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Relevant bibliographies by topics / Approximate dynamic programming / Dissertations / Theses

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Dissertations / Theses on the topic 'Approximate dynamic programming'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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1

Sadiq, Mohammad. "Approximate Dynamic Programming Methods in HEVs." Thesis, KTH, Maskinkonstruktion (Inst.), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-182762.

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Hybrid Electric Vehicles (HEV) have been gaining popularity worldwide for their efficient fuel consumption and therefore an overall reduction in the oil demand. This greatly benefits the environment since this leads to lesser emissions and hence lower greenhouse effect. Therefore research in this field is very active with a demand for new and better fuel consumption strategies. Many different methods for the energy management of HEV are being used, one particular method which promises global optimality is Dynamic Programming. Dynamic Programming yields a global optimum results but suffers from

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Vyzas, Elias. "Approximate dynamic programming for some queueing problems." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/10282.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1997, and Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering, 1997.<br>Includes bibliographical references (p. 81-82).<br>by Elias Vyzas.<br>M.S.

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Sauré, Antoine. "Approximate dynamic programming methods for advance patient scheduling." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/43448.

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This dissertation studies an advance multi-priority patient scheduling problem. Patrick et al. (2008) formulated a version of this problem as a discounted infinite-horizon Markov decision process (MDP) and studied it using a linear programming method based on an affine value function approximation. This thesis starts by presenting an alternative solution approach for this problem based on the use of simulation, a policy iteration framework and a non-linear value function approximation. It then extends the dynamic multi-priority patient scheduling model and solution approach developed by Patric

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Child, Christopher H. T. "Approximate dynamic programming with parallel stochastic planning operators." Thesis, City University London, 2011. http://openaccess.city.ac.uk/1109/.

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This thesis presents an approximate dynamic programming (ADP) technique for environment modelling agents. The agent learns a set of parallel stochastic planning operators (P-SPOs) by evaluating changes in its environment in response to actions, using an association rule mining approach. An approximate policy is then derived by iteratively improving state value aggregation estimates attached to the operators using the P-SPOs as a model in a Dyna-Q-like architecture. Reinforcement learning and dynamic programming are powerful techniques for automated agent decision making in stochastic environme

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Liu, Ning. "Approximate dynamic programming algorithms for production-planning problems." Thesis, Wichita State University, 2013. http://hdl.handle.net/10057/10636.

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The capacitated lot-sizing problem (CLSP) is a core problem for successfully reducing overall costs in any production process. The exact approaches proposed for solving the CLSP are based on two major methods: mixed-integer programming and dynamic programming. This thesis provides a new idea for approximating the inventory cost function to be used in a truncated dynamic program for solving the CLSP. In the proposed method, by using only a partial dynamic process, the inventory cost function is approximated, and then the resulting approximate cost function is used as a value function in each st

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Demir, Ramazan. "An approximate dynamic programming approach to discrete optimization." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/9137.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2000.<br>Includes bibliographical references (leaves 181-189).<br>We develop Approximate Dynamic Programming (ADP) methods to integer programming problems. We describe and investigate parametric, nonparametric and base-heuristic learning approaches to approximate the value function in order to break the curse of dimensionality. Through an extensive computational study we illustrate that our ADP approach to integer programming competes successfully with existing methodologies including

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Cai, C. "Adaptive traffic signal control using approximate dynamic programming." Thesis, University College London (University of London), 2010. http://discovery.ucl.ac.uk/20164/.

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This thesis presents a study on an adaptive traffic signal controller for real-time operation. An approximate dynamic programming (ADP) algorithm is developed for controlling traffic signals at isolated intersection and in distributed traffic networks. This approach is derived from the premise that classic dynamic programming is computationally difficult to solve, and approximation is the second-best option for establishing sequential decision-making for complex process. The proposed ADP algorithm substantially reduces computational burden by using a linear approximation function to replace th

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Nadarajah, Selvaprabu. "Approximate Dynamic Programming for Commodity and Energy Merchant Operations." Research Showcase @ CMU, 2014. http://repository.cmu.edu/dissertations/350.

