distribution of the electric truck and the unmanned aerial vehicle; the problem of how to cooperate with the truck and the unmanned aerial vehicle for logistics distribution is solved.

Description

Technical Field

[0001] The present application relates to the technical field of route planning, and in particular to a logistics route planning method and system based on collaborative distribution. Background Art

[0002] With the rapid expansion of the e-commerce market, it poses many challenges to the logistics industry, especially in the costly and inefficient last-mile delivery process.

[0003] The traditional logistics distribution model relies on trucks and is easily affected by ground traffic conditions, which not only affects the timeliness of delivery, but may also damage the quality of goods. Drone delivery, with its advantage of ignoring ground traffic restrictions, can significantly shorten the delivery time. However, drones are limited by battery life and load capacity, making it difficult to achieve long-term and long-distance delivery. Therefore, how to achieve coordinated delivery of trucks and drones to effectively combine the advantages of both and improve delivery efficiency and scope is an urgent problem to be solved.

[0004] Currently, there is no effective solution to the problem of how to coordinate trucks and drones for logistics distribution in related technologies. Summary of the invention

[0005] The embodiments of the present application provide a logistics path planning method and system based on collaborative distribution, so as to at least solve the problem in the related art of how to coordinate trucks and drones to perform logistics distribution.

[0006] In a first aspect, an embodiment of the present application provides a logistics path planning method based on collaborative distribution, the method comprising:

[0007] Constructing a mixed integer programming model, wherein the mixed integer programming model is used to plan the coordinated delivery path of electric trucks and drones, and the battery swapping path of battery swapping vehicles;

[0008] An optimization solution is performed based on the mixed integer programming model to obtain a collaborative delivery path of the electric truck and the drone;

[0009] Based on the coordinated delivery path of the electric truck and the drone, the battery swap node of the electric truck is determined, and then the battery swap path of the battery swap vehicle is obtained.

[0010] In some embodiments, constructing the mixed integer programming model includes:

[0011] Constructing an objective function of a mixed integer programming model, wherein the objective function is used to minimize the total logistics delivery cost of electric trucks, drones, and battery-swap vehicles;

[0012] Construct routing constraints, load constraints, energy consumption constraints and time constraints of the mixed integer programming model, wherein the routing constraints are used to regulate the coordinated delivery path of the electric truck and the drone, and the battery swapping path of the battery swapping vehicle; the load constraints are used to regulate the load changes of the electric truck, the drone and the battery swapping vehicle; the energy consumption constraints are used to regulate the energy changes of the electric truck, the drone and the battery swapping vehicle; and the time constraints are used to regulate the coordinated delivery time of the electric truck and the drone, and the battery swapping time of the battery swapping vehicle.

[0013] In some embodiments, the routing constraint includes a first routing constraint formula, a second routing constraint formula, a third routing constraint formula, a fourth routing constraint formula, a fifth routing constraint formula, a sixth routing constraint formula group, a seventh routing constraint formula, an eighth routing constraint formula, a ninth routing constraint formula, a tenth routing constraint formula, and an eleventh routing constraint formula group;

[0014] The first routing constraint formula is used to ensure that each customer must be served once;

[0015] The second routing constraint formula is used to ensure that each electric truck starts from the warehouse and eventually returns to the warehouse;

[0016] The third routing constraint formula is used to ensure the balance of electric truck flow;

[0017] The fourth routing constraint formula is used to ensure that each customer is visited by a drone at most once;

[0018] The fifth routing constraint formula is used to ensure flow balance at the drone service point;

[0019] The sixth routing constraint formula group is used to determine the starting node and the ending node of each trip of the drone;

[0020] The seventh route constraint formula is used to characterize the relationship between two consecutive trips of the drone;

[0021] The eighth routing constraint formula is used to ensure that each battery-swapping vehicle starts from the warehouse and eventually returns to the warehouse;

[0022] The ninth routing constraint formula group is used to ensure the balance of battery swapping vehicle flow;

[0023] The tenth route constraint formula is used to ensure that an electric truck passes by the battery swapping position of the battery swapping vehicle;

[0024] The eleventh routing constraint formula group is used to ensure that the electric truck passes by the take-off position and landing position of the drone.

[0025] In some embodiments, the load constraint includes a first load constraint formula, a second load constraint formula group, a third load constraint formula group, a fourth load constraint formula, a fifth load constraint formula group, a sixth load constraint formula group, and a seventh load constraint formula;

[0026] The first load constraint formula is used to ensure that the load of the electric truck does not exceed the maximum load;

[0027] The second load constraint formula group is used to calculate the load of the electric truck when it leaves the warehouse, including when the drone is launched from the warehouse and when the drone is not launched from the warehouse;

[0028] The third load constraint formula group is used to track the load change of the electric truck at the node j, including the UAV being launched from the node j and the UAV not being launched from the node j;

[0029] The fourth load constraint formula is used to ensure that the load of the UAV does not exceed the maximum load;

[0030] The fifth load constraint formula group is used to calculate the load of the drone at the beginning of each trip;

[0031] The sixth load constraint formula group is used to track the load change of the UAV at the node i, including when the node i is the starting node of the trip and when the node i is not the starting node of the trip;

[0032] The seventh load constraint formula is used to ensure that the load of the battery-swapping vehicle does not exceed the maximum load.

[0033] In some embodiments, the energy consumption constraint includes a first energy consumption constraint formula group, a second energy consumption constraint formula group, a third energy consumption constraint formula, a fourth energy consumption constraint formula group, a fifth energy consumption constraint formula group, a sixth energy consumption constraint formula, a seventh energy consumption constraint formula group, an eighth energy consumption constraint formula group and a ninth energy consumption constraint formula;

[0034] The first energy consumption constraint formula group is used to ensure that the battery of the electric truck is fully charged when leaving the warehouse;

[0035] The second energy consumption constraint formula group is used to track the energy change of the electric truck at the node j, including whether the electric truck needs to replace the battery at the node j and whether the electric truck does not need to replace the battery at the node j;

[0036] The third energy consumption constraint formula is used to ensure that the remaining energy of the electric truck is higher than the minimum allowed energy;

[0037] The fourth energy consumption constraint formula group is used to ensure that the battery of each drone is fully charged at the beginning of each trip;

[0038] The fifth energy consumption constraint formula group is used to track the energy changes of the UAV;

[0039] The sixth energy consumption constraint formula is used to ensure that the drone has enough energy to complete each trip;

[0040] The seventh energy consumption constraint formula group is used to ensure that the battery of the battery swap vehicle is fully charged when leaving the warehouse;

[0041] The eighth energy consumption constraint formula group is used to track the energy changes of the battery-swapping vehicle;

[0042] The ninth energy consumption constraint formula is used to ensure that the remaining energy of the battery-swapping vehicle is higher than the minimum allowable energy.

