https://hdl.handle.net/20.500.12960/31 2026-05-14T09:02:13Z https://hdl.handle.net/20.500.12960/1824 Modeling Gradual and Joint Coverage in Location Problems Karatas, Mumtaz; Eriskin, Levent; Yakici, Ertan Location problems are a core area of research within OR/MS and decision sciences, with diverse applications in logistics, facility location, healthcare, defense, energy and transportation. One important feature of location problems is the coverage of demand or service areas by facilities, which can have significant economic, social, and environmental implications. Conventional models often assume binary or deterministic coverage, where a facility either fully covers a demand point or does not cover it at all. Although this simplification is useful for theoretical derivations, back-of-the-envelope calculations, and performance comparison, it overlooks the nuances and complexities of real-world scenarios. In this study, we provide an overview of the modeling challenges in location problems that incorporate gradual and joint coverage, where multiple facilities provide partial and cooperative coverage to demand points. Based on previous studies in this domain, we present mathematical formulations, and discuss techniques for linearization and approximation. As an illustrative example, we discuss a capacitated minimal covering location problem (MCLP) adapted from [21], which aims to determine the location and size of undesirable facilities in a given region. We start by introducing the nonlinear formulation that minimizes the sum of demand covered by those undesirable facilities. Subsequently, we introduce three integer linear programming formulations given in [21], two of which involve linear approximations based on a separable programming approach and a tangent line approximation method, while the third involves an exact reformulation of the problem. We also discuss the impact of linearization approximation errors on solution quality and time. © 2026 by World Scientific Publishing Co. 2025-01-01T00:00:00Z https://hdl.handle.net/20.500.12960/1794 Predictive Maintenance for Sustainable Marine Operations Using Machine Learning and Deep Learning Kıyak, Erkan The role of the maritime industry is essential in global trade and economic growth, but it faces challenges, including maintenance and environmental impact. With Industry 4.0 introducing advanced technologies like Machine Learning (ML) and Deep Learning (DL) that provide innovative solutions for predictive maintenance, this section delves into how the use of ML and DL can lead to a complete overhaul of traditional maintenance approaches. It discusses operational efficiency through prediction-based systems due to their effective implementation, which involves adoption from the theories as explained using real-world examples, thus stopping reactive practices that only manage equipment when it breaks down. We also review data considerations—as well as implementation strategies—to equip stakeholders with practical knowledge that will allow them to easily navigate through this complicated field of technology in the future. In conclusion, the chapter presents a three-dimensional view of how tapping artificial intelligence can help slash maintenance costs—minimizing equipment downtime—greasing the wheels for more eco-friendly marine operations. 2025-01-01T00:00:00Z https://hdl.handle.net/20.500.12960/1789 Drone selection for forest surveillance and fire detection using interval valued neutrosophic edas method Gül, Alize Yaprak; Çakmak, Emre; Karakaş, Atiye Ece Forest fires are one of the major causes for deforestation resulting in significant economic and environmental losses. The application of drones has been extended to various areas including disaster management. Since drones offer numerous advantages like real-time surveillance, task planning capabilities and autonomy, they are utilized in early detection systems for forest fires. The selection of a drone type for this purpose involves a complex system of multiple factors and conflicting information, for which the use of multi-criteria decision-making (MCDM) methods have been found to be yielding effective results. The aim of this study is to present a decision framework for drone selection problem in the context of forest fire surveillance and detection. This study contributes by (i) pointing out to the gap that the drone selection problem for forest surveillance and fire detection has been sparsely addressed, (ii) presenting an extensive literature review, (iii) extracting the relevant criteria through a literature review and interviews with the experts in field, (iv) assessing the alternatives by the proposed framework based on interval valued neutrosophic evaluation based on distance from average solution (IVN EDAS) method. The proposed framework is demonstrated by a case study consisting of four drone alternatives and 14 criteria. In accordance with the extant literature, the criteria related to the visual capabilities and diagnosis are evaluated as the most crucial features. A sensitivity analysis is carried out to check for the robustness by varying the criteria weights and a comparative analysis is conducted with interval valued neutrosophic technique for preference by similarity to the ideal solution (IVN TOPSIS) and interval valued neutrosophic combinative distance-based assessment (IVN CODAS) methods to validate the veracity of the method. https://hdl.handle.net/20.500.12960/1758 Risk Assessment of Employing Digital Robots in Process Automation Doğan, Onur; Arslan, Özlem; Cengiz Tırpan, Esra; Cebi, Selçuk Using digital technologies is essential to gain a competitive advantage in the global market by adapting to new business models. While digital technologies make business processes efficient, they enable companies to make faster and more accurate decisions by automating daily and routine process tasks. Robotic process automation (RPA) automates routine and repetitive business processes, allowing many jobs performed by humans to be performed faster. This way, advantages such as reduced error rates, reduced costs, increased production speed, and labor productivity are provided. For the successful implementation of RPA, potential risks need to be considered. In this study, failure mode and effect analysis (FMEA) based on decomposed fuzzy sets (DFSs), a new extension of intuitionistic fuzzy sets, has been used to evaluate subjectiveness in expert judgments. Differing from the other extensions of fuzzy set theory, the advantage of DFSs is to simultaneously consider decision-makers’ optimistic and pessimistic answers. Thus, the answer given by the decision-maker to the positive and negative questions on the same subject defines the indeterminacy of the decision-maker, and the method takes this indeterminacy into account in the evaluation. This study assesses and evaluates the potential risks of six digital robots in process automation. Thirteen risks were individually assessed for each automated process. This study found “Sustainability challenge” critical in three processes, “Absence of governance management” in two, and “Security“ in one. Variability in risk importance arose from process vulnerabilities. 2024-01-01T00:00:00Z