CN114186924A - Collaborative distribution path planning method and device, electronic equipment and storage medium - Google Patents
Collaborative distribution path planning method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention provides a collaborative distribution path planning method, a collaborative distribution path planning device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring distribution information; establishing a collaborative distribution model according to the distribution information; obtaining a rural road punishment cost, and processing the distribution information according to the rural road punishment cost; applying an initial solution algorithm according to the processed distribution information and the collaborative distribution model to calculate an initial solution of a collaborative distribution path; and iterating the initial solution by using an optimization algorithm to obtain the optimal solution of the cooperative distribution path. Therefore, in the coordinated distribution path, partial customer points are served by rural supermarkets instead of receiving points, and rural road punishment cost is introduced on the basis of rural scene road information, so that the unmanned aerial vehicle is used for replacing the road sections, which are difficult to pass, of the trucks, the overall distribution cost is reduced, and the distribution efficiency is greatly improved.
Description
Technical Field
The invention relates to the technical field of path planning, in particular to a collaborative distribution path planning method and device, electronic equipment and a storage medium.
Background
With the development of modern agriculture, the living standard of farmers is improved, and the function of rural logistics is increasingly shown. Compared with other regions, the rural areas have the characteristics of poor road conditions, wide regions, more logistics bodies, small scale and the like. Due to cost constraints, rural areas are currently distributed by a truck in combination with a collection point in a partitioned block, which is inefficient.
With the development of science and technology, in recent years, unmanned aerial vehicle-truck cooperative distribution is attracting attention, and certain research results are formed. The unmanned aerial vehicle has no characteristic of requirement on the terrain, and has high adaptability to rural scenes naturally, so that the truck serves as a mobile warehouse for the unmanned aerial vehicle, and the cruising ability of the unmanned aerial vehicle can be improved; therefore, each truck is provided with one unmanned aerial vehicle, and the distribution efficiency can be greatly improved. Unfortunately, the current unmanned aerial vehicle-truck collaborative distribution research for rural logistics is few, and fails to comprehensively consider the situation specific to the rural scene.
At present, aiming at the research of rural areas, the route planning is only carried out by applying an unmanned aerial vehicle-truck cooperative distribution scheme in urban areas, so that the distribution efficiency is low.
Disclosure of Invention
The invention solves the problems that the existing rural areas only apply the urban unmanned aerial vehicle-truck cooperative distribution scheme for path planning, the consideration of rural specific scenes is lacked, and the distribution efficiency is low.
To solve the above problems, the present invention first provides a collaborative distribution route planning method, which includes:
acquiring delivery information, wherein the delivery information at least comprises: rural scene road information, customer point information, truck information, unmanned aerial vehicle information and collection point information;
establishing a collaborative distribution model according to the distribution information;
obtaining a rural road punishment cost, and processing the distribution information according to the rural road punishment cost;
applying an initial solution algorithm according to the processed distribution information and the collaborative distribution model to calculate an initial solution of a collaborative distribution path;
and iterating the initial solution by using an optimization algorithm to obtain the optimal solution of the cooperative distribution path.
Therefore, in the cooperative distribution path, partial customer points are served by rural supermarkets instead of receiving points, rural road punishment cost is introduced on the basis of rural scene road information, and goods delivery service is performed on the road section where the truck is not easy to pass through by replacing the unmanned aerial vehicle, so that the total distribution cost is reduced, and the distribution efficiency is greatly improved.
Preferably, establishing a collaborative distribution model according to the distribution information includes:
determining a target function of cooperative distribution;
determining constraints of a client point, a truck, an unmanned aerial vehicle and a collection point; determining a time constraint of a joint point of the unmanned aerial vehicle and the truck in a distribution network;
and establishing the collaborative distribution model according to the constraint and the objective function.
Preferably, the calculating an initial solution of the collaborative distribution path by applying an initial solution algorithm according to the processed distribution information and the collaborative distribution model includes:
distributing the client points in the collection point range to the collection points to obtain opened collection points and unallocated client points;
constructing an initial truck delivery route using a greedy insertion algorithm for the turned-on collection points and unassigned customers;
dividing the client points into a point set to be distributed of the unmanned aerial vehicle service according to a cost comparison principle and the unmanned aerial vehicle bearing capacity;
determining service customers, routes, and positions of the launching points and landing points of the unmanned aerial vehicles based on the principle that the waiting time of the truck and the unmanned aerial vehicles is shortest, the maximum flight capacity limit condition of the unmanned aerial vehicles, and the front and back sequence of the launching points and the landing points of the unmanned aerial vehicles;
and for the clients of which the points to be distributed for the unmanned aerial vehicle service are concentrated and do not meet the unmanned aerial vehicle limiting conditions when the unmanned aerial vehicle route is constructed, reinserting the clients into the truck delivery route by using a greedy algorithm to obtain an initial solution of the collaborative delivery route.
Preferably, the iterating the initial solution by using an optimization algorithm to obtain an optimal solution of the collaborative distribution path includes:
selecting a damage operator and a repair operator according to the weight, wherein the damage operator is a customer point damage operator or a collection point damage operator;
sequentially executing a destruction operator and a repair operator, wherein in the execution of the destruction operator, the collection point destruction operator is executed in a specified iteration period, and the client point destruction operator is executed in the rest iteration periods; updating a new solution according to a rule of simulated annealing after each iteration cycle is finished, and updating an operator score according to the obtained quality of the new solution;
recording the total iteration times, the iteration times of using a customer point damage operator, the updated weight iteration times and the iteration times of not improving the new solution quality for each iteration;
if the total iteration number is smaller than a first threshold value, re-executing the operator selected according to the weight;
if the iteration number of the customer point damage operator is smaller than a second threshold value, selecting the customer point damage operator, otherwise, selecting a collection point damage operator, and resetting the iteration number of the customer point damage operator to 0; if it is
If the iteration times of the updated weight are equal to a third threshold value, updating the weight of the operator according to the score of the operator, resetting the iteration times of the updated weight to be 0 and returning to the operator selected according to the weight;
if the iteration number of the new solution quality not improved is equal to a fourth threshold, executing all local search operators in the local search operator set, and returning to the selection of the destruction and repair operators according to the weight after the execution is finished;
and if the total iteration number is equal to a first threshold value or the iteration number of the new solution quality which is not improved is equal to a fifth threshold value, stopping iteration and outputting the current solution as the optimal solution.