[0043] In some embodiments, the time constraint includes a first time constraint formula, a second time constraint formula group, a third time constraint formula group, a fourth time constraint formula group, a fifth time constraint formula, a sixth time constraint formula group, a seventh time constraint formula, an eighth time constraint formula group, a ninth time constraint formula group, a tenth time constraint formula group, an eleventh time constraint formula group, a twelfth time constraint formula, a thirteenth time constraint formula group, a fourteenth time constraint formula, a fifteenth time constraint formula group, a sixteenth time constraint formula group and a seventeenth time constraint formula group;

[0044] The first time constraint formula is used to determine the time when the electric truck leaves the warehouse;

[0045] The second time constraint formula group is used to calculate the time it takes for the electric truck to arrive at the customer node;

[0046] The third time constraint formula group is used to calculate the time when the electric truck starts delivering to the customer and ensure that the delivery time window specified by the customer is met;

[0047] The fourth time constraint formula group is used to calculate the time when the electric truck leaves the customer node;

[0048] The fifth time constraint formula is used to ensure that the electric truck must return to the warehouse before the warehouse closing time;

[0049] The sixth time constraint formula group is used to calculate the time for the drone to arrive at the customer node;

[0050] The seventh time constraint formula is used to ensure that the UAV and the electric truck can meet in time;

[0051] The eighth time constraint formula group is used to calculate the time when the drone starts delivering to the customer and ensure that the delivery time window specified by the customer is met;

[0052] The ninth time constraint formula group is used to calculate the time when the drone leaves the client node;

[0053] The tenth time constraint formula group is used to ensure that the UAV can complete the preparation work in idle time;

[0054] The eleventh time constraint formula group is used to calculate the start time of the next trip of the drone;

[0055] The twelfth time constraint formula is used to ensure that the drone returns to the warehouse before the warehouse closing time;

[0056] The thirteenth time constraint formula group is used to calculate the time it takes for the battery swap vehicle to arrive at the battery swap node;

[0057] The fourteenth time constraint formula is used to ensure that the battery-swapping vehicle can promptly swap batteries for the electric truck;

[0058] The fifteenth time constraint formula group is used to calculate the time when the battery-swapping vehicle starts to swap batteries for the electric truck;

[0059] The sixteenth time constraint formula group is used to calculate the time when the battery swap vehicle leaves the battery swap node;

[0060] The seventeenth time constraint formula group is used to ensure that the travel time of the battery-swapping vehicle meets the operating time window of the warehouse.

[0061] In some embodiments, an optimization solution is performed based on the mixed integer programming model to obtain a collaborative delivery path of the electric truck and the drone, including:

[0062] The total logistics distribution cost of electric trucks, drones and battery-swapping vehicles is minimized through the objective function of the mixed integer programming model, and on the basis of the route constraints, the load constraints, the energy consumption constraints and the time constraints, an adaptive large neighborhood search algorithm ALNS is used to perform optimization and solve to obtain the collaborative distribution path of the electric trucks and the drones.

[0063] In some embodiments, the coordinated delivery path of the electric truck and the drone is obtained by optimizing and solving through the adaptive large neighborhood search algorithm ALNS, including:

[0064] Constructing the delivery path R of electric trucks via nearest neighbor greedy insertion strategy e ;

[0065] In the delivery path R of the electric truck e The collaborative delivery path of the electric truck and the UAV is generated by a destruction operator and a repair operator, and the collaborative delivery path is iteratively updated by a simulated annealing acceptance criterion to obtain an iteratively updated collaborative delivery path.

[0066] In some embodiments, based on the coordinated delivery path of the electric truck and the drone, determining the battery swap node of the electric truck, and then obtaining the battery swap path of the battery swap vehicle includes:

[0067] Based on the coordinated delivery path of the electric truck and the drone, a battery swap node of the battery swap vehicle is determined from the delivery path of the electric truck that needs battery swapping by using a greedy algorithm;

[0068] Based on the battery-swapping nodes of the battery-swapping vehicle, a battery-swapping path of the battery-swapping vehicle that does not affect the collaborative delivery path is obtained.

[0069] In a second aspect, an embodiment of the present application provides a logistics path planning system based on collaborative distribution, the system is used to execute the method described in the first aspect above, the system includes a planning model building module, a distribution path calculation module and a battery replacement path calculation module;

[0070] The planning model building module is used to build a mixed integer programming model, wherein the mixed integer programming model is used to plan the coordinated delivery path of the electric truck and the drone, and the battery swapping path of the battery swapping vehicle;

[0071] The delivery path calculation module is used to perform optimization and solution based on the mixed integer programming model to obtain a collaborative delivery path for the electric truck and the drone;

[0072] The battery-swapping path calculation module is used to determine the battery-swapping node of the electric truck according to the coordinated delivery path of the electric truck and the drone, and then obtain the battery-swapping path of the battery-swapping vehicle.

[0073] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect when executing the computer program.

[0074] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect above.

[0075] Compared with the related art, the embodiment of the present application provides a logistics path planning method and system based on collaborative distribution, wherein the method constructs a mixed integer programming model, wherein the mixed integer programming model is used to plan the collaborative distribution path of electric trucks and drones, and the battery replacement path of battery-swapping vehicles; optimization and solution are performed on the basis of the mixed integer programming model to obtain the collaborative distribution path of electric trucks and drones; based on the collaborative distribution path of electric trucks and drones, the battery replacement nodes of electric trucks are determined, and then the battery replacement path of the battery-swapping vehicle is obtained, thereby realizing the synchronous movement and battery replacement of electric trucks with insufficient energy during the collaborative distribution process by battery-swapping vehicles, effectively avoiding obstacles such as high construction cost and difficult site selection of battery charging stations, greatly improving the flexibility and efficiency of distribution in the collaborative distribution of goods between electric trucks and drones, and solving the problem of how to coordinate trucks and drones for logistics distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0077] Figure 1 is a flowchart of the steps of a logistics path planning method based on collaborative distribution according to an embodiment of the present application;

[0078] Figure 2 is a schematic diagram of coordinated delivery by an electric truck, a drone, and a battery-swapping vehicle according to an embodiment of the present application;

[0079] Figure 3 is a schematic diagram of a flow chart of an adaptive large neighborhood search algorithm according to an embodiment of the present application;

[0080] Figure 4 It is a schematic diagram of a process of constructing a battery swapping path of a battery swapping vehicle according to an embodiment of the present application;

[0081] Figure 5 It is a schematic diagram of the internal structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0082] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0083] Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed in this application, some changes in design, manufacturing or production based on the technical content disclosed in this application are just conventional technical means, and should not be understood as insufficient content disclosed in this application.

