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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3

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Department: search.filters.department.Department of Industrial EngineeringSponsors: search.filters.sponsors.TÜBİTAK

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    Transshipment network design for express air cargo operations in China
    (Elsevier B.V., 2023) Savelsbergh, Martin; Dogru, Ali K.; Department of Industrial Engineering; Yıldız, Barış; Department of Industrial Engineering; College of Engineering
    We introduce a novel multimodal (ground and air transportation) network design model with transshipments for the transport of express cargo with heterogeneous service classes (i.e., next morning delivery, and next day delivery). We formulate this problem using a novel path-based mixed-integer program which seeks to maximize the demand (weight) served. We investigate the value of the proposed transshipment network under various operational conditions and by benchmarking against a direct shipment network and a network with a single transshipment point which mimics a classical star-shaped hub-and-spoke network. Our extensive computational study with real-world data from ShunFeng (SF) Express reveals that the integration of ground and air transportation improves the coverage and that transshipment enables serving a large number of origin–destination pairs with a small number of cargo planes. Importantly, we show that by simplifying handling, i.e., employing cross-docking rather than time-consuming sortation, a transshipment network can transport express cargo fast enough to meet demanding delivery deadlines. Finally, we find that increasing the efficiency of intra-city operations and extending the nightly operating time window are the most effective operational adjustments for further improving the performance of the proposed transshipment network.
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    Hub network design problem with capacity, congestion, and stochastic demand considerations
    (Informs, 2023) Bayram, Vedat; Farham, M. Saleh; Department of Industrial Engineering; Yıldız, Barış; Department of Industrial Engineering; College of Engineering
    Our study introduces the hub network design problem with congestion, capacity, and stochastic demand considerations (HNDC), which generalizes the classical hub location problem in several directions. In particular, we extend state-of-the-art by integrating capacity acquisition decisions and congestion cost effect into the problem and allowing dynamic routing for origin-destination (OD) pairs. Connecting strategic and operational level decisions, HNDC jointly decides hub locations and capacity acquisitions by considering the expected routing and congestion costs. A path-based mixed-integer second-order cone programming (SOCP) formulation of the HNDC is proposed. We exploit SOCP duality results and propose an exact algorithm based on Benders decomposition and column generation to solve this challenging problem. We use a specific characterization of the capacity-feasible solutions to speed up the solution procedure and develop an efficient branch-and-cut algorithm to solve the master problem. We conduct extensive computational experiments to test the proposed approach's performance and derive managerial insights based on realistic problem instances adapted from the literature. In particular, we found that including hub congestion costs, accounting for the uncertainty in demand, and whether the underlying network is complete or incomplete have a significant impact on hub network design and the resulting performance of the system.
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    Fair and effective vaccine allocation during a pandemic
    (Elsevier Science Ltd, 2024) Erdoğan, Güneş; Yücel, Eda; Department of Industrial Engineering; Kiavash, Parinaz; Salman, Fatma Sibel; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering
    This paper presents a novel model for the Vaccine Allocation Problem (VAP), which aims to allocate the available vaccines to population locations over multiple periods during a pandemic. We model the disease progression and the impact of vaccination on the spread of the disease and mortality to minimise total expected mortality and location inequity in terms of mortality ratios under total vaccine supply and hospital and vaccination centre capacity limitations at the locations. The spread of the disease is modelled through an extension of the well -established Susceptible-Infected-Recovered (SIR) epidemiological model that accounts for multiple vaccine doses. The VAP is modelled as a nonlinear mixed -integer programming model and solved to optimality using the Gurobi solver. A set of scenarios with parameters regarding the COVID-19 pandemic in the UK over 12 weeks are constructed using a hypercube experimental design on varying disease spread, vaccine availability, hospital capacity, and vaccination capacity factors. The results indicate the statistical significance of vaccine availability and the parameters regarding the spread of the disease.
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    Resensitization to colistin results in rapid and stable recovery of adherence, serum resistance and ompW in Acinetobacter baumannii
    (Public Library of Science, 2024) Menekşe, Şirin; Department of Industrial Engineering; Boral, Jale; Vatansever, Cansel; Özcan, Gülin; Keske, Şiran; Gönen, Mehmet; Can, Füsun; Department of Industrial Engineering; Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); College of Engineering; Graduate School of Health Sciences; School of Medicine; Koç University Hospital
    Background Colistin resistance in Acinetobacter baumannii is an emerging problem that limits antimicrobial therapy options. Materials & methods We isolated two pairs of colistin susceptible and colistin-resistant A. baumannii (K1007/ K1006 and K408/K409) from two patients diagnosed with carbapenem-resistant A. baumannii infection. Colistin susceptible isolates were exposed to in vitro colistin induction for 50 generations. The selected cell populations were subjected to DNA and RNA sequencing and phenotypic assays. Results In the in vitro induction assay, K408 gained colistin resistance on the corresponding day of clinical resistance (K408-G25) and got resensitized to colistin in the consecutive generation (K408-G26). A significant upregulation of ompW, ata, adeFGH genes on K408-G25 was followed by a downregulation upon resensitization to colistin (G26). Despite the upregulation of the ompW gene in transcriptomic analysis, the ompW protein disappeared on K408-G25 and recovered in the resensitized generation (G26). In parallel, disrupted cell membrane integrity recovered in K408-G26. In the K408-G25, downregulation of pbpG and upregulation of pbp1a/pbp3 genes decreased serum-resistance which was reversed in the resensitized generation (G26). The K1007 did not gain colistin resistance amongst 50-generations, however, the generation corresponding to clinical resistance day (K1007-G9) had a similar trend with K408-G25. The clinical colistin-resistant K409 and K1006 had SNPs on pmrA and pmrB genes. Conclusion In this study, we observed that A. baumannii regulates adhesion, efflux pumps and serum-resistance associated genes as an early response to colistin stress. Besides, the ompW protein disappears in the cell membrane of colistin resistant cells which recovers after resensitization to colistin. The lack of ompW protein in colistin-resistant cells should be taken into consideration for escape mutants in development of antivirulence vaccination or treatment options. © 2024 Public Library of Science. All rights reserved.
