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KOYUNCU, Burcu Balçık

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Burcu Balçık

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KOYUNCU
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    ArticlePublicationOpen Access
    A machine learning approach to deal with ambiguity in the humanitarian decision-making
    (Wiley, 2023-09) Grass, E.; Ortmann, J.; Koyuncu, Burcu Balçık; Rei, W.; Industrial Engineering; KOYUNCU, Burcu Balçık
    One of the major challenges for humanitarian organizations in response planning is dealing with the inherent ambiguity and uncertainty in disaster situations. The available information that comes from different sources in postdisaster settings may involve missing elements and inconsistencies, which can hamper effective humanitarian decision-making. In this paper, we propose a new methodological framework based on graph clustering and stochastic optimization to support humanitarian decision-makers in analyzing the implications of divergent estimates from multiple data sources on final decisions and efficiently integrating these estimates into decision-making. To the best of our knowledge, the integration of ambiguous information into decision-making by combining a cluster machine learning method with stochastic optimization has not been done before. We illustrate the proposed approach on a realistic case study that focuses on locating shelters to serve internally displaced people (IDP) in a conflict setting, specifically, the Syrian civil war. We use the needs assessment data from two different reliable sources to estimate the shelter needs in Idleb, a district of Syria. The analysis of data provided by two assessment sources has indicated a high degree of ambiguity due to inconsistent estimates. We apply the proposed methodology to integrate divergent estimates in making shelter location decisions. The results highlight that our methodology leads to higher satisfaction of demand for shelters than other approaches such as a classical stochastic programming model. Moreover, we show that our solution integrates information coming from both sources more efficiently thereby hedging against the ambiguity more effectively. With the newly proposed methodology, the decision-maker is able to analyze the degree of ambiguity in the data and the degree of consensus between different data sources to ultimately make better decisions for delivering humanitarian aid.
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    Site selection and vehicle routing for post-disaster rapid needs assessment
    (Elsevier, 2017-05) Koyuncu, Burcu Balçık; Industrial Engineering; KOYUNCU, Burcu Balçık
    In the immediate aftermath of a disaster, relief agencies perform rapid needs assessment to investigate the effects of the disaster on the affected communities. Since assessments must be performed quickly, visiting all of the sites in the affected region may not be possible. Therefore, assessment teams must decide which sites to select and visit during the assessment horizon. In this paper, we address site selection and routing decisions of the rapid needs assessment teams which aim to evaluate the post-disaster conditions of different community groups, each carrying a distinct characteristic. We define the Selective Assessment Routing Problem (SARP) that constructs an assessment plan to cover different characteristics in a balanced way. The SARP is formulated as a variant of the team orienteering problem with a coverage objective. We develop an efficient tabu search heuristic, which produces high-quality solutions for the SARP. We illustrate our approach with a case study, which is based on real-world data from the 2011 Van earthquake in Turkey.
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    Multi-vehicle sequential resource allocation for a nonprofit distribution system
    (Informa Group, 2014) Koyuncu, Burcu Balçık; Iravanib, S.; Smilowitz, K.; Industrial Engineering; KOYUNCU, Burcu Balçık
    This article introduces a multi-vehicle sequential allocation problem that considers two critical objectives for nonprofit operations: providing equitable service and minimizing unused donations. This problem is motivated by an application in food redistribution from donors such as restaurants and grocery stores to agencies such as soup kitchens and homeless shelters. A set partitioning model is formulated that can be used to design vehicle routes; it primarily focuses on equity maximization and implicitly considers waste. The behavior of the model in clustering agencies and donors on routes is studied, and the impacts of demand variability and supply availability on route composition and solution performance are analyzed. A comprehensive numerical study is performed in order to develop insights on optimal solutions. Based on this study, an efficient decomposition-based heuristic for the problem that can handle an additional constraint on route length is developed and it is shown that the heuristic obtains high-quality solutions in terms of equity and waste.
