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 11 of 11 for “"Two-stage Stochastic Optimization"”.
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Parallel algorithms for two-stage stochastic optimization
We develop scalable algorithms for two-stage stochastic program optimizations. We propose performance optimizations such as cut-window mechanism in Stage 1 and scenario clustering in Stage 2 of benders method for solving two-stage stochastic programs. A naive implementation of benders method has …
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Approaches to Joint Base Station Selection and Adaptive Slicing in Virtualized Wireless Networks
Wireless network virtualization is a promising avenue of research for next-generation 5G cellular networks. This work investigates the problem of selecting base stations to construct virtual networks for a set of service providers, and adaptive slicing of the resources between the service providers …
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Toward Microtransit: Design and Operations of Reservation-based Systems
… In this thesis, we discuss new strategic optimization frameworks for microtransit planning and operations. We develop decomposition algorithms to achieve insights at scale, and we evaluate new decision-making prototypes for transportation planners over case studies based on real-world …
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Operational efficiency through resource planning optimization and work process improvement
… leaks that may be called in. At the execution stage, when the jobs are carried out by crews, the lack of standardization in work processes dealing with granting and approval of overtime, productivity tracking, data collection, and imperfect alignment of incentives make it difficult to get the …
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Optimization of retrofit decisions as risk mitigation strategies for infrastructure
… three aspects, this study formulates a general stochastic optimization problem to find the optimal retrofit for infrastructure. The optimization problem is formulated to examine two objectives, i.e., to minimize the total retrofit cost and the performance losses of the infrastructure. The …
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Dynamic Ridesharing under Travel Time Uncertainty: Passenger Preference and Optimal Assignment Methods
… time uncertainty. Travel times on urban road networks on which DRS services operate are often highly variable, and the potential for vehicle detours due to pooling increases travel time uncertainty for DRS passengers when compared to exclusive ridehailing. This dissertation investigates the …
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Data-driven decision-making under uncertainty in power systems
… nine publications, each of which lays out an optimization framework under uncertainty or a decision-support tool, on which SOs can capitalize in ensuring a reliable power system operation around the clock. The deepening penetration of renewables greatly exacerbates the uncertainty and …
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Distributionally robust solution schemes for two-stage optimization and interdiction problems under uncertainty
… in the presence of uncertainty. One can use optimization models with uncertain parameters to formulate the decision problems. Despite its wide applications in real-world problems, optimization under uncertainty gives rise to computational challenges. This thesis aims to design tractable …
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Integrated Microgrid Expansion Planning and Policy Making under Uncertainty in Power Electricity Market
… scale integration of microgrids into energy networks, which poses several principle challenges to be addressed. First, deploying a microgrid on the main-grid depends on estimated profits for potential power investors in the electricity capacity market. Hence, there is a clear need for …
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Aligning Machine Learning and Robust Decision-Making
… of existing methods. We present a meta-optimization machine learning framework to learn fast approximations to general convex problems. We further apply this within an end-to-end learning framework which trains ML models with an optimization-based loss function to minimize the decision …
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Distributed Optimization Algorithms for Networked Systems
<p>Distributed optimization methods allow us to decompose an optimization problem</p><p>into smaller, more manageable subproblems that are solved in parallel. For this</p><p>reason, they are widely used to solve large-scale problems arising in areas as diverse</p><p>as wireless communications, …