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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only 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; Çınar, Ahmet; Salman, Fatma Sibel; Parçaoğlu, Mert; Department of Industrial Engineering; ; 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.Publication Metadata only 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; Çınar, Ahmet; Salman, Fatma Sibel; Parçaoğlu, Mert; PhD Student; Faculty Member; Master Student; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; Graduate School of Sciences and Engineering; N/A; 178838; N/AWe 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.Publication Metadata only Effects of system parameters on the optimal cost and policy in a class of multidimensional queueing control problems(The Institute for Operations Research and the Management Sciences (INFORMS), 2018) Vercraene, Samuel; Gayon, Jean-Philippe; Department of Industrial Engineering; Karaesmen, Fikri; Faculty Member; Department of Industrial Engineering; College of Engineering; 3579We consider a class of Markov Decision Processes frequently employed to model queueing and inventory control problems. For these problems, we explore how changes in different system input parameters (transition rates, costs, discount rates etc.) affect the optimal cost and the optimal policy when the state space of the problem is multidimensional. To address a large class of problems, we introduce two generic dynamic programming operators to model different types of controlled events. For these operators, we derive sufficient conditions to propagate monotonicity and supermodularity properties of the value function. These properties allow to predict how changes in system input parameters affect the optimal cost and policy. Finally, we explore the case when several parameters are changed at the same time.Publication Metadata only Shift scheduling in call centers with multiple skill sets and transportation costs(IEEE, 2007) Emil, Emre; Department of Industrial Engineering; Department of Industrial Engineering; Örmeci, Lerzan; Salman, Fatma Sibel; Faculty Member; Faculty Member; Department of Industrial Engineering; College of Engineering; College of Engineering; 32863; 178838Workforce plans in call centers, mostly working 24 hours a day and 7 days a week, have to sAtışfy both custÖmer service levels and personnel constraints. Moreover, in large metropolitans such as Istanbul, call centers provide the transportation of the staff, so that shuttle costs constitute a major part of the total operational costs. We present a mathematical model which minimizes the transportation costs while sAtışfying service level and personnel constraints. We test our model with data from call centers.Publication Metadata only Modeling customer reactions to sales attempts: if cross-selling backfires(Sage Publications Inc, 2010) Department of Business Administration; Department of Business Administration; Department of Industrial Engineering; N/A; Güneş, Evrim Didem; Karaesmen, Zeynep Akşin; Örmeci, Lerzan; Özden, S. Hazal; Faculty Member; Faculty Member; Faculty Member; Other; Department of Business Administration; Department of Industrial Engineering; College of Administrative Sciences and Economics; College of Administrative Sciences and Economics; College of Engineering; N/A; 51391; 4534; 32863; N/ACross-selling attempts, based on estimated purchase probabilities, are not guaranteed to succeed and such failed attempts may annoy customers. There is a general belief that cross-selling may backfire if not implemented cautiously, however, there is not a good understanding of the nature and impact of this negative reaction or appropriate policies to counter-balance it. This article focuses on this issue and develops a modeling framework that makes use of a Markov decision model to account for negative customer reactions to failed sales attempts and the effect of past contacts in managing cross-selling initiatives. Three models are analyzed, where purchase probabilities are affected from customer maturity, the number of failed attempts since the last purchase, or both. The analysis shows that customer reactions to cross-sell attempts make the purchase probabilities endogenous to the firm's cross-selling decisions; hence, the optimal cross-selling policy becomes a function of customer state. The results highlight the role that the cost of excessive cross-selling (direct as well as in the form of customer reactions) plays in optimal policies. Cross-sell data from a retail bank illustrate in what context the modeling framework can be applied and underline the importance of customizing cross-sell policies to individual customers.Publication Metadata only Itinerary-based nesting control with upsell(Palgrave Macmillan Ltd, 2016) Pun, Chan Seng; Klabjan, Diego; Shebalov, Sergey; Department of Industrial Engineering; Karaesmen, Fikri; Faculty Member; Department of Industrial Engineering; College of Engineering; 3579In order to accept future high-yield booking requests, airlines protect seats from low-yield passengers. More seats may be reserved when passengers faced with closed fare classes can upsell to open higher fare classes. We address the airline revenue management problem with capacity nesting and customer upsell, and formulate this problem by a stochastic optimization model to determine a set of static protection levels for each itinerary. We apply an approximate dynamic programming framework to approximate the objective function by piecewise linear functions, whose slopes (marginal revenue) are iteratively updated and returned by an efficient heuristic that simultaneous handles both nesting and upsells. The resulting allocation policy is tested over a real airline network and benchmarked against the randomized linear programming bid-price policy under various demand settings. Simulation results suggest that the proposed allocation policy significantly outperforms when incremental demand or upsell probability are high. Structural analyses are also provided for special demand dependence cases.