Preferably, the optimization algorithm stopping condition is: and the total iteration number is equal to a first threshold, or the iteration number of the new solution quality which is not improved is equal to a fifth threshold, and the requirement on one of the two is met.
Preferably, the condition of using the local search operator is that the number of iterations in which the quality of the new solution is not improved is equal to a fourth threshold.
Preferably, the set of local search operators comprises: unmanned aerial vehicle-truck crossover operator, collection point-unmanned aerial vehicle crossover operator, unmanned aerial vehicle launch-landing point crossover operator, two-point crossover operator.
Secondly, a collaborative distribution route planning device is provided, which comprises:
an information acquisition unit configured to acquire delivery information including at least: rural scene road information, customer point information, truck information, unmanned aerial vehicle information and collection point information;
the model establishing unit is used for establishing a collaborative distribution model according to the distribution information;
the information processing unit is used for acquiring the penalty cost of the rural road and processing the distribution information according to the penalty cost of the rural road;
an initial solution calculation unit, configured to apply an initial solution algorithm to calculate an initial solution of a collaborative distribution path according to the processed distribution information and the collaborative distribution model;
and the optimization unit is used for iterating the initial solution by using an optimization algorithm to obtain an optimal solution of the collaborative distribution path.
Therefore, in the cooperative distribution path, partial customer points are served by rural supermarkets instead of receiving points, rural road punishment cost is introduced on the basis of rural scene road information, and goods delivery service is performed on the road section where the truck is not easy to pass through by replacing the unmanned aerial vehicle, so that the total distribution cost is reduced, and the distribution efficiency is greatly improved.
Still further, an electronic device is provided, which includes a computer readable storage medium storing a computer program and a processor, and when the computer program is read and executed by the processor, the method for planning a collaborative distribution path as described above is implemented.
Finally, a computer-readable storage medium is provided, in which a computer program is stored, and when the computer program is read and executed by a processor, the method for planning a collaborative distribution route according to the foregoing is implemented.
Therefore, in the coordinated distribution path, partial customer points are served by rural supermarkets instead of receiving points, and rural road punishment cost is introduced on the basis of rural scene road information, so that the unmanned aerial vehicle is used for replacing the road sections, which are difficult to pass, of the trucks, the overall distribution cost is reduced, and the distribution efficiency is greatly improved.
Drawings
Fig. 1 is a flowchart of a collaborative distribution route planning method according to an embodiment of the present invention;
fig. 2 is a flowchart of a coordinated distribution path planning method S20 according to an embodiment of the present invention;
fig. 3 is a flowchart of a coordinated distribution path planning method S40 according to an embodiment of the present invention;
fig. 4 is a flowchart of a coordinated distribution path planning method S50 according to an embodiment of the present invention;
fig. 5 is a block diagram of a collaborative distribution route planning apparatus according to an embodiment of the present invention;
fig. 6 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
The embodiment of the application provides a collaborative distribution path planning method, which can be executed by a collaborative distribution path planning device, and the collaborative distribution path planning device can be integrated in electronic equipment such as a computer, a server and a computer. Fig. 1 is a flowchart of a collaborative distribution route planning method according to an embodiment of the present invention; the collaborative distribution path planning method comprises the following steps:
s10, obtaining distribution information, where the distribution information at least includes: rural scene road information, customer point information, truck information, unmanned aerial vehicle information and collection point information;
the obtained distribution information may be data in a standard example (during experiment or verification), or may be actually acquired data; the rural scene road data, the position information and the like can be obtained by crawling the data through a map; the corresponding delivery information can also be acquired through the GPS data of the vehicle.
S20, establishing a collaborative distribution model according to the distribution information;
the cooperative distribution model can be an operation research model and can be composed of a plurality of constraints of actual conditions and an objective function;
s30, obtaining a rural road punishment cost, and processing the distribution information according to the rural road punishment cost;
the penalty cost of the rural road can be determined according to historical data or map information, and can also be obtained by comprehensive judgment according to other actual conditions; it can also be derived by loop feedback.
And processing the distribution information to apply unit penalty cost to each customer point according to the position of the customer point.
Preferably, the unit road penalty cost is sequentially increased outwards from the geometric center of the customer point set until the travel cost is 1.85 times; the penalty cost for a link between customer points is the link length x 1/2.
Wherein, the landform of most rural areas is: the middle part is a village and a town, and the periphery is a suburb or a mountain area, so the data processing is based on the following modes: and sequentially increasing the unit road penalty cost outwards from the geometric center of the customer point set until the travel cost is 1.85 times. Namely, the unit road penalty cost of the customer point in the center of the village and the town is close to 0, and the unit road penalty cost of the customer point in the mountainous area is close to 1.85 times of the travel cost.
The penalty cost of the road between the customer points is the length of the road multiplied by 1/2 (the penalty cost of the unit road of the customer point i + the penalty cost of the unit road of the customer point j), so that the conditions of the road where the customer is located are gradually excessive from good road conditions to poor road conditions.
