2023
doi: 10.12720/jait.14.5.1124-1131
Sequential Decision Making for Elevator Control
Emre Oner Tartan1,*
,
Cebrail Ciflikli2
- 1Vocational School of Technical Sciences, Baskent University, Ankara, Turkey
- 2Vocational School of Technical Sciences, Kayseri University, Kayseri, Turkey
- Manuscript receivedJanuary 20, 2023
- revisedFebruary 25, 2023
- acceptedMarch 31, 2023
- publishedOctober 26, 2023
Abstract
In the last decade Reinforcement Learning (RL) has significantly changed the conventional control paradigm in many fields. RL approach is spreading with many applications such as autonomous driving and industry automation. Markov Decision Process (MDP) forms a mathematical idealized basis for RL if the explicit model is available. Dynamic programming allows to find an optimal policy for sequential decision making in a MDP. In this study we consider the elevator control as a sequential decision making problem, describe it as a MDP with finite state space and solve it using dynamic programming. At each decision making time step we aim to take the optimal action to minimize the total of hall call waiting times in the episodic task. We consider a sample 6-floor building and simulate the proposed method in comparison with the conventional Nearest Car Method (NCM).
Keywords
- elevator control
- Markov decision process
- dynamic programming
- optimal policy
- sequential decision making
How to Cite
Emre Oner Tartan and Cebrail Ciflikli, "Sequential Decision Making for Elevator Control," Journal of Advances in Information Technology, vol. 14, no. 5, pp. 1124-1131, 2023. https://doi.org/10.12720/jait.14.5.1124-1131
Copyright & License
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