[0084] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0085] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the" and the like involved in this application do not indicate a quantitative limitation, and may represent the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there may be three relationships, for example, "A and / or B" can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0086] The present application embodiment provides a logistics path planning method based on collaborative distribution. Figure 1 is a flowchart of the steps of the logistics path planning method based on collaborative distribution according to an embodiment of the present application, such as Figure 1As shown, the method comprises the following steps:

[0087] Step S102, constructing a mixed integer programming model, wherein the mixed integer programming model is used to plan the coordinated delivery path of the electric truck and the drone, and the battery swapping path of the battery swapping vehicle;

[0088] Specifically, step S102 constructs an objective function of a mixed integer programming model, wherein the objective function is used to minimize the total logistics distribution cost of electric trucks, drones, and battery-swapping vehicles;

[0089] The routing constraints, load constraints, energy consumption constraints and time constraints of the mixed integer programming model are constructed. Among them, the routing constraints are used to regulate the collaborative delivery path of electric trucks and drones, as well as the battery swapping path of battery swapping vehicles. The load constraints are used to regulate the load changes of electric trucks, drones and battery swapping vehicles. The energy consumption constraints are used to regulate the energy changes of electric trucks, drones and battery swapping vehicles. The time constraints are used to regulate the collaborative delivery time of electric trucks and drones, as well as the battery swapping time of battery swapping vehicles.

[0090] Step S102 preferably includes the following steps:

[0091] Step S1021, the logistics path planning method provided in this embodiment is aimed at the EVRPTW-D-SMBS problem (the battery swap vehicle-electric truck-drone collaborative delivery path problem considering the mobile battery swap mode and time window constraints). Therefore, before constructing the mixed integer programming model, the entire logistics distribution problem needs to be defined as follows:

[0092] a) Each EV (electric truck) is equipped with a UAV (drone) to provide delivery services to customers;

[0093] b) Customers arrived by EV must be served by EV;

[0094] c) EV not only serves customers, but also acts as an operating platform to provide UAV with loading and battery replacement services;

[0095] d) UAVs can serve multiple customers in one flight;

[0096] e) The EV continues to provide services to the next customer point after launching the UAV;

[0097] f) BSV (Battery Swapping Vehicle) only provides door-to-door battery replacement service for EVs and does not participate in delivery work;

[0098] g) If the UAV arrives at the customer before the EV, it will land and wait safely. Otherwise, the EV will wait for the UAV at the customer, but a maximum delay time is set for the UAV;

[0099] h) If the UAV does not transmit, it will be carried by the EV to the next node;

[0100] i) Delivery service can only start during each customer’s time window.

[0101] Further based on the above logistics distribution problem, the following assumptions are made:

[0102] a) The travel distances of electric trucks, drones, and battery-swapping vehicles in the network are known constants;

[0103] b) Each customer can only be served once by an electric truck or drone, and electric trucks and drones cannot revisit nodes that have already been visited;

[0104] c) Each drone is fully charged at the beginning of each trip, and each electric truck is fully charged after leaving the warehouse or obtaining a mobile battery replacement service;

[0105] d) The meeting point of the electric truck and the drone is the customer's location, and providing services to customers cannot be carried out at the same time as preparing the drone;

[0106] e) Battery replacement must be performed at the customer's location, and the battery swap service of the electric vehicle can be performed at the same time as the electric truck provides services to customers;

[0107] f) The electric truck can automatically recover the drone.

[0108] It should be noted that Figure 2 is a schematic diagram of coordinated delivery of electric trucks, drones, and battery-swapping vehicles according to an embodiment of the present application, such as Figure 2As shown, the EVRPTW-D-SMBS problem targeted by the embodiment of the present application includes an Electric Vehicle Routing Problem (EVRP), a time window constraint (TW), a drone (Drone) and a synchronized mobile battery swap (SMBS). Among them, EVRP is an extended version of the Vehicle Routing Problem (VRP). Considering that the vehicle is an electric vehicle, it is necessary to consider factors such as battery life and battery recharge; TW stipulates that the customer's delivery needs must be met within the specified time window, that is, the vehicle must arrive at the service location at the specified time; D means that all electric trucks carry drones for collaborative delivery to improve flexibility and efficiency; SMBS stipulates that the recharge method of electric vehicles is mobile battery replacement, and the battery replacement must be performed at an appropriate time to ensure the completion of the task. In summary, EVRPTW-D-SMBS refers to an environment with time window constraints, in which electric trucks and drones work together to serve customers, while considering the battery life limitations and mobile battery swap strategies. The goal is usually to find the most optimized path that minimizes total cost while satisfying all customer constraints.

[0109] Step S1022, constructing the objective function of the mixed integer programming model:

[0110]

[0111] The definitions of sets, parameters, and variables in the objective function are detailed in Table 1 below.

[0112] It can be seen that the objective function of the model is to minimize the purchase cost and driving energy consumption cost required for coordinated delivery of trucks, drones and battery-swap vehicles; among them, the purchase cost can be calculated by the number of vehicles used in delivery and the unit price of the vehicle truck; the driving energy consumption cost is related to the vehicle load and driving distance.

[0113] Table 1

[0114]

[0115]

[0116] Step S1023, constructing the routing constraints, load constraints, energy consumption constraints and time constraints of the mixed integer programming model.

[0117] Step S1023 specifically constructs a routing constraint of a mixed integer programming model, the routing constraint comprising a first routing constraint formula (2), a second routing constraint formula (3), a third routing constraint formula (4), a fourth routing constraint formula (5), a fifth routing constraint formula (6), a sixth routing constraint formula group (7)-(8), a seventh routing constraint formula (9), an eighth routing constraint formula (10), a ninth routing constraint formula (11), a tenth routing constraint formula (12) and an eleventh routing constraint formula group (13)-(14). Preferably, the routing constraint is preferably:

[0118]

[0119]

[0120] The definitions of the sets, parameters, and variables in the routing constraints are detailed in Table 1 above.

[0121] It can be seen that the first routing constraint formula of the model is used to ensure that each customer must be served once; the second routing constraint formula is used to ensure that each electric truck starts from the warehouse and eventually returns to the warehouse; the third routing constraint formula is used to ensure the balance of electric truck flow; the fourth routing constraint formula is used to ensure that each customer is visited by the drone at most once; the fifth routing constraint formula is used to ensure the balance of flow at the drone service point; the sixth routing constraint formula group is used to determine the starting node and end node of each drone trip; the seventh routing constraint formula is used to characterize the relationship between two consecutive trips of the drone; the eighth routing constraint formula is used to ensure that each battery swap vehicle starts from the warehouse and eventually returns to the warehouse; the ninth routing constraint formula group is used to ensure the balance of battery swap vehicle flow; the tenth routing constraint formula is used to ensure that an electric truck passes by the battery swap location of the battery swap vehicle; the eleventh routing constraint formula group is used to ensure that an electric truck passes by the take-off position and landing position of the drone.

