Endüstri Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1942
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Article Robust HMM-Based Remaining Useful Life Estimation Using a Ridge-Regularized EM Algorithm(MDPI, 2026-02-18) Kucukdag, Halime Beyza; Kirkil, Gokhan; Hekimoglu, MustafaEstimating the remaining useful life (RUL) of engineering systems is crucial for maintenance planning and the reliability of complex mechanical units. Accurate RUL predictions support timely interventions and help to prevent unexpected failures. This study proposes a statistically robust framework that models degradation signals up to the end of life using a hidden Markov model (HMM) with a simple-failure structure and an absorbing terminal state. The proposed method estimates state-dependent linear emission parameters and transition probabilities using a ridge-regularized expectation-maximization (EM) algorithm. The ridge penalty stabilizes slope estimates under limited data, while a robust Huber-based scale estimator reduces sensitivity to outliers in the sensor-derived health indicator. RUL is computed as a weighted expected time to absorption, combining transient-state survival characteristics with smoothed posterior-state probabilities obtained via the forward-backward algorithm. This yields a low-variance state-aware estimator that preserves the probabilistic structure of the HMM. Simulation studies show that the proposed ridge-regularized EM significantly reduces parameter variance and improves predictive accuracy compared with the baseline weighted least squares EM (WLS-EM). A real-data case analysis demonstrates further improvements in RUL estimation accuracy and smoother, more reliable prediction trajectories. Overall, the framework provides a robust and interpretable approach for practical prognostics applications.Book Part Critical Social Perspectives on Automation and Job Impacts(Elsevier, 2026) Toktaş-Palut, PeralArticle Empowering Electric Vehicle Adoption: Innovative Strategies for Optimizing Charging Station Placement Based on Projected Demand(Wiley, 2025-01-01) Cekyay, Bora; Kabak, Ozgur; Ozaydin, Ozay; Isik, Mine; Toktas-Palut, Peral; Topcu, Y. Ilker; Ulengin, FusunElectric vehicles (EVs) are pivotal for reducing transportation-related emissions; however, the lack of adequate charging infrastructure remains a significant barrier to their widespread adoption. This study presents a comprehensive methodology for optimizing EV charging station placement. It combines a gravity model, scenario analysis, and mixed-integer linear programming (MILP) to ensure a thorough and robust approach. The model aims to maximize accessibility by ensuring both path-level and overall system demand coverage across diverse scenarios, providing reassurance about the validity of the findings. The methodology is tested on the Bursa-& Idot;zmir motorway in Turkey, a strategic intercity route with rapidly growing EV penetration. Results reveal that the optimal configuration involves locating charging stations in seven of the nine service areas. This allocation secures a minimum path coverage ratio of 0.903, meaning 90.3% of the route is covered by charging stations, and an overall demand coverage ratio of 0.935, indicating that 93.5% of total demand is covered across all scenarios. A sensitivity analysis further shows that increasing the network to 45 chargers elevates reachability levels to above 97%, indicating the infrastructure scale required for reliable service quality. The findings underscore the practical applicability of the proposed framework, providing policymakers and infrastructure planners with robust, data-driven guidance for charging network expansion. By integrating demand forecasting with resilient optimization, this study advances both methodological and empirical insights, empowering the audience to make informed decisions for sustainable EV adoption.Article A Comparative Study of Branch-And Algorithms for Vehicle Routing With Time Windows and Waiting Time Costs(Wiley, 2026-02-09) Michelini, Stefano; Kucukaydin, Hande; Arda, YaseminBranch-and-price is one of the most commonly used methodologies for solving routing problems. In recent years, several studies have investigated advanced labeling algorithms to solve the related pricing problem, which is usually a variant of the elementary shortest path problem with resource constraints. Such algorithms include efficient techniques such as decremental state space relaxation, ng-route relaxation, and several hybridizations of these two relaxation methods. In this study, we compare the performance of these labeling algorithms in a branch-and-price framework when applied to the vehicle routing problem with time windows and a variant of this problem in which waiting times have a linear cost. For the latter problem, we also propose an appropriate label structure with associated resource extension functions and dominance rules. We perform these comparisons by using a rigorous methodology, which consists of parameterizing several features of these algorithms, obtaining a good parameter configuration for each algorithm, and analyzing the performance of these configurations on benchmark instances. In order to obtain good configurations, we make use of irace, which is a tool for automated parameter tuning, while statistical tests are used for performance comparisons. Our results show that a class of hybrid algorithms with certain features based on ng-route relaxation outperforms all the others.Article Citation - WoS: 3Citation - Scopus: 5Qubo Formulations and Characterization of Penalty Parameters for the Multi-Knapsack Problem(IEEE-Inst Electrical Electronics Engineers Inc, 2025) Guney, Evren; Ehrenthal, Joachim; Hanne, ThomasThe Multi-Knapsack Problem (MKP) is a fundamental challenge in operations research and combinatorial optimization. Quantum computing introduces new possibilities for solving MKP using Quadratic Unconstrained Binary Optimization (QUBO) models. However, a key challenge in QUBO formulations is the selection of penalty parameters, which directly influence solution feasibility and algorithm performance. In this work, we develop QUBO formulations for two MKP variants-the Multidimensional Knapsack Problem (MDKP) and the Multiple Knapsack Problem (MUKP)-and provide an algebraic characterization of their penalty parameters. We systematically evaluate their impact through quantum simulation experiments and compare the performance of the two leading quantum optimization approaches: Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing, alongside a state-of-the-art classical solver. Our results indicate that while classical solvers remain superior, careful tuning of penalty parameters has a strong impact on quantum optimization outcomes. QAOA is highly sensitive to parameter choices, whereas quantum annealing produces more stable results on small to mid-sized instances. Further, our results reveal that MDKP instances can maintain feasibility at penalty values below theoretical bounds, while MUKP instances show greater sensitivity to penalty reductions. Finally, we outline directions for future research in solving MKP, including adaptive penalty parameter tuning, hybrid quantum-classical approaches, and practical optimization strategies for QAOA, as well as real-hardware evaluations.