S40, calculating an initial solution of the cooperative distribution path by applying an initial solution algorithm according to the processed distribution information and the cooperative distribution model;
and S50, iterating the initial solution by using an optimization algorithm to obtain the optimal solution of the cooperative distribution path.
The steps S40-S50 are adaptive large-scale neighborhood search algorithms, that is, after an initial solution is obtained by using the initial algorithm, the initial solution is input into the adaptive large-scale neighborhood search algorithms for optimization.
Therefore, in the cooperative distribution path, partial customer points are served by rural supermarkets instead of receiving points, rural road punishment cost is introduced on the basis of rural scene road information, and goods delivery service is performed on the road section where the truck is not easy to pass through by replacing the unmanned aerial vehicle, so that the total distribution cost is reduced, and the distribution efficiency is greatly improved. Preferably, as shown in fig. 2, the S20, building a collaborative distribution model according to the distribution information, includes:
s21, determining a target function of cooperative distribution;
the objective function of cooperative distribution can be divided into four parts: the first part is the cost of travel of the truck; the second part is the travel cost of the unmanned aerial vehicle; the third part is the punishment cost of the running of the truck under different road conditions in rural areas; the fourth part is the collection point operating cost.
Wherein the penalty cost can be understood as: when the road condition of a truck to a certain customer point is poor, the fuel consumption of the truck is increased due to the increase of the times of turning and braking, and at the moment, a punishment cost is applied to the travel of the truck. Due to the addition of penalty cost, under the effect of a minimized target, the unmanned aerial vehicle is selected to carry out distribution service under the condition of poor road conditions.
Preferably, the collection of operating costs, comprises two sub-parts: one part is the fixed cost and the other part is the compensation cost, where it is assumed that the compensation cost is proportional to the distance of the customer from the collection point.
Wherein, the compensation cost can be understood as: when the customer is far away from the collection point (still within the service range of the collection point), the customer needs to spend more energy for self-service, and a certain compensation cost needs to be paid to the customer, so that the customer can self-service at the collection point.
S22, determining the constraints of a client point, a truck, an unmanned aerial vehicle and a collection point; determining time constraints of a combining point of a truck and an unmanned aerial vehicle in a distribution network;
preferably, the constraint portion may comprise four portions, respectively: constraints on customer points, constraints on trucks, constraints on drones (this section includes constraints on truck-drone junctions), and constraints on collection points.
Preferably, the constraints on the customer points are: each client must be accessed only once, and the access modes which can be selected are as follows: the truck, the unmanned aerial vehicle and the collection point are lifted by themselves.
Preferably, the constraints on the truck are: requiring the truck to exit and return to the distribution center exactly once; the repeated driving of the truck on a certain road section is avoided, and the constraint of a truck sub-loop is eliminated; since the truck must leave the customer site to continue participating in the delivery system after visiting the customer site, constraints require that the truck visiting the site must leave the site.
Preferably, the constraints on the drone are: the truck is used as a mobile warehouse of the unmanned aerial vehicle while participating in distribution, so that the unmanned aerial vehicle can be launched from a point i only when the truck visits the point i and the point k, and the unmanned aerial vehicle is landed at the point k and recovered by the truck after visiting the point j; the constraint of the unmanned aerial vehicle route under the special condition that the unmanned aerial vehicle is launched from the warehouse is the same as the previous constraint mode; launching and landing of the unmanned aerial vehicle have a sequence, and the constraint requires that the launching of the unmanned aerial vehicle for a certain trip needs to be before landing; the unmanned aerial vehicle is required to be launched and landed from any node at most once; since the truck serves as a mobile warehouse and the drone needs to be launched from and landed on the truck, the order of access to the trucks needs to be constrained, and the truck needs to visit the drone launch point first and then the drone landing point.
Preferably, the constraints on the collection points are: the drop-off point can only be turned on when it serves the customer, in order to avoid special situations: the collection point serves as a launching point or a landing point of the unmanned aerial vehicle under the condition of not serving the client point; the customer can carry out self-lifting to the collection point, and can carry out self-lifting to the collection point only when the customer is in the service range of the opened collection point; the collection point can be started only when the truck passes through the collection point, namely the collection point can provide service for customers only after the truck goes to the collection point for delivery; the package carrying capacity of the collection point is limited, and the sum of the demands of customers served by the collection point cannot exceed the maximum capacity of the collection point.
Preferably, a time constraint of the distribution network may be included, and since the unmanned aerial vehicle and the truck perform coordinated distribution, the truck needs to receive back the unmanned aerial vehicle completing the distribution within a specified time (i.e. meeting the flight capability of the unmanned aerial vehicle), so that the time spent by the unmanned aerial vehicle for traveling in the distribution network, i.e. the time used by the unmanned aerial vehicle to launch from the truck to land on the truck, needs to be calculated. The part to be calculated is: in the unmanned aerial vehicle path, the time when the unmanned aerial vehicle reaches the launching point, the time when the unmanned aerial vehicle reaches the customer point of the service, and the time when the unmanned aerial vehicle reaches the landing point; the time when the truck arrives at the launching point in the unmanned aerial vehicle path and the time when the truck and the unmanned aerial vehicle leave the launching point and the landing point; the unmanned aerial vehicle and the truck leave the same time at the launching point because the truck immediately goes to the next access point while the unmanned aerial vehicle launches on the truck; after receiving the unmanned aerial vehicle, the truck leaves the landing point together with the unmanned aerial vehicle, so that the time for the truck to leave the landing point is the same as that for the unmanned aerial vehicle; using the time obtained above, the time spent by the drone in the path from launch to service the customer site and from service the customer site to landing can be calculated; after obtaining these two kinds of time, can combine unmanned aerial vehicle battery weight and payload to retrain unmanned aerial vehicle flight ability: this is because the power consumed by the drone is approximately linear with battery weight and payload. Only when the energy consumption required for a single trip is within the range of the flight capabilities of the drone will the drone execute that trip. The node at the end of the travel of the unmanned aerial vehicle is that the unmanned aerial vehicle lands on a truck, because if the unmanned aerial vehicle reaches the landing point first, the hovering waiting time of the unmanned aerial vehicle also needs to be calculated;
only after the unmanned aerial vehicle is recovered at the landing point, the unmanned aerial vehicle can be arranged for next customer service, so that the sequence of the last travel landing point and the next travel launching point of the unmanned aerial vehicle needs to be restricted; the package bearing capacity of the unmanned aerial vehicle is limited, only when the requirement of the customer point is smaller than the maximum load of the unmanned aerial vehicle, the unmanned aerial vehicle can serve the customer point, the bearing capacity of the unmanned aerial vehicle is not met, and the unmanned aerial vehicle can be served by a truck or a collection point.
