CN118154067A - Unmanned aerial vehicle distribution scheduling method, device, equipment and storage medium - Google Patents
Unmanned aerial vehicle distribution scheduling method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention provides an unmanned aerial vehicle distribution scheduling method, device, equipment and storage medium, comprising the following steps: acquiring map information of an unmanned aerial vehicle dispatching system; acquiring battery information of each unmanned aerial vehicle; acquiring order information of each to-be-processed order, wherein the order information comprises distribution position information and menu information; and determining an optimal scheduling scheme of the unmanned aerial vehicle based on the order information, the map information and the battery information of each unmanned aerial vehicle. The present invention configures the unmanned aerial vehicle to be dispatched from the landing station to the designated meal delivery destination in order to maximize the overall profit of on-time meal delivery. According to the unmanned aerial vehicle meal delivery system, special constraints of unmanned aerial vehicle meal delivery, such as delivery profits, meal delivery time, limited energy of an unmanned aerial vehicle battery and the like, are comprehensively considered, and whether the battery needs to be replaced when the unmanned aerial vehicle returns to a transmitting field is considered, so that the reliability and the safety of the unmanned aerial vehicle meal delivery system are improved by taking a battery replacement decision into consideration, and the benefit of the delivery system is further improved.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle dispatching, in particular to an unmanned aerial vehicle dispatching method, an unmanned aerial vehicle dispatching device, unmanned aerial vehicle dispatching equipment and a storage medium.
Background
Along with the acceleration of life rhythm, the progress of science and technology and the rise of a plurality of online meal ordering platforms, the convenience of meal ordering service is continuously improved, so that the online meal ordering requirement is rapidly increased. As demand for orders grows, take-away platforms face challenges in ensuring on-time delivery, with the speed of delivery becoming a critical factor in maintaining customer satisfaction. To address this problem, efforts are typically made to complete the delivery prior to the delivery time promised for each order. In order to provide high quality service, these platforms typically rely on a large number of part-time couriers to fulfill orders. However, takeaway platforms are actively seeking alternative solutions due to rising labor costs and road congestion.
Unmanned aerial vehicles have the advantage of faster flight speeds and are not limited by roads, and therefore unmanned aerial vehicle delivery is an attractive alternative. However, the battery power of the drone is limited, which presents a potential risk. Unlike traditional human couriers, any energy interruption of the unmanned aerial vehicle may pose a serious threat to public safety, and the limited battery energy and energy consumption problems of the unmanned aerial vehicle increase the need for energy replenishment. Therefore, for improving the reliability and safety of the unmanned aerial vehicle meal delivery system, taking the energy consumption into consideration and exploring an innovative solution for unmanned aerial vehicle power conversion decisions is important.
Disclosure of Invention
The invention provides a method, a device, equipment and a storage medium for unmanned aerial vehicle distribution scheduling, and aims to solve at least one problem in the prior art.
The invention provides an unmanned aerial vehicle distribution scheduling method, which comprises the following steps:
acquiring map information of an unmanned aerial vehicle dispatching system;
acquiring battery information of each unmanned aerial vehicle;
acquiring order information of each to-be-processed order, wherein the order information comprises distribution position information and menu information;
And determining an optimal scheduling scheme of the unmanned aerial vehicle based on the order information, the map information and the battery information of each unmanned aerial vehicle.
According to the unmanned aerial vehicle distribution scheduling method provided by the invention, the optimal scheduling scheme of the unmanned aerial vehicle is determined based on the order information, the map information and the battery information of each unmanned aerial vehicle, and the method comprises the following steps:
Combining the order information, the map information and battery information of each unmanned aerial vehicle, establishing a time-expanded flow network to establish an integer linear programming model based on the flow network, wherein each time of delivery scheme of the unmanned aerial vehicle is represented by one node in the flow network, and the complete scheduling scheme of one unmanned aerial vehicle is represented by a flow path formed by combining different nodes;
and solving the integer linear programming model based on a pre-constructed mixed variable domain searching algorithm to obtain an optimal scheduling scheme of the unmanned aerial vehicle.
According to the unmanned aerial vehicle distribution scheduling method provided by the invention, the time-extended stream network is established by combining the order information, the map information and the battery information of each unmanned aerial vehicle, so as to establish an integer linear programming model based on the stream network, and the method comprises the following steps:
determining the delivery period of each order to be processed and the energy consumption information of the unmanned aerial vehicle;
Determining whether a delivery trip of the order to be processed is feasible or not based on the battery information, the delivery deadline and the energy consumption information of the unmanned aerial vehicle;
Constructing and obtaining a search tree based on all feasible delivery routes of any unmanned aerial vehicle, wherein a root node of the search tree represents that the unmanned aerial vehicle starts delivering service from a landing station; each node of the tree represents a viable delivery scheme for one order, or a viable scheduling scheme for one pass of the drone; each node is provided with a group of child nodes, and the child nodes represent feasible scheduling schemes of subsequent passes of the unmanned aerial vehicle;
Optimally traversing the search tree to construct a time-expanded flow network, and setting value arcs among nodes in the flow network, wherein the value arcs are used for representing distribution profits of an order to be processed;
And on the basis of the flow network, the unmanned aerial vehicle scheduling problem is expressed as an integer linear programming model.