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    Capacitated mobile facility location problem with mobile demand: efficient relief aid provision to en route refugees
    (Pergamon-Elsevier Science Ltd, 2024) Gunnec, Dilek; Yucel, Eda; Department of Industrial Engineering; Pashapour, Amirreza; Salman, Fatma Sibel; Department of Industrial Engineering;  ; Graduate School of Sciences and Engineering; College of Engineering;  
    As a humanity crisis, the tragedy of forced displacement entails relief aid distribution efforts among en route refugees to alleviate their migration hardships. This study aims to assist humanitarian organizations in cost-efficiently optimizing the logistics of capacitated mobile facilities utilized to deliver relief aid to transiting refugees in a multi-period setting. The problem is referred to as the Capacitated Mobile Facility Location Problem with Mobile Demands (CMFLP-MD). In CMFLP-MD, refugee groups follow specific paths, and meanwhile, they receive relief aid at least once every fixed number of consecutive periods, maintaining continuity of service. To this end, the overall costs associated with capacitated mobile facilities, including fixed, service provision, and relocation costs, are minimized. We formulate a mixed integer linear programming (MILP) model and propose two solution methods to solve this complex problem: an accelerated Benders decomposition approach as an exact solution method and a matheuristic algorithm that relies on an enhanced fix-and-optimize agenda. We evaluate our methodologies by designing realistic instances based on the Honduras migration crisis that commenced in 2018. Our numerical results reveal that the accelerated Benders decomposition excels MILP with a 46% run time improvement on average while acquiring solutions at least as good as the MILP across all instances. Moreover, our matheuristic acquires high-quality solutions with a 2.4% average gap compared to best-incumbents rapidly. An in-depth exploration of the solution properties underscores the robustness of our relief distribution plans under varying migration circumstances. Across several metrics, our sensitivity analyses also highlight the managerial advantages of implementing CMFLP-MD solutions.
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    Nek2A prevents centrosome clustering and induces cell death in cancer cells via KIF2C interaction
    (Springernature, 2024) Department of Industrial Engineering; Kalkan, Batuhan Mert; Özcan, Selahattin Can; Çiçek, Enes; Gönen, Mehmet; Ayhan, Ceyda Açılan; Department of Industrial Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); Graduate School of Health Sciences; College of Engineering; School of Medicine
    Unlike normal cells, cancer cells frequently exhibit supernumerary centrosomes, leading to formation of multipolar spindles that can trigger cell death. Nevertheless, cancer cells with supernumerary centrosomes escape the deadly consequences of unequal segregation of genomic material by coalescing their centrosomes into two poles. This unique trait of cancer cells presents a promising target for cancer therapy, focusing on selectively attacking cells with supernumerary centrosomes. Nek2A is a kinase involved in mitotic regulation, including the centrosome cycle, where it phosphorylates linker proteins to separate centrosomes. In this study, we investigated if Nek2A also prevents clustering of supernumerary centrosomes, akin to its separation function. Reduction of Nek2A activity, achieved through knockout, silencing, or inhibition, promotes centrosome clustering, whereas its overexpression results in inhibition of clustering. Significantly, prevention of centrosome clustering induces cell death, but only in cancer cells with supernumerary centrosomes, both in vitro and in vivo. Notably, none of the known centrosomal (e.g., CNAP1, Rootletin, Gas2L1) or non-centrosomal (e.g., TRF1, HEC1) Nek2A targets were implicated in this machinery. Additionally, Nek2A operated via a pathway distinct from other proteins involved in centrosome clustering mechanisms, like HSET and NuMA. Through TurboID proximity labeling analysis, we identified novel proteins associated with the centrosome or microtubules, expanding the known interaction partners of Nek2A. KIF2C, in particular, emerged as a novel interactor, confirmed through coimmunoprecipitation and localization analysis. The silencing of KIF2C diminished the impact of Nek2A on centrosome clustering and rescued cell viability. Additionally, elevated Nek2A levels were indicative of better patient outcomes, specifically in those predicted to have excess centrosomes. Therefore, while Nek2A is a proposed target, its use must be specifically adapted to the broader cellular context, especially considering centrosome amplification. Discovering partners such as KIF2C offers fresh insights into cancer biology and new possibilities for targeted treatment.
