CN112163788A - Real-time data-based scheduling method for Internet pile-free single vehicle - Google Patents
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Abstract
The invention relates to the technical field of information data, in particular to an internet pile-free single-vehicle scheduling method based on real-time data, wherein in a plurality of scheduling areas, standard single-vehicle quantity values of each scheduling area are obtained based on the real-time data, real-time position data of a single vehicle are obtained through the internet, and when the single vehicle scheduling needs to be carried out on a target scheduling area, the number of the single vehicles needing to be scheduled in or out of each scheduling area is firstly counted based on a genetic algorithm; and (3) taking the current single vehicle information data of each scheduling area as an initial population, iterating to preset iteration times through a genetic algorithm, obtaining an optimal scheduling strategy of the target scheduling area after comprehensive evaluation, and then taking the target scheduling area as a scheduling center to execute single vehicle scheduling according to the optimal scheduling strategy. The invention can generate the scheduling path by calling algorithms in different time windows, quickly obtain the optimal scheduling strategy in a short time, effectively reduce the scheduling cost of the shared bicycle and improve the scheduling efficiency of the shared bicycle.
Description
Technical Field
The invention relates to the technical field of information data, in particular to a real-time data-based scheduling method for an internet pile-free single vehicle.
Background
Genetic Algorithm (GA) was originally proposed by John holland in the united states in the 70 th 20 th century, and was designed according to the rules of organism evolution in nature, and is a computational model of the biological evolution process that simulates the natural selection and Genetic mechanism of darwinian biological evolution theory, and is a method for searching for an optimal solution by simulating the natural evolution process. The algorithm converts the solving process of the problem into the processes of crossover, variation and the like of chromosome genes in the similar biological evolution by a mathematical mode and by utilizing computer simulation operation. When a complex combined optimization problem is solved, a better optimization result can be obtained faster compared with some conventional optimization algorithms. Genetic algorithms have been widely used by people in the fields of combinatorial optimization, machine learning, signal processing, adaptive control, artificial life, and the like.
With the arrival of sharing economy, sharing single cars develop rapidly and become important transportation means for people to go out. The shared bicycle refers to the fact that an enterprise provides bicycle and bicycle sharing services in a campus, a subway station, a bus station, a residential area, a commercial area, a public service area and the like, and the shared bicycle is in a time-sharing rental mode and is novel environment-friendly and economical to share. Urban travelers use a shared bicycle to travel in the traveling process, the traveling requirement of the last kilometer is met, and meanwhile, new problems are brought: if travelers use shared vehicles to arrive at bus stations and subway stations for public transportation transfer in the morning and the situation is opposite when the travelers leave work, the demand of the travelers in residential districts in the morning is increased, the number of the actually usable shared vehicles is reduced sharply, and meanwhile, the data of the shared vehicles at the bus stations and the subway stations are increased, so that a large amount of public resources are occupied. The phenomenon of random parking and random placement also exists, and the urban public space land is greatly wasted.
Therefore, in order to dispatch the shared bicycle more reasonably and efficiently and realize the balance of supply and demand of the shared bicycle, the invention provides the Internet pile-free bicycle dispatching method based on real-time data, which is simple in method and convenient to use.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an internet pile-free single-vehicle scheduling method based on real-time data, which can generate scheduling paths by calling algorithms at different time windows, quickly obtain an optimal scheduling strategy in a short time, effectively reduce the scheduling cost of shared single vehicles, improve the scheduling efficiency of the shared single vehicles, meet the scheduling requirements of the shared single vehicles in different areas and at different moments and effectively solve the problem of supply and demand balance of the shared single vehicles.
The purpose of the invention is realized by the following technical scheme:
a real-time data-based scheduling method for Internet pile-free single vehicles is characterized in that standard single vehicle quantity values of each scheduling area are set in a plurality of scheduling areas, real-time position data of single vehicles are obtained through the Internet, and when the single vehicles need to be scheduled in a target scheduling area, the single vehicle scheduling is completed through the following steps based on a genetic algorithm:
s1, counting the number of the single vehicles entering each dispatching area and the number of the single vehicles leaving each dispatching area, and calculating to obtain the number of the single vehicles needing to be dispatched to or dispatched from each dispatching area;
s2, using the current bicycle information data of each scheduling area as an initial population, based on a genetic algorithm, regarding each generation of population, using a target scheduling area as a scheduling center, randomly simulating and executing scheduling strategies, simultaneously calculating the length of a scheduling path of each scheduling strategy, using the length of the scheduling path as a first evaluation scale, calculating the scheduling satisfaction degree of each scheduling strategy, using the scheduling satisfaction degree as a second evaluation scale, then respectively inheriting the optimal scheduling strategies under respective evaluation of the first evaluation scale and the second evaluation scale in the previous population to the next generation of population, arranging the optimal scheduling strategies in the previous population in the previous generation, simultaneously regenerating the next generation of population according to the crossing rate and the variation rate, and keeping the number of the scheduling strategies unchanged;
s3, iterating to a preset iteration number, and obtaining an optimal scheduling strategy of the target scheduling area by adopting a comprehensive evaluation method;
and S4, taking the target scheduling area as a scheduling center, and executing the bicycle scheduling according to the optimal scheduling strategy.
