A Double-Deck Elevator Systems Controller with Idle Cage Assignment Algorithm Using Genetic Network Programming
Shingo Mabu, Lu Yu, Jin Zhou, Shinji Eto,
and Kotaro Hirasawa
Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan
So far, many studies on Double-Deck Elevator Systems (DDES) have been done for exploring more efficient algorithms to improve the system transportation capacity, especially in a heavy traffic mode. The main idea of these algorithms is to decrease the number of stops during a round trip by grouping the passengers with the same destination as much as possible. Unlike what occurs in this mode, where all cages almost always keep moving, there is the case, where some cages become idle in a light traffic mode. Therefore, how to dispatch these idle cages, which is seldom considered in the heavy traffic mode, becomes important when developing the controller of DDES. In this paper, we propose a DDES controller with idle cage assignment algorithm embedded using Genetic Network Programming (GNP) for a light traffic mode, which is based on a timer and event-driven hybrid model. To verify the efficiency and effectiveness of the proposed method, some experiments have been done under a special down-peak pattern where passengers emerge especially at the 7th floor. Simulation results show that the proposed method improves the performance compared with the case when the cage assignment algorithm is not employed and works better than six other heuristic methods in a light traffic mode.
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