CN113177762B - Multi-center electric vehicle-unmanned aerial vehicle distribution path optimization method - Google Patents
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Abstract
The invention provides a multi-center electric vehicle-unmanned aerial vehicle distribution path optimization method, which comprises the following steps: constructing an electric vehicle energy consumption calculation model based on continuous changes of vehicle running speed; establishing a time-dependent multi-center electric vehicle-unmanned aerial vehicle coordination and cooperative delivery path optimization model with the aim of minimizing the total delivery cost; and designing a genetic large neighborhood search hybrid algorithm to solve the time-dependent multi-center electric vehicle-unmanned aerial vehicle matching collaborative delivery path optimization model to obtain a delivery path optimization scheme. According to the invention, the mixed integer planning model is built by fully considering the traffic information of the distribution area road network, the maximum flight distance of the unmanned aerial vehicle, the charge state of the battery of the electric vehicle in the distribution process, the influence of the running speed, the load capacity and the like of the vehicle on the energy consumption of the electric vehicle and the like, so that the method is closer to the actual distribution production activity, and the vehicle path problem research is expanded and deepened.
Description
Technical Field
The invention relates to the technical field of path optimization, in particular to a multi-center electric vehicle-unmanned aerial vehicle distribution path optimization method.
Background
Distribution path optimization is an important part of modern logistics development. Under the large environment of protecting the environment and reducing the carbon emission, the electric vehicle gradually replaces the fuel vehicle, and meanwhile, the unmanned aerial vehicle distribution is intelligent, high in efficiency, low in cost and the like, and is also deeply concerned by enterprises such as express delivery, electronic commerce and the like. However, the unmanned aerial vehicle has small bearing capacity and short flying mileage, is influenced by airspace limitation and the like, and cannot be used alone to complete the long-distance and multi-client distribution tasks. The vehicle is used as a temporary bin, an apron and a charging and level changing platform of the unmanned aerial vehicle and is jointly distributed with the unmanned aerial vehicle, so that the distribution efficiency of the electric vehicle distribution alone can be improved, the distribution cost can be reduced, and the defect of unmanned aerial vehicle distribution can be overcome.
Meanwhile, in the related research of the optimization of the existing vehicle-unmanned aerial vehicle combined delivery path, the delivery vehicle is only provided with one unmanned aerial vehicle, and the limit of the delivery area road network on the vehicle running speed is ignored, so that the vehicle and the unmanned aerial vehicle cannot cooperate well to complete the delivery task.
Disclosure of Invention
According to the technical problem that the vehicle and the unmanned aerial vehicle cannot be distributed cooperatively, the multi-center electric vehicle-unmanned aerial vehicle distribution path optimization method is provided. According to the invention, the mixed integer planning model is built by fully considering the traffic information of the distribution area road network, the maximum flight distance of the unmanned aerial vehicle, the charge state of the battery of the electric vehicle in the distribution process, the influence of the running speed, the load capacity and the like of the vehicle on the energy consumption of the electric vehicle and the like, so that the method is closer to the actual distribution production activity, and the vehicle path problem research is expanded and deepened.
The invention adopts the following technical means:
a multi-center electric vehicle-unmanned aerial vehicle distribution path optimization method comprises the following steps:
constructing an electric vehicle energy consumption calculation model based on continuous changes of vehicle running speed;
establishing a time-dependent multi-center electric vehicle-unmanned aerial vehicle coordination and cooperative delivery path optimization model with the aim of minimizing the total delivery cost;
and designing a genetic large neighborhood search hybrid algorithm to solve the time-dependent multi-center electric vehicle-unmanned aerial vehicle matching collaborative delivery path optimization model to obtain a delivery path optimization scheme.
Further, the construction of the electric vehicle energy consumption calculation model based on the continuous change of the vehicle running speed includes:
obtaining a speed function of the electric vehicle according to the road type of the pre-divided distribution area road network, wherein the speed function continuously changes along with time;
calculating the traction force required by the vehicle to overcome the resistance to advance on a gentle highway, and deducing the output power of the battery of the electric vehicle based on the traction force;
and constructing an electric vehicle energy consumption calculation model according to the speed function and the output power of the electric vehicle battery.
Further, the established time-dependent multi-center electric vehicle-unmanned aerial vehicle cooperation delivery path optimization model comprehensively considers the traffic information of the delivery area road network, the maximum flight distance and the bearing capacity of the unmanned aerial vehicle, and the charge state of the electric vehicle battery in the delivery process.
Further, the genetic large neighborhood search hybrid algorithm is obtained by embedding two sets of destruction and reconstruction operators into a conventional genetic algorithm.