According to the unmanned aerial vehicle dispatching method provided by the invention, each node of the search tree represents a feasible delivery route, wherein each node of the search tree is represented by (k, y, p, z, alpha), k is an unmanned aerial vehicle number for realizing the delivery route, y is a binary variable for indicating whether a battery needs to be replaced before the unmanned aerial vehicle realizes the delivery route, p represents an order number of an order to be processed, alpha represents the arrival time of the unmanned aerial vehicle at a delivery destination, and z is the residual electric quantity of the unmanned aerial vehicle for completing the delivery route;
the optimizing traverses the search tree to construct a time-expanded stream network and set value arcs among nodes in the stream network, comprising:
introducing a source node o and a sink node d;
For any two nodes And node/>: If/>, in unmanned aerial vehicle k's search treeIs/>A slave node/>, is establishedTo/>Directed arc/>; For any node/>, in a streaming networkConstructing a node/>, from a source node o to a nodeConnected arc/>Building a slave node/>Connection arcs to sink node d to construct a streaming network/>Wherein the streaming network is a node/>And arc/>Is a loop-free directed network of (1) >, set/>Representing arc/>For arc/>: Set unit flow cost/>For all connection arcs, the unit flow cost is zero;
the method expresses the unmanned aerial vehicle scheduling problem as an integer linear programming model on the basis of the flow network and comprises the following steps:
the goal is to maximize the total profit obtained from on-time meal delivery;
in the constructed flow network, the delivery profit of an order is expressed as a negative value of the unit flow cost on a certain arc line, and the objective function of maximizing the total profit obtained by on-time meal delivery is set to minimize the total flow cost in the flow network.
According to the unmanned aerial vehicle distribution scheduling method provided by the invention, the integer linear programming model is solved based on a pre-constructed mixed variable domain searching algorithm to obtain an optimal scheduling scheme of the unmanned aerial vehicle, and the method comprises the following steps:
Selecting an order with the largest distribution profit from the orders to be processed to be distributed to the unmanned aerial vehicles, distributing the order to each unmanned aerial vehicle, and determining a distribution plan with the shortest distribution completion time of each unmanned aerial vehicle;
updating the distribution plan of the unmanned aerial vehicle according to the order distribution scheme with the largest distribution profit until all the orders to be processed are distributed, so as to obtain a target feasible scheme;
and carrying out neighborhood iterative search updating on the target feasible scheme to obtain the optimal scheduling scheme.
According to the unmanned aerial vehicle dispatching and dispatching method provided by the invention, the target feasible scheme is subjected to neighborhood iterative search and update to obtain the optimal dispatching scheme, and the method comprises the following steps:
Determining four preset destruction operators: randomly deleting the order of the distributed delivery; greedy removes orders for which the profit of the allocated delivery is minimal; randomly deleting all meal delivery plans of the unmanned aerial vehicle; greedy deletes the unmanned plane's meal delivery plan with minimum total delivery profit;
determining a preset reconstruction operator: a scenario that requires minimal additional time to deliver the order;
Combining the four destructive operators and the reconstruction operator respectively to obtain four combined results;
Based on the selected combination result, deleting and reassigning the target feasible scheme to obtain a solution;
If the overall distribution profit of the solution is greater than the total distribution profit of the target feasible scheme, taking the solution as a new target feasible scheme, and setting the number of non-improved continuous times corresponding to the combined result to be 0; returning to execute the deleting and reassigning operation on the target feasible scheme based on the current combination result to obtain a solution;
If the overall distribution profit of the solution is not greater than the total distribution profit of the target feasible scheme, recording the non-improved continuous times until the continuous times reach a preset time threshold, and calling the next combined result to execute deleting and reassigning operations on the target feasible scheme based on the new combined result;
And outputting the optimal solution with the maximum profit in the iterative process after the iteration of all the combined results is completed, so as to obtain the optimal scheduling scheme.
According to the unmanned aerial vehicle distribution scheduling method provided by the invention, the integer linear programming model is as follows:
Wherein equation 1 is an objective function for characterizing the minimum total traffic cost in the streaming network; equation 2 shows that all unmanned aerial vehicles start from the landing station; equation 3 shows that all the landing stations return to the landing station after completing the task; equation 4 represents a stream equalization constraint; equation 5 shows that each order is serviced at most once; equation 6 represents a decision variable value constraint; is a binary variable representing arc/> Flow on,/>Representing unmanned aerial vehicle execution node/>A delivery trip represented; /(I)Representing the same order sequence number/>, involved in a streaming networkIs a node set of (1); /(I)Characterizing each node in the streaming network that does not include a source node and a sink node; /(I)A sequence number set representing an order to be processed; a represents all arcs in the streaming network; k represents the number of unmanned aerial vehicles.
The invention also provides an unmanned aerial vehicle distribution scheduling device, which comprises:
the first acquisition module is used for acquiring map information of the unmanned aerial vehicle dispatching system;
The second acquisition module is used for acquiring battery information of each unmanned aerial vehicle;
The third acquisition module is used for acquiring order information of each to-be-processed order, wherein the order information comprises distribution position information and menu information;
And the scheduling module is used for determining an optimal scheduling scheme of the unmanned aerial vehicle based on the order information, the map information and the battery information of each unmanned aerial vehicle.
The invention also provides electronic equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the unmanned aerial vehicle dispatching scheduling method when executing the program.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of unmanned aerial vehicle dispatch scheduling as described in any of the above.
The invention also provides a computer program product comprising a computer program which when executed by a processor implements a method of unmanned aerial vehicle dispatch scheduling as described in any of the above.