S23, establishing the collaborative distribution model according to the constraint and the objective function;
wherein the collaborative distribution model may be derived from a combination of constraints and objective functions.
In this way, a cooperative distribution model is established through an objective function and constraints, so that the problem of cooperative distribution of the truck and the unmanned vehicle is scientifically described; penalty costs of different road conditions are introduced into the objective function, so that the matching degree of the whole model and actual distribution is greatly increased; by introducing constraints such as road conditions into the constraints, the matching degree of the model is further improved, and the accuracy of path planning is greatly improved.
Preferably, the objective function is:
wherein the model symbols and variables involved are defined as shown in the following table:
preferably, as shown in fig. 3, S40, applying an initial solution algorithm to calculate an initial solution of the collaborative distribution path according to the processed distribution information and the collaborative distribution model, includes:
s41, distributing the client points in the collection point range to the collection points; obtaining an opened collection point and an unallocated customer point;
and if the customer is not served by the collecting point, closing the collecting point after the step is finished.
The formula is:
in the formula, pikRepresenting the probability of a customer point i being assigned to a collection point k, rSTo order a service scope, dikThe distance from the client point i to the collection point k, N is the set of client points, NSIs a collection of collection points.
Through the step, two types of points of the opened collection point and the scattered customer points which are not distributed to the collection point can be obtained.
S42, constructing an initial truck delivery route by a greedy insertion algorithm aiming at the opened collection point and the unallocated customers;
s43, dividing the client points into the point sets to be distributed of the unmanned aerial vehicle service according to the cost comparison principle and the unmanned aerial vehicle bearing capacity limit;
s44, determining the distribution client, the route and the positions of the transmitting point and the landing point of the unmanned aerial vehicle based on the principle that the waiting time of the truck and the unmanned aerial vehicle is the shortest, the maximum flight capacity limit condition of the unmanned aerial vehicle and the front-back sequence of the transmitting point and the landing point of the unmanned aerial vehicle;
and S45, for the customers whose unmanned aerial vehicle service points to be distributed are concentrated and do not meet the unmanned aerial vehicle limiting conditions when the unmanned aerial vehicle route is constructed, reinserting the customers into the truck distribution route by using a greedy algorithm to obtain an initial solution of the collaborative distribution route.
In the steps S43-S45, all scattered customer points on the truck route need to be processed, and the service points of the unmanned aerial vehicle are divided, that is, the customer points are selected according to the cost comparison principle and the limitation of the carrying capacity of the unmanned aerial vehicle, and if the cost for using the unmanned aerial vehicle to perform service is lower, the customer points are divided into the point sets to be distributed of the unmanned aerial vehicle. After the processing is finished, all the truck stop points are traversed in sequence aiming at each unmanned aerial vehicle to be allocated with customer points, and the following requirements are met: 1) the driving mileage of the unmanned aerial vehicle does not exceed the maximum flight capacity of the unmanned aerial vehicle; 2) the sequence of the truck accessing the last launch point of the unmanned aerial vehicle must be based on the principle that the truck and the unmanned aerial vehicle have the shortest mutual waiting time to select the launch point and the landing point of the unmanned aerial vehicle under the condition that the truck accesses the sequence of the last landing point of the unmanned aerial vehicle, so that the distribution path of the unmanned aerial vehicle is determined. And circulating the point set to be distributed of the unmanned aerial vehicle, and if any customer point does not meet the two conditions, taking the point set as the point to be distributed of the truck route.
Preferably, if the customer point to be allocated by the unmanned aerial vehicle is inserted in the unmanned aerial vehicle path at any position of the truck path, the customer point to be allocated is violated by the two conditions, and the customer point to be allocated is reinserted into the truck route through a greedy insertion algorithm, and delivery service is provided by the truck.
In this way, an initial solution of the collaborative delivery path may be obtained, facilitating subsequent iterations.
Preferably, as shown in fig. 4, S50, iterating the initial solution by using an optimization algorithm to obtain an optimal solution of the collaborative distribution path, includes:
s51, selecting a destruction operator and a repair operator according to the weight, wherein the destruction operator is a customer point destruction operator or a collection point destruction operator;
the customer point destruction operator destroys the customer points, and the collection point destruction operator destroys the collection points.
It should be noted that, the destruction operator is destroyed for the collection point and the client point, so as to achieve a better destruction effect and enable the destruction to be performed more completely.
The customer point destruction operator is selected from the first destruction operator set; the collection point damage operator is selected from the second damage operator set, and the repair operator is selected from the repair operator set; each operator has own weight, a damage operator (a client point damage operator or a collection point damage operator) and a repair operator can be randomly selected from the operators according to the weight, and the larger the weight of the operator is, the higher the probability of being selected is.