Further, solving the time-dependent multi-center electric vehicle-unmanned aerial vehicle cooperative delivery path optimization model, including:
constructing an initial solution based on the constructed vehicle and unmanned aerial vehicle collaborative distribution model, and calculating an objective function value of the initial solution;
randomly selecting one client in each vehicle path for feasible decomposition and destruction for each parent in the initial solution, and greedy inserting and reconstructing the feasible solution to obtain child individuals; calculating the objective function value of the child individual, and updating the child individual into a new solution when judging that the objective function value of the parent individual and the objective function value of the child individual meet the preset relationship;
selecting a client with the largest distance cost in each vehicle delivery path for deleting the father individuals in the updated solutions to perform feasible decomposition and destruction, and greedy inserting and reconstructing the feasible solutions to obtain child individuals; calculating the objective function value of the child individual, and updating the child individual into a new solution when judging that the objective function value of the parent individual and the objective function value of the child individual meet the preset relationship;
and circularly executing the feasible decomposition and destruction reconstruction step until the preset optimization times are reached, and obtaining a final distribution path scheme.
Compared with the prior art, the invention has the following advantages:
1. according to the invention, the mixed integer planning model is built by fully considering the traffic information of the distribution area road network, the maximum flight distance of the unmanned aerial vehicle, the charge state of the battery of the electric vehicle in the distribution process, the influence of the vehicle running speed, the load capacity and the like on the energy consumption of the electric vehicle and the like, so that the method is closer to the actual distribution production activity, and the vehicle path problem research is expanded and deepened.
2. The invention designs a genetic large neighborhood search hybrid algorithm, two groups of destroying and rebuilding operators are embedded on the basis of the traditional genetic algorithm, client points are purposefully removed and inserted, the problem of low efficiency and time consumption can be solved, the solving quality is ensured, and the convergence rate of the algorithm can be improved.
Based on the reasons, the intelligent distribution system can be widely popularized in the fields of intelligent distribution and the like.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to the drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flowchart illustrating a path optimization method according to an embodiment.
Fig. 2 shows the overall trend of the vehicle running speed in the embodiment.
Fig. 3 is a schematic diagram of multi-center vehicle-drone co-delivery in an embodiment.
Fig. 4 is a schematic diagram of vehicle-drone co-delivery in an embodiment.
Fig. 5 is a schematic diagram of an initial solution configuration in an embodiment.
FIG. 6 is a schematic diagram of a removal operator in an embodiment.
FIG. 7 is a diagram of a greedy insert operator in an embodiment.
Fig. 8 is a distribution area network diagram in an embodiment.
Fig. 9 is a time-dependent function of vehicle speed in an embodiment.
FIG. 10 is a diagram of an example distribution path in an embodiment.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only 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 present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The invention provides a multi-center electric vehicle-unmanned aerial vehicle distribution path optimization method, which mainly comprises the following steps:
s1, constructing an electric vehicle energy consumption calculation model based on continuous change of vehicle running speed.
Specifically, according to the related studies, the traction force F required for the vehicle to travel on a gentle road against the resistance is:
the output power P of the battery can be derived from the above equation:
the present invention divides the road types of the distribution area road network and expresses the continuous change of speed with time by a polynomial function, as shown in fig. 2. The power consumption e of the vehicle k from the node i to the node j can be obtained by combining the above ijk (kW.h) as follows:
wherein m is the total mass (kg) of the vehicle; g is gravity acceleration; f is the rolling friction coefficient; c (C) D Is the air resistance coefficient; a is the frontal area (m) 2 ) The method comprises the steps of carrying out a first treatment on the surface of the Delta is a vehicle rotating mass conversion coefficient; a is the acceleration (m/s) of the vehicle 2 )；η T The transmission efficiency for the vehicle driveline; η (eta) V Conversion efficiency for a vehicle inverter; η (eta) m Motor efficiency for the vehicle; v is the vehicle speed (km/h).
S2, establishing a time-dependent multi-center electric vehicle-unmanned aerial vehicle coordination and cooperative delivery path optimization model with the aim of minimizing the total delivery cost.
