Public bicycle scheduling model and solving method

Abstract

The invention discloses a public bicycle scheduling model and a solving method. The method comprises the following steps: obtaining the local data of public bicycles, and carrying out the preprocessing; constructing a public bicycle scheduling model in advance according to the preprocessed data; converting the public bicycle scheduling model into a pseudo traveling salesman model; solving the obtained pseudo traveling salesman model by adopting an improved discrete brainstorm optimization algorithm; and outputting a target function value and a scheduling path planning result of the optimal public bicycle scheduling model. The method has the advantages that the problem of infeasible solutions generated by a common brain storm optimization algorithm is effectively eliminated; transport vehicle resources can be reasonably allocated, reasonable scheduling of urban public transport institutions for the transport vehicle resources can be met, and optimization of transport vehicle service paths is achieved.

Description

technical field [0001] The invention relates to a public bicycle dispatching model and a solution method, belonging to the technical field of urban intelligent public transportation systems. Background technique [0002] With the continuous acceleration of the urbanization process, the urban traffic problem is becoming more and more serious, which seriously hinders the development of the city. Public bicycles have developed rapidly in major cities because of their low-carbon environmental protection, convenience and flexibility, and unique short-distance travel advantages. Since public bicycles can achieve "seamless docking" with public transportation, the green transportation mode of "bus + public bicycle" has been favored by the general public. As a result, a new problem of "difficult to rent a car and difficult to change a car" has been brought along, that is, the number of bicycles in some bicycle rental sites is not enough for some time periods, so that users cannot re...

AI Technical Summary

Problems solved by technology

The neural network method needs a large amount of training data, the algorithm converges slowly, and the search ability is not high; the ant colony algorithm has the disadvantages of long search time and easy to fall into local optimum; the genetic algorithm has the disadvantages of slow calculation speed and easy premature convergence.

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