The invention provides an unmanned aerial vehicle distribution scheduling method, device, equipment and storage medium, which comprise the following steps: acquiring map information of an unmanned aerial vehicle dispatching system; acquiring battery information of each unmanned aerial vehicle; acquiring order information of each to-be-processed order, wherein the order information comprises distribution position information and menu information; and determining an optimal scheduling scheme of the unmanned aerial vehicle based on the order information, the map information and the battery information of each unmanned aerial vehicle. The present invention configures the unmanned aerial vehicle to be dispatched from the landing station to the designated meal delivery destination in order to maximize the overall profit of on-time meal delivery. According to the unmanned aerial vehicle meal delivery system, special constraints of unmanned aerial vehicle meal delivery, such as delivery profits, meal delivery time, limited energy of an unmanned aerial vehicle battery and the like, are comprehensively considered, and whether the battery needs to be replaced when the unmanned aerial vehicle returns to a transmitting field is considered, so that the reliability and the safety of the unmanned aerial vehicle meal delivery system are improved by taking a battery replacement decision into consideration, and the benefit of the delivery system is further improved.
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In order to more clearly illustrate the invention or the technical solutions in the prior art, the drawings that are used in the description of the embodiments or the prior art will be briefly described one by one, it being obvious that the drawings in the description below are some embodiments of the invention, and that other drawings can be obtained from these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for dispatching unmanned aerial vehicle delivery provided by the invention;
fig. 2 is a schematic structural diagram of an unmanned aerial vehicle dispatching device provided by the invention;
fig. 3 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terminology used in the one or more embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of the invention. As used in one or more embodiments of the invention, the singular forms "a," "an," "the," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used in one or more embodiments of the present invention refers to and encompasses any or all possible combinations of one or more of the associated listed items.
It should be understood that, although the terms first, second, etc. may be used in one or more embodiments of the invention to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, a first may also be referred to as a second, and similarly, a second may also be referred to as a first, without departing from the scope of one or more embodiments of the invention. The word "if" as used herein may be interpreted as "at … …" or "when … …", depending on the context.
Fig. 1 is a schematic flow chart of a method for dispatching unmanned aerial vehicle delivery. As shown in fig. 1, the unmanned aerial vehicle delivery scheduling method includes:
Step S11, obtaining map information of an unmanned aerial vehicle dispatching system;
It should be noted that the map information may be obtained by a positioning system, for example, a beidou positioning system in china, a GPS system, a GLONASS system, and a galileo system in europe. The positioning system can be used for multidimensional positioning of the transportation path, and the unmanned aerial vehicle meal delivery path can be checked through the positioning system on the mobile terminal or the PC terminal. The map information comprises an unmanned aerial vehicle meal delivery line, a primary starting point, an intersection point, a safe distance, a conveying speed, a loading point, an unloading point and a waiting area, wherein the unmanned aerial vehicle meal delivery line is a line for providing unmanned aerial vehicle meal delivery, the primary starting point is the starting point of the unmanned aerial vehicle on the unmanned aerial vehicle meal delivery line, the intersection point is the intersection point of branches of the unmanned aerial vehicle conveying lines on the unmanned aerial vehicle meal delivery line, the safe distance is the distance between two adjacent unmanned aerial vehicles, the conveying speed is the conveying speed of the unmanned aerial vehicle on the unmanned aerial vehicle conveying line, the loading point is the position of the unmanned aerial vehicle for loading the meal, the unloading point is the position of the unmanned aerial vehicle for stopping and unloading the meal, and the waiting area is the waiting area of the unmanned aerial vehicle, for example, when the unmanned aerial vehicle arrives and the previous unmanned aerial vehicle does not finish unloading the meal immediately, the unmanned aerial vehicle waits for the waiting area, and then goes to the unloading point from the waiting area.
Step S12, obtaining battery information of each unmanned aerial vehicle;
It is to be noted that, unmanned aerial vehicle monitored control system can real-time supervision unmanned aerial vehicle's battery service condition, and unmanned aerial vehicle battery service condition includes when and the power consumption of electricity and the remaining condition, can realize unmanned aerial vehicle and arrive the electric quantity tracking of unmanned aerial vehicle on the meal delivery line of unmanned aerial vehicle any moment, helps carrying out the decision of changing the electricity.
In some embodiments, a set of drone numbers is setAs the identification of unmanned aerial vehicle to realize on-demand meal delivery, the availability status of unmanned aerial vehicle k is used/>Representation of/>And/>Indicating the time and battery power available to drone k at the transmit field. In order to ensure reliability of unmanned aerial vehicle delivery and minimize the risk of unmanned aerial vehicle failure, in this embodiment, the system employs a small unmanned aerial vehicle with a low payload capacity. It will be appreciated that each unmanned aerial vehicle is configured to fly between the launch site and a particular customer location with only a single meal at a time, wherein the single pass flight time of the unmanned aerial vehicle from the launch site of menu p to the delivery destination is recorded as/>The time to load the meal onto the drone at the launch site or to unload the meal at the delivery destination is denoted by s.
In addition, in view of the limited flight endurance of the unmanned aerial vehicle, a symbol Q is introduced to represent the energy level of a fully charged battery equipped on the unmanned aerial vehicle. Each time the drone returns to the firing field, the battery remaining energy is checked and a decision is made as to whether to replace its battery with a fully charged battery. If the battery is replaced, the duration time required for replacing the battery is set asThe battery energy is updated to q, the energy consumed by the round trip of transporting menu p, use/>The specific calculation formula for the consumed energy is shown as follows:
wherein W represents the weight of the unmanned aerial vehicle frame, m represents the weight of the equipped battery, Representing gravity,/>Representing the fluid density of air,/>Representing the area of the rotating blade disc,/>Representing a single pass time of flight from the emission site to the delivery destination,The weight of the order is represented, and n represents the number of rotor wings of the unmanned aerial vehicle; /(I)Representing the energy consumed by delivery menu p from the delivery site to the delivery destination,/>Representing the energy expended by returning the delivery destination to the emission site
Step S13, order information of each order to be processed is obtained, wherein the order information comprises distribution position information and menu information;
It should be noted that, the order platform monitors order information of different client points in the map, and the order information includes distribution position information and menu information. After a customer places a bill, the bill is reacted on the unmanned aerial vehicle meal delivery line through the positioning system. In some embodiments, the menu information includes the weight of the menu, the time the menu was prepared, and the profit for dispensing the menu. In one embodiment, the sequence number set of the pending order is represented as . Each pending order p is characterized byWherein/>Representing the weight of the order,/>Representing delivery destination,/>Representing the time that an order may be dispatched at a take-off and landing station,/>The profit of the time distribution order p is shown. If order p promises on-time delivery, order p should arrive at the designated delivery destination within the promised time, specifying delivery deadline as/>。
Step S14, determining an optimal scheduling scheme of the unmanned aerial vehicle based on the order information, the map information and the battery information of each unmanned aerial vehicle.