Specifically, the invention aims at the problem of time-dependent multi-center electric vehicle-unmanned aerial vehicle cooperative delivery path, and specifically expresses as follows: in a complete directed graph g= (V, E) formed by the distribution area road network, there are different types of roads, and the running speed V (t) of each type of road vehicle is continuously changed; the node set isWherein V is 0 For a set of distribution centers from which the vehicle can leave, V 1 For the client Point set, +.>For a distribution center set which can return to the vehicle, is provided withCustomer point set for unmanned aerial vehicle service, V L ＝V 0 ∪V 1 Unmanned aerial vehicle take-off node and perform cyclic delivery tasks (returnThe same node as the departure point) and then returning to the vehicle, and +.>The unmanned aerial vehicle returns to the node set of the vehicle after executing the non-circulating delivery task; edge set e= { (i, j) |i, j E V }, no path connection between distribution centers, l ij For the distance of travel of the vehicle between the two nodes i, j +.>For the flight distance of the unmanned aerial vehicle between the two nodes i and j, considering that the unmanned aerial vehicle is not limited by a road network, supposing +.>K is any vehicle in the available distribution vehicle set K, and the moment of leaving the distribution center is T 0 Rated loading mass of vehicle Q k The battery capacity is B, the allowable lowest charge state of the battery is epsilon, and the electric quantity of the vehicle k when leaving the distribution center is B 0k The power consumption between the i and j nodes is e ijk The dispatching cost of a vehicle (including the unmanned aerial vehicle) of a unit carrying unmanned aerial vehicle is c 1 The unit energy consumption cost is c 2 The method comprises the steps of carrying out a first treatment on the surface of the All vehicles are equipped with the same drone, k u Any one frame in unmanned plane set U (U epsilon U) equipped for vehicle k, with mass of m and maximum flight distance of unmanned plane of l u The bearing capacity is Q u The flying speed is v 0 The preparation time before flying is t 1 The operation time period required for landing when returning the vehicle is t 2 The flying cost per unit distance is c 3 The method comprises the steps of carrying out a first treatment on the surface of the The path of each time the unmanned aerial vehicle executes the delivery task is (i, w, j), which means that the unmanned aerial vehicle is at node i (i epsilon V) L ) Leaving vehicle to go to w (w.epsilon.V) U I.noteq.w) executing the delivery task, and after the delivery task is executed, the delivery task is executed by the node j (j E V) R W+.j) is returned to the vehicle, the drone delivery path is (i, w, i) when the drone departure point is the same as the return point. The demand of client point i is d i The service duration is t h The method comprises the steps of carrying out a first treatment on the surface of the The moment when vehicle k reaches node i is +.>The departure time is +.>Unmanned aerial vehicle k carried by vehicle k u The moment of execution of the delivery task in preparation for flying of node i is +.>Unmanned aerial vehicle k carried by vehicle k u The moment of preparing to leave (including unmanned plane preparing to fly to execute non-cyclic delivery task after the cyclic delivery task is executed and the carried vehicle leaves) node i is +.>
Decision variable x ijk Indicating whether the vehicle k is from a node i to a node j, wherein the nodes i and j are two adjacent nodes in the route of the vehicle k, and are 1, and are 0; decision variablesUnmanned plane k mounted on vehicle k at node i u Whether to perform (i, w, j) operation, 1 or 0; decision variable z ijk Indicating whether the node i is accessed by the vehicle k before the node j, wherein the nodes i and j can be two non-adjacent nodes in the vehicle k route, and are 1 or 0; decision variable n ik An integer variable with a lower limit of 0, representing the position of the node i in the k route of the vehicle; decision variable->Representing unmanned plane k u Whether the vehicle k takes off as a node w before reaching the node i, and returns to the vehicle k at a subsequent node p after the vehicle k reaches the node j, if yes, the vehicle k is 1, and if not, the vehicle k is 0; decision variable->Unmanned aerial vehicle k carried by vehicle k u Whether the delivery task can be scheduled at node i, 1, 0.
Taking fig. 3 as an example, the steps of the co-dispensing of the vehicle and the unmanned aerial vehicle are as follows:
(1) The vehicles 1, 2 and 3 carry a plurality of unmanned aerial vehicles which are respectively started by the distribution centers 1, 1 and 2;
(2) When the vehicle reaches a certain customer point, the vehicle can provide delivery service for the customer, and meanwhile, a plurality of unmanned aerial vehicles can be flown to other customer points to execute delivery tasks, the vehicle can return to the vehicle after waiting for the unmanned aerial vehicle to execute the delivery tasks at the current node, and can also go to a subsequent customer point to execute the delivery tasks, and the unmanned aerial vehicle can follow the vehicle to be assembled with the vehicle at the subsequent node after completing the delivery tasks. Unmanned aerial vehicle k carried by vehicle k u Flying by node i as client m Several situations for returning to the vehicle after delivery service is performed are shown in fig. 4;
(3) After the distribution task is completed, the vehicles return to the nearby distribution centers, as in the figure, the vehicles 1, 2 and 3 return to the distribution centers 2, 1 and 1 respectively.
