Hybrid heuristic algorithm-based public bicycle dynamic scheduling method

Abstract

The invention discloses a hybrid heuristic algorithm-based public bicycle dynamic scheduling method. The method comprises the following steps of 1, performing region management on stations; 2, obtaining related information of the stations, and dividing a whole working region into multiple small sub-regions, wherein the information comprises coordinates of the stations and calculated demand information, and a scheduling center exists in each sub-region and manages all stations belonging to the same sub-region as the scheduling center; 3, judging whether a scheduling scheme needs to be recalculated or not according to a dynamic scheduling policy by an event manager, and if yes, establishing a static scheduling instance by the event manager, wherein the static scheduling instance comprises departure positions, departure time and basic demand information of transport vehicles; and 4, solving a problem by using an improved variable neighborhood ant colony algorithm. According to the method,a better scheduling scheme can be calculated out; the solving efficiency is higher; and the satisfaction of citizens on use of public bicycles can be improved to the maximum extent through a more cost-saving scheduling scheme.

Description

Technical field

[0001] The invention belongs to the technical field of urban intelligent public transportation systems, and relates to a public bicycle dynamic dispatch method based on a hybrid heuristic algorithm. Background technique

[0002] With the rapid development of cities, the urban population continues to increase, and the number of motor vehicles also increases, making traffic congestion and environmental pollution increasingly serious. Giving full play to the role of urban public bicycles can effectively alleviate these problems. However, at present, there are some problems in the operation of public bicycles, which affect the efficiency of operation. "Difficulty in renting and changing bicycles" is the problem that users have the strongest feedback during bicycle use, that is, some bicycle rental sites have a certain period of time. The number of bicycles is not large enough for users to rent bicycles. There are no parking spaces at certain bicycle sites for some t...

Examples

Embodiment

[0115] In this section, 20 data samples are designed to be used for experiments. The format of the experimental data is shown in Table 2. The experiment requires data to provide information such as the location of the dispatch center, the coordinates of multiple public bicycle stations, the dispatch amount of each station, the corresponding time window for dispatch and the total parking space.

[0116] Table 2 Description of experimental data format

[0117]

[0118] The important parameters of the model and algorithm are set as follows: T co = 21:00, c p =0.6(RMB / KM), t ij =150*d ij , E i =a i -120, f i = B i +120, T ac = 20 (minute), c w = 0.3. The length of the time slice is set to 15 minutes, and the capacity of the transporter is set to carry 30 bicycles. According to time limit rules: f i ≤T ac +now, dynamic demand information will be given in turn.

[0119] The results of the algorithm by testing 20 samples are shown in Table 3. The simulation time for testing is 25 min...

Technical field

[0001] The invention belongs to the technical field of urban intelligent public transportation systems, and relates to a public bicycle dynamic dispatch method based on a hybrid heuristic algorithm. Background technique

[0002] With the rapid development of cities, the urban population continues to increase, and the number of motor vehicles also increases, making traffic congestion and environmental pollution increasingly serious. Giving full play to the role of urban public bicycles can effectively alleviate these problems. However, at present, there are some problems in the operation of public bicycles, which affect the efficiency of operation. "Difficulty in renting and changing bicycles" is the problem that users have the strongest feedback during bicycle use, that is, some bicycle rental sites have a certain period of time. The number of bicycles is not large enough for users to rent bicycles. There are no parking spaces at certain bicycle sites for some t...

Examples

Embodiment

[0115] In this section, 20 data samples are designed to be used for experiments. The format of the experimental data is shown in Table 2. The experiment requires data to provide information such as the location of the dispatch center, the coordinates of multiple public bicycle stations, the dispatch amount of each station, the corresponding time window for dispatch and the total parking space.

[0116] Table 2 Description of experimental data format

[0117]

[0118] The important parameters of the model and algorithm are set as follows: T co = 21:00, c p =0.6(RMB / KM), t ij =150*d ij , E i =a i -120, f i = B i +120, T ac = 20 (minute), c w = 0.3. The length of the time slice is set to 15 minutes, and the capacity of the transporter is set to carry 30 bicycles. According to time limit rules: f i ≤T ac +now, dynamic demand information will be given in turn.

[0119] The results of the algorithm by testing 20 samples are shown in Table 3. The simulation time for testing is 25 min...

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