Browsing by Author "Dincer, M. Cemali"

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Citation - WoS: 4
Citation - Scopus: 4
Analysis of Transient Throughput Rates of Transfer Lines With Pull Systems
(Springer, 2012) Gokce, M. A.; Dincer, M. Cemali; Örnek, Mustafa Arslan
Transfer lines represent the basic manufacturing system of many high volume production systems. Analysis and understanding of transfer lines are of great importance to improve design and operation performance of many manufacturing systems. Majority of research on the throughput of transfer lines concentrate on the steady state results. Due to the changes in manufacturing environment and increasing importance of JIT and pull systems, many transfer lines now have to changeover to different parts' production quickly, probably most of the time, before enough time passes to reach steady state for a specific configuration. In such situations, steady state may never be reached and hence results relating to the steady state do not make sense. In these situations, one would be more interested in transient behavior of the system. In this study, we offer a novel analytical model for transient throughput analysis of transfer lines. Defining throughput as the number of units produced by a transfer line with buffers per unit time, this chapter shows how to calculate mean and variance of and interval estimates for throughput for a pull type transfer line. Derivation of distribution of transient throughput of transfer lines is presented and sample calculations are provided.
Citation - WoS: 16
Citation - Scopus: 15
Determining optimal treatment rate after a disaster
(Taylor & Francis Ltd, 2014) Kilic, Asli; Dincer, M. Cemali; Gökçe, Mahmut Ali
From the standpoint of medical services, a disaster is a calamitous event resulting in an unexpected number of casualties that exceeds the therapeutic capacities of medical services. In these situations, effective medical response plays a crucial role in saving life. To model medical rescue activities, a two-priority non-preemptive S-server, and a finite capacity queueing system is considered. After constructing Chapman-Kolmogorov differential equations, Pontryagin's minimum principle is used to calculate optimal treatment rates for each priority class. The performance criterion is to minimize both the expected value of the square of the difference between the number of servers and the number of patients in the system, and also the cost of serving these patients over a determined time period. The performance criterion also includes a final time cost related to deviations from the determined value of the desired queue length. The two point boundary value problem is numerically solved for different arrival rate patterns and selected parameters.
Reinforcement Learning in Condition-Based Maintenance: A Survey
(Springer International Publishing AG, 2025) Erdem, Gamze; Dincer, M. Cemali; Fadiloglu, M. Murat
This literature review examines the convergence of Reinforcement Learning (RL) and Condition-Based Maintenance (CBM), emphasizing the transformative impact of RL methodologies on maintenance decision-making in complex industrial settings. By integrating insights from a diverse array of studies, the review critically assesses the use of various RL techniques such as Q-learning, deep reinforcement learning, and policy gradient approaches in forecasting equipment failures, optimizing maintenance schedules, and reducing operational downtime. It outlines the shift from conventional, rule-based maintenance practices to adaptive, data-driven strategies that exploit real-time sensor data and probabilistic modeling. Key challenges highlighted include computational complexity, the extensive training data requirements, and the integration of RL models into existing industrial frameworks. Furthermore, the review explores literature on CBM within multi-component systems, where prevalent approaches include numerical analyses, Markov Decision Processes (MDPs), and case studies, all of which demonstrate notable cost reductions and decreased downtime. Relevant studies were identified through searches on databases such as Google Scholar, Scopus, and Web of Science. Overall, this review provides a comprehensive analysis of the current state and prospects of employing reinforcement learning in conditionbased maintenance, offering valuable insights for both academic researchers and industry practitioners.