It should be noted that, in order to maximize the total profit of on-time meal delivery, the meal delivery schedule of each unmanned aerial vehicle is optimized, including menu allocation and battery energy management. This task can be described in terms of several decisions: (1) Order formWhether to be delivered. (2) If order/>Promises to deliver goods on time, and distributes which unmanned plane to deliver goods. (3) designating an order delivery order. (4) Whether the unmanned aerial vehicle battery needs to be replaced by a fully charged battery before each delivery. Unmanned aerial vehicle scheduling in real-time meal delivery is facilitated, and the fulfillment of meal order orders with a tighter advance period is realized under the constraint of batteries and the constraint of unmanned aerial vehicle weight. Accordingly, the delivery schedule for the drone has the following features:
Wherein, Representation and/>Decision making relating to secondary delivery trip. /(I)Is a binary variable represented at the/>Whether to replace the unmanned aerial vehicle battery before the next delivery of goods,/>Representing an order for a meal delivered during the delivery.
In addition, delivery time is constrained by feasibility requirements, including delivery time of order commitments and energy consumption associated with drone loading. First consider a viable continuous stroke condition. Suppose unmanned aerial vehicle k is in order p 1 on the current trip, during which unmanned aerial vehicle k is in timeReach delivery destination and carry battery remaining energy/>And returning to the landing station. The unmanned plane k can deliver the menu p 2 in time in the next trip, provided that one of the following conditions is satisfied:
Where s represents the time for loading the meal onto the drone or unloading the meal at the delivery destination, Representing the one-way time of flight of order p 1 from the launch site to the delivery destination,/>Representing the one-way time of flight of order p 2 from the launch site to the delivery destination,/>Representing the delivery deadline specified by order p 2,/>Representing the energy consumed by the round trip of order p 2; /(I)Representing the time that order p 2 may be dispensed at the take-off and landing station.
Without battery replacement, the drone must reach the delivery destination before a specified delivery expiration date, and the battery charge must be sufficient to cover the entire trip.
Wherein,Representing the duration of time required to replace the battery, Q represents the energy level of a fully charged battery equipped on the drone; so that the delivery deadline can be achieved even in consideration of the time required to replace the battery.
Unmanned planeFor example, the menu p can be delivered on time only if one of the following conditions is satisfied:
Wherein, And/>Respectively represent unmanned plane/>Time available at the transmit field and battery power. These constraints ensure the feasibility of ordering delivery deadlines and drone energy without and with battery replacement, respectively. The method is characterized in that in order to keep the energy efficiency of the unmanned aerial vehicle-based distribution system, a load-dependent energy consumption model is considered, the model considers a scheduling scheme under the load constraint of the unmanned aerial vehicle, and meanwhile, the power conversion decision of the unmanned aerial vehicle is considered.
To further simplify the decision to replace the battery, the present embodiment is preconfigured with the following theorem:
Theorem 1: for two menu sheets p and q, there are And/>If they are delivered by the same drone, there is an optimal solution for the menu p to deliver before menu q. This means that the drone dispatch should prioritize earlier preparation times and closer destinations. Theorem 2: consider that drone k delivers menu p on the next trip. If the unmanned aerial vehicle is at time/>And then back to the field of emission, and the battery energy is sufficient to deliver the menu p, the battery need not be replaced.
The order information, the map information and the battery information of each unmanned aerial vehicle are combined, a time-expanded flow network is established, and an integer linear programming model is established based on the flow network, wherein each time of delivery scheme of each unmanned aerial vehicle is represented by one node in the flow network, and the complete scheduling scheme of one unmanned aerial vehicle is represented by a flow path formed by combining different nodes; solving the integer linear programming model to obtain an optimal scheduling scheme of the unmanned aerial vehicle, wherein the construction process and the solving process of the integer linear programming model are specifically described in the following embodiments and are not described herein. Therefore, unmanned aerial vehicle scheduling in real-time meal delivery is realized, and the fulfillment of meal order orders with tighter advance period is realized under the constraint of load-dependent energy consumption.
In order to maximize the overall profit of on-time meal delivery, the embodiment of the invention configures the unmanned aerial vehicle to be dispatched to the designated meal delivery destination from the take-off and landing station, comprehensively considers special constraints of unmanned aerial vehicle meal delivery, such as delivery profit, meal delivery time, limited energy of an unmanned aerial vehicle battery and the like, and considers whether the unmanned aerial vehicle needs to be replaced when returning to a transmitting field, thereby realizing the improvement of the reliability and safety of the unmanned aerial vehicle meal delivery system by incorporating a battery replacement decision and further improving the benefit of the delivery system.
