CN111709561A - Real-time adding and optimizing method for solving dynamic vehicle path problem - Google Patents
Real-time adding and optimizing method for solving dynamic vehicle path problem Download PDFInfo
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Abstract
The invention discloses a real-time adding and optimizing method for solving a dynamic vehicle path problem, which is applied to the design and optimization of the dynamic vehicle path problem and mainly relates to the two fields of vehicle path dynamic planning and intelligent optimization. The inventive method comprises the following steps: first, a path is planned for known customer points using a hybrid ant colony algorithm. And then sequentially adding the new customer to the path of the vehicle nearest to the new customer according to the dynamic customer service request time. And immediately judging whether the path service vehicle of the newly added customer is overloaded or not every time a new customer point is added, if so, arranging the vehicles to carry out centralized service, and if not, replanning the path of the newly added customer and the unserviced customers by using a hybrid ant colony algorithm. And finally, finishing until all the new client points are added. The method disclosed by the invention tests the disclosed data set, and proves that the method is real and effective in designing and optimizing the vehicle path problem.
Description
Technical Field
The invention relates to a real-time adding and optimizing method for solving the problem of dynamic vehicle paths, and belongs to the two fields of vehicle path dynamic planning and intelligent optimization.
Background
Dynamic vehicle routing problems are common in the field of logistics. When logistics companies arrange vehicles to deliver goods to known customers, new demands are often made around the planned service path. If the planned service path can be dynamically changed, the cost of the logistics company is reduced, and the service quality is improved.
The problem of dynamic vehicle paths is an NP-hard problem, and since constraints are many, node scale is large, and it is difficult to solve with an accurate algorithm, a heuristic algorithm is generally required for solving the problem, such as: genetic algorithm, ant colony algorithm, tabu search, particle swarm algorithm, etc. For the adding mode of the dynamic demand, a time slice mode is generally adopted for solving, namely, a working day is divided into a plurality of time slices, and then the dynamic demand is processed in each time slice.
Disclosure of Invention
The invention aims to provide a real-time adding and optimizing method for solving the problem of dynamic vehicle paths, aiming at improving the efficiency of solving the problem of dynamic vehicle paths.
The technical scheme adopted by the invention for solving the technical problem comprises the following steps:
step (1) using a hybrid ant colony algorithm to carry out path planning on known customer nodes to form n initial service paths (R)1、R2、…、Rn)。
And (2) inserting the new client node into the nearest path according to the time and the position requested by the new client node.
And (3) judging whether the vehicle serving the newly added customer node exceeds the maximum vehicle load or not, if so, removing the new customer node from the new path, and then arranging the vehicle to process the requirement independently.
And (4) keeping the served path unchanged under the condition that the maximum load of the vehicle is not exceeded, and replanning the path by using a hybrid ant colony algorithm for the new customer node and the previously planned but unserved customer node.
And (5) repeating and circularly executing the steps (2) to (4) until all the new client nodes are completely served.
The mixed ant colony algorithm of the steps (1) and (4) is specifically as follows:
the method comprises the steps of firstly, using mutation operation to integrally optimize a path. And (3) adopting the idea of mutation operation in the genetic algorithm, setting the probability of mutation to be m equal to 0.2, and then generating a random number r. The method comprises the following specific steps: and selecting a path, randomly exchanging every two client nodes of 30% of the path once if the generated random number r is less than or equal to m, calculating the path length of each exchange, and recording the path with the shortest length as the optimal path.
Local optimization is performed using a modified 2-Opt algorithm.
Firstly, the optimal path obtained in the mutation operation is taken out, and the optimal path is divided according to different vehicles to form a single path for one vehicle.
Then, a 2-Opt algorithm is independently used for the path of each vehicle, namely, the first customer node sequentially exchanges positions with other customer nodes, and then the second customer node sequentially exchanges positions with the subsequent customer nodes until all the customer nodes are completely exchanged, and the local optimal path of each vehicle is found.