S52, sequentially executing a destroy operator and a repair operator, wherein in the execution of the destroy operator, the collection point destroy operator is executed in a specified iteration period, and the client point destroy operator is executed in the rest iteration periods;
the ALNS algorithm main framework comprises the following steps: and setting the inner layer iteration times and the outer layer iteration times. When the inner layer iteration number is less than N, executing a customer point damage operator; and when the inner layer iteration number is equal to N, executing a destroy operator of the collection point, and resetting the inner layer iteration number to 0. And after the damage operator is executed, executing a repair operator, and after the repair operator is executed, adding 1 to the outer layer iteration number.
For the break operator of the collection point, as long as the operation of closing the collection point is adopted in the operator, all the client points served by the closed collection point are added into the break list. The collection points that are turned on in this phase will be inserted into the path of the truck according to the principle of greedy insertion, and the client points in the destruction list will be allocated again in the repair phase. The break for a collection point can be considered as a break-fix operator because the opening and closing of the collection point is not operated again in the subsequent repair process.
After the completion of the destroy (to the customer site or the collection site) operation, all elements in the delivery system can be divided into: a damaged truck path, a drone to be assigned customer point (if the deleted truck service point is exactly the launch or landing point of the drone, then the drone path is removed, the drone service customer points are divided into a set of drone to be assigned points), a damage list, a non-opened collection point, and a collection point that has been opened in the truck path.
In the operator repairing stage, only the client points and the points to be distributed of the unmanned aerial vehicle in the damage list are considered to be inserted into the distribution system, and the closing and opening of the collection points are not operated.
And S53, finishing each iteration cycle, updating a new solution according to the rule of simulated annealing, and updating the operator score according to the quality of the new solution.
And receiving the new solution according to the simulated annealing criterion, wherein the scoring rule of the solution is as follows: (a, B, C) ═ 5, 10, 20. Wherein, A, B, C may represent better than the historical optimal solution, better than the current solution, and worse than the current solution, respectively.
S54, recording the total iteration times, the iteration times of using the customer point damage operator, the iteration times after updating the weight and the iteration times of not improving the new solution quality in each iteration;
the total iteration number may be the outer layer iteration number, and each time the destruction operator (the client point destruction operator or the collection point destruction operator) and the repair operator are executed, one iteration is considered to be performed.
S55, if the total iteration number is less than a first threshold value, re-executing the operator selection and the repair operator according to the weight;
s56, if the iteration number of the customer point damage operator is smaller than a second threshold value, selecting the customer point damage operator, otherwise, selecting the collection point damage operator, and resetting the iteration number of the customer point damage operator to 0;
s57, if the iteration number of the updated weight is equal to a third threshold value, updating the weight of the operator according to the score of the operator, resetting the iteration number of the updated weight to be 0 and returning to the operator selected according to the weight;
s58, if the iteration number of the new solution quality not improved is equal to a fourth threshold, executing all local search operators in the local search operator set, and after the execution is finished, returning to the operation of selecting the destruction operator and the repair operator according to the weight;
when the new solution quality is not improved over multiple iteration cycles, it may be trapped in local optima. In this case, a local search operator is performed, thereby avoiding the case of local optimality.
And S59, if the total iteration number is equal to the first threshold or the iteration number of the new solution with the quality not improved is equal to the fifth threshold, stopping iteration and outputting the current solution as the optimal solution.
In this way, iterative optimization is performed on the initial solution through the ALNS algorithm, so that the current optimal cooperative distribution path is obtained.
It should be noted that the optimal solution is only the best solution in the current search range, and is not necessarily the global optimal solution.
The conditions for performing the local search are: the new solution quality fails to improve the number of iterations equal to a fourth threshold. That is, if the quality of the new solution is not optimized in none of the successive fourth threshold number of iterations, it means that a local optimum may have occurred.
The iteration stop condition is as follows: and the total iteration number is equal to a first threshold, or the iteration number of which the new solution quality cannot be improved is equal to a fifth threshold, and the total iteration number is equal to one of the first threshold and the fifth threshold.
The second condition for iteration to stop is that if the quality of the current solution is not optimized in none of the consecutive iterations of the fifth threshold, the solution is less likely to continue to improve quality. Or the iteration times are too many, and the iteration is also forcibly stopped in order to avoid occupying too much calculation time.
Wherein the fifth threshold is greater than the fourth threshold.
Preferably, the second set of destruction operators comprises: randomly starting the destruction operators, randomly closing the starting destruction operators, closing the minimum number of customers, starting the destruction operators by the maximum number of customers, and closing the destruction operators by the maximum number of customers.
Randomly starting a destruction operator: randomly selecting one collection point from the unopened collection points to open.
Randomly turning off the destruction operator: and randomly selecting one collection point from the opened collection points to close.
Closing and opening the destruction operator randomly: randomly selecting one of the started collection points to close, and starting another collection point. The newly turned on collection point is not based on a random selection but on a distance, the more likely it is that the collection point is turned on if the unopened collection point is closer to the collection point that was turned off.
Minimum number of customers off maximum number of customers on destruction operators: and selecting the collection point with the least number of service clients to close, and adding the service clients into the destruction list. And meanwhile, selecting the generation receiving point with the largest number of client points in the service range from the non-opened generation receiving points for starting. The destroy operator may also be referred to as a proxy position swap operator.
Maximum number of customers shutdown destruction operator: and selecting the collection point with the largest number of service clients to close, and adding the service clients into the destruction list.
Preferably, the first set of destruction operators comprises: a random destruction operator, a greedy destruction operator with perturbation.
Random destruction operator: and randomly selecting the customer points to delete until the customer point removal number is reached.
Greedy destruction operator: the customer site that increases the total cost the most is deleted until the customer site removal number is reached.