In one embodiment of the present invention, the establishing a time-extended flow network in combination with the order information, the map information, and the battery information of each of the unmanned aerial vehicles to establish an integer linear programming model based on the flow network includes:
Determining the delivery period of each order to be processed and the energy consumption information of the unmanned aerial vehicle; determining whether a delivery trip of the order to be processed is feasible or not based on the battery information, the delivery deadline and the energy consumption information of the unmanned aerial vehicle; constructing and obtaining a search tree based on all feasible delivery routes of any unmanned aerial vehicle, wherein a root node of the search tree represents that the unmanned aerial vehicle starts delivering service from a landing station; each node of the tree represents a viable delivery scheme for one order, or a viable scheduling scheme for one pass of the drone; each node is provided with a group of child nodes, and the child nodes represent feasible scheduling schemes of subsequent passes of the unmanned aerial vehicle; optimally traversing the search tree to construct a time-expanded flow network, and setting value arcs among nodes in the flow network, wherein the value arcs are used for representing distribution profits of an order to be processed; and on the basis of the flow network, the unmanned aerial vehicle scheduling problem is expressed as an integer linear programming model.
In order to determine the feasibility of the delivery trip, it is first determined whether the delivered menu requires replacement of the battery of the unmanned aerial vehicle, and then the feasibility condition related to the delivery period and the energy consumption information of the unmanned aerial vehicle is checked. Based on the battery information, delivery deadline, and energy consumption information, it is determined whether a delivery trip of the pending order is viable, wherein the delivery trip is viable if the unmanned aerial vehicle is able to reach the delivery destination within a specified time and the battery charge is sufficient to complete the delivery trip.
Further, a search tree is constructed based on all possible delivery trips of any drone, wherein the root node of the tree represents the drone to deliver service from the landing station. Each node of the tree represents a viable delivery scheme for one order, or a viable scheduling scheme for one pass of the drone. Each node may have a set of child nodes that represent a continuous trip after the delivery trip represented. Each node of the search tree represents a feasible delivery trip, wherein each node of the search tree is represented by (k, y, p, z, a), k is the number of the unmanned aerial vehicle for realizing the delivery trip, y is a binary variable for indicating whether the unmanned aerial vehicle needs to replace a battery before realizing the delivery trip, p represents the order number of an order to be processed, a represents the arrival time of the unmanned aerial vehicle at the delivery destination, and z is the residual electric quantity of the unmanned aerial vehicle for completing the delivery trip.
Still further, the entire search tree is traversed to determine all feasible delivery trips, e.g., traversed using a depth-first search algorithm. In more detail, for a given nodeThe continuous delivery trip is first found without the need to replace the battery, and then the extra trip to replace the battery at the firing site is considered. By utilizing theorem 1 and theorem 2, the process of enumerating all possible delivery runs may be made more computationally efficient, where theorem 1: for two menu sheets p and q, there is/>And/>If they are delivered by the same drone, there is an optimal solution for the menu p to deliver before menu q. This means that the drone dispatch should prioritize earlier preparation times and closer destinations. Theorem 2: consider that drone k delivers menu p on the next trip. If the unmanned aerial vehicle is at time/>And then back to the field of emission, and the battery energy is sufficient to deliver the menu p, the battery need not be replaced. As one example: given father node/>Theorem 1 allows for eliminating a continuous dispensing stroke of meal with earlier preparation time and closer destination than menu p. Furthermore, if node/>The indicated dispensing stroke satisfies the condition specified in the theorem 2, and only the continuous dispensing stroke requiring no replacement of the battery may be paid attention to. By utilizing these attributes, unnecessary delivery runs can be eliminated, thereby eliminating redundant nodes in the search tree, and filtering the nodes in the search tree to obtain the search tree, which helps to simplify the search process and improve the computational efficiency.
Further, a time-extended streaming network is constructed based on the search tree of the viable delivery routes of the unmanned aerial vehicle, in more detail: first, a source node o with K supply units and a sink node d with K demand units are introduced into a streaming network. Then, for the root node of the search tree of unmanned aerial vehicle k, a node is definedTo represent the initial state of drone k at the transmit field. Furthermore, for each node/>, in the search treeA node/>, is defined in the flow networkAnd a set of nodes involving the same p in a streaming network is denoted/>. And then for any two nodes/>And node/>: If/>, in unmanned aerial vehicle k's search treeIs/>A slave node/>, is establishedTo/>Directed/>. Connection arcs introduce ensuring flow connections from source nodes to sink nodes: for each node/>, in the streaming networkConstructing a node/>, from a source node o to a nodeConnected arc/>; For any node/>, in a streaming networkIntroducing a slave node/>Connection arcs to the sink (endpoint) node d. Accordingly, a streaming network/>, is constructedThe streaming network is a node/>And arc/>In some embodiments, value arcs are set to represent delivery profits for orders, e.g., set/>Representing arc/>For arc/>: Set unit flow cost/>For all connection arcs, the unit flow cost is zero. In the flow network, the unmanned plane is scheduled from the source node o to the sink node/>Is representative of an arcuate disjoint flow path. Thus, a binary variable/>, is definedTo represent arc/>Upper flow rate. /(I)Representing unmanned aerial vehicle execution node/>The delivery travel represented.
Still further, for unmanned scheduling problems with on-demand meal delivery, the goal is set to maximize the total profit gained by on-time meal delivery. Reviewing the flow network, the delivery profit of the order is expressed in terms of a negative value of the unit flow cost on a certain arc line, which means that the objective function of maximizing the total profit obtained by on-time delivery is set to minimize the total flow cost in the flow network, thereby expressing the unmanned aerial vehicle scheduling problem as an integer linear programming model.