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We study the merchant operations of commodity and energy conversion assets. Examples of such assets include natural gas pipelines systems, commodity swing options, and power plants. Merchant operations involves managing these assets as real options on commodity and energy prices with the objective of maximizing the market value of these assets. The economic relevance of natural gas conversion assets has increased considerably since the occurrence of the oil and gas shale boom; for example, the Energy Information Agency expects natural gas to be the source of 30% of the world's electricity prod

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Bethke, Brett (Brett M. ). "Kernel-based approximate dynamic programming using Bellman residual elimination." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/57544.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2010.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student submitted PDF version of thesis.<br>Includes bibliographical references (p. 207-221).<br>Many sequential decision-making problems related to multi-agent robotic systems can be naturally posed as Markov Decision Processes (MDPs). An important advantage of the MDP framework is the ability to utilize stochastic system models

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Valenti, Mario J. (Mario James) 1976. "Approximate dynamic programming with applications in multi-agent systems." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/40330.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>MIT Institute Archives copy: contains CDROM of thesis in .pdf format.<br>Includes bibliographical references (p. 151-161).<br>This thesis presents the development and implementation of approximate dynamic programming methods used to manage multi-agent systems. The purpose of this thesis is to develop an architectural framework an

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11

Keng, Leng Hui. "Approximate String Matching With Dynamic Programming and Suffix Trees." UNF Digital Commons, 2006. http://digitalcommons.unf.edu/etd/196.

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The importance and the contribution of string matching algorithms to the modern society cannot be overstated. From basic search algorithms such as spell checking and data querying, to advanced algorithms such as DNA sequencing, trend analysis and signal processing, string matching algorithms form the foundation of many aspects in computing that have been pivotal in technological advancement. In general, string matching algorithms can be divided into the categories of exact string matching and approximate string matching. We study each area and examine some of the well known algorithms. We prob

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Boussios, Constantinos I. "An approach for nonlinear control design via approximate dynamic programming." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/9792.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1998.<br>Includes bibliographical references (p. 173-181).<br>This thesis proposes and studies a methodology for designing controllers for nonlinear dynamic systems. We are interested in state feedback controllers (policies) that stabilize the state in a given region around an equilibrium point while minimizing a cost functional that captures the performance of the closed loop system. The optimal control problem can be solved in principle using dynamic programming algorithms such as policy iteration. Exact

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Keller, Philipp Wilhelm. "Automatic basis function construction for reinforcement learning and approximate dynamic programming." Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=19284.

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We address the problem of automatically constructing basis functions for linear approximation of the value function of a Markov decision process (MDP). Our work builds on results by Bertsekas and Casta˜non (1989) who proposed a method for automatically aggregating states to speed up value iteration. We propose to use neighbourhood component analysis , a dimensionality reduction technique created for supervised learning, in order to map a high-dimensional state space to a low-dimensional space, based on the Bellman error, o

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Hwang, Daw-sen. "Projected equation and aggregation-based approximate dynamic programming methods for Tetris." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66033.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 65-67).<br>In this thesis, we survey approximate dynamic programming (ADP) methods and test the methods with the game of Tetris. We focus on ADP methods where the cost-to- go function J is approximated with [phi]r, where [phi] is some matrix and r is a vector with relatively low dimension. There are two major categories of methods: projected equation methods and aggregation methods. In projected equ

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Löhndorf, Nils, David Wozabal, and Stefan Minner. "Optimizing Trading Decisions for Hydro Storage Systems using Approximate Dual Dynamic Programming." INFORMS, 2013. http://dx.doi.org/10.1287/opre.2013.1182.