And finally, combining each vehicle according to the local optimal path to form a new path, and comparing the new path with the original path. If the optimized new path is better than the original path, the optimized new path is used, otherwise, the original path is kept unchanged.
In the step (2), inserting the client node into the nearest path according to the time and the position of the new client request specifically comprises:
firstly, taking out a new client according to the request time of the new client, and respectively calculating the distance between a newly inserted client node and a vehicle which is in service;
and then inserting the new client node into the path where the vehicle nearest to the new client node is located, and finally forming a new path.
The vehicle load judgment and the individual vehicle arrangement in the step (3) are specifically as follows:
inserting a new client node into a nearest path, judging whether the vehicle heavy load served by the path exceeds, if the maximum load exceeds, moving the newly added client node out of the nearest path and placing the client node into an individual list, and finally arranging vehicles to intensively process the client nodes in the individual list after all the client nodes are added.
The step (4) of replanning the path for the new customer node and the previously planned but unserved point by using the hybrid ant colony algorithm specifically comprises the following steps:
the known path is divided into two parts after each new client is added to the known path. The first part is the customers that have been served before, and this part of the path remains unchanged.
The second part is a newly added customer node and a customer node which is not served before, and the path of the second part is re-planned by using a mixed ant colony algorithm to realize real-time addition and optimization. And (4) repeating the steps (2) to (4) until all the new client nodes are served.
The invention has the following beneficial effects:
in the present invention, the hybrid ant colony algorithm will be used to generate initial paths and later dynamic optimizations. The hybrid ant colony algorithm fuses the global optimization path of the mutation operation of the genetic algorithm, and simultaneously adopts 2-opt to carry out the local optimization operation of the path. The real-time adding strategy is to immediately add the current path according to the request time of each new demand for synchronous optimization.
The invention is real and effective for the design and optimization of the vehicle path problem.
Drawings
FIG. 1 is a flow chart of the dynamic addition and optimization of the present invention;
FIG. 2 is a flowchart illustrating the hybrid ant colony algorithm mutation operation of the present invention;
fig. 3 is a schematic diagram of adding path selection and path reconnection according to the present invention.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
FIG. 1 is a flow chart of the dynamic addition and optimization of the present invention.
In the present invention, a simulation experiment was performed using Matlab language. As shown in fig. 1, a path is first planned for known customer nodes using a hybrid ant colony algorithm. And then sequentially adding the new customer to the path of the vehicle nearest to the new customer according to the dynamic customer service request time. And immediately judging whether the path service vehicle of the newly added client is overloaded or not every time a new client node is added, if so, independently arranging the vehicles to carry out centralized service, and otherwise, immediately replanning the path of the newly added client and the unserved client by using a hybrid ant colony algorithm. And finally, finishing until all the new client nodes are added.
The following are the detailed steps of the whole method:
(1) path planning is carried out on known customer nodes by using a hybrid ant colony algorithm to form n initial service paths (R)1...R2)。
The following are the detailed steps of the hybrid ant colony algorithm:
initializing basic parameters of the ant colony algorithm, placing the ant colony at the train yard and setting the train yard as an initial node. Manually setting parameters such as: ant number, heuristic factor, iteration times and initial node.
Each ant represents a vehicle and starts from the initial node, the next client node to be visited is selected according to the transition probability, and the client node visited by the ant is added into the taboo table. The next accessed client node is selected by the following formula.
Wherein p isij(k) Denotes the probability of selecting client j as the next access point for client i, τ(i,j)And η(i,j)Pheromone intensity and route visibility representing routes between customers i and j, respectively, α and β representing importance of pheromone intensity and route visibilitykRespectively representing the set of all visited customer points and the set of k-th vehicle visited customer points.
Fig. 2 is a flowchart of the hybrid ant colony algorithm mutation operation in the present invention. The entire optimization path of the mutation operation is used. By adopting the idea of mutation operation in genetic algorithm, we set the probability of mutation as m ═ 0.2 and then generate random number r.
The method comprises the following specific steps: and selecting a path, randomly exchanging every two client nodes of 30% of the path once if the generated random number r is less than or equal to m, calculating the path length of each exchange, and recording the path with the shortest length.
