GA based hybrid fuzzy rule optimization approach for elevator group control system

Elevators are the essential transportation tools in high buildings so that Elevator Group Control System (EGCS) is developed to dynamically layout the schedule of elevators in a group. In this study, a fuzzy rules based intelligent elevator group control system has been proposed in which the structure of rules including the related parameters are generated optimally based on the traffic data so as to maximize service quality. In literature, the fuzzy related approaches have been applied in EGCS but the fuzzy rules were all pre-defined. However, how to create the most suitable fuzzy rule set in EGCS for dispatching elevators more efficiently and economically are never discussed in literature. The aim of the proposed approach is to minimize the average waiting time at peak hours as well as to minimize the power energy at off-peak hours by using the proposed fuzzy rule based ECGS. Moreover, there are many decision variables are considered in the GCGS to provide the most appropriate elevator assignment whenever any hall call is given. In this study, a fuzzy rule based elevator-dispatching approach has been proposed for the EGCS in which the fuzzy rules and related parameters are derived optimally by using genetic algorithm based on the historical elevator transportation data. The experimental results show that the performance of the proposed approach is superior to these of traditional approaches in literatures.