Greedy with perturbation destroy operator: similar to the greedy breaker, the cost is calculated by multiplying the disturbance factor. To increase randomness, the perturbation coefficients are random numbers uniformly distributed in [0.7,1 ].
Preferably, the set of repair operators comprises: a greedy insertion repair operator, a two-stage greedy repair operator, a two-stage repair operator, a greedy penalty repair operator.
Greedy insertion repair operator: first, the customer points in the destruction list are scored. That is, the scores of customers within the collection point service range are lower than the scores of customers not within the collection point service range; the higher the customer point score, the further the customer point is from the shortest distance of the customers of the existing path. During the insertion process, the insertion is performed according to the customer score from high to low. There are three ways of insertion: (a) inserting into a truck path; (b) inserting an unmanned aerial vehicle path; (c) inserting a collection point service range; and calculating the cost of the three modes, and selecting the service mode and the insertion position with the lowest cost.
Two-stage greedy repair operator: in the first stage, if the client point in the destroy list is within the range of the opened proxy receiving point service, the client is inserted into the proxy receiving point service. Otherwise, the customer point is inserted into the truck path according to a greedy strategy. In the second phase, for a customer who has just inserted the truck path, if both conditions are met: (a) the maximum bearing requirement and flight capability requirement of the unmanned aerial vehicle; (b) the delivery cost of using the drone is lower than the truck delivery cost, it is inserted into the drone path.
Two-stage repair operator: different from the two-stage greedy repair operator in that (1) when a customer point inserts into the truck path, the customer point inserts an increased distance multiplied by noise to increase randomness
The range of (1.2) is (0.8). (2) In the second stage, the customer point only needs to meet the maximum bearing requirement and flight capability requirement of the unmanned aerial vehicle, namely, the customer point is randomly inserted into the unmanned aerial vehicle path.
Greedy penalty repair operator: the operator firstly calculates penalty values for the customer points in the destruction list according to the following formula:
customer points with high penalty value are inserted first. And in the repairing process, repairing according to a two-stage greedy repairing operator.
Preferably, the set of local search operators comprises: unmanned aerial vehicle-truck crossover operator, collection point-unmanned aerial vehicle crossover operator, unmanned aerial vehicle launch-landing point crossover operator, two-point crossover operator.
The set of local search operators includes two types of operators: local search operators for service mode (first three operators), local search operators for truck path (last two operators).
Unmanned aerial vehicle-truck swap operator: for all customer sites served by the drone, the cost of its service by the truck is calculated. If the cost of service by the truck is less than the cost of service by the drone, then the drone path is deleted and the customer site is inserted into the truck path. This operator is designed to avoid that the best way of serving the customer site of the drone after the truck path is changed is not a drone.
Collection point-truck swap operator: for all customer sites served by the collection site, the cost of their service by truck is calculated. If the cost of service by the truck is lower than the cost of service by the collection point, the point is inserted into the truck path. For the case that the collection point only serves one customer point, when the cost of the customer point served by the collection point is calculated, the fixed operation cost of the collection point is added. This operator is designed to avoid the appearance of: the position of a customer point of the collection point service is in a path from the previous point to the collection point of the truck, and meanwhile, the situation that the cost is increased possibly caused by the collection point service of an isolated point can be avoided.
Collection point-unmanned aerial vehicle exchange operator: for all customer sites served by the collection points, if they are within the delivery capabilities of the drone, the cost of their serving by the drone is calculated. If the cost of service by the drone is lower than the cost of the collection point, the customer is added to the drone path.
The unmanned aerial vehicle launching-landing point exchange operator changes the access sequence of the trucks by exchanging the launching points and the landing points of the unmanned aerial vehicle so as to find a better access sequence of the trucks;
and the two-point exchange operator randomly selects any two points except the unmanned aerial vehicle launching point and the landing point in the truck path and exchanges the access sequence of the two points.
In this way, the truck carries the drone and the package and starts from the distribution center, the launch of the drone is performed at the drone launch point, and due to the road penalty cost imposed on the truck, customer points located in an environment with poor road conditions are more likely to be serviced by the drone. After the truck launches the unmanned aerial vehicle, the delivery work is continued, and if the collection point is opened, the truck needs to send the package to the collection point. Based on the flight capabilities of the drone, the truck needs to receive the landed drone at the drone landing point. This process follows the principle of cost minimization.
In order to verify the feasibility of the model and the planning method provided by the invention, experiments were performed, and all codes were programmed with python 3.7. The final experimental data are not shown in detail, and according to the experimental result, the distribution network model has the advantage of cost compared with other cooperative distribution models; compared with other heuristic algorithms, the path planning algorithm has great advantages in both time and cost, and most of the algorithms can find an accurate solution in a short time.
The embodiment of the present invention provides a collaborative distribution path planning apparatus, which is used for executing the collaborative distribution path planning method according to the above-mentioned contents of the present invention, and the collaborative distribution path planning apparatus is described in detail below.
As shown in fig. 5, the collaborative distribution route planning apparatus includes:
an information acquisition unit 101 configured to acquire delivery information including at least: rural scene road information, customer point information, truck information, unmanned aerial vehicle information and collection point information;
a model establishing unit 102, configured to establish a collaborative distribution model according to the distribution information;
the information processing unit 103 is used for acquiring the penalty cost of the rural road and processing the distribution information according to the penalty cost of the rural road;
an initial solution calculation unit 104, configured to apply an initial solution algorithm to calculate an initial solution of a collaborative distribution path according to the processed distribution information and the collaborative distribution model;
and an optimizing unit 105, configured to iterate the initial solution by using an optimization algorithm to obtain an optimal solution of the collaborative distribution path.