Further, for genetic algorithms, the population size is set to an even number.
Further, the preset value of the iteration times for the genetic algorithm is 100-1000. The iteration times refer to the calculation algebra with the position information of each bicycle as an initial point.
Further, the preset value of the mutation rate is 0.01-0.1 for the genetic algorithm.
Further, for the genetic algorithm, the preset value of the cross rate was 0.8. In genetic algorithms, two solutions are selected from a solution set with a certain probability, which is the crossover probability, to be crossed over to generate a new solution.
Further, in step S3, the optimal scheduling policy of the final population is evaluated by using a comprehensive evaluation method, whether the optimal scheduling policy meets the requirements is evaluated, if the optimal scheduling policy meets the requirements, step S4 is performed, and if the optimal scheduling policy does not meet the requirements, the final population is used as the initial population, and the step S2 is returned to perform genetic iteration again.
Further, in step S2, the scheduling policy is to schedule the single cars in the scheduling area that need to be dispatched to the single cars in the scheduling area that need to be dispatched.
Further, the shorter the scheduling path length, the better the scheduling policy.
Further, the scheduling policy with higher degree of satisfaction of the scheduling is better.
Further, when the target scheduling area is not designated, each scheduling area is respectively used as a target scheduling area scheduling center, the steps S1 to S3 are sequentially executed, the optimal scheduling policy when each area is used as the target scheduling area scheduling center is calculated, the optimal target scheduling area scheduling center and the optimal scheduling policy are obtained by adopting a comprehensive evaluation method, and then the step S4 is executed. When the target dispatching area dispatching center is not appointed, the optimal target dispatching area dispatching center and the optimal dispatching strategy are selected from all dispatching areas by the method, and the site selection of the dispatching center is realized.
Further, the dispatching path length refers to the shortest distance between the position of the bicycle and the target dispatching area.
The invention has the beneficial effects that: the invention relates to an internet pile-free single vehicle scheduling method based on real-time data, which comprises the steps of taking single vehicle information data of each current scheduling area as an initial population, iterating to preset iteration times through a genetic algorithm, obtaining an optimal scheduling strategy and an optimal scheduling center of a target scheduling area after comprehensive evaluation, selecting the optimal scheduling center in the target area to select a site, obtaining the optimal scheduling strategy, meanwhile, also specifying the target scheduling area as a scheduling center, obtaining the optimal strategy of a specific scheduling center after comprehensive evaluation, and executing single vehicle scheduling according to the optimal scheduling strategy; in addition, the scheduling paths can be generated by calling algorithms in different time windows, an optimal scheduling strategy can be quickly obtained in a short time, the scheduling cost of the shared bicycle is effectively reduced, the scheduling efficiency of the shared bicycle is improved, the scheduling requirements of the shared bicycle in different areas and at different moments are met, and the problem of shared supply and demand of the shared bicycle is effectively solved.
Drawings
Fig. 1 is a schematic flow chart of the non-pile single vehicle scheduling method of the present invention.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.
As shown in fig. 1, in a scheduling method of an internet pile-free single vehicle based on real-time data, standard single vehicle quantity values of each scheduling area are set in a plurality of scheduling areas, real-time position data of the single vehicle is obtained through the internet, and when the single vehicle scheduling needs to be performed on a target scheduling area, the single vehicle scheduling is completed through the following steps based on a genetic algorithm:
s1, counting the number of the single vehicles entering each dispatching area and the number of the single vehicles leaving each dispatching area, and calculating to obtain the number of the single vehicles needing to be dispatched to or dispatched from each dispatching area;
s2, using the current bicycle information data of each scheduling area as an initial population, based on a genetic algorithm, regarding each generation of population, using a target scheduling area as a scheduling center, randomly simulating and executing scheduling strategies, simultaneously calculating the length of a scheduling path of each scheduling strategy, using the length of the scheduling path as a first evaluation scale, calculating the scheduling satisfaction degree of each scheduling strategy, using the scheduling satisfaction degree as a second evaluation scale, then respectively inheriting the optimal scheduling strategies under respective evaluation of the first evaluation scale and the second evaluation scale in the previous population to the next generation of population, arranging the optimal scheduling strategies in the previous population in the previous generation, simultaneously regenerating the next generation of population according to the crossing rate and the variation rate, and keeping the number of the scheduling strategies unchanged;
s3, iterating to a preset iteration number, and obtaining an optimal scheduling strategy of the target scheduling area by adopting a comprehensive evaluation method;
and S4, taking the target scheduling area as a scheduling center, and executing the bicycle scheduling according to the optimal scheduling strategy.