Optionally, the integer linear programming model is as follows:
Wherein equation 1 is an objective function for characterizing the minimum total traffic cost in the streaming network; equation 2 shows that all unmanned aerial vehicles start from the landing station; equation 3 shows that all the landing stations return to the landing station after completing the task; equation 4 represents a stream equalization constraint; equation 5 shows that each order is serviced at most once; equation 6 represents a decision variable value constraint; is a binary variable representing arc/> Flow on,/>Representing unmanned aerial vehicle execution node/>A delivery trip represented; /(I)Representing the same order sequence number/>, involved in a streaming networkIs a node set of (1); /(I)Characterizing each node in the streaming network that does not include a source node and a sink node; /(I)A sequence number set representing an order to be processed; a represents all arcs in the streaming network; k represents the number of unmanned aerial vehicles.
In one embodiment of the present invention, the solving the integer linear programming model based on the pre-constructed mixed variable domain searching algorithm to obtain an optimal scheduling scheme of the unmanned aerial vehicle includes:
Selecting an order with the largest distribution profit from the orders to be processed to be distributed to the unmanned aerial vehicles, distributing the order to each unmanned aerial vehicle, and determining a distribution plan with the shortest distribution completion time of each unmanned aerial vehicle; updating the distribution plan of the unmanned aerial vehicle according to the order distribution scheme with the largest distribution profit until all the orders to be processed are distributed, so as to obtain a target feasible scheme; and carrying out neighborhood iterative search updating on the target feasible scheme to obtain the optimal scheduling scheme.
It should be noted that the unmanned plane delivery schedule is represented as a series of delivery trips and decisions for battery replacement, usingAnd (3) representing. In this embodiment, specifically, orders are continuously allocated to the unmanned aerial vehicle for delivery from the delivery task of the unmanned aerial vehicle until all orders are allocated. Specifically: and selecting an order with the largest distribution profit from the rest orders to be distributed to the unmanned aerial vehicles, distributing the order to each unmanned aerial vehicle, and determining a distribution plan with the shortest distribution completion time of each unmanned aerial vehicle. And then, updating the delivery plan of the unmanned aerial vehicle according to the order distribution scheme with the maximum delivery profit. Until all the orders to be processed are distributed, and a target feasible scheme is obtained. Further, in order to improve profit of overall distribution, the target feasible scheme is iteratively updated through neighborhood search, and the optimal scheduling scheme is obtained.
In an embodiment, the performing a neighborhood iterative search update on the target feasible scheme to obtain the optimal scheduling scheme includes:
determining four preset destruction operators: randomly deleting the order of the distributed delivery; greedy removes orders for which the profit of the allocated delivery is minimal; randomly deleting all meal delivery plans of the unmanned aerial vehicle; greedy deletes the unmanned plane's meal delivery plan with minimum total delivery profit; determining a preset reconstruction operator: a scenario that requires minimal additional time to deliver the order; combining the four destructive operators and the reconstruction operator respectively to obtain four combined results; based on the selected combination result, deleting and reassigning the target feasible scheme to obtain a solution; if the overall distribution profit of the solution is greater than the total distribution profit of the target feasible scheme, taking the solution as a new target feasible scheme, and setting the number of non-improved continuous times corresponding to the combined result to be 0; returning to execute the deleting and reassigning operation on the target feasible scheme based on the current combination result to obtain a solution; if the overall distribution profit of the solution is not greater than the total distribution profit of the target feasible scheme, recording the non-improved continuous times until the continuous times reach a preset time threshold, and calling the next combined result to execute deleting and reassigning operations on the target feasible scheme based on the new combined result; and outputting the optimal solution with the maximum profit in the iterative process after the iteration of all the combined results is completed, so as to obtain the optimal scheduling scheme.
It should be noted that, the neighborhood operations of the traditional 1-1 Exchange, 1-2 Exchange, etc. are not applicable to different strokes of the same unmanned aerial vehicle, or different strokes of different unmanned aerial vehicles, but all fail to improve the overall distribution profit. Thus, in this embodiment, the following four custom delete operations are introduced to destroy the current viable solution: randomly deleting orders: Randomly deleting order/>, of allocated delivery. Greedy deleted order/>: Greedy deleting orders with minimal profit for distributed delivery. Randomly deleting unmanned aerial vehicle/>: Randomly deleting unmanned aerial vehicle/>All meal delivery plans. Greedy unmanned aerial vehicle/>: Greedy deleted unmanned aerial vehicle/>, with minimum total delivery profitIs a food delivery plan of (a).
In addition, a reconstruction operator is preset: the solution with the least additional time required for delivering the order is to be noted, and the order may have multiple allocation solutions or no allocation solution. In the case where there are multiple solutions, the solution that minimizes the additional time required to fulfill the order is selected. If there is no viable allocation scheme, the order is proved to be unable to be delivered on time, and will remain in the set of orders to be allocated.
Specifically, four destructive operators and the reconstruction operator are respectively combined to obtain four combined results, one combined result is selected randomly from the four combined results, and further, deleting and reassigning operations are performed on the target feasible scheme based on the selected combined result to obtain a solution. And further comparing the overall distribution profit of the newly obtained solution with the overall distribution profit of the target feasible solution, and if the overall distribution profit of the solution is greater than the overall distribution profit of the target feasible solution, taking the solution as the new target feasible solution, that is, replacing the solution with the target feasible solution. Further, the number of consecutive times corresponding to the combination result, which is not improved, is set to 0, and the deleting and reassigning operations for the target feasible scheme are performed in a return manner, that is, the deleting and reassigning operations for the target feasible scheme are performed by continuing to use the current combination result. In addition, if the overall distribution profit of the solution is not greater than the total distribution profit of the target feasible scheme, recording the number of non-improved continuous times, wherein the initial value of the number of non-improved continuous times is 0, that is, recording the number of continuous times corresponding to the total distribution profit of the target feasible scheme until the number of continuous times reaches a preset time threshold, at this time, calling the next combined result to execute the deleting and reassigning operation on the target feasible scheme by using the new combined result until all the combined results are called to execute the deleting and reassigning operation, and outputting the optimal solution with the greatest profit in the iterative process after the iteration is completed, thereby obtaining the optimal scheduling scheme.