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    Multi-vehicle synchronized arc routing problem to restore post-disaster network connectivity
    (Elsevier Science Bv, 2017) Department of Industrial Engineering; Department of Industrial Engineering; Akbari, Vahid; Salman, Fatma Sibel; Teaching Faculty; Faculty Member; Department of Industrial Engineering; College of Engineering; College of Engineering; N/A; 178838
    After a natural disaster roads can be damaged or blocked by debris, while bridges and viaducts may collapse. This commonly observed hazard causes some road sections to be closed and may even disconnect the road network. In the immediate disaster response phase work teams are dispatched to open a subset of roads to reconnect the network. Closed roads are traversable only after they are unblocked/cleared by one of the teams. The main objective of this research is to provide an efficient solution method to generate a synchronized work schedule for the road clearing teams. The solution should specify the synchronized routes of each clearing team so that: 1) connectivity of the network is regained, and 2) none of the closed roads are traversed unless their unblocking/clearing procedure is finished. In this study we develop an exact Mixed Integer Programming (MIP) formulation to solve this problem. Furthermore, we propose a matheuristic that is based on an MIP-relaxation and a local search algorithm. We prove that the optimality gap of the relaxation solution is bounded by K times the lower bound obtained from the relaxed model, where K is the number of teams. We show computationally that the matheuristic obtains optimal or near-optimal solutions. (C) 2016 Elsevier B.V. All rights reserved.
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    A bi-criteria optimization model to analyze the impacts of electric vehicles on costs and emissions
    (Elsevier, 2017) N/A; N/A; Department of Industrial Engineering; Kabatepe, Bora; Türkay, Metin; Master Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 24956
    Electric vehicles (EV) are emerging as a mobility solution to reduce emissions in the transportation sector. The studies environmental impact analysis of EVs in the literature are based on the average energy mix or pre-defined generation scenarios and construct policy recommendations with a cost minimization objective. However, the environmental performance of EVs depends on the source of the marginal electricity provided to the grid and single objective models do not provide a thorough analysis on the economic and environmental impacts of EVs. In this paper, these gaps are addressed by a four step methodology that analyzes the effects of EVs under different charging and market penetration scenarios. The methodology includes a bi-criteria optimization model representing the electricity market operations. The results from a real-life case analysis show that EVs decrease costs and emissions significantly compared to conventional vehicles.
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    Mean-variance newsvendor model with random supply and financial hedging
    (Taylor and Francis Inc, 2015) N/A; Department of Industrial Engineering; Tekin, Müge; Özekici, Süleyman; Master Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 32631
    In this paper, we follow a mean-variance (MV) approach to the newsvendor model. Unlike the risk-neutral newsvendor that is mostly adopted in the literature, the MV newsvendor considers the risks in demand as well as supply. We further consider the case where the randomness in demand and supply is correlated with the financial markets. The MV newsvendor hedges demand and supply risks by investing in a portfolio composed of various financial instruments. The problem therefore includes both the determination of the optimal ordering policy and the selection of the optimal portfolio. Our aim is to maximize the hedged MV objective function. We provide explicit characterizations on the structure of the optimal policy. We also present numerical examples to illustrate the effects of risk-aversion on the optimal order quantity and the effects of financial hedging on risk reduction.
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    Discriminating early- and late-stage cancers using multiple kernel learning on gene sets
    (Oxford Univ Press, 2018) N/A; N/A; Department of Industrial Engineering; Rahimi, Arezou; Gönen, Mehmet; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 237468
    Motivation: Identifying molecular mechanisms that drive cancers from early to late stages is highly important to develop new preventive and therapeutic strategies. Standard machine learning algorithms could be used to discriminate early-and late-stage cancers from each other using their genomic characterizations. Even though these algorithms would get satisfactory predictive performance, their knowledge extraction capability would be quite restricted due to highly correlated nature of genomic data. That is why we need algorithms that can also extract relevant information about these biological mechanisms using our prior knowledge about pathways/gene sets. Results: In this study, we addressed the problem of separating early- and late-stage cancers from each other using their gene expression profiles. We proposed to use a multiple kernel learning (MKL) formulation that makes use of pathways/gene sets (i) to obtain satisfactory/improved predictive performance and (ii) to identify biological mechanisms that might have an effect in cancer progression. We extensively compared our proposed MKL on gene sets algorithm against two standard machine learning algorithms, namely, random forests and support vector machines, on 20 diseases from the Cancer Genome Atlas cohorts for two different sets of experiments. Our method obtained statistically significantly better or comparable predictive performance on most of the datasets using significantly fewer gene expression features. We also showed that our algorithm was able to extract meaningful and disease-specific information that gives clues about the progression mechanism.