Research On Optimal Scheduling Technology Of Multi-Car EGCS Using Particle Swarm Optimization

With the increasing of high-rise or ultrahigh buildings,people become more and more demanding with vertical traffic in buildings for high efficiency,energy conservation and taking up as little building space as possible.But once the traffic flow increases to a certain extent,the operating efficiency of the traditional single-car elevator group control system(EGCS)will be hard to be improved,and increasing the number of elevators will be the plausible way which increases the footprint taken up by the shafts.It is of great importance to develop new elevator systems structure and related scheduling technology to solve the bottleneck problem of the traditional single-car elevator system.Therefore,the optimal scheduling technology of multi-car EGCS is the current research highlight.The research of optimal scheduling of EGCS is conducted based on the concept that plural cars operate in a single elevator shaft.Firstly,an elevator traffic pattern recognition method with SOM based fuzzy BP neural network is proposed to identify elevator traffic pattern,Secondly,based on the traffic characteristics of different traffic patterns,the optimal scheduling strategies of MCES based on an improved particle swarm optimization algorithm are presented to solve the dispatching problems of multi-car EGCS.The main work of this dissertation is as follows:(1)Based on the related literatures,the research objective is determined by analyzing research quo at home and abroad.The existing scheduling algorithms are summarized based on analyzing the deficiencies of the traditional EGCS scheduling techniques employed in current single car elevator system.In connection with the structural characteristics of MECS,the research program of in improved particle swarm optimization based scheduling technology of EGCS of MCES is formulated.(2)The characteristics of the EGCS,including uncertainty,nonlinearity,disturbance,multiple objectives and incompleteness of the information,are analyzed,and based on which the intensive study of group control of MCES is conducted relating the characteristics of MCES,modeling of the EGCS,performance indexes,the group control strategies and so on.(3)The importance of recognizing elevator traffic pattern to scheduling EGCS is expounded.An elevator traffic pattern recognition method with SOM based fuzzy BP neural network is proposed to identify elevator traffic pattern,and verified by MATLAB simulation which shows that the method is effective and lays the foundation for the implementation of multi-car elevator group control strategy.(4)An optimal scheduling method for MCES based on an improved particle swarm optimization algorithm is presented with the goal of passenger waiting time,riding time and long waiting percent,energy consumption,etc.Considering the feature of MCES,particle swarm optimization based optimal scheduling strategy is established.The MATLAB based simulation verification of the presented method is conducted showing that the method is valid for the optimal scheduling of MECS.(5)Summary of the research and the future work prospects.