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We propose a new approach to optimize operations of hydro storage systems with multiple connected reservoirs whose operators participate in wholesale electricity markets. Our formulation integrates short-term intraday with long-term interday decisions. The intraday problem considers bidding decisions as well as storage operation during the day and is formulated as a stochastic program. The interday problem is modeled as a Markov decision process of managing storage operation over time, for which we propose integrating stochastic dual dynamic programming with approximate dynamic programming. We

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Jeria, David (David O. Jeria López). "An approximate dynamic programming approach to risk sensitive control of execution costs." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/55112.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2009.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 43-44).<br>We study the problem of optimal execution within a dynamic programming framework. Given an exponential objective function, system variables which are normally distributed, and linear market dynamics, we derive a closed form solution for optimal trading trajectories. We show that a trader lacking private information has trajectories which are static in nature, whilst a trader w

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Pratikakis, Nikolaos. "Multistage decisions and risk in Markov decision processes towards effective approximate dynamic programming architectures /." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/31654.

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Thesis (Ph.D)--Chemical Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: Jay H. Lee; Committee Member: Martha Grover; Committee Member: Matthew J. Realff; Committee Member: Shabbir Ahmed; Committee Member: Stylianos Kavadias. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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Astaraky, Davood. "A Simulation Based Approximate Dynamic Programming Approach to Multi-class, Multi-resource Surgical Scheduling." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/23622.

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The thesis focuses on a model that seeks to address patient scheduling step of the surgical scheduling process to determine the number of surgeries to perform in a given day. Specifically, provided a master schedule that provides a cyclic breakdown of total OR availability into specific daily allocations to each surgical specialty, we look to provide a scheduling policy for all surgeries that minimizes a combination of the lead time between patient request and surgery date, overtime in the ORs and congestion in the wards. We cast the problem of generating optimal control strategies into the fr

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Nachmani, Gil. "Minimum-energy flight paths for UAVs using mesoscale wind forecasts and approximate dynamic programming." Thesis, Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion-image.exe/07Dec%5FNachmani.pdf.

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Thesis (M.S. in Operations Research)--Naval Postgraduate School, December 2007.<br>Thesis Advisor(s): Royset, Johannes O. Description based on title screen as viewed on January 22, 2007. Includes bibliographical references (p. 57-60). Also available in print.

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Xu, Jinbiao. "An approximate dynamic programming approach for coordinated charging control at vehicle-to-grid aggregator." Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/36970.

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A vehicle-to-grid (V2G) aggregator is an agent between the power grid and the plug-in hybrid electrical vehicles (PHEVs). In this thesis, we study the coordinated charging control at a V2G aggregator. The coordinated charging control brings the advantages of minimizing the charging cost and reducing the power losses, by coordinating the control sequences of a group of PHEVs. On one hand, the lower cost of charging gives the users of PHEVs an incentive to cooperate. On the other hand, with an increasing popularity of PHEVs, the impact on the power distribution grid such as power losses should b

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Hearnes, Warren E. II. "Near-optimal intelligent control for continuous set-point regulator problems via approximate dynamic programming." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/24882.

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Chen, Xiaoting. "Optimal Control of Non-Conventional Queueing Networks: A Simulation-Based Approximate Dynamic Programming Approach." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1427799942.

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23

Yang, Xinan. "Top-percentile traffic routing problem." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/5883.

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Multi-homing is a technology used by Internet Service Provider (ISP) to connect to the Internet via multiple networks. This connectivity enhances the network reliability and service quality of the ISP. However, using multi-networks may imply multiple costs on the ISP. To make full use of the underlying networks with minimum cost, a routing strategy is requested by ISPs. Of course, this optimal routing strategy depends on the pricing regime used by network providers. In this study we investigate a relatively new pricing regime – top-percentile pricing. Under top-percentile pricing, network prov

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Li, Dong. "An approximate dynamic programming approach to the scheduling of impatient jobs in a clearing system." Thesis, Lancaster University, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.557156.