As a specific example: for each combination (4*1, total four combination results) of the destruction operator and the reconstruction operator, firstly randomly utilizing one combination result (1 destruction operator and 1 reconstruction operator), presetting the continuous times of which the current scheme is not improved by any combination result as n, presetting the initial value of n as 0, and for the current best scheme (initially the target initial scheme), taking the solution as a new target feasible scheme if the overall distribution profit of the solution is greater than the total distribution profit of the target feasible scheme, setting the continuous times of which the current combination result is not improved as n=0, and continuing to iteratively improve by using the current combination result; if the overall delivery profit for each iteration solution is not greater than the total delivery profit for the target viable solution, n=n+1 is set, and if n increases to 100 (the preset number of times threshold), the next combined result is invoked to improve the current solution. And after the 4 combination results are called and finished to complete iteration, outputting an optimal solution with the maximum profit in the iteration process, thereby obtaining the optimal scheduling scheme and improving the system benefit.
The unmanned aerial vehicle dispatching device provided by the invention is described below, and the unmanned aerial vehicle dispatching device described below and the unmanned aerial vehicle dispatching method described above can be referred to correspondingly.
Fig. 2 is a schematic structural diagram of an unmanned aerial vehicle dispatching device provided by the present invention, and as shown in fig. 2, the unmanned aerial vehicle dispatching device according to an embodiment of the present invention includes:
A first obtaining module 21, configured to obtain map information of the unmanned aerial vehicle scheduling system;
A second acquiring module 22, configured to acquire battery information of each unmanned aerial vehicle;
A third obtaining module 23, configured to obtain order information of each pending order, where the order information includes distribution position information and menu information;
The scheduling module 24 is configured to determine an optimal scheduling scheme of the unmanned aerial vehicle based on the order information, the map information, and battery information of each unmanned aerial vehicle.
It should be noted that, the above device provided in the embodiment of the present invention can implement all the method steps implemented in the method embodiment and achieve the same technical effects, and detailed descriptions of the same parts and beneficial effects as those of the method embodiment in the embodiment are omitted.
Fig. 3 is a schematic structural diagram of an electronic device provided by the present invention, and as shown in fig. 3, the electronic device may include: processor 310, memory 320, communication interface (Communications Interface) 330, and communication bus 340, wherein processor 310, memory 320, and communication interface 330 communicate with each other via communication bus 340. The processor 310 may invoke logic instructions in the memory 320 to perform the drone dispatch scheduling method.
Further, the logic instructions in the memory 320 described above may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In yet another aspect, the present invention further provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform the unmanned aerial vehicle dispatch scheduling method provided by the above methods.
In another aspect, the present invention also provides a computer program product, where the computer program product includes a computer program, where the computer program can be stored on a non-transitory computer readable storage medium, and when the computer program is executed by a processor, the computer can execute the unmanned aerial vehicle delivery scheduling method provided by the above methods.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (10)
1. The unmanned aerial vehicle distribution scheduling method is characterized by comprising the following steps of:
acquiring map information of an unmanned aerial vehicle dispatching system;
acquiring battery information of each unmanned aerial vehicle;
acquiring order information of each to-be-processed order, wherein the order information comprises distribution position information and menu information;
And determining an optimal scheduling scheme of the unmanned aerial vehicle based on the order information, the map information and the battery information of each unmanned aerial vehicle.
2. The unmanned aerial vehicle delivery scheduling method of claim 1, wherein the determining an optimal scheduling scheme for an unmanned aerial vehicle based on the order information, the map information, and battery information for each unmanned aerial vehicle comprises:
Combining the order information, the map information and battery information of each unmanned aerial vehicle, establishing a time-expanded flow network to establish an integer linear programming model based on the flow network, wherein each time of delivery scheme of the unmanned aerial vehicle is represented by one node in the flow network, and the complete scheduling scheme of one unmanned aerial vehicle is represented by a flow path formed by combining different nodes;
and solving the integer linear programming model based on a pre-constructed mixed variable domain searching algorithm to obtain an optimal scheduling scheme of the unmanned aerial vehicle.
3. The unmanned aerial vehicle delivery scheduling method of claim 2, wherein the establishing a time-extended streaming network in combination with the order information, the map information, and the battery information of each unmanned aerial vehicle to establish an integer linear programming model based on the streaming network comprises:
determining the delivery period of each order to be processed and the energy consumption information of the unmanned aerial vehicle;
Determining whether a delivery trip of the order to be processed is feasible or not based on the battery information, the delivery deadline and the energy consumption information of the unmanned aerial vehicle;
Constructing and obtaining a search tree based on all feasible delivery routes of any unmanned aerial vehicle, wherein a root node of the search tree represents that the unmanned aerial vehicle starts delivering service from a landing station; each node of the tree represents a viable delivery scheme for one order, or a viable scheduling scheme for one pass of the drone; each node is provided with a group of child nodes, and the child nodes represent feasible scheduling schemes of subsequent passes of the unmanned aerial vehicle;
Optimally traversing the search tree to construct a time-expanded flow network, and setting value arcs among nodes in the flow network, wherein the value arcs are used for representing distribution profits of an order to be processed;
And on the basis of the flow network, the unmanned aerial vehicle scheduling problem is expressed as an integer linear programming model.
