CN114118616A - Multi-target vehicle path planning method with soft time window - Google Patents

Multi-target vehicle path planning method with soft time window Download PDF

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CN114118616A
CN114118616A CN202111479361.5A CN202111479361A CN114118616A CN 114118616 A CN114118616 A CN 114118616A CN 202111479361 A CN202111479361 A CN 202111479361A CN 114118616 A CN114118616 A CN 114118616A
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孙家泽
耿扬
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Abstract

The invention discloses a multi-target vehicle path planning method with a soft time window, which aims at the problem of multi-target path planning and belongs to the field of intelligent transportation. Firstly, reading a vehicle path data set to construct a road network topological graph; setting the path length and the number of compatible passengers as targets, solving all non-dominated solutions in the initial population, and adding a soft time window to update the current optimal solution; and adopting a 0-1 random number to judge the state transition probability and add penalty values to globally update the human traffic, and selecting an optimal solution set meeting constraint conditions. The invention provides an effective and stable multi-target vehicle path planning method with a soft time window, which is beneficial to exploring a condition that the path length is shorter, the number of compatible passengers is more, the conveying time is as short as possible under the condition that a constraint condition is met, and the method has a good application prospect.

Description

Multi-target vehicle path planning method with soft time window
Technical Field
The invention belongs to the field of intelligent traffic, particularly relates to a multi-target vehicle path planning problem, and provides a multi-target vehicle path planning method with a soft time window.
Background
The vehicle path planning plays a very important role in urban traffic, the path transportation cost accounts for a large proportion of the urban traffic system cost at present, and the reasonable path planning is one of important methods for improving the current situation of urban traffic. In vehicle path planning, the optimal solutions of multiple targets need to be considered comprehensively in combination with real road conditions, so that the research on the multi-target vehicle path planning method has practical significance.
The path problem of the vehicle in path planning is a core problem to be solved. Vehicle routing problems: the method comprises the steps that a vehicle distribution center, a customer point position and a cargo demand are known, the vehicle starts from the distribution center, passengers are conveyed from a starting point to a destination, and finally the vehicle returns to the distribution center, meanwhile, each customer can only be conveyed by one vehicle, the cargo demand of the customer point is guaranteed, a reasonable operation scheme is determined, and the optimal solution (including shortest path, the largest number of compatible passengers, the lowest conveying cost and the like) of a plurality of targets is obtained under the multi-constraint condition.
The multi-target vehicle path planning method with the soft time window has the characteristics of strong practicability and easiness in implementation, and is widely applied. Different from a vehicle path planning method with a hard time window, the soft time window constraint sets the elastic time, allows the waiting time and delay time caused by the fact that the arrival is not constrained according to the time window, and has obvious practicability. In addition, the soft time window method is adopted, so that the expansibility and the practicability of the model can be effectively improved, and the overall service quality is improved. The invention provides a multi-target vehicle path planning method with a soft time window.
Disclosure of Invention
A multi-target vehicle path planning method with soft time windows is characterized in that a real track data set is used, a soft distribution time window which is more in line with the actual situation is designed, the time constraint of a vehicle is reasonably normalized, the solutions of multiple targets are comprehensively realized, and the accuracy of the solutions is improved. In multi-objective vehicle path planning using soft time windows, the goal is to minimize the resulting path length, maximize the number of compatible passengers, and minimize the latency and delay time that results from not having to reach in accordance with the customer point time window constraints.
The invention relates to a multi-target vehicle path planning method with a soft time window, which is characterized by comprising the following steps of:
the method comprises the following steps: a trajectory dataset is acquired and N ═ 0, 1., i., m } is used to represent delivery centers and customer points, where i ∈ N,0 ≦ i ≦ m, and i ≦ 0 represents delivery centers, i ≦ 1., m represents m customer points, and (x ≦ 1., m represents m customer points, the trajectory dataset is obtained using (x ≦ 0, 1.,. i.,. m)i,yi) Indicating location coordinates of delivery centres and of customers' points, used in the delivery centresA maximum of M vehicles of the same type and a maximum load capacity R, to carry out the delivery tasks at all customer sites, ciExpresses the cargo demand of the ith customer point and satisfies maxci≤R，
Figure BDA0003394407380000021
Representing the maximum number of compatible passengers on a single path, and ensuring that C ≦ R, using [ E ≦ Ri,Li]As time windows for the distribution center and customer sites, where EiIndicating the time, L, at which customer point i was serviced earliestiRepresenting the time of serving a customer point i at the latest, firstly, generating an initial path set PT with the customer scale of S by adopting an optimal point set, wherein the dereferenceable value of S is 25, 50 and 100, and constructing a road network topological graph according to position coordinates of a distribution center and the customer point, the cargo demand, time window information, the maximum compatible passenger number of a vehicle and the like;
step two: using the path length of the solution and the number of compatible passengers on the path as the objective function f1And f2Wherein f is1The definition domain is the shortest distance from the starting point to the end point of a single path in the road network, f2The definition domain of (a) is R, and the value domains thereof are real numbers, when f is1The smaller the value of (f)2The larger the value of (A), the better the current solution is, and for different solutions T satisfying the constraint condition1And T2And T is1,T2E to PT, solve all satisfied f in PT1(T1)＞f1(T2) And f2(T1)＞f2(T2) Or f is1(T1)＜f1(T2) And f2(T1)＜f2(T2) The method comprises the steps that non-dominant solutions of a pareto dominant relationship are individual, all solved non-dominant solutions are stored in an external non-dominant solution set PT _ set, and a multi-target vehicle path problem model is constructed;
step three: setting a soft time window on the basis of a multi-target vehicle path problem model, namely taking waiting time and delay time which are not caused by the constraint of arrival according to a client point time window as a target function f3And f4Wherein f is3、f4The definition domain of (1) is PT, the value domain is real, and in this case, f is required3And f4The value of (A) is as small as possible, the time for returning the vehicle to the distribution center is ensured not to exceed the time window constraint, so that the current solution is better, for different solutions meeting the soft time window constraint, the non-dominated solution is solved according to the pareto dominance relation, all the solved non-dominated solutions are stored in an external non-dominated solution set PT _ set, and the client scale is set to be S, a fixed parameter alpha, and adjusting parameters theta, beta and w1、w2And the maximum iteration number, making the iteration number j equal to 1, and starting iteration;
step four: during the jth iteration, randomly selecting a solution from a non-dominated solution set PT _ set to select the next state, accumulating and calculating the state transition probability, judging whether the state transition probability is larger than the current accumulated probability by generating a 0-1 random number, selecting the next available client point, calculating the state transition probability according to a formula (1),
Figure BDA0003394407380000031
wherein tau isuvIs the flow of people on the side (u, v) (. eta.)uvIs a heuristic value, Jk(u) is the set of customers that vehicle k did not serve at point u, tuIs the time when the vehicle reaches the customer point u, [ E ]u,Lu]Is the time window of the client point u, then by the parameter w1And w2Comprehensively considering the target, where w1+w2The theta and beta parameters are used to balance the heuristic values with the flow rate, the larger the value the larger the ratio,
Figure BDA0003394407380000032
the method has the functions of calculating the probability of state transition, wherein a part consisting of the pedestrian volume and the heuristic value points to an edge with a shorter path and the maximum number of compatible passengers, and a calculation time window and an overall time part point to an edge conforming to the time window constraint;
step five: at the j-th iteration, randomly selecting the human flow in the solution updating path from the non-dominated solution set PT _ set, namely updating the human flow through a formula (2) and a formula (3),
τuv＝τuv*(1-α) (2)
Figure BDA0003394407380000033
Figure BDA0003394407380000034