[0122] Step S1023 specifically constructs a load constraint of a mixed integer programming model, the load constraint comprising a first load constraint formula (15), a second load constraint formula group (16)-(19), a third load constraint formula group (20)-(23), a fourth load constraint formula (24), a fifth load constraint formula group (25)-(26), a sixth load constraint formula group (27)-(30) and a seventh load constraint formula (31). Preferably, the load constraint is preferably:

[0123]

[0124]

[0125] The definitions of sets, parameters, and variables in the load constraints are detailed in Table 1 above.

[0126] It can be seen that the first load constraint formula of the model is used to ensure that the load of the electric truck does not exceed the maximum load; the second load constraint formula group is used to calculate the load of the electric truck when it leaves the warehouse, including when the UAV is launched from the warehouse and when the UAV is not launched from the warehouse; the third load constraint formula group is used to track the load changes of the electric truck at node j, including when the UAV is launched from node j and when the UAV is not launched from node j; the fourth load constraint formula is used to ensure that the load of the UAV does not exceed the maximum load; the fifth load constraint formula group is used to calculate the load of the UAV at the beginning of each trip; the sixth load constraint formula group is used to track the load changes of the UAV at node i, including when node i is the starting node of the trip and when node i is not the starting node of the trip; the seventh load constraint formula is used to ensure that the load of the battery swap vehicle does not exceed the maximum load.

[0127] Step S1023 specifically constructs an energy consumption constraint of a mixed integer programming model, the energy consumption constraint comprising a first energy consumption constraint formula group (32)-(33), a second energy consumption constraint formula group (34)-(37), a third energy consumption constraint formula (38), a fourth energy consumption constraint formula group (39)-(40), a fifth energy consumption constraint formula group (41)-(42), a sixth energy consumption constraint formula (43), a seventh energy consumption constraint formula group (44)-(45), an eighth energy consumption constraint formula group (46)-(47) and a ninth energy consumption constraint formula (48). Preferably, the energy consumption constraint is preferably:

[0128]

[0129]

[0130] The definitions of sets, parameters and variables in energy consumption constraints are detailed in Table 1 above.

[0131] It can be seen that the first energy consumption constraint formula group of the model is used to ensure that the battery of the electric truck is fully charged when it leaves the warehouse; the second energy consumption constraint formula group is used to track the energy changes of the electric truck at node j, including the need for the electric truck to replace the battery at node j and the electric truck not needing to replace the battery at node j; the third energy consumption constraint formula is used to ensure that the remaining energy of the electric truck is higher than the minimum allowable energy; the fourth energy consumption constraint formula group is used to ensure that the battery of each drone is fully charged at the beginning of each trip; the fifth energy consumption constraint formula group is used to track the energy changes of the drone; the sixth energy consumption constraint formula is used to ensure that the drone has enough energy to complete each trip; the seventh energy consumption constraint formula group is used to ensure that the battery of the battery swap vehicle is fully charged when it leaves the warehouse; the eighth energy consumption constraint formula group is used to track the energy changes of the battery swap vehicle; the ninth energy consumption constraint formula is used to ensure that the remaining energy of the battery swap vehicle is higher than the minimum allowable energy.

[0132] Step S1023 specifically constructs a time constraint of a mixed integer programming model, the time constraint comprising a first time constraint formula (49), a second time constraint formula group (50)-(51), a third time constraint formula group (52)-(54), a fourth time constraint formula group (55)-(58), a fifth time constraint formula (59), a sixth time constraint formula group (60)-(61), a seventh time constraint formula (62), an eighth time constraint formula group (63)-(65), a ninth time constraint formula group (66)-(67), a tenth time constraint formula group (68)-(70), an eleventh time constraint formula group (71)-(76), a twelfth time constraint formula (77), a thirteenth time constraint formula group (78)-(79), a fourteenth time constraint formula (80), a fifteenth time constraint formula group (81)-(82), a sixteenth time constraint formula group (83)-(84) and a seventeenth time constraint formula group (85)-(86). Preferably, the time constraint is preferably:

[0133]

[0134]

[0135]

[0136] The definitions of sets, parameters, and variables in the time constraints are detailed in Table 1 above.

[0137] It can be seen that the first time constraint formula of the model is used to determine the time when the electric truck leaves the warehouse, taking into account the time when the drone equipped by the electric truck takes off and loads goods at the warehouse; the second time constraint formula group is used to calculate the time when the electric truck arrives at the customer node; the third time constraint formula group is used to calculate the time when the electric truck starts delivering to the customer and ensures that the delivery time window specified by the customer is met; the fourth time constraint formula group is used to calculate the time when the electric truck leaves the customer node, which is divided into three situations: whether the electric truck launches the drone at customer i and whether it has been completed before starting to serve the customer; the fifth time constraint formula is used to ensure that the electric truck must return to the warehouse before the warehouse closing time; the sixth time constraint formula group is used to calculate the time when the drone arrives at the customer node; the seventh time constraint formula is used to ensure that the drone and the electric truck can meet in time; the eighth time constraint formula group is used to calculate The ninth time constraint formula group is used to calculate the time when the drone starts delivering to the customer and ensure that the delivery time window specified by the customer is met; the tenth time constraint formula group is used to ensure that the drone can use its idle time to complete maintenance work; the eleventh time constraint formula group is used to calculate the start time of the drone's next trip; the twelfth time constraint formula is used to ensure that the drone returns to the warehouse before the warehouse closes; the thirteenth time constraint formula group is used to calculate the time when the battery swap vehicle arrives at the battery swap node; the fourteenth time constraint formula is used to ensure that the battery swap vehicle can swap batteries for electric trucks in time; the fifteenth time constraint formula group is used to calculate the time when the battery swap vehicle starts swapping batteries for electric trucks; the sixteenth time constraint formula group is used to calculate the time when the battery swap vehicle leaves the battery swap node; the seventeenth time constraint formula group is used to ensure that the travel time of the battery swap vehicle meets the warehouse's operating time window.

[0138] It should be noted that step S102 in this embodiment successfully constructs a mixed integer programming model that accurately reflects the core of the problem for the collaborative logistics distribution path planning problem of electric trucks and drones under the constraints of synchronous mobile battery replacement and customer time windows. The model comprehensively considers the carrying capacity, endurance, impact of load on energy consumption of electric trucks and drones, as well as multiple constraints such as customer time windows, and has extremely high practical application value.

[0139] Step S104, performing optimization and solving based on the mixed integer programming model to obtain a coordinated delivery path for the electric truck and the drone;

[0140] Specifically, step S104 minimizes the total logistics distribution cost of electric trucks, drones and battery-swapping vehicles through the objective function of the mixed integer programming model, and optimizes and solves the route constraints, load constraints, energy consumption constraints and time constraints through the adaptive large neighborhood search algorithm ALNS to obtain the collaborative distribution path of electric trucks and drones.