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A single server is faced with a collection of jobs of varying duration and urgency. Before service starts, all jobs are subject to an initial triage, i.e., an assessment of both their urgency and of their service requirement, and are allocated to distinct classes. Jobs in one class have independent and identically distributed lifetimes, during which they are available for service. Should a job's lifetime expire before its service begins then it is lost from the system unserved. The goal is to schedule the jobs for service to maximise the expected number served to completion. Two heuristic poli

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Lee, Jong Min. "A Study on Architecture, Algorithms, and Applications of Approximate Dynamic Programming Based Approach to Optimal Control." Diss., Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/5048.

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This thesis develops approximate dynamic programming (ADP) strategies suitable for process control problems aimed at overcoming the limitations of MPC, which are the potentially exorbitant on-line computational requirement and the inability to consider the future interplay between uncertainty and estimation in the optimal control calculation. The suggested approach solves the DP only for the state points visited by closed-loop simulations with judiciously chosen control policies. The approach helps us combat a well-known problem of the traditional DP called 'curse-of-dimensionality,' while it

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Seelhof, Michael. "Long term infrastructure investments under uncertainty in the electric power sector using approximate dynamic programming techniques." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90724.

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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 179-183).<br>A computer model was developed to find optimal long-term investment strategies for the electric power sector under uncertainty with respect to future regulatory regimes and market conditions. The model is based on a multi-stage problem formulation and uses approximate dynamic programming techniques to find an optimal solution. The model was

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Hanselmann, Thomas. "Approximate dynamic programming with adaptive critics and the algebraic perceptron as a fast neural network related to support vector machines." University of Western Australia. School of Electrical, Electronic and Computer Engineering, 2003. http://theses.library.uwa.edu.au/adt-WU2004.0005.

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[Truncated abstract. Please see the pdf version for the complete text. Also, formulae and special characters can only be approximated here. Please see the pdf version of this abstract for an accurate reproduction.] This thesis treats two aspects of intelligent control: The first part is about long-term optimization by approximating dynamic programming and in the second part a specific class of a fast neural network, related to support vector machines (SVMs), is considered. The first part relates to approximate dynamic programming, especially in the framework of adaptive critic designs (ACDs

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Stellato, Bartolomeo. "Mixed-integer optimal control of fast dynamical systems." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:b8a7323c-e36e-45ec-ae8d-6c9eb4350629.

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Many applications in engineering, computer science and economics involve mixed-integer optimal control problems. Solving these problems in real-time is a challenging task because of the explosion of integer combinations to evaluate. This thesis focuses on the development of new algorithms for mixed-integer programming with an emphasis on optimal control problems of fast dynamical systems with discrete controls. The first part proposes two reformulations to reduce the computational complexity. The first reformulation avoids integer variables altogether. By considering a sequence of switched dyn

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Ramirez, Jose A. "Optimal and Simulation-Based Approximate Dynamic Programming Approaches for the Control of Re-Entrant Line Manufacturing Models." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1282329260.

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Tosukhowong, Thidarat. "Dynamic Real-time Optimization and Control of an Integrated Plant." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/14087.

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Applications of the existing steady-state plant-wide optimization and the single-scale fast-rate dynamic optimization strategies to an integrated plant with material recycle have been impeded by several factors. While the steady-state optimization formulation is very simple, the very long transient dynamics of an integrated plant have limited the optimizers execution rate to be extremely low, yielding a suboptimal performance. In contrast, performing dynamic plant-wide optimization at the same rate as local controllers requires exorbitant on-line computational load and may increase the sensiti

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Keerthisinghe, Chanaka. "Fast Solution Techniques for Energy Management in Smart Homes." Thesis, The University of Sydney, 2016. http://hdl.handle.net/2123/16033.

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In the future, residential energy users will seize the full potential of demand response schemes by using an automated smart home energy management system (SHEMS) to schedule their distributed energy resources. The underlying optimisation problem facing a SHEMS is a sequential decision making problem under uncertainty because the states of the devices depend on the past state. There are two major challenges to optimisation in this domain; namely, handling uncertainty, and planning over suitably long decision horizons. In more detail, in order to generate high quality schedules, a SHEMS should

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Wong, Wee Chin. "Estimation and control of jump stochastic systems." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31775.

