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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only Risk management through financial hedging in inventory systems with stochastic price processes(Elsevier, 2024) Canyakmaz, Caner; Department of Industrial Engineering; Özekici, Süleyman; Karaesmen, Fikri; Department of Industrial Engineering; College of EngineeringWe consider the financial hedging problem of a firm whose operational cash flow from its inventory operation is affected by both price and demand uncertainties. We assume that selling prices and demand arrival process are governed by an exogenous continuous stochastic price process which is assumed to be correlated with prices of various products in financial markets. During the selling horizon, the firm dynamically invests in a financial portfolio of these products to manage its exposure to price and demand risks by observing the current inventory and price levels. We explore the problem in a minimum -variance framework where we look for the varianceminimizing financial hedge for a given operational policy and a martingale price process. The framework leads to explicit results for the optimal static and dynamic financial hedges in single -period problems with complicated within -period dynamics. We also obtain characterizations of optimal dynamic hedges for multiperiod problems using dynamic programming. We explore the risk reduction effects of minimum -variance financial hedges through numerical examples and show that significant risk reductions may be possible by using the right hedge.Publication Metadata only 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 EngineeringOur 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.Publication Metadata only Sustainability analysis of cement supply chains considering economic, environmental and social effects(Elsevier, 2023) Suhaib, Seyyed Amir Babak; Rasmi, Seyyed Amir Babak; Department of Industrial Engineering; Türkay, Metin; Department of Industrial Engineering; College of EngineeringCement is a fundamental ingredient in the construction industry and infrastructure development; these sectors depend on this raw material and the demand proportionally increases as the population of the world grows and the urbanization rate accelerates. Despite being a vital element of the development, cement manufacturing sector is a major source of GHG emissions and depletes the natural capital. In this paper we examine the effects of incorporating sustainability indicators in cement supply chains under the Triple Bottom Line (TBL) accounting of sustainability using multi-Objective optimization. We implement a tailored multi-objective optimization algorithm that generates unique optimal solutions hence giving an accurate and well-defined Pareto front to decision makers. Our model shows that even by including additional environmental and social considerations cement manufacturing is economically feasible.Publication Metadata only Predicting graft survival in paediatric kidney transplant recipients using machine learning(Springer , 2024) Aksoy, Gulsah Kaya; Akcay, Huseyin Gokhan; Adar, Mehtap; Koyun, Mustafa; Comak, Elif; Akman, Sema; Department of Industrial Engineering; Department of Industrial Engineering; College of EngineeringBackground Identification of factors that affect graft survival in kidney transplantation can increase graft survival and reduce mortality. Artificial intelligence modelling enables impartial evaluation of clinician bias. This study aimed to examine factors that affect the survival of grafts in paediatric kidney transplantation through the use of machine learning. Methods A retrospective review was conducted on records of paediatric patients who underwent kidney transplantation between 1994 and 2021 and had post-transplant follow-up > 12 months. The nearest neighbour method was used to impute missing fields from a total of 48 variables in the dataset. Models including Naive Bayes, logistic regression, support vector machine (SVM), multi-layer perceptron, and XGBoost were trained to predict graft survival. The study used 80% of the patients for training and the remaining 20% for testing. Modelling success was evaluated based on accuracy and F1 score metrics. Results The study analysed 465 kidney transplant recipients. Of these, 56.7% were male. The mean age at transplantation was 12.08 +/- 5.01 years. Of the kidney transplants, 73.1% (n = 339) were from living donors, 34.5% (n = 160) were pre-emptive transplants, and 2.2% (n = 10) were second-time transplants. The machine learning model identified several features associated with graft survival, including antibody-mediated rejection (+ 0.7), acute cellular rejection (+ 0.66), eGFR at 3 years (+ 0.43), eGFR at 5 years (+ 0.34), pre-transplant peritoneal dialysis (+ 0.2), and cadaveric donor (+ 0.2). The successes of the logistic regression and SVM models were similar. The F1 score was 91.9%, and accuracy was 96.5%. Conclusion Machine learning can be used to identify factors that affect graft survival in kidney transplant recipients. By expanding similar studies, risk maps can be created prior to transplantation.Publication Metadata only Crowdshipping problem with dynamic compensations and transshipments(Pergamon-Elsevier Science Ltd, 2024) Kizil, Kerim U.; Department of Industrial Engineering; Şardağ, Ali; Yıldız, Barış; Department of Industrial Engineering; College of Engineering; Graduate School of Sciences and EngineeringRapid urban growth and consequent increase in e-commerce demand make urban logistics a harder task than ever. The growing size of urban delivery operations not only entails operational challenges but also generates several negative externalities, such as increased traffic, pollution, noise, and accidents. This trend creates a pressing need for efficient delivery mechanisms that are more economical and environmentally friendly than existing systems. Crowdshipping, wherein ordinary members of the society partake in