Specifically, for genetic algorithms, the population size is set to an even number.
Specifically, for the genetic algorithm, the preset value of the iteration times is 100-1000. The iteration times refer to the calculation algebra with the position information of each bicycle as an initial point.
Specifically, for the genetic algorithm, the preset value of the mutation rate is 0.01-0.1.
Specifically, for the genetic algorithm, the preset value of the crossover rate was 0.8. In genetic algorithms, two solutions are selected from a solution set with a certain probability, which is the crossover probability, to be crossed over to generate a new solution.
Specifically, in step S3, the optimal scheduling policy of the final population is evaluated by using a comprehensive evaluation method, whether the optimal scheduling policy meets the requirements is evaluated, if the optimal scheduling policy meets the requirements, step S4 is performed, and if the optimal scheduling policy does not meet the requirements, the final population is used as the initial population, and the step S2 is returned to perform genetic iteration again.
Specifically, in step S2, the scheduling policy is to schedule the single cars in the scheduling area that need to be dispatched to the single cars in the scheduling area that need to be dispatched.
Specifically, the shorter the scheduling path length, the better the scheduling policy.
Specifically, the higher the degree of the scheduling satisfaction, the better the scheduling policy.
Specifically, when the target scheduling area is not designated, each scheduling area is used as a target scheduling area scheduling center, steps S1 to S3 are sequentially performed, an optimal scheduling policy when each area is used as the target scheduling area scheduling center is calculated, an optimal target scheduling area scheduling center and an optimal scheduling policy are obtained by using a comprehensive evaluation method, and then step S4 is performed. When the target dispatching area dispatching center is not appointed, the optimal target dispatching area dispatching center and the optimal dispatching strategy are selected from all dispatching areas by the method, and the site selection of the dispatching center is realized.
Specifically, the comprehensive evaluation method is to measure the sample gap by using a distance scale by using a TOPSIS method, and the index attribute needs to be subjected to the same-direction processing by using the distance scale (if the data of one dimension is larger, the better, and the data of the other dimension is smaller, the better, the scale confusion is caused), specifically,
initial matrix
Wherein X represents a matrix formed by evaluation indexes, the nth row represents the evaluation index result of the nth strategy, specifically the scheduling satisfaction degree and the reciprocal of the scheduling path length; the mth column represents the mth type evaluation index of all measurements; in the patent, m is set to be 2 and corresponds to the reciprocal of the scheduling satisfaction degree and the scheduling path length respectively, and n is set to be the initial population number;
constructing a weighting normalization matrix, and carrying out vector normalization:
the normalized matrix Z is obtained:
determining an optimal solution Z+And the worst case Z-The optimal strategy is formed by the maximum value of each column of elements in the matrix, specifically, the maximum value of the scheduling satisfaction degree column is obtained, and the maximum value of the scheduling path evaluation index is obtained:
the worst scheme is composed of the minimum value of each column of elements in the matrix, specifically, the minimum value of the scheduling satisfaction degree column is obtained, and the minimum value of the scheduling path evaluation index is obtained:
calculating the closeness degree of each evaluation object to the optimal scheme and the worst scheme,
indicating the closeness of the ith evaluation object to the optimal solution,
the approach degree of the ith evaluation object to the worst scheme is represented, in the algorithm, the scheduling satisfaction degree evaluation and the scheduling path evaluation index are considered to be equally important, w is 0.5, and the calculation formula is as follows:
calculating the closeness degree of each evaluation object to the optimal scheme, CiRepresenting the closeness degree of the ith evaluation object to the optimal scheme:
0≤≤Ci≤1，Ci→ 1 indicates evaluation pairThe better the image.
Specifically, the dispatching path length refers to the shortest distance between the position of the single vehicle and the target dispatching area.