The following assumptions were made before modeling of the present invention:
(1) The delivery vehicles and the unmanned aerial vehicles are the same in model number, all performances are consistent, the trucks can fly and retract the unmanned aerial vehicles simultaneously, and the unmanned aerial vehicles return to the vehicles without manual operation.
(2) The unmanned aerial vehicle can only fly and retract at each node (comprising a distribution center), and only carries one package when flying.
(3) After the vehicle reaches the customer point, the distribution service is provided for the current node, and then the power is replaced and loaded for the unmanned aerial vehicle, so that the unmanned aerial vehicle is flown.
On the basis of related research of unmanned aerial vehicle path optimization, a mixed integer programming model of the problem of the invention is established with the aim of minimizing the total distribution cost, and the mixed integer programming model is as follows:
the objective function is:
a brief description of the formulae in the above optimization model is as follows: wherein the variables are defined as follows:
the target function expression is used for minimizing the distribution cost, wherein the first term is the dispatching cost of the unmanned aerial vehicle, the second term is the power consumption cost of the electric vehicle, and the third term is the transportation cost of the unmanned aerial vehicle; the sum of the demand of the customer points served by the constraint vehicle k and the carrying unmanned aerial vehicle does not exceed the maximum loading quality of the vehicle; equation (2) indicates that the demand of the client points served by the unmanned aerial vehicle is within the bearing capacity of the unmanned aerial vehicle; the formula (3) shows that the vehicle can leave the distribution center only once, returns to the distribution center after completing the task, and the distribution centers can be different from each other; equation (4) indicates that the number of vehicles returned for any one distribution center is equal to the number of vehicles leaving the distribution center; equation (5) is the lowest state of charge constraint of the vehicle during delivery; formula (6) constrains the path length of the unmanned aerial vehicle to perform the delivery task to not exceed its maximum flight distance; equation (7) indicates that the vehicle accessing the customer point must leave the customer point after the delivery task is performed; equation (8) represents the client point w (w.epsilon.V) U ) Allowing the vehicle or drone to visit only once; formula (9) ensures that client points that are not accessed by the unmanned aerial vehicle are available and can be accessed by only one vehicle; formula (10) is a cooperative constraint between the unmanned aerial vehicle and the vehicle when the unmanned aerial vehicle flies from the distribution center, and represents unmanned aerial vehicle k u May not fly away from the delivery center from which the vehicle left, if unmanned plane k u Flying from a distribution center and unmanned plane k u The vehicle k must reach a return point j where the unmanned aerial vehicle performs the delivery task; equation (11) shows that if the unmanned aerial vehicle performs the delivery task of the non-cyclic (i not equal to j) path (i, w, j), the vehicle must access node i and node j, ensuring that the unmanned aerial vehicle smoothly flies and returns; equation (12) indicates that the drone can perform the delivery task of the cyclical (i=j) path (i, w, i) at this point only if the vehicle accesses node i; formula (13) represents decision variable x ijk A relationship with the locations of node i and node j in the vehicle k path; formula (14) represents a decision variableA relationship with the locations of node i and node j in the vehicle k path; equation (15) represents decision variable x ijk And decision variable z ijk If node i and node j are two adjacent nodes in the k path of the vehicle, the constraint ensures that the policy variable x ijk And decision variable z ijk Strong coupling between them; equation (16) and equation (17) represent the decision variable z ijk And decision variable n ik 、 n jk A relationship between; formula (18) and formula (19) represent decision variable +.>And decision variable->z ijk The relation between them is defined by decision variables +.>z ijk The value of (2) determines the decision variable +.>Is a value of (2); equations (20) and (21) represent the time when the vehicle k arrives at node j when traveling from point i to point j, while also avoiding the path of the vehicle k from forming a sub-loop, where t ij The moment i, i at which the vehicle can leave the node i ij And v (t) obtaining an upper integral limit, wherein M is an infinite positive number; equation (22) represents the relationship between the time when the vehicle k leaves the client point i and the time when the vehicle k reaches the client point i; formula (23) represents unmanned plane k u A relationship between the time of departure of node j and the time of departure of node i; equation (24) represents if unmanned plane k u Unmanned plane k when node i executes cyclic delivery task u The earliest moment of departure from node i; formula (25) constrains the vehicle to wait for the unmanned aerial vehicle on which it is mounted to leave after all operations are completed at node i; formula (26) constrains the drone performing the acyclic distribution task to arrive after vehicle k arrives at node j when node j returns to the vehicle; equation (27) shows that the vehicle k leaves the node j at a time not earlier than the time of completing the node delivery task and the unmanned aerial vehicle mounted thereonk u At the moment the node completes landing; formulas (28) - (30) ensure that the vehicle k carries the unmanned aerial vehicle k during the period from node i to node j u Scheduling a number of executable delivery tasks, M being an infinite positive number; equation (28) shows that if the unmanned plane u mounted on the vehicle k performs the operation around the nodes i, j, the decision variable +.>The value is 0, namely the unmanned plane k u It is not possible to schedule a delivery task at node i, instead the decision variable +.>A value of 1, and a distribution task can be arranged at the node i; formulas (39) and (30) represent and are only when unmanned plane k u It can be arranged to perform delivery tasks when available; formulas (31) - (33) are decision variables n ik 、p ijk Defining a value; equations (34) - (38) are decision variable attributes.
