Graduate School of Engineering and Science

Permanent URI for this communityhttps://hdl.handle.net/10679/8952

Browse

Browsing by Institution Author "KOYUNCU, Burcu Balçık"

Filter results by typing the first few letters
Now showing 1 - 3 of 3
  • Placeholder
    Book PartPublicationMetadata only
    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.
  • Placeholder
    ArticlePublicationMetadata only
    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.
  • Placeholder
    Book PartPublicationMetadata only
    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.