Abstract
Elevator Group Supervisory Control System (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. Recently, many solutions for EGSCS using Artificial Intelligence (AI) technologies have been reported. Genetic Network Programming (GNP), which is proposed as a new evolutionary computation method several years ago, is also proved to be efficient when applied to EGSCS problem. In this paper, we propose an extended algorithm for EGSCS by introducing Reinforcement Learning (RL) into GNP framework, and expect to make an improvement of the EGSCS' performances since the efficiency of GNP with RL has been clarified in some other studies like tile-world problem. Simulation tests using traffic flows in a typical office building have been made, and the results show an actual improvement of the EGSCS' performances comparing to the algorithms using original GNP and conventional control methods.
| Original language | English |
|---|---|
| Title of host publication | 2006 IEEE Congress on Evolutionary Computation, CEC 2006 |
| Pages | 3035-3041 |
| Number of pages | 7 |
| Publication status | Published - 2006 |
| Event | 2006 IEEE Congress on Evolutionary Computation, CEC 2006 - Vancouver, BC Duration: 2006 Jul 16 → 2006 Jul 21 |
Other
| Other | 2006 IEEE Congress on Evolutionary Computation, CEC 2006 |
|---|---|
| City | Vancouver, BC |
| Period | 06/7/16 → 06/7/21 |
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ASJC Scopus subject areas
- Artificial Intelligence
- Software
- Theoretical Computer Science
Cite this
A study of applying genetic network programming with reinforcement learning to elevator group supervisory control system. / Zhou, Jin; Eguchi, Toru; Mabu, Shingo; Hirasawa, Kotaro; Furuzuki, Takayuki; Markon, Sandor.
2006 IEEE Congress on Evolutionary Computation, CEC 2006. 2006. p. 3035-3041 1688692.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A study of applying genetic network programming with reinforcement learning to elevator group supervisory control system
AU - Zhou, Jin
AU - Eguchi, Toru
AU - Mabu, Shingo
AU - Hirasawa, Kotaro
AU - Furuzuki, Takayuki
AU - Markon, Sandor
PY - 2006
Y1 - 2006
N2 - Elevator Group Supervisory Control System (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. Recently, many solutions for EGSCS using Artificial Intelligence (AI) technologies have been reported. Genetic Network Programming (GNP), which is proposed as a new evolutionary computation method several years ago, is also proved to be efficient when applied to EGSCS problem. In this paper, we propose an extended algorithm for EGSCS by introducing Reinforcement Learning (RL) into GNP framework, and expect to make an improvement of the EGSCS' performances since the efficiency of GNP with RL has been clarified in some other studies like tile-world problem. Simulation tests using traffic flows in a typical office building have been made, and the results show an actual improvement of the EGSCS' performances comparing to the algorithms using original GNP and conventional control methods.
AB - Elevator Group Supervisory Control System (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. Recently, many solutions for EGSCS using Artificial Intelligence (AI) technologies have been reported. Genetic Network Programming (GNP), which is proposed as a new evolutionary computation method several years ago, is also proved to be efficient when applied to EGSCS problem. In this paper, we propose an extended algorithm for EGSCS by introducing Reinforcement Learning (RL) into GNP framework, and expect to make an improvement of the EGSCS' performances since the efficiency of GNP with RL has been clarified in some other studies like tile-world problem. Simulation tests using traffic flows in a typical office building have been made, and the results show an actual improvement of the EGSCS' performances comparing to the algorithms using original GNP and conventional control methods.
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M3 - Conference contribution
SN - 0780394879
SN - 9780780394872
SP - 3035
EP - 3041
BT - 2006 IEEE Congress on Evolutionary Computation, CEC 2006
ER -