And S3, designing a genetic large neighborhood search hybrid algorithm to solve the time-dependent multi-center electric vehicle-unmanned aerial vehicle matching collaborative delivery path optimization model, and obtaining a delivery path optimization scheme. The method specifically comprises the following steps:
s310, constructing an initial solution. It comprises the following steps:
s311, randomly generating an initial population by adopting an integer coding mode, and performing S312 and S315 on each chromosome in the population.
S312, screening and removing client nodes in the bearing capacity of the unmanned aerial vehicle for the chromosome shown in the figure 5 (a), judging whether the minimum round trip distance from the client in the selected client node set to the client node which is not removed exceeds the maximum flight distance of the unmanned aerial vehicle, if so, moving the client node to the tail part of the chromosome, and removing part of the chromosome after the client is removed as shown in the figure 5 (b).
S313, selecting a vehicle from available vehicles to provide delivery service for the non-removed clients from the virtual delivery center 0, sequentially checking whether the vehicle loading quality and the charge state of the battery of the electric vehicle (the electric vehicle is supposed to be fully loaded in the beginning) can provide delivery service for the current clients from the first client in the chromosome, when the next client is checked to find that the current vehicle loading quality or the charge state of the battery cannot meet the requirement, the newly dispatched vehicle provides service for the client and inserts the virtual delivery center 0 after the last client meeting the checking requirement and before the client not meeting the checking requirement, and so on until the non-removed last client is checked, inserting the vehicle into the delivery center 0 after the whole chromosome head and the non-removed last client point, and completing the division of the chromosome. As shown in fig. 5 (b), the customer demand and the battery power consumption are calculated by accumulating from the first customer point 3 which is not removed from the chromosome, assuming that the constraint condition cannot be satisfied at the customer 10 position, the customer 10 is recorded and the virtual distribution center 0 is inserted after the customer 2 and before the customer 10, the vehicles are rearranged at the recorded customer position, the above procedure is repeated, and finally the virtual distribution center 0 is inserted before the customer 3 and after the customer 13, and the division of the chromosome is completed, and the divided chromosome is shown in fig. 5 (c).
S314, selecting the nearest distribution center to the first customer of the vehicle service as the distribution center of the vehicle departure, and simultaneously selecting the nearest distribution center to the last customer of the vehicle service as the distribution center of the vehicle return on the basis of balancing the quantity of the vehicle return to each distribution center.
S315, according to the determined vehicle path, the removed clients are clustered to unmanned aerial vehicle landing nodes in sequence according to the minimum distance principle, the removed clients are arranged at the tail of the current path in sequence according to the near-far principle, unmanned aerial vehicle landing nodes are marked above the current path, and the chromosome after operation is shown in fig. 5 (d).
S316, sequentially judging whether the number n of unmanned aerial vehicles needing to fly at a take-off node of the unmanned aerial vehicle in a vehicle path exceeds the number |D| of unmanned aerial vehicles carried by the vehicle, if so, taking the path of the unmanned aerial vehicle for executing the delivery tasks of the previous n|D| as a circulation path, executing the delivery tasks of the next |D| of the unmanned aerial vehicle, selecting the node with the shortest vehicle waiting time before the unmanned aerial vehicle flies at a node (delivery center) for executing the delivery tasks within the flight distance as a return point, and re-arranging the next |D| of the customers from small to large according to the moment when the unmanned aerial vehicle reaches the return nodeJudging node set V meeting the condition that the maximum flight distance of all unmanned aerial vehicles is not exceeded m And the first client of the following |D| clients is at V according to the minimum vehicle waiting time principle m Determining a return node of the unmanned aerial vehicle, correspondingly placing the lower part of a delivery client node of the unmanned aerial vehicle, updating the moment when the vehicle reaches a subsequent node after determining the return node of the unmanned aerial vehicle, and repeating the operation to obtain an initial solution S 0 As shown in fig. 5 (e).
