Industrial Engineering

Permanent URI for this collectionhttps://hdl.handle.net/10679/45

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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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Collaborative prepositioning network design for regional disaster response
(Wiley, 2019-10) Koyuncu, Burcu Balçık; Silvestri, S.; Rancourt, M.‐È.; Laporte, G.; Industrial Engineering; KOYUNCU, Burcu Balçık
We present a collaborative prepositioning strategy to strengthen the disaster preparedness of the Caribbean countries, which are frequently hit by hurricanes. Since different subsets of countries are affected in each hurricane season, significant risk pooling benefits can be achieved through horizontal collaboration, which involves joint ownership of prepositioned stocks. We worked with the intergovernmental Caribbean Disaster and Emergency Management Agency to design a collaborative prepositioning network in order to improve regional response capacity. We propose a novel insurance-based method to allocate the costs incurred to establish and operate the proposed collaborative prepositioning network among the partner countries. We present a stochastic programming model, which determines the locations and amounts of relief supplies to store, as well as the investment to be made by each country such that their premium is related to the cost associated with the expected value and the standard deviation of their demand. We develop a realistic data set for the network by processing real-world data. We conduct extensive numerical analyses and present insights that support practical implementation. We show that a significant reduction in total inventory can be achieved by applying collaborative prepositioning as opposed to a decentralized policy. Our results also demonstrate that reducing the replenishment lead time during the hurricane season and improving sea connectivity are essential to increasing the benefits resulting from the network.
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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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Contributions to humanitarian and non-profit operations: Equity impacts on modeling and solution approaches
(Springer, 2019-09-14) 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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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 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 decomposition-based heuristic for a waste cooking oil collection problem
(Springer, 2020-01-01) Gültekin, Ceren; Ölmez, Ömer Berk; Koyuncu, Burcu Balçık; Ekici, Ali; Özener, Okan Örsan; Industrial Engineering; KOYUNCU, Burcu Balçık; EKİCİ, Ali; ÖZENER, Okan Örsan; Gültekin, Ceren; Ölmez, Ömer Berk
Every year, a tremendous amount of waste cooking oil (WCO) is produced by households and commercial organizations, which poses a serious threat to the environment if disposed improperly. While businesses such as hotels and restaurants usually need to have a contract for their WCO being collected and used as a raw material for biodiesel production, such an obligation may not exist for households. In this study, we focus on designing a WCO collection network, which involves a biodiesel facility, a set of collection centers (CCs), and source points (SPs) each of whom represents a group of households. The proposed locationrouting problem (LRP) determines: (i) the CCs to be opened, (ii) the number of bins to place at each CC, (iii) the assignment of each SP to one of the accessible CCs, and (iv) the vehicle routes to collect the accumulated oil from the CCs. We formulate the problem as a mixed-integer mathematical model and solve it by using commercial solvers by setting a 1-h time limit. We also propose a decompositionbased heuristic and conduct a computational study. Our decomposition algorithm obtains the same or better solutions in 95% of all the test instances compared to the proposed mathematical model.
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Developing a national pandemic vaccination calendar under supply uncertainty
(Elsevier, 2024-04) Karakaya, Sırma; Koyuncu, Burcu Balçık; Industrial Engineering; KOYUNCU, Burcu Balçık; Karakaya, Sırma
During the COVID-19 pandemic, many countries faced challenges in developing and maintaining a reliable national pandemic vaccination calendar due to vaccine supply uncertainty. Deviating from the initial calendar due to vaccine delivery delays eroded public trust in health authorities and the government, hindering vaccination efforts. Motivated by these challenges, we address the problem of developing a long-term national pandemic vaccination calendar under supply uncertainty. We propose a novel two-stage mixed integer programming model that considers critical factors such as multiple doses, varying dosing schemes, and uncertainties in vaccine delivery timing and quantity. We present an efficient aggregation-based algorithm to solve this complex problem. Additionally, we extend our model to allow for dynamic adjustments to the vaccine schedule in response to mandatory policy changes, such as modifications to dose intervals, during ongoing vaccinations. We validate our model based on a case study developed by using real COVID-19 vaccination data from Norway. Our results demonstrate the advantages of the proposed stochastic optimization approach and heuristic algorithm. We also present valuable managerial insights through extensive numerical analysis, which can aid public health authorities in preparing for future pandemics.
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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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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.
Open 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.