equation (3) is determined by the total route of dispatching the optimal vehicle each time, namely the current optimal solution, and adding a penalty value shown in equation (4)
Figure BDA0003394407380000035
The super solution exceeding the global optimal solution is rewarded, and meanwhile, symmetrical processing is adopted in the formula (3), namely when the current customer point in the current optimal path is processed, the symmetrical customer point in the current optimal path is processed at the same time, and the solving time is reduced;
step six: judging a termination condition, wherein the iteration condition is that the current iteration times are larger than the maximum iteration times, if the iteration termination condition is satisfied, the external non-dominated solution set is an optimal solution set of the multi-target vehicle path planning problem with the soft time window, outputting the optimal solution set and stopping iteration; otherwise, let the iteration number j equal to j +1, return to step three.
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FIG. 1 is a flow chart of a multi-goal vehicle path planning method with soft time windows in accordance with the present invention.
Detailed Description
Taking standard test packets C101-25, R101-25 and RC101-25 with the client scale of 25 in the Solomon reference example as an example, a specific implementation mode of the multi-target vehicle path planning method with the soft time window provided by the invention is described with reference to the attached drawing 1.
The method comprises the following steps: a trajectory data set is acquired, where N ═ 0, 1., i., m } represents delivery centers and customer points, where i ∈ N,0 ≦ i ≦ m, and i ≦ 0 represents delivery centers, i ≦ 1., m represents m customer points, and (x ≦ 1., m represents m customer points, where (x ≦ m) is usedi,yi) Showing distribution centers and customersPosition coordinates of the customer sites, using at most M vehicles with the same model and the maximum load capacity R in the distribution center to complete the delivery tasks of all the customer sites, ciExpresses the cargo demand of the ith customer point and satisfies maxci≤R，
Figure BDA0003394407380000041
Representing the maximum number of compatible passengers on a single path, and ensuring that C ≦ R, using [ E ≦ Ri,Li]As time windows for the distribution center and customer sites, where EiIndicating the time, L, at which customer point i was serviced earliestiRepresenting the time of the latest service customer point i, firstly generating an initial path set PT with the customer scale of 25 by adopting an optimal point set, and constructing a road network topological graph according to the position coordinates of a distribution center and the customer point, the cargo demand, the time window information, the maximum number of compatible passengers of a vehicle and the like, wherein the maximum number of compatible passengers is 200; the client point information is stored in the form of a data table, as shown in table 1.
Table 1 distribution center and customer site information
Figure BDA0003394407380000042
Figure BDA0003394407380000051
After the detailed information is obtained, the distribution center, the ith customer point and the distance d between every two customer points are calculatedio(i ≠ o; i ≠ 0,1,2.. m; o ═ 0,1,2.. m), using the formula
Figure BDA0003394407380000052
And (5) obtaining the linear distance between any two points.
Step two: using the path length of the solution and the number of compatible passengers on the path as the objective function f1And f2Wherein f is1The definition domain is the shortest distance from the starting point to the end point of a single path in the road network, f2The definition domain of (a) is R, and the value domains thereof are real numbers, when f is1The smaller the value of (f)2The larger the value of (A), the better the current solution is, and for different solutions T satisfying the constraint condition1And T2And T is1,T2E to PT, solve all satisfied f in PT1(T1)＞f1(T2) And f2(T1)＞f2(T2) Or f is1(T1)＜f1(T2) And f2(T1)＜f2(T2) And (4) non-dominant solution individuals of the pareto dominant relationship, and storing all the solved non-dominant solutions in an external non-dominant solution set PT _ set to construct a multi-target vehicle path problem model.
Step three: setting a soft time window on the basis of a multi-target vehicle path problem model, namely taking waiting time and delay time which are not caused by the constraint of arrival according to a client point time window as a target function f3And f4Wherein f is3、f4The definition domain of (1) is PT, the value domain is real, and in this case, f is required3And f4The value of (a) is as small as possible, the time for returning the vehicle to the distribution center is ensured not to exceed the time window constraint, so that the current solution is better, for different solutions meeting the soft time window constraint, the non-dominated solution is solved according to the pareto dominated relation, all the solved non-dominated solutions are stored in an external non-dominated solution set PT _ set, the client size is set to be 25, the fixed parameter alpha is 0.05, the parameter beta is 1, theta is 5, w is1＝0.9、w2The maximum number of iterations is 500, and the iteration is started with the number of iterations j equal to 1.
