Academic literature on the topic 'Dynamic stochastic optimization'

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Dissertations / Theses on the topic "Dynamic stochastic optimization"

1

Wei, Wei. "Stochastic Dynamic Optimization and Games in Operations Management." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1354751981.

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2

Azad, Saeed. "Combined Design and Control Optimization of Stochastic Dynamic Systems." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1602153122063302.

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3

Lin, Wuqin. "Dynamic Control in Stochastic Processing Networks." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7105.

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A stochastic processing network is a system that takes materials of various kinds as inputs, and uses processing resources to produce other materials as outputs. Such a network provides a powerful abstraction of a wide range of real world, complex systems, including semiconductor wafer fabrication facilities, networks of data switches, and large-scale call centers. Key performance measures of a stochastic processing network include throughput, cycle time, and holding cost. The network performance can dramatically be affected by the choice of operational policies. We propose a family of operational policies called maximum pressure policies. The maximum pressure policies are attractive in that their implementation uses minimal state information of the network. The deployment of a resource (server) is decided based on the queue lengths in its serviceable buffers and the queue lengths in their immediate downstream buffers. In particular, the decision does not use arrival rate information that is often difficult or impossible to estimate reliably. We prove that a maximum pressure policy can maximize throughput for a general class of stochastic processing networks. We also establish an asymptotic optimality of maximum pressure policies for stochastic processing networks with a unique bottleneck. The optimality is in terms of minimizing workload process. A key step in the proof of the asymptotic optimality is to show that the network processes under maximum pressure policies exhibit a state space collapse.
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Bastani, Spencer, and Olov Andersson. "Stochastic Optimization in Dynamic Environments : with applications in e-commerce." Thesis, Linköping University, Department of Mathematics, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-8509.

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<p>In this thesis we address the problem of how to construct an optimal algorithm for displaying banners (i.e advertisements shown on web sites). The optimization is based on the revenue each banner generates, with the aim of selecting those banners which maximize future total revenue. Banner optimality is of major importance in the e-commerce industry, in particular on web sites with heavy traffic. The 'micropayments' from showing banners add up to substantial profits due to the large volumes involved. We provide a broad, up-to-date and primarily theoretical treatment of this global optimization problem. Through a synthesis of mathematical modeling, statistical methodology and computer science we construct a stochastic 'planning algorithm'. The superiority of our algorithm is based on empirical analysis conducted by us on real internet-data at TradeDoubler AB, as well as test-results on a selection of stylized data-sets. The algorithm is flexible and adapts well to new environments.</p>
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Sivakumar, Ishwar Krishnan Ashok 1980. "Stochastic optimization of electricity transmission : dynamic programming algorithms under uncertainties." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/80655.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.<br>Includes bibliographical references (leaves 141-144).<br>by Ishwar Krishnan Ashok Sivakumar.<br>M.Eng.
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Angulo, Olivares Gustavo I. "Integer programming approaches for semicontinuous and stochastic optimization." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51862.

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This thesis concerns the application of mixed-integer programming techniques to solve special classes of network flow problems and stochastic integer programs. We draw tools from complexity and polyhedral theory to analyze these problems and propose improved solution methods. In the first part, we consider semi-continuous network flow problems, that is, a class of network flow problems where some of the variables are required to take values above a prespecified minimum threshold whenever they are not zero. These problems find applications in management and supply chain models where orders in small quantities are undesirable. We introduce the semi-continuous inflow set with variable upper bounds as a relaxation of general semi-continuous network flow problems. Two particular cases of this set are considered, for which we present complete descriptions of the convex hull in terms of linear inequalities and extended formulations. We also consider a class of semi-continuous transportation problems where inflow systems arise as substructures, for which we investigate complexity questions. Finally, we study the computational efficacy of the developed polyhedral results in solving randomly generated instances of semi-continuous transportation problems. In the second part, we introduce and study the forbidden-vertices problem. Given a polytope P and a subset X of its vertices, we study the complexity of optimizing a linear function on the subset of vertices of P that are not contained in X. This problem is closely related to finding the k-best basic solutions to a linear problem and finds applications in stochastic integer programming. We observe that the complexity of the problem depends on how P and X are specified. For instance, P can be explicitly given by its linear description, or implicitly by an oracle. Similarly, X can be explicitly given as a list of vectors, or implicitly as a face of P. While removing vertices turns to be hard in general, it is tractable for tractable 0-1 polytopes, and compact extended formulations can be obtained. Some extensions to integral polytopes are also presented. The third part is devoted to the integer L-shaped method for two-stage stochastic integer programs. A widely used model assumes that decisions are made in a two-step fashion, where first-stage decisions are followed by second-stage recourse actions after the uncertain parameters are observed, and we seek to minimize the expected overall cost. In the case of finitely many possible outcomes or scenarios, the integer L-shaped method proposes a decomposition scheme akin to Benders' decomposition for linear problems, but where a series of mixed-integer subproblems have to be solved at each iteration. To improve the performance of the method, we devise a simple modification that alternates between linear and mixed-integer subproblems, yielding significant time savings in instances from the literature. We also present a general framework to generate optimality cuts via a cut-generating problem. Using an extended formulation of the forbidden-vertices problem, we recast our cut-generating problem as a linear problem and embed it within the integer L-shaped method. Our numerical experiments suggest that this approach can prove beneficial when the first-stage set is relatively complicated.
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7

Moser, Irene. "Applying external optimisation to dynamic optimisation problems." Swinburne Research Bank, 2008. http://hdl.handle.net/1959.3/22526.