When the method is used, firstly, parameters of a genetic algorithm are set, the population size is set to be an even number, the numerical value is set to be 20-100, the preset iteration number is set, the crossing rate is set to be 0.8, the variation rate is set to be 0.01-0.1, and the optimal value is 0.05; setting standard bicycle quantity values of each scheduling area, then acquiring real-time position data of bicycles, counting the number of bicycles entering each scheduling area and the number of bicycles leaving each scheduling area corresponding to a certain moment when the target scheduling area needs to be subjected to bicycle scheduling at the moment, and calculating the number of the bicycles needing to be scheduled in or out of each scheduling area by using the difference value of the two data; taking the current bicycle information data of each scheduling area as an initial population, taking a target scheduling area as a scheduling center for each generation of population based on a genetic algorithm, randomly simulating and executing scheduling strategies, simultaneously calculating the length of a scheduling path of each scheduling strategy, taking the length of the scheduling path as a first evaluation scale, calculating the allocation satisfaction degree of each scheduling strategy, taking the allocation satisfaction degree as a second evaluation scale, then, inheriting the optimal scheduling strategies under the joint evaluation of the first evaluation scale and the second evaluation scale in the previous population to the next generation of population, arranging the optimal scheduling strategies in the front, regenerating the next generation of population according to the crossing rate and the variation rate, and keeping the quantity of the scheduling strategies unchanged;
iterating to a preset iteration number to obtain an optimal scheduling strategy of the target scheduling area; evaluating the optimal scheduling strategy of the final population by adopting a comprehensive evaluation method, evaluating whether the optimal scheduling strategy meets the requirements, if the optimal scheduling strategy meets the requirements, performing step S4, if the optimal scheduling strategy does not meet the requirements, taking the final population as an initial population, and returning to step S2 to perform genetic iteration again; and finally, taking the target scheduling area as a scheduling center, executing single-vehicle scheduling according to an optimal scheduling strategy, meeting the requirement of sharing single-vehicle scheduling among different areas at a specific moment, and effectively solving the problem of sharing supply and demand of sharing single vehicles.
The foregoing is illustrative of the preferred embodiments of this invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the concept as disclosed herein, either as described above or as apparent to those skilled in the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. The Internet pile-free single-vehicle scheduling method based on real-time data is characterized in that standard single-vehicle quantity values of each scheduling area are set in a plurality of scheduling areas, real-time position data of a single vehicle are obtained through the Internet, and when the single-vehicle scheduling is required to be carried out on a target scheduling area, the single-vehicle scheduling is completed through the following steps based on a genetic algorithm:
s1, counting the number of the single vehicles entering each dispatching area and the number of the single vehicles leaving each dispatching area, and calculating to obtain the number of the single vehicles needing to be dispatched to or dispatched from each dispatching area;
s2, using the current bicycle information data of each scheduling area as an initial population, based on a genetic algorithm, regarding each generation of population, using a target scheduling area as a scheduling center, randomly simulating and executing scheduling strategies, simultaneously calculating the length of a scheduling path of each scheduling strategy, using the length of the scheduling path as a first evaluation scale, calculating the scheduling satisfaction degree of each scheduling strategy, using the scheduling satisfaction degree as a second evaluation scale, then respectively inheriting the optimal scheduling strategies under respective evaluation of the first evaluation scale and the second evaluation scale in the previous population to the next generation of population, arranging the optimal scheduling strategies in the previous population in the previous generation, simultaneously regenerating the next generation of population according to the crossing rate and the variation rate, and keeping the number of the scheduling strategies unchanged;
s3, iterating to a preset iteration number, and obtaining an optimal scheduling strategy of the target scheduling area by adopting a comprehensive evaluation method;
and S4, taking the target scheduling area as a scheduling center, and executing the bicycle scheduling according to the optimal scheduling strategy.
2. The real-time data-based scheduling method for Internet stub-free single vehicles according to claim 1, wherein the population size is set to be even for genetic algorithm.
3. The real-time data-based scheduling method for the Internet pile-free single vehicles according to claim 1, wherein the preset value of the iteration number is 100-1000 for the genetic algorithm.
4. The real-time data-based scheduling method for the Internet stub-free single vehicles according to claim 1, wherein the preset value of the variation rate for the genetic algorithm is 0.01-0.1.
5. The real-time data-based scheduling method for Internet stub-free single vehicles according to claim 1, wherein the preset value of the crossing rate is 0.8 for the genetic algorithm.
