Double-deck elevator systems adaptive to traffic flows using Genetic Network Programming

Abstract

Double-deck elevator system (DDES) has been invented firstly as a solution to improve the transportation capacity of elevator group systems in the up-peak traffic pattern. The transportation capacity could be even doubled when DDES runs in a pure up-peak traffic pattern where two connected cages stop at every two floors in an elevator round trip. However, the specific features of DDES make the elevator system intractable when it runs in some other traffic patterns. Moreover, since almost all of the traffic flows vary continuously during a day, an optimized controller of DDES is required to adapt the varying traffic flow. In this paper, we have proposed a controller adaptive to traffic flows for DDES using genetic network programming (GNP) based on our past studies in this field, where the effectiveness of DDES controller using GNP has been verified in three typical traffic patterns. A traffic flow judgment part was introduced into the GNP framework of DDES controller, and the different parts of GNP were expected to be functionally localized by the evolutionary process to make the appropriate cage assignment in different traffic flow patterns. Simulation results show that the proposed method outperforms a conventional approach and two heuristic approaches in a varying traffic flow during the work time of a typical office building.