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Thesis (Ph.D)--Chemical Engineering, Georgia Institute of Technology, 2010.<br>Committee Chair: Jay H. Lee; Committee Member: Alexander Gray; Committee Member: Erik Verriest; Committee Member: Magnus Egerstedt; Committee Member: Martha Grover; Committee Member: Matthew Realff. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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Papageorgiou, Dimitri Jason. "Optimization in maritime inventory routing." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/50117.

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The primary aim of this thesis is to develop effective solution techniques for large-scale maritime inventory routing problems that possess a core substructure common in many real-world applications. We use the term “large-scale” to refer to problems whose standard mixed-integer linear programming (MIP) formulations involve tens of thousands of binary decision variables and tens of thousands of constraints and require days to solve on a personal computer. Although a large body of literature already exists for problems combining vehicle routing and inventory control for road-based applications,

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Löhndorf, Nils, and Stefan Minner. "Simulation Optimization for the Stochastic Economic Lot Scheduling Problem." Taylor and Francis, 2013. http://dx.doi.org/10.1080/0740817X.2012.662310.

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We study simulation optimization methods for the stochastic economic lot scheduling problem. In contrast to prior research, we focus on methods that treat this problem as a black box. Based on a large-scale numerical study, we compare approximate dynamic programming with a global search for parameters of simple control policies. We propose two value function approximation schemes based on linear combinations of piecewise- constant functions as well as control policies that can be described by a small set of parameters. While approximate value iteration worked well for small problems with

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Regatti, Jayanth Reddy. "Dynamic Routing for Fuel Optimization in Autonomous Vehicles." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1524145002064074.

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Cakir, Fahrettin. "Data-centric solution methodologies for vehicle routing problems." Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/2052.

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Data-driven decision making has become more popular in today’s businesses including logistics and vehicle routing. Leveraging historical data, companies can achieve goals such as customer satisfaction management, scalable and efficient operation, and higher overall revenue. In the management of customer satisfaction, logistics companies use consistent assignment of their drivers to customers over time. Creating this consistency takes time and depends on the history experienced between the company and the customer. While pu

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Bountourelis, Theologos. "Efficient pac-learning for episodic tasks with acyclic state spaces and the optimal node visitation problem in acyclic stochastic digaphs." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/28144.

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Thesis (M. S.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: Reveliotis, Spyros; Committee Member: Ayhan, Hayriye; Committee Member: Goldsman, Dave; Committee Member: Shamma, Jeff; Committee Member: Zwart, Bert.

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Jeddi, Babak. "A coordinated energy management scheme in a residential neighborhood under given market framework." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/200710/1/Babak_Jeddi_Thesis.pdf.

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This thesis proposes a computationally efficient home energy management system to optimize the electricity payment and improve the occupant's comfort degree by appropriately scheduling all devices of the home. It incorporates solar panels, battery systems, thermostatically controlled appliances, and deferrable appliances. Also, this thesis develops a coordinated framework for the operation of multiple home energy management systems in a residential neighborhood based on the optimal and secure operation of the grid. The coordinated load scheduling framework enables customers to cooperate to opt

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Fiocchi, Leonardo. "A Reinforcement Learning strategy for Satellite Attitude Control." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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In recent years space missions for both scientific and commercial purposes have substantially increased. More and more spacecrafts have flexible multibody structures, are subject to liquid volume changes, fuel utilization, and other behaviours that alter the parameters of the spacecraft's model. Moreover, varying disturbances such as the gravity angle torque due to Earth's gravitational field, aerodynamic torque, and others may lead to unwanted effects on the satellite's dynamics. These uncertainties in the model and environment descriptions make it difficult to set up an exact mathematical m

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Goodson, Justin Christopher. "Solution methodologies for vehicle routing problems with stochastic demand." Diss., University of Iowa, 2010. https://ir.uiowa.edu/etd/675.