delivery operations for a small compensation, is one of the answers that cater to this need and has attracted considerable research interest recently. However, designing compensation mechanisms to prompt efficient participation from the public remains largely unexplored in the literature. In this study, we devise a dynamic compensation scheme for crowdshipping operations in a many-to-many express delivery framework, where the crowdshipper compensations are determined based on spatial and temporal distributions of the delivery demand and continually updated during the service time to leverage the crowd participation as needed. To address the resulting complex network management problem, we derive analytical solutions for compensation optimization and use these results along with effective pruning strategies to build a lookup table to simultaneously determine package routes and compensation offers in real time. Computational studies and extensive simulations conducted with real-world data show that our proposed approach can provide significant cost savings and considerably reduce operational costs and other transport- related negative externalities when compared to classical delivery modes, crowdshipping with static compensations, and crowdshipping without transshipment.Publication Metadata only Relief item inventory planning under centralized and decentralized bilateral cooperation and uncertain transshipment quantities(Elsevier Science Inc, 2024) Coşkun, Abdullah; Department of Industrial Engineering; Salman, Fatma Sibel; Pashapour, Amirreza; Department of Industrial Engineering; College of Engineering; Graduate School of Sciences and EngineeringPre-positioning relief inventory ensures timely delivery of in-kind aid after a catastrophe. Tragic disasters like major earthquakes are rare and unpredictable;therefore, stockpiled items may not be used. To avoid overstocking and reduce shortage risk, the cooperation of two humanitarian agencies in supporting each other in case of shortages is suggested in the literature. In this study, we utilize newsvendor-based quantitative models to optimize the pre-disaster stocking decisions of agencies under centralized and decentralized cooperation mechanisms. In the former, both agencies jointly determine their inventory levels to maximize their combined benefits of relief operations, whereas, in the latter, each agency establishes its stocking level in isolation via a game theoretic approach. In both systems, the two agencies agree to transship their excessive items to the other party if needed. In this regard, we investigate the situation where only a portion of the transshipped items, denoted as the reliability factor, can be received and effectively utilized at the destination due to the chaotic nature of the disaster. Considering a deterministic reliability factor, we obtain the singular optimal inventory levels in the centralized system and identify the unique Nash Equilibrium in the decentralized system. Subsequently, we formulate a two-stage stochastic program, considering a random reliability factor for both cooperation systems. The study concludes by offering a range of managerial insights. Our analyses quantify the sub-optimality resulting from decentralized decision-making across diverse parameter settings using the concept of the price of anarchy. The findings highlight that centralized cooperation becomes particularly advisable when the average demand within either agency is high, the transshipment process is secure (i.e., the reliability factor is high), and transshipment costs remain low.Publication Metadata only 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 EngineeringThis 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.Publication Metadata only 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 HospitalBackground 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.Publication Metadata only 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.Publication Metadata only Managing home health-care services with dynamic arrivals during a public health emergency(IEEE-Inst Electrical Electronics Engineers Inc, 2024) Araz, Özgür M.; Department of Industrial Engineering; Çınar, Ahmet; Salman, Fatma Sibel; Parçaoğlu, Mert; Department of Industrial Engineering; ; Graduate School of Sciences and Engineering; College of Engineering;We consider a public health emergency, during which a high number of patients and their varying health conditions necessitate prioritizing patients receiving home health care. Moreover, the dynamic emergence of patients needing urgent care during the day should be handled by rescheduling these patients. In this article, we present a reoptimization framework for this dynamic problem to periodically determine which patients will be visited in which order on each day to maximize the total priority of visited patients and to minimize the overtime for the health-care provider. This optimization framework also aims to minimize total routing time. A mixed-integer programming (MIP) model is formulated and solved at predetermined reoptimization times, to assure that urgent patients are visited within the current day, while visits of others may be postponed, if overtime is not desired or limited. The effectiveness of a schedule is evaluated with respect to several performance metrics, such as the number of patients whose visits are postponed to the next day, waiting time of urgent patients, and required overtime. The MIP-based approach is compared to two practical heuristics that achieve satisfactory performance under a nervous service system by excelling in different criteria. The MIP-based reoptimization approach is demonstrated for a case during the COVID-19 pandemic. We contribute to the home health-care literature by managing dynamic/urgent patient arrivals under a multiperiod setting with prioritized patients, where we optimize different rescheduling objectives via three alternative reoptimization approaches. © 1988-2012 IEEE.