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KOYUNCU, Burcu Balçık

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Burcu Balçık

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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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    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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    Conference paperPublicationMetadata only
    Selective routing for post-disaster needs assessments
    (Springer International Publishing, 2016) Koyuncu, Burcu Balçık; Industrial Engineering; KOYUNCU, Burcu Balçık
    In the immediate aftermath of a disaster, relief agencies perform needs assessment operations to investigate the effects of the disaster and understand the needs of the affected communities. Since assessments must be performed quickly, it may not be possible to visit each site in the affected region. In practice, sites to be visited during the assessment period are selected considering the characteristics of the target communities. In this study, we address site selection and routing decisions of the rapid needs assessment teams that aim to evaluate the post-disaster conditions of a diverse set of community groups with different characteristics (e.g., ethnicity, income level, etc.) within a limited period of time. In particular, we study the Selective Assessment Routing Problem (SARP) that determines sites to be visited and the order of site visits for each team while ensuring sufficient coverage of the given set of characteristics. We present a mathematical model and greedy heuristics for the SARP. We perform numerical analysis to evaluate the performance of the greedy heuristics and show that the heuristic version that balances the tradeoff between coverage and travel times provides reasonable solutions for realistic problem instances.
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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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    Book ChapterPublicationMetadata only
    Contributions to humanitarian and non-profit operations: Equity impacts on modeling and solution approaches
    (Springer) Koyuncu, Burcu Balçık; Smilowitz, K.; Industrial Engineering; KOYUNCU, Burcu Balçık
    Equity has been acknowledged as an important concern in designing and managing humanitarian and non-profit operations over the past decade. Given the significant demands for relief supplies created by a disaster and the scarcity of resources (such as supplies, vehicles, equipment), it is inevitable that some needs will be satisfied later than others, and effective prioritization is crucial. Relief organizations are faced with the challenge of finding ways to deliver resources in an equitable way to increase the chances of survival of people. These issues also emerge in the operations of non-profit organizations that allocate distribute scarce resources. Important contributions have been made by women in studying equity in humanitarian and non-profit operations, both in terms of practical insights and methodological advances. In this chapter, we review key papers written by women, which have advanced the literature in characterizing equity in humanitarian and non-profit operations and exploring the methodological implications of equity.
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    Disaster preparedness using risk-assessment methods from earthquake engineering
    (Elsevier, 2018-09-01) Battarra, M.; Koyuncu, Burcu Balçık; Xu, H.; Industrial Engineering; KOYUNCU, Burcu Balçık
    Analyzing the uncertainties associated with disaster occurrences is critical to make effective disaster preparedness plans. In this study, we focus on pre-positioning emergency supplies for earthquake preparedness. We present a new method to compute earthquake likelihood and the number of the affected people. Our approach utilizes forecasting methods from the earthquake engineering literature, and avoids using probabilistic scenarios to represent the uncertainties related to earthquake occurrences. We validate the proposed technique by using historical earthquake data from Turkey, a country under significant earthquake risk. We also present a case study that illustrates the implementation of our method to solve the inventory allocation problem of the Turkish Red Crescent
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    Supplier selection for framework agreements in humanitarian relief
    (Wiley, 2014-06) Koyuncu, Burcu Balçık; Ak, D.; Industrial Engineering; KOYUNCU, Burcu Balçık
    In this study, we consider the supplier selection problem of a relief organization that wants to establish framework agreements (FAs) with a number of suppliers to ensure quick and cost-effective procurement of relief supplies in responding to sudden-onset disasters. Motivated by the FAs in relief practice, we focus on a quantity flexibility contract in which the relief organization commits to purchase a minimum total quantity from each framework supplier over a fixed agreement horizon, and, in return, the suppliers reserve capacity for the organization and promise to deliver items according to pre-specified agreement terms. Due to the uncertainties in demand locations and amounts, it may be challenging for relief organizations to assess candidate suppliers and the offered agreement terms. We use a scenario-based approach to represent demand uncertainty and develop a stochastic programming model that selects framework suppliers to minimize expected procurement and agreement costs while meeting service requirements. We perform numerical experiments to understand the implications of agreement terms in different settings. The results show that supplier selection decisions and costs are generally more sensitive to the changes in agreement terms in settings with high-impact disasters. Finally, we illustrate the applicability of our model on a case study.
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    A robust optimization approach for humanitarian needs assessment planning under travel time uncertainty
    (Elsevier, 2020-04-01) Koyuncu, Burcu Balçık; Yanıkoğlu, İhsan; Industrial Engineering; KOYUNCU, Burcu Balçık; YANIKOĞLU, Ihsan
    We focus on rapid needs assessment operations conducted immediately after a disaster to identify the urgent needs of the affected community groups, and address the problem of selecting the sites to be visited by the assessment teams during a fixed assessment period and constructing assessment routes under travel time uncertainty. Due to significant uncertainties in post-disaster transportation network conditions, only rough information on travel times may be available during rapid needs assessment planning. We represent uncertain travel times simply by specifying a range of values, and implement robust optimization methods to ensure that each constructed route is feasible for all realizations of the uncertain parameters that lie in a predetermined uncertainty set. We present a tractable robust optimization formulation with a coaxial box uncertainty set due to its advantages in handling uncertainty in our selective assessment routing problem, in which the dimension of the uncertainty (number of arcs traversed) is implicitly determined. To solve the proposed model efficiently, we develop a practical method for evaluating route feasibility with respect to the robust route duration constraints, and embed this feasibility check procedure in a tabu search heuristic. We present computational results to evaluate the effectiveness of our solution method, and illustrate our approach on a case study based on a real-world post-disaster network.
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    Book ChapterPublicationMetadata only
    Drone routing for post-disaster damage assessment
    (Springer, 2021) Adsanver, Birce; Göktürk, Elvin Çoban; Koyuncu, Burcu Balçık; Industrial Engineering; GÖKTÜRK, Elvin Çoban; KOYUNCU, Burcu Balçık; Adsanver, Birce
    We consider drones to support post-disaster damage assessment operations when the disaster-affected area is divided into grids and grids are clustered based on their attributes. Specifically, given a set of drones and a limited time for assessments, we address the problem of determining the grids to scan by each drone and the sequence of visits to the selected grids. We aim to maximize the total priority score collected from the assessed grids while ensuring that the pre-specified coverage ratio targets for the clusters are met. We adapt formulations from the literature developed for electric vehicle routing problems with recharging stations and propose two alternative mixed-integer linear programming models for our problem. We use an optimization solver to evaluate the computational difficulty of solving different formulations and show that both formulations perform similarly. We also develop a practical constructive heuristic to solve the proposed drone routing problem, which can find high-quality solutions rapidly. We evaluate the performance of the heuristic with respect to both mathematical models in a variety of instances with the different numbers of drones and grids.
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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.