The modelling and simulation of elevator group control sytems for public service buildings

Elevator traffic control systems have become more and more complicated due to their nature of intelligence. Artificial intelligence methods employing neural networks have been proved to be successful in many fields, such as process modeling, pattern recognition and classification problems. They have also been applied to basic problems in elevator traffic control systems, such as the prediction and control of elevator movements In particular, neural networks can offer better solutions to the passenger call allocation process when compared to the classical traffic control methods. Elevator control algorithms utilizing neural networks aims at distributing the most suitable cars to the floors by considering the passenger service demand. Neural networks can dynamically learn the behavior of an elevator system and predict the next floors to stop, based on what has been learnt. In this paper the neural network approach has been applied to Duplex/Tnplex group control systems for improving passenger waiting time and a lift simulation software has been developed and implemented in order to assess the learning capability by measuring the performance of the control algorithm. The lift traffic analysis have been carried out by examining the simulation results obtained.