[0141] It should be noted that the Adaptive Large Neighborhood Search (ALNS) algorithm is a meta-heuristic algorithm for solving combinatorial optimization problems. It searches for the global optimal solution by performing large-scale local searches in the solution space, and is suitable for solving complex optimization problems such as vehicle routing problems (VRP) and scheduling problems. The ALNS algorithm combines two operations, destruction and repair, to explore the solution space. The core idea is to selectively destroy some solutions (such as deleting some paths or task assignments) based on the current solution, and then rebuild the solution through the repair operation, so as to achieve the purpose of jumping out of the local optimum.

[0142] Step S104 preferably includes the following steps:

[0143] Step S1041, by minimizing the total logistics distribution cost of electric trucks, drones and battery-swap vehicles through the objective function of the mixed integer programming model, and on the basis of routing constraints, load constraints, energy consumption constraints and time constraints, the distribution path R of the electric truck is constructed through the nearest neighbor greedy insertion strategy e ;

[0144] Step S1042: In the delivery path R of the electric truck e In this paper, the collaborative delivery path of electric trucks and drones is generated by the destruction operator and the repair operator, and the collaborative delivery path is iteratively updated through the simulated annealing acceptance criterion to obtain the iteratively updated collaborative delivery path.

[0145] Step S106, based on the coordinated delivery path of the electric truck and the drone, determine the battery replacement node of the electric truck, and then obtain the battery replacement path of the battery replacement vehicle.

[0146] Specifically, step S106 determines the battery swapping node of the battery swapping vehicle from the delivery path of the electric truck that needs to swap batteries based on the coordinated delivery path of the electric truck and the drone through a greedy algorithm; based on the battery swapping node of the battery swapping vehicle, obtains the battery swapping path of the battery swapping vehicle that does not affect the coordinated delivery path.

[0147] It should be noted that step S104 and step S106 in this embodiment, the adaptive large neighborhood search algorithm - the solution framework of the synchronous mobile battery exchange strategy, is not only widely applicable, but also highly scalable and can achieve high-quality solution results. It effectively addresses the problem of multiple visits by drones, ensuring that drones can serve multiple customers with a single takeoff, while solving the problem of synchronous scheduling of electric vehicles and drones, as well as the problem of synchronous mobile battery exchange between electric trucks and battery exchange vehicles. It significantly improves the overall delivery efficiency of drones and trucks, and provides the logistics industry with a new and efficient solution in terms of energy supply and on-time delivery.

[0148] Through the above steps S102 to S106 in the embodiment of the present application, it is possible to synchronously move and replace batteries of electric trucks that are insufficient in energy during the coordinated distribution process through battery replacement vehicles, effectively avoiding obstacles such as high construction costs and difficult site selection of battery charging stations, greatly improving the flexibility and efficiency of distribution in the coordinated distribution of goods by electric trucks and drones, and solving the problem of how to coordinate trucks and drones for logistics distribution.

[0149] The specific embodiment of the present application provides a logistics path planning method based on collaborative distribution. For step S104 in the above embodiment, the specific embodiment of the present application is based on a mixed integer programming model (EVRPTW-D-SMBS problem - the objective function, routing constraints, load constraints, energy consumption constraints and time constraints of the above model), and obtains two stages of first constructing an electric truck route and then constructing a drone itinerary, thereby completing the construction of the initial collaborative distribution path. Figure 3 is a flow chart of an adaptive large neighborhood search algorithm according to an embodiment of the present application, such as Figure 3 shown.

[0150] Phase 1: Based on the nearest neighbor greedy insertion strategy, the route R of the electric truck serving the customer is constructed by considering the payload capacity constraint of the electric truck and the customer time window constraint. e ;

[0151] Phase 2: Find customers that can be served by drones in the constructed electric truck routes and try to construct drone trips. For example, the steps to check the route i→j→l→m→q are as follows.

[0152] Step 1: The drone is launched from node i;

[0153] Step 1: The drone is launched from node i;

[0154] Step 2: Determine whether it is feasible to construct a drone itinerary: If d j ≤Q u , Then (i, j, l) is a feasible drone itinerary and we go to step 3. Otherwise, the electric truck must carry the drone from node i to node j and then return to step 1.

[0155] Step 3: Continue to determine whether it is feasible to transfer the next node to the drone itinerary: If d j +d l ≤Q u ,

[0156] It indicates that (i, j, l, m) is a feasible drone trip and repeat step 3. Otherwise, (i, j, l) is a scheduled drone trip and return to step 1.

[0157] Step 4: R e Other nodes not served by drones are served by electric trucks.

[0158] Based on the above two stages, an initial electric truck-UAV route (initial collaborative delivery path) can be constructed that meets constraints such as electric truck payload, UAV payload, UAV battery energy consumption, and customer time window. It should be noted that the initial electric truck-UAV route may violate the battery energy consumption constraints of the electric truck, but this can be solved by subsequently implementing synchronous mobile battery swapping of battery swapping vehicles.

[0159] After completing the above two stages and obtaining the initial collaborative delivery path, it is necessary to iteratively update the collaborative delivery path, such as Figure 3 As shown, a roulette wheel strategy is used to select and apply the destruction operator and the repair operator to generate a new collaborative delivery route.

[0160] Specifically, the embodiment of the present application proposes 4 destruction operators and 3 repair operators. The main function of the destruction operator is to remove some elements from the current solution, thereby creating an incomplete solution. The purpose of this is to be able to explore new neighborhoods in the subsequent repair process and possibly find a better solution. The function of the repair operator is to fill the gaps created by the destruction operator, thereby constructing a new, complete solution. The detailed application methods of these operators are as follows:

[0161] Random destruction operator (D1): Randomly remove customers from the electric truck and drone routes. If the removed electric truck service customer performs takeoff or landing operations on a drone trip, the service customer on the corresponding drone trip is also removed. If the removed customer is the only service customer on the drone trip, the corresponding drone trip is also deleted.

[0162] Longest distance destruction operator (D2): First, calculate the distance between all customers and the two nodes before and after them. For example, the distance of customer j is l(j) = l ij +l jl , and then the farthest customer j * = argmax{l(j)} from R e The other steps are the same as the random destruction operator operation.

[0163] Site vicinity destruction operator (D3): First, a customer is randomly selected, then a circle with a radius of r is drawn with the selected customer as the center, and a customer is randomly removed from the circle. The other steps are the same as the random destruction operator operation.

[0164] Similarity destruction operator (D4): First, randomly remove a customer i, and then calculate the correlation between this customer and the remaining customers The smaller S(i,j) is, the stronger the correlation is. The most similar customer j is deleted, and then the most similar customer k to j is deleted, and so on. The other steps are the same as the random destruction operator operation.

