TY - GEN
T1 - Service area-based elevator group supervisory control system using GNP with RL
AU - Zhou, Jin
AU - Yu, Lu
AU - Mabu, Shingo
AU - Hirasawa, Kotaro
AU - Hu, Jinglu
AU - Markon, Sandor
PY - 2006/12/1
Y1 - 2006/12/1
N2 - Genetic Network Programming (GNP) was proposed several years ago as a new evolutionary computation method. Its unique features, such as highly compact structure, potential memory function, etc, are verified by many studies mainly on virtual world problems. Recently, GNP is also applied to some complicated real world problems like Elevator Group Supervisory Control Systems (EGSCS) and stock price prediction systems. As we know, EGSCS is a very large scale stochastic dynamic optimization problem. Due to its vast state space, significant uncertainty and numerous resource constraints such as finite car capacities and registered hall/car calls, it is hard to manage EGSCS using conventional control methods. In this paper, we propose an enhanced algorithm of EGSCS using GNP with Reinforcement Learning (RL) where an importance weight tuning method and a car assignment policy based on service area are introduced.
AB - Genetic Network Programming (GNP) was proposed several years ago as a new evolutionary computation method. Its unique features, such as highly compact structure, potential memory function, etc, are verified by many studies mainly on virtual world problems. Recently, GNP is also applied to some complicated real world problems like Elevator Group Supervisory Control Systems (EGSCS) and stock price prediction systems. As we know, EGSCS is a very large scale stochastic dynamic optimization problem. Due to its vast state space, significant uncertainty and numerous resource constraints such as finite car capacities and registered hall/car calls, it is hard to manage EGSCS using conventional control methods. In this paper, we propose an enhanced algorithm of EGSCS using GNP with Reinforcement Learning (RL) where an importance weight tuning method and a car assignment policy based on service area are introduced.
KW - Elevator group supervisory control system
KW - Genetic network programming
KW - Importance weight
KW - Reinforcement learning
KW - Service area
UR - http://www.scopus.com/inward/record.url?scp=34250739426&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34250739426&partnerID=8YFLogxK
U2 - 10.1109/SICE.2006.315839
DO - 10.1109/SICE.2006.315839
M3 - Conference contribution
AN - SCOPUS:34250739426
SN - 8995003855
SN - 9788995003855
T3 - 2006 SICE-ICASE International Joint Conference
SP - 5967
EP - 5972
BT - 2006 SICE-ICASE International Joint Conference
T2 - 2006 SICE-ICASE International Joint Conference
Y2 - 18 October 2006 through 21 October 2006
ER -