Researcher:
Çınar, Ahmet

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Ahmet

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Çınar

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Çınar, Ahmet

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    Publication
    Managing home health-care services with dynamic arrivals during a public health emergency
    (IEEE-Inst Electrical Electronics Engineers Inc, 2024) Araz, Özgür M.; Department of Industrial Engineering; Department of Industrial Engineering; Çınar, Ahmet; Salman, Fatma Sibel; Parçaoğlu, Mert;  ; Graduate School of Sciences and Engineering; College of Engineering;  
    We consider a public health emergency, during which a high number of patients and their varying health conditions necessitate prioritizing patients receiving home health care. Moreover, the dynamic emergence of patients needing urgent care during the day should be handled by rescheduling these patients. In this article, we present a reoptimization framework for this dynamic problem to periodically determine which patients will be visited in which order on each day to maximize the total priority of visited patients and to minimize the overtime for the health-care provider. This optimization framework also aims to minimize total routing time. A mixed-integer programming (MIP) model is formulated and solved at predetermined reoptimization times, to assure that urgent patients are visited within the current day, while visits of others may be postponed, if overtime is not desired or limited. The effectiveness of a schedule is evaluated with respect to several performance metrics, such as the number of patients whose visits are postponed to the next day, waiting time of urgent patients, and required overtime. The MIP-based approach is compared to two practical heuristics that achieve satisfactory performance under a nervous service system by excelling in different criteria. The MIP-based reoptimization approach is demonstrated for a case during the COVID-19 pandemic. We contribute to the home health-care literature by managing dynamic/urgent patient arrivals under a multiperiod setting with prioritized patients, where we optimize different rescheduling objectives via three alternative reoptimization approaches. © 1988-2012 IEEE.
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    Publication
    Managing home health-care services with dynamic arrivals during a public health emergency
    (Institute of Electrical and Electronics Engineers (IEEE), 2022) Araz, Ozgur M.; N/A; Department of Industrial Engineering; N/A; Department of Industrial Engineering; Çınar, Ahmet; Salman, Fatma Sibel; Parçaoğlu, Mert; PhD Student; Faculty Member; Master Student; Graduate School of Sciences and Engineering; College of Engineering; Graduate School of Sciences and Engineering; N/A; 178838; N/A
    We consider a public health emergency, during which a high number of patients and their varying health conditions necessitate prioritizing patients receiving home health care. Moreover, the dynamic emergence of patients needing urgent care during the day should be handled by rescheduling these patients. In this article, we present a reoptimization framework for this dynamic problem to periodically determine which patients will be visited in which order on each day to maximize the total priority of visited patients and to minimize the overtime for the health-care provider. This optimization framework also aims to minimize total routing time. A mixed-integer programming (MIP) model is formulated and solved at predetermined reoptimization times, to assure that urgent patients are visited within the current day, while visits of others may be postponed, if overtime is not desired or limited. The effectiveness of a schedule is evaluated with respect to several performance metrics, such as the number of patients whose visits are postponed to the next day, waiting time of urgent patients, and required overtime. The MIP-based approach is compared to two practical heuristics that achieve satisfactory performance under a nervous service system by excelling in different criteria. The MIP-based reoptimization approach is demonstrated for a case during the COVID-19 pandemic. We contribute to the home health-care literature by managing dynamic/urgent patient arrivals under a multiperiod setting with prioritized patients, where we optimize different rescheduling objectives via three alternative reoptimization approaches.
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    Publication
    Prioritized single nurse routing and scheduling for home healthcare services
    (Elsevier, 2021) Bozkaya, Burçin; N/A; Department of Industrial Engineering; Department of Industrial Engineering; Çınar, Ahmet; Salman, Fatma Sibel; PhD Student; Faculty Member; Graduate School of Sciences and Engineering; College of Engineering; N/A; 178838
    We study a real-life problem in which a nurse is required to check upon patients she is responsible for either by home visits or phone calls. Due to the large number of patients and their varying conditions, she has to select carefully which patients to visit at home for the upcoming days. We propose assigning priorities to patients according to factors such as the last visit time and the severity of their condition so that the priorities of unvisited patients increase exponentially by day. The solution to this problem should simultaneously specify which patients to visit on each day of the planning horizon, as well as the sequence of the visits to the selected patients on each day that obeys patients' time window requests. The objective is to maximize the total priority of the visited patients primarily and to minimize the total traveling time secondarily. After having observed the computational limits of an exact formulation, we develop an Adaptive Large Neighborhood Search (ALNS) algorithm and a matheuristic to generate near optimal solutions for realistic-sized instances. We measure the quality of both algorithms by computing the optimality gaps using upper bounds generated by Lagrangean relaxation. Tests on real-life data show that both algorithms yield high quality solutions, but the matheuristic outperforms ALNS in large instances. On the other hand, the ALNS algorithm provides very short running times, while the running times of the matheuristic increase exponentially with problem size. (C) 2019 Elsevier B.V. All rights reserved.