Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 20 for “"Data-driven Optimization"”.
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Data-Driven Optimization of Automated Speed Enforcement Logistics
… during a planning period. This study proposes a data-driven model to classify camera site locations based on the effectiveness of ASE enforcement. Then, a Markov decision process optimization model is presented to find the optimal camera locations at each cycle and the length of the cycles for …
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Data-driven optimization and analytics for operations management applications
In this thesis, we study data-driven decision making in operation management contexts, with a focus on both theoretical and practical aspects. The first part of the thesis analyzes the well-known newsvendor model but under the assumption that, even though demand is stochastic, its probability …
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Data-driven optimization with behavioral considerations : applications to pricing
… perception observed from the experimental data and consumers' potential loss aversion. We embed this consumer model into the retailer's markdown optimization and examine the impact of these behavioral factors on the retailer's optimal strategy.
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Data-driven optimization for police beat design in South Fulton, Georgia
… Police Department (SFPD), using a predictive data-driven optimization approach. Due to rapid urban development and population growth, the original police beats arrangement designed in the 1970s was far from efficient, which leads to low policing efficiency and long 911 call response time. We …
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Robust data-driven optimization for dynamic and decision-dependent systems under uncertainty
… decision-making systems increasingly rely on data-driven models to guide actions in dynamic environments. However, in many real-world applications, the data available for learning is limited, biased, or influenced by previous decisions, and the resulting uncertainty can significantly degrade …
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Three essays on data-driven optimization and causal inference for online platform operations
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms
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Trustworthy decision-making in data-driven optimization: Fairness, generalization, and interpretability for revenue management and inventory control in e-commerce
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms
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Empirical comparison of robust, data driven and stochastic optimization
… compare computationally four methods for solving optimization problems under uncertainty: * Robust Optimization (RO) * Adaptive Robust Optimization (ARO) * Data Driven Optimization (DDO) * stochastic Programming (SP) We have implemented several computation experiments to demonstrate the different …
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Simulation of lipid membrane models at coarse-grained resolution Towards novel methodologies with improved capabilities
… of lipid types. Building on this foundation, a data-driven optimization protocol based on automatic differentiation is introduced, systematically refining CG FFs by minimizing a multi-objective loss function that combines bottom-up and top-down parametrization targets. To move beyond the …
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Efficient development and uncertainty propagation of aviation fuel chemical kinetic models
… The method uses experimental ignition delay data from a shock tube and a rapid compression machine as primary validation targets, which are then used for data-driven optimization of chemical kinetic mechanisms. To efficiently propagate and reduce model uncertainty, a hybrid response surface …
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Análisis en Tiempo Real para el Internet Industrial de las Cosas
… generating continuous streams of heterogeneous data. While this transformation enables intelligent automation, predictive maintenance, and data-driven optimization, traditional centralized cloud-based architectures are unable to satisfy the strict latency, scalability, and contextual-awareness …
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Measurement of ignition delay time of jet fuels in shock tube for the development of the chemical kinetic mechanism
… shock tube with regularization-constrained, data-driven optimization of lumped reaction rates. GC×GC compositional analysis, together with prior work on F-24 and Jet-A, is used to justify Jet-A HyChem as the base mechanism and to highlight the distinct character of alternative fuels such as …
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Prediction and optimization in school choice
In this thesis, I study how data-driven optimization can be used to improve school choice. In a typical school choice system, each student receives a set of school options, called the student's menu. Based on his/her menu, each student submits a preference ranking of schools in the menu. Based on …
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Deploying EV Charging Infrastructure using Optimization with Real-Life Data
… operators. This research develops and validates optimization models for Level 2 EV charging station deployment and pricing in parking garages, utilizing a novel API-available parking occupancy dataset from Santa Monica spanning 2018-2022. Three optimization approaches— GridSearch, Gurobi …
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Towards A Robust Integrated Urban Mobility System: Public Transit and Ride-Sharing Systems
… further protection against uncertainty, robust optimization (RO) techniques are introduced to construct a robust version of the MIVR model. Problem-specific uncertainty sets are designed for the robust MIVR model. The second study further explores different approaches for handling demand …
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Essays on Decision Making Under Uncertainty
… the evolution of cooperation. In Chapter 3, "Data-Driven Optimization: A Reproducing Kernel Hilbert Space Approach," we present two methods, based on regression in reproducing kernel Hilbert spaces, for solving an optimization problem with uncertain parameters for which we have historical …
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Robust and Efficient AI-models for Medical Image Reconstruction, Segmentation, and Multimodal Knowledge Distillation
… must operate reliably with limited annotated data, generalize across diverse imaging modalities and patient populations, function efficiently within computationally constrained environments, and remain robust against noise and adversarial perturbations. Overcoming these barriers requires …
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Data based predictive control: Application to water distribution networks
In this thesis, the main goal is to propose novel data based predictive controllers to cope with complex industrial infrastructures such as water distribution networks. This sort of systems have several inputs and out- puts, complicate nonlinear dynamics, binary actuators and they are usually …
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Computational biology in plant tissue culture: Systematic evaluation of machine learning approaches for callus induction in Solanum L.: A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University
Optimization of plant tissue culture systems remains a major biotechnological challenge; yet successful callus induction, the formation of unorganized, proliferating cell masses from differentiated tissues, enables transformative applications in agriculture, medicine, and fundamental research. …
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MATHEMATICAL PROGRAMMING METHODS FOR PARTIALLY UNDEFINED OPTIMIZATION MODELS
Questa tesi propone nuovi metodi per formulare e risolvere modelli di programmazione matematica parzialmente indefiniti. Mediante l'utilizzo di metodi di programmazione matematica e solutori per modelli di programmazione lineare e quadratica, puntiamo ad automatizzare il processo di generare …