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A literature review on inventory management in humanitarian supply chains
(Elsevier, 2016) Koyuncu, Burcu Balçık; Bozkir, Cem Deniz Çağlar; Kundakcıoğlu, Ömer Erhun; Industrial Engineering; KOYUNCU, Burcu Balçık; KUNDAKCIOĞLU, Ömer Erhun; Bozkir, Cem Deniz Çağlar
In this paper, we present a review and analysis of studies that focus on humanitarian inventory planning and management. Specifically, we focus on papers which develop policies and models to determine how much to stock, where to stock, and when to stock throughout the humanitarian supply chain. We categorize papers according to the disaster management cycle addressed; specifically, we focus on pre-disaster and post-disaster inventory management. We evaluate existing literature in terms of problem aspects addressed such as decision makers, stakeholders, disaster types, commodities, facility types, performance measures as well as methodological aspects (i.e., types of policies, models, and solution approaches). We identify current gaps in the literature and propose directions for future research.
Open 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.
Open 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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A multi-cover routing problem for planning rapid needs assessment under different information-sharing settings
(Springer Nature, 2020-03) Pamukcu, D.; Koyuncu, Burcu Balçık; Industrial Engineering; KOYUNCU, Burcu Balçık
In this paper, we introduce a multi-cover routing problem (MCRP), which is motivated by post-disaster rapid needs assessment operations performed to evaluate the impact of the disaster on different affected community groups. Given a set of sites, each carrying at least one community group of interest, the problem involves selecting the sites to be visited and constructing the routes. In practice, each community group is observed multiple times at different sites to make reliable evaluations; therefore, the MCRP ensures that pre-specified coverage targets are met for all community groups within the shortest time. Moreover, we assume that the completion time of the assessment operations depends on the information-sharing setting in the field, which depends on the availability of information and communication technologies (ICT). Specifically, if remote communication is possible, each assessment team can share its findings with the central coordinator immediately after completing the site visits; otherwise, all teams must return to the origin point to share information and finalize the assessments. To address these different information-sharing settings, we define two MCRP variants with different objectives and present alternative formulations for these variants. We propose two constructive heuristics and a tabu search algorithm to solve the MCRP, and conduct an extensive computational study to evaluate the performance of our heuristics with respect to different benchmark solutions. Our results show that the proposed tabu search algorithm can achieve high-quality solutions for both MCRP variants quickly. The results also highlight the importance of considering the availability of ICT in the field while devising assessment plans.
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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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Operations research approaches for improving coordination, cooperation, and collaboration in humanitarian relief chains: A framework and literature review
(Elsevier, 2023) Adsanver, B.; Koyuncu, Burcu Balçık; Bélanger, V.; Rancourt, M. E.; Industrial Engineering; KOYUNCU, Burcu Balçık
Given the considerable number of actors in the humanitarian space, coordination is essential for successful disaster response. Furthermore, the sheer size of challenges and limited resources increasingly highlight the need for improved cooperation and collaboration in humanitarian supply chains. A significant number of studies in the literature explore the 3Cs (coordination, cooperation and collaboration), using conceptual, empirical and analytical methods. This paper aims to provide an overview and analysis of the Operations Research (OR) approaches that support decision making for improved 3Cs in humanitarian relief chains and to identify future research directions. To achieve this aim, we first present a holistic view of the discussions in the literature and derive a conceptual framework for 3C mechanisms in humanitarian operations. Based on our framework, we analyse studies that develop OR methods to address the design and management of 3C mechanisms in humanitarian relief chains. We also identify current gaps and future research directions.
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Post-disaster damage assessment using drones in a remote communication setting
(Springer, 2023) Yücesoy, Ecem; Göktürk, Elvin Çoban; Koyuncu, Burcu Balçık; Industrial Engineering; GÖKTÜRK, Elvin Çoban; KOYUNCU, Burcu Balçık; Yücesoy, Ecem
After a disaster event, obtaining fast and accurate information about the damaged built-in structure is crucial for planning life-saving response operations. Unmanned aerial vehicles (UAVs), known otherwise as drones, are increasingly utilized to support damage assessment activities as a part of humanitarian operations. In this study, we focus on a post-disaster setting where the drones are utilized to scan a disaster-affected area to gather information on the damage levels. The affected area is assumed to be divided into grids with varying criticality levels. We consider en-route recharge stations to address battery limitations and remote information transmission to a single operation center. We address the problem of determining the routes of a set of drones across a given assessment horizon to maximize the number of visited grids considering their criticality levels and transmit the collected assessment information as quickly as possible along the routes. We propose a mixed integer linear programming formulation to solve this problem and also adapt it to a setting where the information transmission is only possible at the end of the routes for comparison purposes. We propose performance metrics to evaluate the performance of our model and present results on small-sized instances with sensitivity analysis. We present results that highlight the tradeoff between attained coverage (visiting more grids) and response time (the timing of information transmission in the scanned areas). Moreover, we show the advantage of en-route data transmission compared to the setting with data transmission at the end of the routes.
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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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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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