S320, calculating an initial solution S 0 Objective function value obj (S) 0 )。
S330, pair S 0 And randomly selecting one client in each current vehicle delivery path to delete, inserting the client which is removed from the path and is not satisfied with the maximum bearing capacity and the maximum flight distance of the unmanned aerial vehicle serving the original unmanned aerial vehicle into the vehicle path with the minimum cost increment on the premise of satisfying all constraints, and if all the insertion points of the constraints which are not satisfied in all the current vehicle paths, arranging a new delivery path by the newly dispatched vehicle. Insertion cost increment deltac for client m to be inserted between client i and client j im ＝C im -C i0 Wherein C i0 Path cost before insertion for customer m, C im Path costs after insertion between for customer m. Taking fig. 6 as an example, the current solution is shown in fig. 6 (a), the clients 5 and 10 in the delivery path of the vehicle are randomly removed, and placed at the tail of the chromosome together with all clients served by the unmanned aerial vehicle, and all delivery centers are replaced by a virtual delivery center 0, the feasible solution after destruction is shown in fig. 6 (b), and the chromosome repaired according to the rule is shown in fig. 7 (a). After the operation is completed, a new solution S is obtained i If obj (S) i )≤obj(S 0 ) Then update S 0 。
S340, pair S 0 First, selecting the customer deletion with the largest distance cost in the current solution of the distribution path of each vehicle, and the distance cost D of the customer k k ＝l ik +l kj ，l ik Is the distance, l, between client k and the previous client point i accessed by the current vehicle kj Is between client k and the next client point j of the current vehicle visitDistance, then insertion is performed by the insertion method described in Step 3. As shown in fig. 6 (c), the customers 6, 2 in the vehicle delivery path are removed and placed on the tail of the chromosome along with all the customers serviced by the unmanned aerial vehicle, and all the delivery centers are replaced with virtual delivery center 0, and the chromosome repaired according to the rules is shown in fig. 7 (b). After the operation is completed, a new solution S is obtained i If obj (S) i )≤obj(S 0 ) Then update S 0 。
S350, judging whether the gen is larger than or equal to Maxgen, if so, ending the program, otherwise, enabling the gen to be equal to gen+1, and entering S330-S340 to circulate.
The following further describes the aspects and effects of the present invention by way of specific application examples.
Because the invention has more consideration factors, no general calculation example set exists at present, and the MDVRP standard calculation example P07 is modified according to the characteristics of the problem to obtain the calculation example. The distribution area is internally provided with 4 distribution centers and 100 clients, the coordinate data of the distribution centers and the client points are obtained by reducing the data of P07 by one time, the client demand is randomly generated within 0-50 kg, the 60% client demand is within 0-5 kg, and the service time t required by the client is long h =2 min. The vehicle carries two unmanned aerial vehicles, when the vehicle is 8:00, the vehicle fully ionizes the distribution center to execute distribution service for clients, and the distance between the i node and the j node is the distance travelledThe vehicle is equipped with 3t of mass, 1.5t of maximum load mass, 86KWh of battery capacity, 20% of minimum charge state allowed by the battery of the electric vehicle, 500 yuan of dispatching cost of unit vehicle, 0.6 yuan/KWh of unit electricity price and other related parameters A=4m of the vehicle energy consumption estimation model 2 、δ＝1.04、η T ＝0.9、η V ＝0.92、η m =0.9. The distance from node i to node j for the unmanned aerial vehicle is +.>The maximum flight distance of the unmanned aerial vehicle is 19km, the bearing capacity is 5kg, the flight speed is 80km/h, the preparation time before flying is 1min, the operation time required for landing when returning to the vehicle is 0.5min, and the unit is thatThe distance flight cost is 0.3 yuan/km. Two types of roads are arranged in the distribution area road network, the road types are divided into a black line road, a route which is not drawn represents a branch road, and the whole-day change condition of the vehicle running speeds of the two types of roads is shown in fig. 9.