Local optimization is performed using a modified 2-Opt algorithm. The method comprises the following specific steps:
firstly, the optimal path obtained in the mutation operation is taken out, and the optimal path is divided according to different vehicles to form a single path for one vehicle.
Then, the 2-Opt is independently used for the path of each vehicle, namely, the first customer node sequentially exchanges positions with other customer nodes, and then the second node sequentially exchanges positions with the following nodes until all the nodes are exchanged, and the local optimal path of each vehicle is found out.
And finally, combining each vehicle according to the original sequence to form a new path, and comparing the new path with the original path. If the optimized path is better than the original path, the optimized path is used, otherwise, the original path is kept unchanged.
After one iteration is completed, the pheromone concentration of each path is updated.
The pheromone is updated using the following formula:
τij(t+1)＝(1-ρ)×τij(t)+Δτij(t,t+1)
in the above formula, τij(t +1) is the updated pheromone concentration between paths (i, j); tau isij(t) is the initial pheromone concentration for pathway (i, j); ρ is a constant representing the rate at which the pheromone is volatilized; delta tauij(t, t +1) is the pheromone increment on path (i, j), and the pheromone increment rule is updated by the following formula:
wherein Q is a constant, LbestIs the optimal path length.
(2) The client node is inserted into the nearest path based on the time and location of the new client request. Specifically, as shown in fig. 3, nodes 1, 2, 3, 4, 5, 6, 7, 8, 9 in the graph are known customer nodes, and N1 and N2 are newly added customer nodes. Selecting and reconnecting the path according to the completed path, the originally planned path and the real-time newly added path, and the specific steps are as follows:
firstly, taking out the new client according to the request time of the new client, respectively calculating the distance between the newly inserted client node and the vehicle being served, then inserting the new client node into the path where the vehicle nearest to the new client node is located, and finally forming a new path.
(3) Determining whether a vehicle servicing the newly added customer node path exceeds a maximum vehicle load, and if so, removing the new customer node from the new path to schedule the vehicle to handle the demand individually. The method specifically comprises the following steps:
and inserting a new client node into the nearest path, judging whether the vehicle heavy load served by the path exceeds, if so, moving the newly added client node out of the path and placing the client node into the single list, and finally arranging the vehicles to intensively process the client nodes in the single list after all the client nodes are added.
(4) For the case that the vehicle maximum load is not exceeded, the already served path is kept unchanged, and the path is re-planned using the hybrid ant colony algorithm for the new customer node and the previously planned but unserviced points. The method specifically comprises the following steps:
the known path is divided into two parts after each new client is added to the known path.
The first part is the customers that have been served before, and this part of the path remains unchanged.
And the second part is a newly added point and a customer node which is not served before, and the path of the newly added point and the customer node is re-planned by using a mixed ant colony algorithm, so that real-time addition and optimization are realized.
(5) And (4) repeating the steps (2) to (4) until all the new client nodes are served.
Simulation experiments prove that the real-time adding and optimizing method can effectively solve the problem of dynamic vehicle paths.
Claims (7)
1. A real-time adding and optimizing method for solving the problem of dynamic vehicle paths is characterized by comprising the following steps:
step (1) using a hybrid ant colony algorithm to carry out path planning on known customer nodes to form n initial service paths (R)1、R2、…、Rn)；
Step (2) inserting the new client node into the nearest path according to the request time and position of the new client node to form a new path;
step (3) judging whether a vehicle serving the newly added client node exceeds the maximum vehicle load, if so, removing the newly added client node from the new path, and then arranging the vehicle to individually process the requirements;
step (4), for the condition that the maximum load of the vehicle is not exceeded, the served path is kept unchanged, and the path is re-planned by using a hybrid ant colony algorithm for the new customer node and the previously planned but unserviced customer nodes;
and (5) repeating and circularly executing the steps (2) to (4) until all the new client nodes are completely served.