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We present solution methodologies for vehicle routing problems (VRPs) with stochastic demand, with a specific focus on the vehicle routing problem with stochastic demand (VRPSD) and the vehicle routing problem with stochastic demand and duration limits (VRPSDL). The VRPSD and the VRPSDL are fundamental problems underlying many operational challenges in the fields of logistics and supply chain management. We model the VRPSD and the VRPSDL as large-scale Markov decision processes. We develop cyclic-order neighborhoods, a general methodology for solving a broad class of VRPs, and use this techniq

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Yin, Biao. "Contrôle adaptatif des feux de signalisation dans les carrefours : modélisation du système de trafic dynamique et approches de résolution." Thesis, Belfort-Montbéliard, 2015. http://www.theses.fr/2015BELF0279/document.

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La régulation adaptative des feux de signalisation est un problème très important. Beaucoup de chercheurs travaillent continuellement afin de résoudre les problémes liés à l’embouteillage dans les intersections urbaines. Il devient par conséquent très utile d’employer des algorithmes intelligents afin d’améliorer les performances de régulation et la qualité du service. Dans cette thèse, nous essayons d'étudier ce problème d’une part à travers une modèlisation microscopique et dynamique en temps discret, et d’autre part en explorant plusieurs approches de résoltion pour une intersection isolée

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Holguin, Mijail Gamarra. "Planejamento probabilístico usando programação dinâmica assíncrona e fatorada." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-14042013-131306/.

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Processos de Decisão Markovianos (Markov Decision Process - MDP) modelam problemas de tomada de decisão sequencial em que as possíveis ações de um agente possuem efeitos probabilísticos sobre os estados sucessores (que podem ser definidas por matrizes de transição de estados). Programação dinâmica em tempo real (Real-time dynamic programming - RTDP), é uma técnica usada para resolver MDPs quando existe informação sobre o estado inicial. Abordagens tradicionais apresentam melhor desempenho em problemas com matrizes esparsas de transição de estados porque podem alcançar eficientemente a convergê

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Filho, Antonio Martins Lima. "Alocação dinâmica de recursos: aplicação ao transporte rodoviário de cargas em longa distância." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/3/3138/tde-25082011-135158/.

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O planejamento operacional de um sistema de transporte de longa distância implica resolver um problema de otimização de rede dinâmica, visando a efetuar, de forma eficaz e eficiente, o atendimento às demandas de cargas, utilizando a capacidade de transporte disponível. A metodologia de solução proposta utiliza a abordagem de Rede de Filas Logísticas, a qual substitui o processo de otimização global da rede (usualmente utilizando Programação Linear Inteira) por um modelo de Programação Dinâmica Estocástica, Aproximada e Adaptativa, que permite a resolução de uma série de subproblemas delimitado

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Heng, Jeremy. "On the use of transport and optimal control methods for Monte Carlo simulation." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:6cbc7690-ac54-4a6a-b235-57fa62e5b2fc.

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This thesis explores ideas from transport theory and optimal control to develop novel Monte Carlo methods to perform efficient statistical computation. The first project considers the problem of constructing a transport map between two given probability measures. In the Bayesian formalism, this approach is natural when one introduces a curve of probability measures connecting the prior to posterior by tempering the likelihood function. The main idea is to move samples from the prior using an ordinary differential equation (ODE), constructed by solving the Liouville partial differential equatio

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Andrade, Gustavo Araújo de. "PROGRAMAÇÃO DINÂMICA HEURÍSTICA DUAL E REDES DE FUNÇÕES DE BASE RADIAL PARA SOLUÇÃO DA EQUAÇÃO DE HAMILTON-JACOBI-BELLMAN EM PROBLEMAS DE CONTROLE ÓTIMO." Universidade Federal do Maranhão, 2014. http://tedebc.ufma.br:8080/jspui/handle/tede/517.