In order to verify the stability of the designed algorithm for solving the problem, a comparison algorithm is adopted for solving the problem for 10 times, the solving result is shown in a table 1, wherein n is the number of dispatched vehicles, m is the number of unmanned aerial vehicles carried by the vehicles, T_cost is the total distribution cost, T_time is the total distribution time, CPU is the running time of the algorithm, and Gap is the deviation of the optimizing result from the average value. As can be seen from table 2, in the solution result of 10 times, the total distribution cost of the optimal scheme is 2183.27 yuan, the total distribution duration is 15.61h, and the corresponding distribution scheme is shown in table 2; the number of vehicles to be dispatched for solving the result for 10 times is 4, the average delivery cost is 2187.58 yuan, the average total delivery time length is 15.72h, and the variances of the delivery cost and the delivery time length are 9.58 and 0.10 respectively. Because the problem herein considers smooth changes in the vehicle travel speed, the travel time and electricity consumption of the vehicle between two nodes are calculated by integrating, and the like, the algorithm run time is slightly longer than the time for solving the traditional VRP, and is about 12 minutes. It can be seen that for the problem herein, the proposed algorithm is able to converge steadily to a better solution, with a solution speed within an acceptable range.
Table 1GA_LNS solves the problem 10 times of the results of the operations herein
Table 2 distribution scheme corresponding to optimal solution in 10 operation results
Note that: black bold fonts are customer points for unmanned services; [] The inner path is the path of the unmanned plane for stopping the vehicle to travel
In order to ensure the diversity of the customer scales of the calculation examples, the distribution schemes under different customer scales are respectively solved, the calculation examples of different scales are generated by randomly selecting the corresponding customer numbers from the problems, the calculation results of 10 times are solved by adopting the algorithm designed in the invention, and the average dispatch vehicle number, the optimal solution, the average value and the statistical results of the average solving time of the calculation results of 10 times are shown in the table 3. As can be seen from Table 3, the distribution of customer locations and the amount of demand thereof have an important effect on the formulation of distribution schemes at the same customer scale, and the distribution costs are significantly different due to the different number of vehicles to be dispatched at the same customer scale, such as the different customer locations and the different amounts of demand, as calculated examples 25-1, 25-2 and 50-1, 50-2. Furthermore, as the size of the customer increases, the algorithm solution time increases significantly. The delivery path of example 50-1 is shown in FIG. 10, where the solid, broken and dotted lines are the vehicle, UAV1, UAV2 delivery paths.
Table 3GA_LNS solves the results of operations for different customer scale cases
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or 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 U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; 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 or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (4)
1. The multi-center electric vehicle-unmanned aerial vehicle distribution path optimization method is characterized by comprising the following steps of:
s1, constructing an electric vehicle energy consumption calculation model based on continuous change of vehicle running speed, wherein the electric vehicle energy consumption calculation model is as follows:
wherein e ijk The power consumption of the vehicle k from the node i to the node j; m is the total mass of the vehicle, kg; g is gravity acceleration; f is the rolling friction coefficient; c (C) D Is the air resistance coefficient; a is the frontal area of the vehicle, m 2 The method comprises the steps of carrying out a first treatment on the surface of the Delta is a vehicle rotating mass conversion coefficient; a is the acceleration of the vehicle, m/s 2 ；η T The transmission efficiency for the vehicle driveline; η (eta) V Conversion efficiency for a vehicle inverter; η (eta) m Motor efficiency for the vehicle; v is the vehicle speed, km/h; p is the output power of the battery;
s2, establishing a time-dependent multi-center electric vehicle-unmanned aerial vehicle collaborative distribution path optimization model by taking total distribution cost as a target, wherein the objective function of the time-dependent multi-center electric vehicle-unmanned aerial vehicle collaborative distribution path optimization model is as follows:
wherein the decision variable x ijk Indicating whether the vehicle k is from a node i to a node j, wherein the nodes i and j are two adjacent nodes in the route of the vehicle k, and are 1, and are 0; c 1 Dispatch cost for a vehicle on which the unmanned aerial vehicle is mounted; c 2 The unit energy consumption cost is; c 3 The flying cost per unit distance;road for each time of unmanned aerial vehicle to execute delivery taskWhen the diameter is (i, w, j), the flight distance between the two nodes i, j is determined as a decision variable +.>Unmanned plane k mounted on vehicle k at node i u Whether to perform (i, w, j) operation, 1 or 0; v (V) L ＝V 0 UV 1 The method is characterized in that the unmanned plane takes off nodes and returns to a node set of a vehicle after performing a cyclic delivery task,returning to the node set of the vehicle after performing the non-cyclic delivery task for the unmanned aerial vehicle, V 0 For a set of distribution centers from which the vehicle can leave, V 1 For the client Point set, +.>Customer point set for unmanned aerial vehicle service, V 0 + The method comprises the steps that a distribution center set capable of returning vehicles is adopted, K is an available distribution vehicle set, and U is an unmanned aerial vehicle set;
s3, designing a genetic large neighborhood search hybrid algorithm to solve the time-dependent multi-center electric vehicle-unmanned aerial vehicle collaborative distribution path optimization model to obtain a distribution path optimization scheme, wherein the method comprises the following steps of:
constructing an initial solution based on the constructed vehicle and unmanned aerial vehicle cooperative distribution model, calculating an objective function value of the initial solution,
randomly selecting one client in each vehicle path for feasible decomposition and destruction for each parent in the initial solution, and greedy inserting and reconstructing the feasible solution to obtain child individuals; calculating the objective function value of the child individual, updating the child individual into a new solution when judging that the objective function value of the parent individual and the objective function value of the child individual meet the preset relationship,
selecting a client with the largest distance cost in each vehicle delivery path for deleting the father individuals in the updated solutions to perform feasible decomposition and destruction, and greedy inserting and reconstructing the feasible solutions to obtain child individuals; calculating the objective function value of the child individual, updating the child individual into a new solution when judging that the objective function value of the parent individual and the objective function value of the child individual meet the preset relationship,
and circularly executing the feasible decomposition and destruction reconstruction step until the preset optimization times are reached, and obtaining a final distribution path scheme.