2. The real-time adding and optimizing method for solving the dynamic vehicle path problem according to claim 1, wherein the hybrid ant colony algorithm of the steps (1) and (4) is specifically as follows:
firstly, optimizing a path by using a mutation operation as a whole; adopting the idea of mutation operation in a genetic algorithm, setting the probability of mutation as m being 0.2, and then generating a random number r; the method comprises the following specific steps: selecting a path, randomly exchanging every two client nodes of 30% of the path once if the generated random number r is less than or equal to m, calculating the path length of each exchange, and recording the path with the shortest length as an optimal path;
using an improved 2-Opt algorithm to carry out local optimization;
firstly, taking out the optimal path obtained in the variation operation, and dividing the optimal path according to different vehicles to form a single original path of one vehicle;
then, a 2-Opt algorithm is independently used for the path of each vehicle, namely, a first customer node exchanges positions with other customer nodes in sequence, and then a second customer node exchanges positions with the following nodes in sequence until all the customer nodes are exchanged, and the local optimal path of each vehicle is found out;
finally, combining each vehicle according to the local optimal path to form a new path, and comparing the new path with the original optimal path; if the optimized new path is better than the original optimal path, the optimized new path is used, otherwise, the original optimal path is kept unchanged.
3. The real-time adding and optimizing method for solving the dynamic vehicle path problem according to claim 2, wherein the step (2) of inserting the client node into the nearest path according to the new client request time and location is specifically as follows:
firstly, taking out a new client according to the request time of the new client, and respectively calculating the distance between a newly inserted client node and a vehicle which is in service;
and then inserting the new client node into the path where the vehicle nearest to the new client node is located, and finally forming a new path.
4. The real-time adding and optimizing method for solving the dynamic vehicle path problem according to claim 3, wherein the vehicle load judgment and the individual vehicle scheduling in the step (3) are specifically as follows:
and inserting the new client node into the nearest path, judging whether the vehicle heavy load served by the path exceeds, if the maximum load exceeds, moving the newly added client node out of the nearest path and placing the newly added client node into the single list, and finally arranging the vehicles to intensively process the client nodes in the single list after all other new client nodes are added.
5. The real-time adding and optimizing method for solving the dynamic vehicle path problem according to claim 4, wherein the re-planning of the path for the new customer node and the previously planned but unserviced point in the step (4) by using the hybrid ant colony algorithm specifically comprises:
dividing the known path into two parts after adding a new client node to the known path; the first part is a client node which is already served before, and the path of the first part is kept unchanged;
the second part is a newly added customer node and a customer node which is not served before, and the path of the second part is re-planned by using a mixed ant colony algorithm to realize real-time addition and optimization.
6. A real-time addition and optimization method to solve the dynamic vehicle path problem according to claim 1 or 2 or 5, characterized by:
initializing basic parameters of an ant colony algorithm, placing an ant colony at a train yard and setting the train yard as an initial node; each ant represents a vehicle and starts from an initial node, the next client node to be visited is selected according to the transition probability, and the client node visited by the ant is added into a taboo table; selecting a next accessed client node through the following formula;
wherein p isij(k) Denotes the probability of selecting client j as the next access point for client i, τ(i,j)And η(i,j)Pheromone concentration and route visibility representing routes between customers i and j, α and β representing importance of pheromone concentration and route visibility, respectively, and tabukRespectively representing the set of all visited customer points and the set of k-th vehicle visited customer points.
7. A real-time addition and optimization method to solve the dynamic vehicle path problem as claimed in claim 6, characterized by updating pheromone concentration of each path after each iteration is completed, updating pheromones using the following formula:
τij(t+1)＝(1-ρ)×τij(t)+Δτij(t,t+1)
in the above formula, τij(t +1) is the updated pheromone concentration between paths (i, j); tau isij(t) is the initial pheromone concentration for pathway (i, j); ρ is a constant representing the rate at which the pheromone is volatilized; delta tauij(t, t +1) is the pheromone increment on path (i, j), and the pheromone increment rule is updated by the following formula:
wherein Q is a constant, LbestIs the optimal path length.
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