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Thesis (Ph.D) - Swinburne University of Technology, Faculty of Information & Communication Technologies, 2008.<br>[A thesis submitted in total fulfillment of the requirements of for the degree of Doctor of Philosophy, Faculty of Information and Communication Technologies, Swinburne University of Technology, 2008]. Typescript. Includes bibliographical references p. 193-201.
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8

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 three products, it was clearly outperformed by the global policy search as soon as problem size increased. The most reliable choice in our study was a globally optimized fixed-cycle policy. An additional analysis of the response surface of model parameters on optimal average cost revealed that the cost effect of product diversity was negligible. (authors' abstract)
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9

Choi, Jaein. "Algorithmic Framework for Improving Heuristics in Stochastic, Stage-Wise Optimization Problems." Diss., Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/4954.

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Algorithmic Framework for Improving Heuristics in Stochastic, Stage-Wise Optimization Problems Jaein Choi 172 Pages Directed by Dr. Jay H. Lee and Dr. Matthew J. Realff The goal of this thesis is the development of a computationally tractable solution method for stochastic, stage-wise optimization problems. In order to achieve the goal, we have developed a novel algorithmic framework based on Dynamic Programming (DP) for improving heuristics. The propose method represents a systematic way to take a family of solutions and patch them together as an improved solution. However, patching is accomplished in state space, rather than in solution space. Since the proposed approach utilizes simulation with heuristics to circumvent the curse of dimensionality of the DP, it is named as Dynamic Programming in Heuristically Restricted State Space. The proposed algorithmic framework is applied to stochastic Resource Constrained Project Scheduling problems, a real-world optimization problem with a high dimensional state space and significant uncertainty equivalent to billions of scenarios. The real-time decision making policy obtained by the proposed approach outperforms the best heuristic applied in simulation stage to form the policy. The proposed approach is extended with the idea of Q-Learning technique, which enables us to build empirical state transition rules through simulation, for stochastic optimization problems with complicated state transition rules. Furthermore, the proposed framework is applied to a stochastic supply chain management problem, which has high dimensional action space as well as high dimensional state space, with a novel concept of implicit sub-action space that efficiently restricts action space for each state in the restricted state space. The resulting real-time policy responds to the time varying demand for products by stitching together decisions made by the heuristics and improves overall performance of the supply chain. The proposed approach can be applied to any problem formulated as a stochastic DP, provided that there are reasonable heuristics available for simulation.
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10

Becker, Jonathan. "Dynamic Beamforming Optimization for Anti - Jamming and Hardware Fault Recovery." Research Showcase @ CMU, 2014. http://repository.cmu.edu/dissertations/331.

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In recent years there has been a rapid increase in the number of wireless devices for both commercial and defense applications. Such unprecedented demand has increased device cost and complexity and also added a strain on the spectrum utilization of wireless communication systems. This thesis addresses these issues, from an antenna system perspective, by developing new techniques to dynamically optimize adaptive beamforming arrays for improved anti-jamming and reliability. Available frequency spectrum is a scarce resource, and therefor e increased interference will occur as the wireless spectrum saturates. To mitig ate unintentional interference, or intentional interference from a jamming source, antenna arrays are used to focus electromagnetic energy on a signal of interest while simultaneously minimizing radio frequency energy in directions of interfering signals. The reliability of such arrays, especially in commercial satellite and defense applications, can be addressed by hardware redundancy, but at the expense of increased volume, mass as well as component and design cost. This thesis proposes the development of new models and optimization algorithms to dynamically adapt beamforming arrays to mitigate interference and increase hardware reliability. The contributions of this research are as follows. First, analytical models are developed and experimental results show that small antenna arrays can thwart interference using dynamically applied stochastic algorithms. This type of insitu optimization, with an algorithm dynamically optimizing a beamformer to thwart interference sources with unknown positions, inside of an anechoic chamber has not been done before to our knowledge. Second, it is shown that these algorithms can recover from hardware failures and localized faults in the array. Experiments were performed with a proof-of-concept four-antenna array. This is the first hardware demonstration showing an antenna array with live hardware fault recovery that is adapted by stochastic algorithms in an anechoic chamber. We also compare multiple stochastic algorithms in performing both anti-jamming and hardware fault recovery. Third, we show that stochastic algorithms can be used to continuously track and mitigate interfering signals that continuously move in an additive white Gaussian noise wireless channel.
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