6. The real-time data-based scheduling method for the Internet single vehicle without the pile is characterized in that in step S3, a comprehensive evaluation method is adopted to evaluate the optimal scheduling strategy of the final population, whether the optimal scheduling strategy meets the requirements is evaluated, if the optimal scheduling strategy meets the requirements, step S4 is carried out, if the optimal scheduling strategy does not meet the requirements, the final population is used as the initial population, and the step S2 is returned to carry out genetic iteration again.
7. The method for dispatching Internet unpopulated single vehicles according to claim 1, wherein in step S2, the dispatching strategy is to dispatch the single vehicle in the dispatching area to be dispatched to the single vehicle in the dispatching area to be dispatched.
8. The real-time data-based scheduling method for the Internet stub-free single vehicle as claimed in claim 1, wherein the shorter the scheduling path length, the better the scheduling policy, and the higher the scheduling satisfaction degree.
9. The real-time data-based scheduling method for the Internet unpopulated single vehicle is characterized in that when a target scheduling area is not specified, each scheduling area is taken as a target scheduling area scheduling center, the steps S1-S3 are sequentially executed, the optimal scheduling strategy when each area is taken as the target scheduling area scheduling center is calculated, the optimal target scheduling area scheduling center and the optimal scheduling strategy are obtained by adopting a comprehensive evaluation method, and the step S4 is executed.
10. The real-time data-based Internet stub-free single vehicle scheduling method as claimed in claim 1, wherein the scheduling path length is the shortest distance between the position of the single vehicle and a target scheduling area.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114626766A (en) * | 2022-05-12 | 2022-06-14 | 深圳市伟创高科电子有限公司 | Shared electric vehicle scheduling method, device, equipment and medium based on big data |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016165392A1 (en) * | 2015-04-17 | 2016-10-20 | 华南理工大学 | Genetic algorithm-based cloud computing resource scheduling method |
| CN108764555A (en) * | 2018-05-22 | 2018-11-06 | 浙江大学城市学院 | A kind of shared bicycle based on Hadoop parks a site selecting method |
| CN109978227A (en) * | 2019-01-30 | 2019-07-05 | 郑州大学 | A kind of shared bicycle Optimization Scheduling based on genetic-ant colony blending algorithm |
| CN110147885A (en) * | 2019-05-07 | 2019-08-20 | 浙江大学城市学院 | A kind of shared bicycle of improved adaptive GA-IAGA parks point distribution method |
| CN110210700A (en) * | 2019-04-19 | 2019-09-06 | 中国科学院遥感与数字地球研究所 | More star dynamic task planing methods of task based access control priority towards emergency response |
| CN111191899A (en) * | 2019-12-23 | 2020-05-22 | 华南理工大学 | Vehicle scheduling method based on region division parallel genetic algorithm |
| CN111582552A (en) * | 2020-04-16 | 2020-08-25 | 浙江大学城市学院 | Shared bicycle parking point distribution method based on multi-target genetic algorithm |
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016165392A1 (en) * | 2015-04-17 | 2016-10-20 | 华南理工大学 | Genetic algorithm-based cloud computing resource scheduling method |
| CN108764555A (en) * | 2018-05-22 | 2018-11-06 | 浙江大学城市学院 | A kind of shared bicycle based on Hadoop parks a site selecting method |
| CN109978227A (en) * | 2019-01-30 | 2019-07-05 | 郑州大学 | A kind of shared bicycle Optimization Scheduling based on genetic-ant colony blending algorithm |
| CN110210700A (en) * | 2019-04-19 | 2019-09-06 | 中国科学院遥感与数字地球研究所 | More star dynamic task planing methods of task based access control priority towards emergency response |
| CN110147885A (en) * | 2019-05-07 | 2019-08-20 | 浙江大学城市学院 | A kind of shared bicycle of improved adaptive GA-IAGA parks point distribution method |
| CN111191899A (en) * | 2019-12-23 | 2020-05-22 | 华南理工大学 | Vehicle scheduling method based on region division parallel genetic algorithm |
| CN111582552A (en) * | 2020-04-16 | 2020-08-25 | 浙江大学城市学院 | Shared bicycle parking point distribution method based on multi-target genetic algorithm |
Non-Patent Citations (1)
| Title |
|---|
| 于德新;张行;王薇;邢雪;刘珩;: "共享单车调度模型及算法研究", 重庆交通大学学报(自然科学版), no. 07, 2 September 2019 (2019-09-02) * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114626766A (en) * | 2022-05-12 | 2022-06-14 | 深圳市伟创高科电子有限公司 | Shared electric vehicle scheduling method, device, equipment and medium based on big data |
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