4. A method of unmanned aerial vehicle dispatch scheduling according to claim 3, wherein each node of the search tree represents a viable delivery trip, wherein each node of the search tree is represented by (k, y, p, z, a), k being the unmanned aerial vehicle number for effecting the delivery trip, y being a binary variable indicating whether battery replacement is required before the unmanned aerial vehicle effects the delivery trip, p representing the order number of the order to be processed, a representing the arrival time of the unmanned aerial vehicle at the delivery destination, z being the remaining power of the unmanned aerial vehicle for completing the delivery trip;
the optimizing traverses the search tree to construct a time-expanded stream network and set value arcs among nodes in the stream network, comprising:
introducing a source node o and a sink node d;
For any two nodes And node/>: If/>, in unmanned aerial vehicle k's search treeIs/>A slave node/>, is establishedTo/>Directed arc/>; For any node/>, in a streaming networkConstructing a node/>, from a source node o to a nodeConnected arc/>Building a slave node/>Connection arcs to sink node d to construct a streaming network/>Wherein the streaming network is a node/>And arc/>Is a loop-free directed network of (1) >, set/>Representing arc/>For arc/>: Set unit flow cost/>For all connection arcs, the unit flow cost is zero;
the method expresses the unmanned aerial vehicle scheduling problem as an integer linear programming model on the basis of the flow network and comprises the following steps:
the goal is to maximize the total profit obtained from on-time meal delivery;
in the constructed flow network, the delivery profit of an order is expressed as a negative value of the unit flow cost on a certain arc line, and the objective function of maximizing the total profit obtained by on-time meal delivery is set to minimize the total flow cost in the flow network.
5. The unmanned aerial vehicle dispatching and dispatching method of claim 2, wherein the solving the integer linear programming model based on the pre-constructed mixed variable domain search algorithm to obtain the optimal dispatching scheme of the unmanned aerial vehicle comprises:
Selecting an order with the largest distribution profit from the orders to be processed to be distributed to the unmanned aerial vehicles, distributing the order to each unmanned aerial vehicle, and determining a distribution plan with the shortest distribution completion time of each unmanned aerial vehicle;
updating the distribution plan of the unmanned aerial vehicle according to the order distribution scheme with the largest distribution profit until all the orders to be processed are distributed, so as to obtain a target feasible scheme;
and carrying out neighborhood iterative search updating on the target feasible scheme to obtain the optimal scheduling scheme.
6. The unmanned aerial vehicle delivery scheduling method of claim 5, wherein the performing a neighborhood iterative search update on the target feasible solution to obtain the optimal scheduling solution comprises:
Determining four preset destruction operators: randomly deleting the order of the distributed delivery; greedy removes orders for which the profit of the allocated delivery is minimal; randomly deleting all meal delivery plans of the unmanned aerial vehicle; greedy deletes the unmanned plane's meal delivery plan with minimum total delivery profit;
determining a preset reconstruction operator: a scenario that requires minimal additional time to deliver the order;
Combining the four destructive operators and the reconstruction operator respectively to obtain four combined results;
Based on the selected combination result, deleting and reassigning the target feasible scheme to obtain a solution;
If the overall distribution profit of the solution is greater than the total distribution profit of the target feasible scheme, taking the solution as a new target feasible scheme, and setting the number of non-improved continuous times corresponding to the combined result to be 0; returning to execute the deleting and reassigning operation on the target feasible scheme based on the current combination result to obtain a solution;
If the overall distribution profit of the solution is not greater than the total distribution profit of the target feasible scheme, recording the non-improved continuous times until the continuous times reach a preset time threshold, and calling the next combined result to execute deleting and reassigning operations on the target feasible scheme based on the new combined result;
And outputting the optimal solution with the maximum profit in the iterative process after the iteration of all the combined results is completed, so as to obtain the optimal scheduling scheme.
7. The unmanned aerial vehicle dispatch scheduling method of claim 4, wherein the integer linear programming model is as follows:
Wherein equation 1 is an objective function for characterizing the minimum total traffic cost in the streaming network; equation 2 shows that all unmanned aerial vehicles start from the landing station; equation 3 shows that all the landing stations return to the landing station after completing the task; equation 4 represents a stream equalization constraint; equation 5 shows that each order is serviced at most once; equation 6 represents a decision variable value constraint; is a binary variable representing arc/> Flow on,/>Representing unmanned aerial vehicle execution node/>A delivery trip represented; /(I)Representing the same order sequence number/>, involved in a streaming networkIs a node set of (1); /(I)Characterizing each node in the streaming network that does not include a source node and a sink node; /(I)A sequence number set representing an order to be processed; a represents all arcs in the streaming network; k represents the number of unmanned aerial vehicles.
8. An unmanned aerial vehicle dispatch device, characterized in that includes:
the first acquisition module is used for acquiring map information of the unmanned aerial vehicle dispatching system;
The second acquisition module is used for acquiring battery information of each unmanned aerial vehicle;
The third acquisition module is used for acquiring order information of each to-be-processed order, wherein the order information comprises distribution position information and menu information;
And the scheduling module is used for determining an optimal scheduling scheme of the unmanned aerial vehicle based on the order information, the map information and the battery information of each unmanned aerial vehicle.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and running on the processor, wherein the processor implements the unmanned aerial vehicle dispatch scheduling method of any one of claims 1 to 7 when the program is executed by the processor.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the unmanned aerial vehicle dispatch scheduling method of any one of claims 1 to 7.
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