2. The multi-center electric vehicle-unmanned aerial vehicle delivery path optimization method of claim 1, wherein the constructing an electric vehicle energy consumption calculation model based on the continuous change of the vehicle travel speed comprises:
obtaining a speed function of the electric vehicle according to the road type of the pre-divided distribution area road network, wherein the speed function continuously changes along with time;
calculating the traction force required by the vehicle to overcome the resistance to advance on a gentle highway, and deducing the output power of the battery of the electric vehicle based on the traction force;
and constructing an electric vehicle energy consumption calculation model according to the speed function and the output power of the electric vehicle battery.
3. The multi-center electric vehicle-unmanned aerial vehicle delivery path optimization method according to claim 1, wherein the established time-dependent multi-center electric vehicle-unmanned aerial vehicle collaborative delivery path optimization model comprehensively considers delivery area road network traffic information, the maximum flight distance of the unmanned aerial vehicle, the bearing capacity and the state of charge of an electric vehicle battery in the delivery process.
4. The multi-center electric vehicle-unmanned aerial vehicle delivery path optimization method of claim 1, wherein the genetic large neighborhood search hybrid algorithm is obtained by embedding two sets of destruction and reconstruction operators into a traditional genetic algorithm.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110782086A (en) * | 2019-10-24 | 2020-02-11 | 山东师范大学 | Rescue vehicle distribution path optimization method and system with unmanned aerial vehicle |
| CN110956317A (en) * | 2019-11-22 | 2020-04-03 | 东南大学 | Modeling and solving method for unmanned vehicle and fuel vehicle mixed distribution mode |
| CN111047087A (en) * | 2019-09-18 | 2020-04-21 | 合肥工业大学 | Intelligent optimization method and device for path under cooperation of unmanned aerial vehicle and vehicle |
| WO2021022637A1 (en) * | 2019-08-06 | 2021-02-11 | 南京赛沃夫海洋科技有限公司 | Unmanned surface vehicle path planning method and system based on improved genetic algorithm |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10692385B2 (en) * | 2017-03-14 | 2020-06-23 | Tata Consultancy Services Limited | Distance and communication costs based aerial path planning |
-
2021
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021022637A1 (en) * | 2019-08-06 | 2021-02-11 | 南京赛沃夫海洋科技有限公司 | Unmanned surface vehicle path planning method and system based on improved genetic algorithm |
| CN111047087A (en) * | 2019-09-18 | 2020-04-21 | 合肥工业大学 | Intelligent optimization method and device for path under cooperation of unmanned aerial vehicle and vehicle |
| CN110782086A (en) * | 2019-10-24 | 2020-02-11 | 山东师范大学 | Rescue vehicle distribution path optimization method and system with unmanned aerial vehicle |
| CN110956317A (en) * | 2019-11-22 | 2020-04-03 | 东南大学 | Modeling and solving method for unmanned vehicle and fuel vehicle mixed distribution mode |
Non-Patent Citations (2)
| Title |
|---|
| An adaptive large neighborhood search heuristic for dynamic vehicle routing problems;Chen Shifeng, et al;COMPUTERS & ELECTRICAL ENGINEERINGCOMPUTERS & ELECTRICAL ENGINEERING;第67卷;第596-607页 * |
| 模糊需求与时间窗的车辆路径问题及混合遗传算法求解;范厚明;吴嘉鑫;耿静;李阳;;***管理学报(01);第107-118页 * |
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