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    ArticlePublicationOpen Access
    A mathematical model for equitable in-country COVID-19 vaccine allocation
    (Taylor and Francis, 2022) Koyuncu, Burcu Balçık; Yücesoy, Ecem; Akça, Berna; Karakaya, Sırma; Kaplan, Asena Ayse; Baharmand, H.; Sgarbossa, F.; Industrial Engineering; KOYUNCU, Burcu Balçık; Yücesoy, Ecem; Akça, Berna; Karakaya, Sırma; Kaplan, Asena Ayse
    Given the scarcity of COVID-19 vaccines, equitable (fair) allocation of limited vaccines across the main administrative units of a country (e.g. municipalities) has been an important concern for public health authorities worldwide. In this study, we address the equitable allocation of the COVID-19 vaccines inside countries by developing a novel, evidence-based mathematical model that accounts for multiple priority groups (e.g. elderly, healthcare workers), multiple vaccine types, and regional characteristics (e.g. storage capacities, infection risk levels). Our research contributes to the literature by developing and validating a model that proposes equitable vaccine allocation alternatives in a very short time by (a) minimising deviations from the so-called ‘fair coverage’ levels that are computed based on weighted pro-rata rations, and (b) imposing minimum coverage thresholds to control the allocation of vaccines to higher priority groups and regions. To describe the merits of our model, we provide several equity and effectiveness metrics, and present insights on different allocation policies. We compare our methodology with similar models in the literature and show its better performance in achieving equity. To illustrate the performance of our model in practice, we perform a comprehensive numerical study based on actual data corresponding to the early vaccination period in Turkey.
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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.
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    A cost-sharing mechanism for multi-country partnerships in disaster preparedness
    (Wiley, 2021-12) Rodríguez-Pereira, J.; Koyuncu, Burcu Balçık; Rancourt, M.-E.; Laporte, G.; Industrial Engineering; KOYUNCU, Burcu Balçık
    We study a multi-country disaster preparedness partnership involving the joint prepositioning of emergency relief items. Our focus is the Caribbean region, which faces increasing disaster threats due to weather-related events and has committed to share its resources for regional integration. We collaborate with the inter-governmental Caribbean Disaster and Emergency Management Agency (CDEMA), which is interested in creating a methodology to equitably (fairly) allocate the costs necessary to operationalize this commitment. We present alternative cost allocation methods among the partner countries by considering their risk level and their ability to pay. Specifically, we adapt some techniques such as the Shapley value, the equal profit method, and the alternative cost avoided method, and we also propose a new insurance-based allocation scheme to determine the country contributions. This mechanism, which is formulated as a linear programming model, sets country premiums by considering the expected value and the standard deviation of country demands and their gross national income. We discuss the structural properties of these methods and numerically evaluate their performance in achieving an equitable allocation scheme with respect to three equity indicators based on the Gini coefficient. Our proposed cost-sharing mechanism not only achieves superior solutions compared with other methodologies with respect to the proposed equity metrics, but is also computationally efficient. We numerically illustrate how it can be used to obtain alternative cost allocation plans by giving different weights to disaster risk and economic standing parameters, and we analyze the benefits and fairness of the partnership in a transparent way.
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    A continuous approximation approach for assessment routing in disaster relief
    (Elsevier, 2013-04) Huang, M.; Smilowitz, K. R.; Koyuncu, Burcu Balçık; Industrial Engineering; KOYUNCU, Burcu Balçık
    In this paper, we focus on the assessment routing problem which routes teams to different communities to assess damage and relief needs following a disaster. To address time-sensitivity, the routing problem is modeled with the objective of minimizing the sum of arrival times to beneficiaries. We propose a continuous approximation approach which uses aggregated instance data to develop routing policies and cost approximations. Numerical tests are performed that demonstrate the effectiveness of the cost approximations at predicting the true implementation costs of the policies and compare the policies against more complex solution approaches. The continuous approximation approach yields solutions which can be easily implemented; further, this approach reduces the need for detailed data and the computational requirements to solve the problem.