Therefore, in the cooperative distribution path, partial customer points are served by rural supermarkets instead of receiving points, rural road punishment cost is introduced on the basis of rural scene road information, and goods delivery service is performed on the road section where the truck is not easy to pass through by replacing the unmanned aerial vehicle, so that the total distribution cost is reduced, and the distribution efficiency is greatly improved.
Preferably, the model building unit 102 is further configured to: determining a target function of cooperative distribution; determining constraints of a client point, a truck, an unmanned aerial vehicle and a collection point; determining a time constraint of a joint point of the unmanned aerial vehicle and the truck in a distribution network; and establishing the collaborative distribution model according to the constraint and the objective function.
Preferably, the initial solution calculation unit 104 is further configured to: distributing the client points in the collection point range to the collection points; obtaining an opened collection point and an unallocated customer point; constructing an initial truck route using a greedy insertion algorithm to access the on-time collection points and unassigned customer points; and dividing the customer points into the point sets to be distributed of the unmanned aerial vehicle service according to a cost comparison principle and the unmanned aerial vehicle bearing capacity limit. Based on the principle that the waiting time of the truck and the unmanned aerial vehicle is the shortest, the maximum flight capacity limiting condition of the unmanned aerial vehicle and the front-back sequence of the launching point and the landing point of the unmanned aerial vehicle, the unmanned aerial vehicle delivery client, the route and the positions of the launching point and the landing point are determined, and the initial solution of the collaborative delivery path is obtained.
Preferably, the initial solution calculation unit 104 is further configured to: and if the unmanned aerial vehicle service customer point of the point set to be distributed is inserted in any position of the truck path in the mode of the unmanned aerial vehicle path and violates the flight capability constraint of the unmanned aerial vehicle, inserting the customer point into the truck route in the mode of the truck service through a greedy algorithm.
Preferably, the optimization unit 105 is further configured to: selecting a damage operator (a customer point damage operator or a collection point damage operator) and a repair operator according to the weight; in the iteration, a destruction operator and a repair operator are executed in sequence. In the execution of the damage operator, executing the collection point damage operator in a specified iteration period, and executing the client point damage operator in the rest iteration periods; and updating a new solution according to the rule of simulated annealing after each iteration period is finished, and updating the operator score according to the obtained quality of the new solution. And recording the total iteration times, the iteration times of using the customer point destruction operator, the iteration times after updating the weight and the iteration times of not improving the quality of a new solution in each iteration. If the total iteration number is smaller than a first threshold value, continuously selecting an operator according to the weight to execute destruction and repair operations; and if the iteration number of the using customer point damage operator is equal to the second threshold value, selecting a collection point damage operator according to the weight, and resetting the iteration number of the customer point damage operator to 0. If the iteration number after the weight is updated is equal to a third threshold value, updating the weight of the operator, resetting the iteration number after the weight is updated and returning to the operator selected according to the weight; if the iteration number of the new solution quality which is not improved is equal to a fourth threshold, executing all local search operators in the local search operator set, returning to the operator selected according to the weight after all the local search operators are executed, and continuing to execute the destruction and repair operation; and if the total iteration number is equal to a first threshold value, or the iteration number of the new solution without quality improvement is equal to a fifth threshold value, stopping iteration and outputting the current solution as the optimal solution.
Preferably, the information processing unit 103 is further configured to: sequentially increasing unit road punishment cost outwards by the geometric center of the customer point set until the cost is 1.85 times of the travel cost; the penalty cost for a link between customer points is the link length x 1/2.
Preferably, the optimization algorithm stopping condition is: and the total iteration number is equal to a first threshold, or the iteration number of the new solution without quality improvement is equal to a fifth threshold, and the total iteration number or the new solution without quality improvement is only required to meet one of the first threshold and the fifth threshold.
Preferably, the condition of using the local search operator is that the number of iterations for which the quality of the new solution is not improved is equal to a fourth threshold.
Preferably, the first set of destruction operators comprises: randomly starting the destruction operators, randomly closing the starting destruction operators, closing the minimum number of customers, starting the destruction operators by the maximum number of customers, and closing the destruction operators by the maximum number of customers.
Preferably, the second set of destruction operators comprises: a random destruction operator, a greedy destruction operator with perturbation.
Preferably, the set of repair operators comprises: a greedy insertion repair operator, a two-stage greedy repair operator, a two-stage repair operator, a greedy penalty repair operator.
Preferably, the set of local search operators comprises: unmanned aerial vehicle-truck crossover operator, collection point-unmanned aerial vehicle crossover operator, unmanned aerial vehicle launch-landing point crossover operator, two-point crossover operator.
Preferably, the customer point destruction operator is a destruction operator for a customer point.
Preferably, the collection point destroy operator is a destroy operator for a collection point.
An electronic device is provided in an embodiment of the present application, as shown in fig. 6, and includes a computer-readable storage medium 301 storing a computer program and a processor 302, where the computer program is read by the processor and executed by the processor to implement the coordinated delivery path planning method as described above.
Therefore, in the cooperative distribution path, partial customer points are served by rural supermarkets instead of receiving points, rural road punishment cost is introduced on the basis of rural scene road information, and goods delivery service is performed on the road section where the truck is not easy to pass through by replacing the unmanned aerial vehicle, so that the total distribution cost is reduced, and the distribution efficiency is greatly improved.
An embodiment of the present application provides a computer-readable storage medium, where a computer program is stored, and when the computer program is read and executed by a processor, the method for planning a collaborative distribution route as described above is implemented.
The technical solution of the embodiment of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be an air conditioner, a refrigeration device, a personal computer, a server, or a network device, etc.) or a processor (processor) to execute all or part of the steps of the method of the embodiment of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.
