Browsing by Author "Kurbanzade, Ali Kaan"

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Aid allocation for camp-based and urban refugees with uncertain demand and replenishments
(Wiley, 2021-12) Azizi, S.; Bozkır, Cem Deniz Çağlar; Trapp, A. C.; Kundakcıoğlu, Ömer Erhun; Kurbanzade, Ali Kaan; Industrial Engineering; KUNDAKCIOĞLU, Ömer Erhun; Bozkır, Cem Deniz Çağlar; Kurbanzade, Ali Kaan
There are 26 million refugees worldwide seeking safety from persecution, violence, conflict, and human rights violations. Camp-based refugees are those that seek shelter in refugee camps, whereas urban refugees inhabit nearby, surrounding populations. The systems that supply aid to refugee camps may suffer from ineffective distribution due to challenges in administration, demand uncertainty and volatility in funding. Aid allocation should be carried out in a manner that properly balances the need of ensuring sufficient aid for camp-based refugees, with the ability to share excess inventory, when available, with urban refugees that at times seek nearby camp-based aid. We develop an inventory management policy to govern a camp's sharing of aid with urban refugee populations in the midst of uncertainties related to camp-based and urban demands, and replenishment cycles due to funding issues. We use the policy to construct costs associated with: (i) referring urban populations elsewhere, (ii) depriving camp-based refugee populations, and (iii) holding excess inventory in the refugee camp system. We then seek to allocate aid in a manner that minimizes the expected overall cost to the system. We propose two approaches to solve the resulting optimization problem, and conduct computational experiments on a real-world case study as well as on synthetic data. Our results are complemented by an extensive simulation study that reveals broad support for our optimal thresholds and allocations to generalize across varied key parameters and distributions. We conclude by presenting related discussions that reveal key managerial insights into humanitarian aid allocation under uncertainty.
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Capacity planning for effective cohorting of hemodialysis patients during the coronavirus pandemic: A case study
(Elsevier, 2023-01-01) Bozkır, Cem Deniz Çağlar; Özmemiş, Çağrı; Kurbanzade, Ali Kaan; Koyuncu, Burcu Balçık; Gunes, E. D.; Tuglular, S.; Industrial Engineering; KOYUNCU, Burcu Balçık; Bozkır, Cem Deniz Çağlar; Özmemiş, Çağrı; Kurbanzade, Ali Kaan
Planning treatments of different types of patients have become challenging in hemodialysis clinics during the COVID-19 pandemic due to increased demands and uncertainties. In this study, we address capacity planning decisions of a hemodialysis clinic, located within a major public hospital in Istanbul, which serves both infected and uninfected patients during the COVID-19 pandemic with limited resources (i.e., dialysis machines). The clinic currently applies a 3-unit cohorting strategy to treat different types of patients (i.e., uninfected, infected, suspected) in separate units and at different times to mitigate the risk of infection spread risk. Accordingly, at the beginning of each week, the clinic needs to allocate the available dialysis machines to each unit that serves different patient cohorts. However, given the uncertainties in the number of different types of patients that will need dialysis each day, it is a challenge to determine which capacity configuration would minimize the overlapping treatment sessions of different cohorts over a week. We represent the uncertainties in the number of patients by a set of scenarios and present a stochastic programming approach to support capacity allocation decisions of the clinic. We present a case study based on the real-world patient data obtained from the hemodialysis clinic to illustrate the effectiveness of the proposed model. We also compare the performance of different cohorting strategies with three and two patient cohorts.