Step four: and during the jth iteration, randomly selecting a solution from the non-dominated solution set PT _ set to select the next state, accumulating and calculating the state transition probability, judging whether the state transition probability is greater than the current accumulated probability by generating a 0-1 random number, selecting the next available client point, and calculating the state transition probability according to a formula (5).
Figure BDA0003394407380000061
Wherein tau isuvIs the flow of people on the side (u, v)，ηuvIs a heuristic value, Jk(u) is the set of customers that vehicle k did not serve at point u, tuIs the time when the vehicle reaches the customer point u, [ E ]u,Lu]Is the time window of the client point u, then by the parameter w1And w2Comprehensively considering the target, where w1+w2The theta and beta parameters are used to balance the heuristic values with the flow rate, the larger the value the larger the ratio,
Figure BDA0003394407380000062
the effect of (a) is to calculate a state transition probability, where the portion consisting of the traffic and heuristic values points to the edge with the shorter path and the highest number of compatible passengers, and the calculation time window and the overall time portion point to the edge that meets the time window constraints.
Step five: and randomly selecting the human flow in the solution updating path from the non-dominated solution set PT _ set at the j iteration, namely updating the human flow through a formula (6) and a formula (7).
τuv＝τuv*(1-α) (6)
Figure BDA0003394407380000063
Figure BDA0003394407380000064
Equation (6) is determined by the total route to dispatch the optimal vehicle each time, i.e., the current optimal solution, adding a penalty value as shown in equation (7)
Figure BDA0003394407380000065
Therefore, a super solution exceeding the global optimal solution is rewarded, and meanwhile, symmetrical processing is adopted in the formula (6), namely when the current customer point in the current optimal path is processed, the symmetrical customer point in the current optimal path is processed at the same time, and the solving time is reduced.
Step six: judging a termination condition, wherein the iteration condition is that the current iteration times are larger than the maximum iteration times, if the iteration termination condition is satisfied, the external non-dominated solution set is an optimal solution set of the multi-target vehicle path planning problem with the soft time window, outputting the optimal solution set and stopping iteration; otherwise, let the iteration number j equal to j +1, return to step three. For example, for three sets of examples including standard test packets C101-25, R101-25 and RC101-25 with a customer scale of 25 in the Solomon reference example, the same parameters and experimental conditions are set, and after 500 iterations, the optimal vehicle path planning conditions obtained when different standard test packets satisfy the multi-constraint conditions are respectively shown in table 2, table 3 and table 4, and the optimal solution under the optimal vehicle path planning condition is described in table 5.
TABLE 2 optimal solution for vehicle Path planning for Standard test pack C101-25
Figure BDA0003394407380000071
TABLE 3 optimal solution for vehicle path planning for Standard test Package R101-25
Figure BDA0003394407380000072
TABLE 4 optimal solution for vehicle path planning for standard test pack RC101-25
Figure BDA0003394407380000073
TABLE 5 Experimental results for different standard test packs
Figure BDA0003394407380000081
According to the multi-target vehicle path planning method with the soft time window, multiple experiments are carried out on standard test packets C101-25, R101-25 and RC101-25 with the client scale of 25 in a Solomon reference example, and after each experiment iteration is carried out for 500 times, the optimal solution containing shorter paths and more compatible passengers can be obtained when the soft time window is added, and under the condition that the same parameters are set, the utilization rate of the vehicle is more average, the total length of the paths is shorter, and the number of the passengers compatible with the paths is more, so that the multi-target vehicle path planning method with the soft time window is stable and effective when the problem of the multi-target vehicle path planning with the soft time window is solved.

