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251 results about "Decision points" patented technology

Cooperative optimization and control method for mixed traffic flow of expressway

The invention discloses a cooperative optimization and control method for a mixed traffic flow of an expressway. The cooperative optimization and control method for the mixed traffic flow of the expressway is applied to vehicle optimal control under a merging scene of a ramp of the expressway. The cooperative optimization and control method for the mixed traffic flow of the expressway comprises the steps of predicting a vehicle track by using a microscopic vehicle following model; determining an optimization and control target vehicle; determining an optimization track section of a controllable vehicle; controlling a decision point and an admissible state set under each time; and carrying out optimization and control on the vehicle track. The cooperative optimization and control method forthe mixed traffic flow of the expressway has the beneficial effects that a microscopic traffic flow simulation environment based on the microscopic vehicle following model is established, and trafficeffects of different traffic states and different automatic driving vehicle permeability are analyzed; a brand-new cooperative merging model is proposed on the basis of the microscopic following model, traffic characteristics, geometric constraint and security constraint of the expressway are considered, and a cooperative merging problem is concluded into an optimization and control problem aboutdiscrete time state constraint; and a solving method based on dynamic programming is proposed to effectively solve the problem.
Owner:SOUTHWEST JIAOTONG UNIV

Production-data-driven dynamic job-shop scheduling rule intelligent selection method

ActiveCN107767022ATimely and accurate dynamic responseScheduling results are excellentGenetic modelsForecastingOptimal schedulingJob shop scheduling
The invention provides a production-data-driven dynamic job-shop scheduling rule intelligent selection method and belongs to the manufacturing enterprise job shop production planning and scheduling application field. The method mainly comprises the following steps: introducing a Multi-Pass algorithm simulation mechanism, establishing a job-shop production scheduling simulation platform, and generating production planning and scheduling sample data; screening the obtained sample data and generating a scheduling parameter set; designing BP neural network models for scheduling knowledge learningunder different scheduling targets; optimizing training of the BP neural networks through a new firefly algorithm to obtain NFA-BP models; integrating the NFA-BP models under various scheduling targets into an intelligent scheduling module, which is integrated with a job shop MES system to guide on-line scheduling; manually adjusting online production planning and scheduling deviation and updatingthe scheduling parameter set, and the intelligent scheduling module carrying out online optimization learning; and the intelligent scheduling module adapted to real workshop production status outputting optimal scheduling rules according to current job conflict decision points.
Owner:CHONGQING UNIV OF POSTS & TELECOMM
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