Use of artificial intelligence systems in control and traffic flow guidance of the group elevators

Nowadays highrise buildings with multicar elevator systems bring the question of optimal car movements as how to convey the big populations of residences in the most quickest and efficient way. The Elevator Group Controller is a control system that accomplishes the systematic management of two or more elevators so that the elevator system is operated at best balance of energy, time and demand. The objective is that, elevator cars shall be assigned correspondingly in response to hall calls, so as to optimize waiting time, travel time, power consumption, passengers? comfort, etc. Artificial Intelligence presents soft computing options in designing a controller that capable of solving aforementioned numerous objectives. In the engineering design perspective, regulation of average waiting time and travel time with minimum energy consumption is an optimization problem. Since this is a complex problem, intelligent system based methods are chosen to be suitable for the solution of the same. This thesis aims to improve the system performance by applying a hybrid method that comprise of genetic algorithm and artificial neural network. This study simulates 20-floor building with a 4-multicar system and compares with the similiar studies.