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    A coordinated repair routing problem for post-disaster recovery of interdependent infrastructure networks
    (Springer, 2022-12) Atsız, Eren; Koyuncu, Burcu Balçık; Danış, Dilek Günneç; Sevindik, Busra Uydasoglu; Industrial Engineering; KOYUNCU, Burcu Balçık; DANIŞ, Dilek Günneç; Atsız, Eren; Sevindik, Busra Uydasoglu
    Disasters may cause significant damages and long-lasting failures in lifeline infrastructure networks (such as gas, power and water), which must be recovered quickly to resume providing essential services to the affected communities. While making repair plans, it is important to consider the interdependencies among network components to minimize recovery times. In this paper, we focus on post-disaster repair operations of multiple interdependent lifeline networks, which involve functional dependencies. We assume that each network component, whether damaged or not, becomes nonfunctional if it depends on another nonfunctional component, and it is recovered when all components that it depends on become functional. We introduce a post-disaster coordinated infrastructure repair routing problem, in which dedicated repair teams of each lifeline infrastructure travel through a road network to visit the sites with damaged network components. We present a mixed integer programming model that assigns repair teams to the sites and constructs routes for each team in order to minimize the sum of the recovery times for all network components. We develop a constructive heuristic and a simulated annealing algorithm to solve the proposed coordinated routing problem. We test the performance of the proposed solution algorithms on a set of instances that are developed based on two interdependent lifeline networks (e.g., power and gas). The computational results show that our heuristics can quickly find high-quality solutions. Our results also indicate that coordinating repair operations can significantly improve the overall recovery time of interdependent infrastructure networks.
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    A variable neighborhood search based matheuristic for a waste cooking oil collection network design problem
    (Elsevier, 2022-10) Ölmez, Ömer Berk; Gültekin, Ceren; Koyuncu, Burcu Balçık; Ekici, Ali; Özener, Okan Örsan; Industrial Engineering; KOYUNCU, Burcu Balçık; EKİCİ, Ali; ÖZENER, Okan Örsan; Ölmez, Ömer Berk; Gültekin, Ceren
    Households produce large amounts of waste cooking oil (WCO), which should be disposed properly to avoid its negative impacts on the environment. Using WCO as raw material in biodiesel production is an effective disposal method. In this study, we focus on designing an efficient WCO collection network, in which households deposit their WCO into the bins to be placed at several collection centers (CCs), which are then regularly collected by capacitated vehicles. We study the problem of determining (i) the number and location of the CCs, (ii) the assignment of households to the CCs, respecting a certain coverage distance threshold, (iii) the number of bins to place at each CC, and (iv) the vehicle routes navigating between the biodiesel facility and CCs to minimize the sum of fixed cost of opening CCs and placing bins at each CC and routing cost. To solve the proposed location-routing problem, we develop a novel matheuristic based on Variable Neighborhood Search, which incorporates an integer programming model to determine the bin allocation and assignment decisions. We evaluate the solution performance of different variants of the proposed algorithm by performing an extensive computational study on a set of hypothetical and case instances. The results show that the proposed matheuristic provides superior solutions with respect to benchmark algorithms.
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    ArticlePublicationOpen Access
    Evaluation of field visit planning heuristics during rapid needs assessment in an uncertain post-disaster environment
    (Springer, 2022-12) Hakimifar, M.; Koyuncu, Burcu Balçık; Fikar, C.; Hemmelmayr, V.; Wakolbinger, T.; Industrial Engineering; KOYUNCU, Burcu Balçık
    A Rapid Needs Assessment process is carried out immediately after the onset of a disaster to investigate the disaster’s impact on affected communities, usually through field visits. Reviewing practical humanitarian guidelines reveals that there is a great need for decision support for field visit planning in order to utilize resources more efficiently at the time of great need. Furthermore, in practice, there is a tendency to use simple methods, rather than advanced solution methodologies and software; this is due to the lack of available computational tools and resources on the ground, lack of experienced technical staff, and also the chaotic nature of the post-disaster environment. We present simple heuristic algorithms inspired by the general procedure explained in practical humanitarian guidelines for site selection and routing decisions of the assessment teams while planning and executing the field visits. By simple, we mean methods that can be implemented by practitioners in the field using primary resources such as a paper map of the area and accessible software (e.g., Microsoft Excel). We test the performance of proposed heuristic algorithms, within a simulation environment , which enables us to incorporate various uncertain aspects of the post-disaster environment in the field, ranging from travel time and community assessment time to accessibility of sites and availability of community groups. We assess the performance of proposed heuristics based on real-world data from the 2011 Van earthquake in Turkey. Our results show that selecting sites based on an approximate knowledge of community groups’ existence leads to significantly better results than selecting sites randomly. In addition, updating initial routes while receiving more information also positively affects the performance of the field visit plan and leads to higher coverage of community groups than an alternative strategy where inaccessible sites and unavailable community groups are simply skipped and the initial plan is followed. Uncertainties in travel time and community assessment time adversely affect the community group coverage. In general, the performance of more sophisticated methods requiring more information deteriorates more than the performance of simple methods when the level of uncertainty increases.