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Made available in DSpace on 2016-08-17T14:53:28Z (GMT). No. of bitstreams: 1 Dissertacao Gustavo Araujo.pdf: 2606649 bytes, checksum: efb1a5ded768b058f25d23ee8967bd38 (MD5) Previous issue date: 2014-04-28<br>In this work the main objective is to present the development of learning algorithms for online application for the solution of algebraic Hamilton-Jacobi-Bellman equation. The concepts covered are focused on developing the methodology for control systems, through techniques that aims to design online adaptive controllers to reject noise sensors, parametric variations and modeling error

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Romão, Oberlan Christo. "O problema de corte não-guilhotinado multiperíodo com sobras aproveitáveis." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-01022018-180800/.

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Neste trabalho, estudamos o problema de corte bidimensional multiperíodo com sobras aproveitáveis, que consiste em cortar objetos grandes visando a produção de um conjunto de itens menores. Supomos um horizonte de planejamento finito com uma quantidade finita de períodos entre os tempos inicial e final. Primeiramente consideramos uma versão determinística em que conhecemos, à priori, os itens solicitados em uma ordem de trabalho e o custo dos objetos a cada período. Algumas das sobras geradas durante o processo de corte dos itens solicitados em um período podem ser utilizadas como objetos no

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Zhang, Jian. "Advance Surgery Scheduling with Consideration of Downstream Capacity Constraints and Multiple Sources of Uncertainty." Thesis, Bourgogne Franche-Comté, 2019. http://www.theses.fr/2019UBFCA023.

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Les travaux de ce mémoire portent sur une gestion optimisée des blocs opératoires dans un service chirurgical. Les arrivées des patients chaque semaine, la durée des opérations et les temps de séjour des patients sont considérés comme des paramètres assujettis à des incertitudes. Chaque semaine, le gestionnaire hospitalier doit déterminer les blocs chirurgicaux à mettre en service et leur affecter certaines opérations figurant sur la liste d'attente. L'objectif est la minimisation d'une part des coûts liés à la réalisation et au report des opérations, et d'autre part des coûts hospitaliers lié

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Qu, Zheng. "Nonlinear Perron-Frobenius theory and max-plus numerical methods for Hamilton-Jacobi equations." Palaiseau, Ecole polytechnique, 2013. http://pastel.archives-ouvertes.fr/docs/00/92/71/22/PDF/thesis.pdf.

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Une approche fondamentale pour la résolution de problémes de contrôle optimal est basée sur le principe de programmation dynamique. Ce principe conduit aux équations d'Hamilton-Jacobi, qui peuvent être résolues numériquement par des méthodes classiques comme la méthode des différences finies, les méthodes semi-lagrangiennes, ou les schémas antidiffusifs. À cause de la discrétisation de l'espace d'état, la dimension des problèmes de contrôle pouvant être abordés par ces méthodes classiques est souvent limitée à 3 ou 4. Ce phénomène est appellé malédiction de la dimension. Cette thèse porte sur

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Molina, Diogenes. "Intelligent control and system aggregation techniques for improving rotor-angle stability of large-scale power systems." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50291.

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A variety of factors such as increasing electrical energy demand, slow expansion of transmission infrastructures, and electric energy market deregulation, are forcing utilities and system operators to operate power systems closer to their design limits. Operating under stressed regimes can have a detrimental effect on the rotor-angle stability of the system. This stability reduction is often reflected by the emergence or worsening of poorly damped low-frequency electromechanical oscillations. Without appropriate measures these can lead to costly blackouts. To guarantee system security, operato

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Divila, Jaroslav. "Vyhledávání přibližných palindromů v DNA sekvencích." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236524.

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This work deals with conception and implemetation of tools for finding approximate palindromes in DNA sequences. The work focuses on the description of DNA structure, and on the function of palindromes in DNA sequences, and on the description of methods for finding approximate palindromes. Main part of thesis is focused on conclusion and description of implementation approximate palidromes finding tool.

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