Therefore, in the cooperative distribution path, partial customer points are served by rural supermarkets instead of receiving points, rural road punishment cost is introduced on the basis of rural scene road information, and goods delivery service is performed on the road section where the truck is not easy to pass through by replacing the unmanned aerial vehicle, so that the total distribution cost is reduced, and the distribution efficiency is greatly improved.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the application are described in a relevant manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. Reference is made to the preceding description of the embodiments.
Although the present invention is disclosed above, the present invention is not limited thereto. Various changes and modifications may be effected therein by one skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A collaborative distribution path planning method is characterized by comprising the following steps:
acquiring delivery information, wherein the delivery information at least comprises: rural scene road information, customer point information, truck information, unmanned aerial vehicle information and collection point information;
establishing a collaborative distribution model according to the distribution information;
obtaining a rural road punishment cost, and processing the distribution information according to the rural road punishment cost;
applying an initial solution algorithm according to the processed distribution information and the collaborative distribution model to calculate an initial solution of a collaborative distribution path;
and iterating the initial solution by using an optimization algorithm to obtain the optimal solution of the cooperative distribution path.
2. The collaborative distribution route planning method according to claim 1, wherein the establishing of the collaborative distribution model according to the distribution information includes:
determining a target function of cooperative distribution;
determining constraints of a client point, a truck, an unmanned aerial vehicle and a collection point; determining a time constraint of a joint point of the unmanned aerial vehicle and the truck in a distribution network;
and establishing the collaborative distribution model according to the constraint and the objective function.
3. The collaborative distribution route planning method according to claim 1, wherein the calculating an initial solution of the collaborative distribution route by applying an initial solution algorithm according to the processed distribution information and the collaborative distribution model comprises:
distributing the client points in the collection point range to the collection points to obtain opened collection points and unallocated client points;
constructing an initial truck delivery route using a greedy insertion algorithm for the turned-on collection points and unassigned customers;
dividing the client points into a point set to be distributed of the unmanned aerial vehicle service according to a cost comparison principle and the unmanned aerial vehicle bearing capacity;
determining the distribution customers, routes, and the positions of the launching points and landing points of the unmanned aerial vehicles based on the principle that the waiting time of the truck and the unmanned aerial vehicles is the shortest, the maximum flight capacity limit condition of the unmanned aerial vehicles, and the front-back sequence of the launching points and the landing points of the unmanned aerial vehicles;
and for the clients of which the points to be distributed for the unmanned aerial vehicle service are concentrated and do not meet the unmanned aerial vehicle limiting conditions when the unmanned aerial vehicle route is constructed, reinserting the clients into the truck delivery route by using a greedy algorithm to obtain an initial solution of the collaborative delivery route.
4. The collaborative distribution path planning method according to any one of claims 1 to 3, wherein the iterating the initial solution using an optimization algorithm to obtain an optimal solution of the collaborative distribution path includes:
selecting a damage operator and a repair operator according to the weight, wherein the damage operator is a customer point damage operator or a collection point damage operator;
sequentially executing a destruction operator and a repair operator, wherein in the execution of the destruction operator, the collection point destruction operator is executed in a specified iteration period, and the client point destruction operator is executed in the rest iteration periods; updating a new solution according to a rule of simulated annealing after each iteration cycle is finished, and updating an operator score according to the obtained quality of the new solution;
recording the total iteration times, the iteration times of using a customer point damage operator, the updated weight iteration times and the iteration times of not improving the new solution quality for each iteration;
if the total iteration number is smaller than a first threshold value, re-executing the operator selected according to the weight;
if the iteration number of the customer point damage operator is smaller than a second threshold value, selecting the customer point damage operator, otherwise, selecting a collection point damage operator, and resetting the iteration number of the customer point damage operator to 0;
if the iteration times of the updated weight are equal to a third threshold value, updating the weight of the operator according to the score of the operator, resetting the iteration times of the updated weight to be 0 and returning to the operator selected according to the weight;
if the iteration number of the new solution quality not improved is equal to a fourth threshold, executing all local search operators in the local search operator set, and returning to the selection of the destruction and repair operators according to the weight after the execution is finished;
and if the total iteration number is equal to a first threshold value or the iteration number of the new solution quality which is not improved is equal to a fifth threshold value, stopping iteration and outputting the current solution as the optimal solution.
5. The collaborative distribution route planning method according to claim 4, wherein the optimization algorithm stopping condition is: and the total iteration number is equal to a first threshold, or the iteration number of the new solution quality which is not improved is equal to a fifth threshold, and the requirement on one of the two is met.
6. The collaborative distribution route planning method according to claim 4, wherein a condition of using the local search operator is that a number of iterations for which the quality of the new solution is not improved is equal to a fourth threshold.
7. The collaborative distribution route planning method according to claim 4, wherein the set of local search operators includes: unmanned aerial vehicle-truck crossover operator, collection point-unmanned aerial vehicle crossover operator, unmanned aerial vehicle launch-landing point crossover operator, two-point crossover operator.
8. A collaborative distribution route planning apparatus, comprising:
an information acquisition unit configured to acquire delivery information including at least: rural scene road information, customer point information, truck information, unmanned aerial vehicle information and collection point information;
the model establishing unit is used for establishing a collaborative distribution model according to the distribution information;
the information processing unit is used for acquiring the penalty cost of the rural road and processing the distribution information according to the penalty cost of the rural road;
an initial solution calculation unit, configured to apply an initial solution algorithm to calculate an initial solution of a collaborative distribution path according to the processed distribution information and the collaborative distribution model;
and the optimization unit is used for iterating the initial solution by using an optimization algorithm to obtain an optimal solution of the collaborative distribution path.
9. An electronic device comprising a computer-readable storage medium storing a computer program and a processor, wherein the computer program is read by the processor and executed to implement the collaborative delivery path planning method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when read and executed by a processor, implements the collaborative distribution path planning method according to any one of claims 1 to 7.
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