[0165] Random insertion followed by drone trip repair operator (R1): First, consider the relevant load constraints, time window constraints, and drone energy consumption constraints to randomly insert customers in the removal customer list into the electric truck route or drone trip. If there is no feasible insertion location, the customer is added to the deletion list; secondly, some customers in the electric truck route are transferred to the drone trip, specifically a customer m is randomly selected from the customers in the current route. If customer m is currently served by an electric truck and does not perform drone launch or recovery operations, its service mode is changed from electric truck to drone by finding an existing drone trip or building a new drone trip, while considering the drone payload capacity constraint, drone energy consumption constraint, and customer time window constraint. If there are customers in the deletion list, new electric truck-drone routes will be established for these customers in the deletion list.

[0166] Greedy insertion followed by drone trip repair operator (R2): First, insert the customer in the removed customer list to the feasible position with the minimum insertion distance, which is divided into three steps. The first step is to randomly select customer j from the removed list; the second step is to calculate the insertion distance Insert(j) = l ij +l jl -l il, where i and l are the two nodes before and after j respectively, insert customer j to the position with the minimum insertion distance; the third step is to check the load constraint, time window constraint and drone energy consumption constraint. If all are met, the insertion operation is successful, otherwise, find the next insertion position with the minimum insertion distance in the path. If there is no feasible insertion position, add the customer to the deletion list; secondly, transfer some customers in the electric truck route to the drone itinerary, specifically randomly select a customer m from the customers in the current route. If customer m is currently served by an electric truck and does not perform drone launch or recovery operations, change its service mode from electric truck to drone by finding an existing drone itinerary or building a new drone itinerary, while considering the drone payload capacity constraint, drone energy consumption constraint and customer time window constraint. If there are customers in the deletion list, a new electric truck-drone route will be established for these customers in the deletion list. .

[0167] Near-domain insertion first, then drone travel repair operator (R3): Similar to R2, but no longer inserts with the shortest insertion distance. Specifically, a feasible position is randomly selected from a set of nearby positions for insertion. The set of nearby positions is defined as a set of positions close to the position with a shorter insertion distance. The other steps are the same as the first two repair operators.

[0168] In the ALNS algorithm, the destruction operator and repair operator are usually adaptively weighted according to their performance and selected using a roulette wheel method to ensure that the algorithm can continuously and effectively explore the solution space and find high-quality new solutions.

[0169] The specific method of updating operator weights is as follows:

[0170]

[0171] The parameter θ represents the coefficient of the score update ratio, and its value range is 0-1. i represents the number of times each operator appears in the iteration process, π i It represents the score of each operation in the iteration process. Specifically, when a new optimal solution is obtained, the score is 10; when a solution that is not the optimal solution but is better than the current solution is obtained, the score is 5; when the solution obtained is worse than the current solution but is selected based on the simulated annealing mechanism, the score is 1.

[0172] Each damage operator and repair operation has a selection weight w i , in each iteration, the probability of the operator being selected is determined according to the following formula:

[0173]

[0174] like Figure 3As shown, the simulated annealing acceptance criterion is used to determine whether the newly generated collaborative delivery route is accepted. Specifically, the cost of the new solution S' is compared with the cost of the optimal solution. If the cost of the new solution S' is smaller, then the optimal solution and the current solution are both updated to the new solution S'; if the cost of the new solution S' is greater than the cost of the optimal solution, but less than the cost of the current solution S, then the current solution is updated to the new solution S'; if the cost of the new solution S' is greater than the cost of the current solution S, then the following formula is used to determine whether to retain the new solution S'.

[0175]

[0176] Finally, determine whether the iteration termination condition is met and output the optimal electric truck-drone collaborative delivery route.

[0177] The specific embodiment of the present application provides a logistics path planning method based on collaborative distribution. For step S106 in the above embodiment, Figure 4 It is a flow chart of constructing a battery swapping path for a battery swapping vehicle according to an embodiment of the present application.

[0178] First, if Figure 4 As shown, this embodiment adopts a route decoupling strategy to ensure that the battery swapping route of the battery swapping vehicle will not affect the coordinated delivery route of the electric truck-UAV. Specifically, if the battery swapping vehicle can meet the relevant constraints of battery replacement in the above mixed integer programming model (that is, it arrives in time when the electric truck needs to replace the battery), then the route of the battery swapping vehicle will not affect the coordinated delivery route of the electric truck-UAV. Therefore, the latest time allowed for the battery swapping vehicle to arrive can be calculated according to the fourteenth time constraint formula ((80).

[0179] Secondly, if Figure 4 As shown in the figure, in the given electric truck-UAV collaborative delivery path, according to the energy limit requirements of the electric truck, the electric truck route that needs to be replaced is found, and the combination of replacement nodes is determined. Specifically, the energy consumption of the electric truck between each node is first calculated, and then the electric truck route that violates the energy limit is determined. Then, the number of times the corresponding electric truck route needs to be replaced is determined through greedy thinking, and finally the combination of replacement nodes is given by enumeration.

[0180] Again, if Figure 4 As shown in the figure, route planning is completed according to the determined battery swapping nodes. This is a VRPTW problem: there are some battery swapping nodes, each battery swapping vehicle can carry a limited number of replaceable batteries, and each battery swapping node has a minimum arrival time requirement. The battery swapping vehicle traverses to provide battery swapping services, minimizing the driving energy consumption cost and vehicle use cost.

[0181] Finally, if Figure 4As shown, based on each enumerated battery swap node combination, the optimal battery swap node combination and the corresponding optimal battery swap vehicle path are obtained by satisfying the above-mentioned VRPTW problem.

[0182] The specific embodiment of the present application provides a logistics path planning method based on collaborative distribution. For steps S102 to S106 in the above embodiment, the embodiment of the present application provides a case for numerical experiments, which are as follows:

[0183] (1) Test example

[0184] Since EVRP-D-SMBS is a complex problem, there is currently no ready-made standard instance for direct use. In order to verify the effectiveness and feasibility of the method proposed in this application, this embodiment adopts a strategy based on the Solomon benchmark instance generation method to create new test instances, one of which contains ten customer nodes. The test instance is shown in Table 2. Through these new test instances, real-world delivery scenarios can be simulated, including customer-specified delivery time window restrictions, electric truck energy replenishment requirements, and drone operating characteristics, so as to comprehensively test and verify the collaborative delivery path planning method of this application.

[0185] Table 2

[0186]

[0187] (2) Parameter settings

[0188] As shown in Table 3 below.

[0189] Table 3

[0190]

[0191]

[0192] (3) Numerical results

[0193] The optimal solution of the test instance test_instance_10 solved by GUROBI is 5029.6, and the solution time is about 6.03 hours; the optimal solution is also obtained by using the logistics path planning method based on collaborative distribution proposed in the present invention, but the solution time is only about 30 seconds on average, which not only verifies the correctness of the model proposed in this application, but also shows the efficiency of the logistics path planning method of this application.

