Teknik Bilimler Meslek Yüksekokulu / Vocational School of Technical Sciences

Permanent URI for this collectionhttps://hdl.handle.net/11727/2031

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Author: search.filters.author.Ciflikli, Cebrailsearch.filters.applied.f.publicationcategory: search.filters.publicationcategory.Makale - Uluslararası Hakemli Dergi

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    Sequential Decision Making for Elevator Control
    (2023) Tartan, Emre Oner; Ciflikli, Cebrail; 0000-0002-5688-4226; JVD-9650-2023
    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).
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    A model for the visualization and analysis of elevator traffic
    (2019) Ciflikli, Cebrail; Tartan, Emre Oner
    Analysis of elevator traffic in high rise buildings is critical to the performance evaluation of elevator group control systems (EGCS). Elevator dispatching methods or parking algorithms in an EGCS can be designed or modified according to analyses of traffic flow. However, interpretation of traffic flow based solely on numerical data may not be explicit and transparent for EGCS experts as well as for other non-expert building administration. In this study, we present a model for visualization and analysis of elevator traffic. First, we present an alternative approach for traffic analysis which we call route visualization. In the proposed approach, we initially decompose elevator traffic into its component parts and investigate each component independently. Then, using superposition of components we obtain a reconstructed model of overall traffic. This modeling approach provides component-based traffic analysis and representation of routes with intensities through data visualization. In the second part we introduce a multi-dimensional analysis of time parameters in ECGS. This approach provides a comparative analysis of several control algorithms such as dispatch or park algorithms for different combinations of traffic components.