Claims (1)

1. A multi-target vehicle path planning method with soft time windows is characterized by comprising the following steps:
the method comprises the following steps: a trajectory dataset is acquired and N ═ 0, 1., i., m } is used to represent delivery centers and customer points, where i ∈ N,0 ≦ i ≦ m, and i ≦ 0 represents delivery centers, i ≦ 1., m represents m customer points, and (x ≦ 1., m represents m customer points, the trajectory dataset is obtained using (x ≦ 0, 1.,. i.,. m)i,yi) Representing the coordinates of the location of the distribution centre and of the customer points, where the delivery tasks of all the customer points are carried out using a maximum of M vehicles of the same type and a maximum load capacity R, ciExpresses the cargo demand of the ith customer point and satisfies maxci≤R，
Figure FDA0003394407370000011
Representing the maximum number of compatible passengers on a single path, and ensuring that C ≦ R, using [ E ≦ Ri,Li]As time windows for the distribution center and customer sites, where EiIndicating the time, L, at which customer point i was serviced earliestiRepresenting the time of serving a customer point i at the latest, firstly, generating an initial path set PT with the customer scale of S by adopting an optimal point set, wherein the dereferenceable value of S is 25, 50 and 100, and constructing a road network topological graph according to position coordinates of a distribution center and the customer point, the cargo demand, time window information, the maximum compatible passenger number of a vehicle and the like;
step two: using the path length of the solution and the number of compatible passengers on the path as the objective function f1And f2Wherein f is1The definition domain is the shortest distance from the starting point to the end point of a single path in the road network, f2The definition domain of (a) is R, and the value domains thereof are real numbers, when f is1The smaller the value of (f)2The larger the value of (A), the better the current solution is, and for different solutions T satisfying the constraint condition1And T2And T is1,T2E to PT, solve all satisfied f in PT1(T1)＞f1(T2) And f2(T1)＞f2(T2) Or f is1(T1)＜f1(T2) And f2(T1)＜f2(T2) The method comprises the steps that non-dominant solutions of a pareto dominant relationship are individual, all solved non-dominant solutions are stored in an external non-dominant solution set PT _ set, and a multi-target vehicle path problem model is constructed;
step three: setting a soft time window on the basis of a multi-target vehicle path problem model, namely taking waiting time and delay time which are not caused by the constraint of arrival according to a client point time window as a target function f3And f4Wherein f is3、f4The definition domain of (1) is PT, the value domain is real, and in this case, f is required3And f4The value of (a) is as small as possible, the time for returning the vehicle to the distribution center is ensured not to exceed the time window constraint, so that the current solution is better, for different solutions meeting the soft time window constraint, the non-dominated solution is solved according to the pareto dominance relation, all the solved non-dominated solutions are stored in an external non-dominated solution set PT _ set, and the client scale S, the fixed parameter alpha, the adjusting parameters theta, beta and w are set1、w2And the maximum iteration number, making the iteration number j equal to 1, and starting iteration;
step four: during the jth iteration, randomly selecting a solution from a non-dominated solution set PT _ set to select the next state, accumulating and calculating the state transition probability, judging whether the state transition probability is larger than the current accumulated probability by generating a 0-1 random number, selecting the next available client point, calculating the state transition probability according to a formula (1),
Figure FDA0003394407370000021
wherein tau isuvIs the flow of people on the side (u, v) (. eta.)uvIs a heuristic value, Jk(u) is the set of customers that vehicle k did not serve at point u, tuIs when the vehicle reaches the customer point uM, [ E ]u,Lu]Is the time window of the client point u, then by the parameter w1And w2Comprehensively considering the target, where w1+w2The theta and beta parameters are used to balance the heuristic values with the flow rate, the larger the value the larger the ratio,
Figure FDA0003394407370000022
the method has the functions of calculating the probability of state transition, wherein a part consisting of the pedestrian volume and the heuristic value points to an edge with a shorter path and the maximum number of compatible passengers, and a calculation time window and an overall time part point to an edge conforming to the time window constraint;
step five: at the j-th iteration, randomly selecting the human flow in the solution updating path from the non-dominated solution set PT _ set, namely updating the human flow through a formula (2) and a formula (3),
τuv＝τuv*(1-α) (2)
Figure FDA0003394407370000023
Figure FDA0003394407370000024
equation (3) is determined by the total route of dispatching the optimal vehicle each time, namely the current optimal solution, and adding a penalty value shown in equation (4)
Figure FDA0003394407370000025
The super solution exceeding the global optimal solution is rewarded, and meanwhile, symmetrical processing is adopted in the formula (3), namely when the current customer point in the current optimal path is processed, the symmetrical customer point in the current optimal path is processed at the same time, and the solving time is reduced;
step six: judging a termination condition, wherein the iteration condition is that the current iteration times are larger than the maximum iteration times, if the iteration termination condition is satisfied, the external non-dominated solution set is an optimal solution set of the multi-target vehicle path planning problem with the soft time window, outputting the optimal solution set and stopping iteration; otherwise, let the iteration number j equal to j +1, return to step three.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114489146A (en) * 2022-04-14 2022-05-13 成都慧简联信息科技有限公司 Scheduling and trajectory planning method for multi-unmanned aerial vehicle data collection system

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114489146A (en) * 2022-04-14 2022-05-13 成都慧简联信息科技有限公司 Scheduling and trajectory planning method for multi-unmanned aerial vehicle data collection system

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