[0194] In summary, the method proposed in this application aims at the problem of collaborative logistics distribution path planning between electric trucks and drones under the constraints of synchronous mobile battery exchange and customer time windows, and successfully constructs a mixed integer programming model that accurately reflects the core of the problem. This model comprehensively considers the carrying capacity, endurance, impact of load on energy consumption of electric trucks and drones, as well as multiple constraints such as customer time windows, so it has extremely high practical application value. In addition, the adaptive large neighborhood search algorithm in the present invention-the solution framework of the synchronous mobile battery exchange strategy, this framework is not only widely applicable, but also highly scalable and can achieve high-quality solution results. It effectively addresses the problem of multiple visits by drones, ensures that drones can serve multiple customers with a single takeoff, and solves the problem of synchronous scheduling of electric vehicles and drones, as well as the problem of synchronous mobile battery exchange between electric trucks and battery exchange vehicles. It significantly improves the overall delivery efficiency of drones and trucks, and provides a new and efficient solution for the logistics industry in terms of energy supply and on-time delivery.

[0195] It should be noted that the steps shown in the above process or the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0196] The embodiment of the present application provides a logistics path planning system based on collaborative distribution, which includes a planning model building module, a distribution path calculation module and a battery replacement path calculation module;

[0197] A planning model building module is used to build a mixed integer programming model, where the mixed integer programming model is used to plan the coordinated delivery path of electric trucks and drones, as well as the battery swapping path of battery swapping vehicles;

[0198] The delivery path calculation module is used to optimize and solve the mixed integer programming model to obtain the coordinated delivery path of electric trucks and drones;

[0199] The battery swap path calculation module is used to determine the battery swap nodes of the electric truck based on the coordinated delivery path of the electric truck and the drone, and then obtain the battery swap path of the battery swap vehicle.

[0200] Through the planning model construction module, distribution path calculation module and battery replacement path calculation module in the embodiments of the present application, it is possible to synchronously move and replace batteries of electric trucks that are insufficient in energy during the coordinated distribution process through battery replacement vehicles, effectively avoiding obstacles such as high construction costs and difficult site selection for battery charging stations, greatly improving the flexibility and efficiency of distribution in the coordinated distribution of goods by electric trucks and drones, and solving the problem of how to coordinate trucks and drones for logistics distribution.

[0201] It should be noted that the above modules can be functional modules or program modules, and can be implemented by software or hardware. For modules implemented by hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0202] This embodiment further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0203] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0204] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0205] In addition, in combination with the logistics path planning method based on collaborative distribution in the above embodiments, the present application embodiment can provide a storage medium for implementation. The storage medium stores a computer program; when the computer program is executed by a processor, any one of the logistics path planning methods based on collaborative distribution in the above embodiments is implemented.

[0206] In one embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a logistics path planning method based on collaborative distribution is implemented. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball, or touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0207] In one embodiment, Figure 5 is a schematic diagram of the internal structure of an electronic device according to an embodiment of the present application, such as Figure 5 As shown, an electronic device is provided, which may be a server, and its internal structure diagram may be as shown in Figure 5 As shown. The electronic device includes a processor, a network interface, an internal memory and a non-volatile memory connected through an internal bus, wherein the non-volatile memory stores an operating system, a computer program and a database. The processor is used to provide computing and control capabilities, the network interface is used to communicate with an external terminal through a network connection, the internal memory is used to provide an environment for the operation of the operating system and the computer program, the computer program is executed by the processor to implement a logistics path planning method based on collaborative distribution, and the database is used to store data.

[0208] Those skilled in the art will understand that Figure 5 The structure shown in the figure is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the electronic device to which the scheme of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0209] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0210] Those skilled in the art should understand that the technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0211] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A logistics path planning method based on collaborative distribution, characterized in that: The method comprises: Constructing a mixed integer programming model, wherein the mixed integer programming model is used to plan the coordinated delivery path of electric trucks and drones, and the battery swapping path of battery swapping vehicles; An optimization solution is performed based on the mixed integer programming model to obtain a collaborative delivery path of the electric truck and the drone; Based on the coordinated delivery path of the electric truck and the drone, the battery swap node of the electric truck is determined, and then the battery swap path of the battery swap vehicle is obtained.

2. The method according to claim 1, characterized in that: Building a mixed integer programming model involves: Constructing an objective function of a mixed integer programming model, wherein the objective function is used to minimize the total logistics distribution cost of electric trucks, drones, and battery-swap vehicles; Construct routing constraints, load constraints, energy consumption constraints and time constraints of the mixed integer programming model, wherein the routing constraints are used to regulate the coordinated delivery path of the electric truck and the drone, and the battery swapping path of the battery swapping vehicle; the load constraints are used to regulate the load changes of the electric truck, the drone and the battery swapping vehicle; the energy consumption constraints are used to regulate the energy changes of the electric truck, the drone and the battery swapping vehicle; and the time constraints are used to regulate the coordinated delivery time of the electric truck and the drone, and the battery swapping time of the battery swapping vehicle.

3. The method according to claim 2, characterized in that The routing constraints include a first routing constraint formula, a second routing constraint formula, a third routing constraint formula, a fourth routing constraint formula, a fifth routing constraint formula, a sixth routing constraint formula group, a seventh routing constraint formula, an eighth routing constraint formula, a ninth routing constraint formula, a tenth routing constraint formula and an eleventh routing constraint formula group; The first routing constraint formula is used to ensure that each customer must be served once; The second routing constraint formula is used to ensure that each electric truck starts from the warehouse and eventually returns to the warehouse; The third routing constraint formula is used to ensure the balance of electric truck flow; The fourth routing constraint formula is used to ensure that each customer is visited by a drone at most once; The fifth routing constraint formula is used to ensure flow balance at the drone service point; The sixth routing constraint formula group is used to determine the starting node and the ending node of each trip of the drone; The seventh route constraint formula is used to characterize the relationship between two consecutive trips of the drone; The eighth routing constraint formula is used to ensure that each battery-swapping vehicle starts from the warehouse and eventually returns to the warehouse; The ninth routing constraint formula group is used to ensure the balance of battery swapping vehicle flow; The tenth route constraint formula is used to ensure that an electric truck passes by the battery swapping position of the battery swapping vehicle; The eleventh routing constraint formula group is used to ensure that the electric truck passes by the take-off position and landing position of the drone.

4. The method according to claim 2, characterized in that: The load constraints include a first load constraint formula, a second load constraint formula group, a third load constraint formula group, a fourth load constraint formula, a fifth load constraint formula group, a sixth load constraint formula group and a seventh load constraint formula; The first load constraint formula is used to ensure that the load of the electric truck does not exceed the maximum load; The second load constraint formula group is used to calculate the load of the electric truck when it leaves the warehouse, including when the drone is launched from the warehouse and when the drone is not launched from the warehouse; The third load constraint formula group is used to track the load change of the electric truck at the node j, including the UAV being launched from the node j and the UAV not being launched from the node j; The fourth load constraint formula is used to ensure that the load of the UAV does not exceed the maximum load; The fifth load constraint formula group is used to calculate the load of the drone at the beginning of each trip; The sixth load constraint formula group is used to track the load change of the UAV at the node i, including when the node i is the starting node of the trip and when the node i is not the starting node of the trip; The seventh load constraint formula is used to ensure that the load of the battery-swapping vehicle does not exceed the maximum load.

5. The method according to claim 2, characterized in that: The energy consumption constraints include a first energy consumption constraint formula group, a second energy consumption constraint formula group, a third energy consumption constraint formula, a fourth energy consumption constraint formula group, a fifth energy consumption constraint formula group, a sixth energy consumption constraint formula, a seventh energy consumption constraint formula group, an eighth energy consumption constraint formula group and a ninth energy consumption constraint formula; The first energy consumption constraint formula group is used to ensure that the battery of the electric truck is fully charged when leaving the warehouse; The second energy consumption constraint formula group is used to track the energy change of the electric truck at the node j, including whether the electric truck needs to replace the battery at the node j and whether the electric truck does not need to replace the battery at the node j; The third energy consumption constraint formula is used to ensure that the remaining energy of the electric truck is higher than the minimum allowed energy; The fourth energy consumption constraint formula group is used to ensure that the battery of each drone is fully charged at the beginning of each trip; The fifth energy consumption constraint formula group is used to track the energy changes of the UAV; The sixth energy consumption constraint formula is used to ensure that the drone has enough energy to complete each trip; The seventh energy consumption constraint formula group is used to ensure that the battery of the battery swap vehicle is fully charged when leaving the warehouse; The eighth energy consumption constraint formula group is used to track the energy changes of the battery-swapping vehicle; The ninth energy consumption constraint formula is used to ensure that the remaining energy of the battery-swapping vehicle is higher than the minimum allowable energy.

6. The method according to claim 2, characterized in that The time constraints include a first time constraint formula, a second time constraint formula group, a third time constraint formula group, a fourth time constraint formula group, a fifth time constraint formula, a sixth time constraint formula group, a seventh time constraint formula, an eighth time constraint formula group, a ninth time constraint formula group, a tenth time constraint formula group, an eleventh time constraint formula group, a twelfth time constraint formula, a thirteenth time constraint formula group, a fourteenth time constraint formula, a fifteenth time constraint formula group, a sixteenth time constraint formula group and a seventeenth time constraint formula group; The first time constraint formula is used to determine the time when the electric truck leaves the warehouse; The second time constraint formula group is used to calculate the time for the electric truck to arrive at the customer node; The third time constraint formula group is used to calculate the time when the electric truck starts delivering to the customer and ensure that the delivery time window specified by the customer is met; The fourth time constraint formula group is used to calculate the time when the electric truck leaves the customer node; The fifth time constraint formula is used to ensure that the electric truck must return to the warehouse before the warehouse closing time; The sixth time constraint formula group is used to calculate the time for the drone to arrive at the customer node; The seventh time constraint formula is used to ensure that the UAV and the electric truck can meet in time; The eighth time constraint formula group is used to calculate the time when the drone starts delivering to the customer and ensure that the delivery time window specified by the customer is met; The ninth time constraint formula group is used to calculate the time when the drone leaves the client node; The tenth time constraint formula group is used to ensure that the UAV can complete the preparation work in idle time; The eleventh time constraint formula group is used to calculate the start time of the next trip of the drone; The twelfth time constraint formula is used to ensure that the drone returns to the warehouse before the warehouse closing time; The thirteenth time constraint formula group is used to calculate the time it takes for the battery swap vehicle to arrive at the battery swap node; The fourteenth time constraint formula is used to ensure that the battery-swapping vehicle can promptly swap batteries for the electric truck; The fifteenth time constraint formula group is used to calculate the time when the battery-swapping vehicle starts to swap batteries for the electric truck; The sixteenth time constraint formula group is used to calculate the time when the battery swap vehicle leaves the battery swap node; The seventeenth time constraint formula group is used to ensure that the travel time of the battery-swapping vehicle meets the operating time window of the warehouse.

7. The method according to claim 2, characterized in that: Based on the mixed integer programming model, an optimization solution is performed to obtain the coordinated delivery path of the electric truck and the drone, which includes: The total logistics distribution cost of electric trucks, drones and battery-swapping vehicles is minimized through the objective function of the mixed integer programming model, and on the basis of the route constraints, the load constraints, the energy consumption constraints and the time constraints, an adaptive large neighborhood search algorithm ALNS is used to perform optimization and solve to obtain the collaborative distribution path of the electric trucks and the drones.

8. The method according to claim 7, characterized in that The coordinated delivery path of the electric truck and the drone is obtained by optimizing and solving the problem through the adaptive large neighborhood search algorithm ALNS, including: Constructing the delivery path R of electric trucks via nearest neighbor greedy insertion strategy e ; In the delivery path R of the electric truck e The collaborative delivery path of the electric truck and the UAV is generated by a destruction operator and a repair operator, and the collaborative delivery path is iteratively updated by a simulated annealing acceptance criterion to obtain an iteratively updated collaborative delivery path.

9. The method according to claim 1, characterized in that: Based on the coordinated delivery path of the electric truck and the drone, the battery swap node of the electric truck is determined, and then the battery swap path of the battery swap vehicle is obtained, including: Based on the coordinated delivery path of the electric truck and the drone, a battery swap node of the battery swap vehicle is determined from the delivery path of the electric truck that needs battery swapping by using a greedy algorithm; Based on the battery-swapping nodes of the battery-swapping vehicle, a battery-swapping path of the battery-swapping vehicle that does not affect the collaborative delivery path is obtained.

10. A logistics path planning system based on collaborative distribution, characterized in that: The system is used to execute the method described in any one of claims 1 to 9, and the system includes a planning model building module, a distribution path calculation module, and a battery replacement path calculation module; The planning model building module is used to build a mixed integer programming model, wherein the mixed integer programming model is used to plan the coordinated delivery path of the electric truck and the drone, and the battery swapping path of the battery swapping vehicle; The delivery path calculation module is used to perform optimization and solution based on the mixed integer programming model to obtain a collaborative delivery path for the electric truck and the drone; The battery-swapping path calculation module is used to determine the battery-swapping node of the electric truck according to the coordinated delivery path of the electric truck and the drone, and then obtain the battery-swapping path of the battery-swapping vehicle.

11. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 4.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

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