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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

A binary dynamic programming problem with affine transitions and reward functions : properties and algorithm

Gatica, Ricardo A. 12 1900 (has links)
No description available.

Stochastic Dynamic Demand Inventory Models with Explicit Transportation Costs and Decisions

Zhang, Liqing 16 December 2013 (has links)
Recent supply chain literature and practice recognize that significant cost savings can be achieved by coordinating inventory and transportation decisions. Although the existing literature on analytical models for these decisions is very broad, there are still some challenging issues. In particular, the uncertainty of demand in a dynamic system and the structure of various practical transportation cost functions remain unexplored in detail. Taking these motivations into account, this dissertation focuses on the analytical investigation of the impact of transportation-related costs and practices on inventory decisions, as well as the integrated inventory and transportation decisions, under stochastic dynamic demand. Considering complicated, yet realistic, transportation-related costs and practices, we develop and solve three classes of models: (1) Pure inbound inventory model impacted by transportation cost; (2) Pure outbound transportation models concerning shipment consolidation strategy; (3) Integrated inbound inventory and outbound transportation models. In broad terms, we investigate the modeling framework of vendor-customer systems for integrated inventory and transportation decisions, and we identify the optimal inbound and outbound policies for stochastic dynamic supply chain systems. This dissertation contributes to the previous literature by exploring the impact of realistic transportation costs and practices on stochastic dynamic supply chain systems while identifying the structural properties of the corresponding optimal inventory and/or transportation policies. Placing an emphasis on the cases of stochastic demand and dynamic planning, this research has roots in applied probability, optimal control, and stochastic dynamic programming.

Target Tracking in Multi-Static Active Sonar Systems Using Dynamic Programming and Hough Transform

El-Jaber, MOHAMMAD 13 August 2009 (has links)
Tracking multiple targets in a high cluttered environment where multiple receivers are used is a challenging task due to the high level of false alarms and uncertainty in the track hypothesis. The multi-static active sonar scenario is an example for such systems where multiple source-receiver combinations are deployed. Due to the nature of the underwater environment and sound propagation characteristics, tracking targets in the underwater environment becomes a complex operation. Conventional tracking approaches (such as the Kalman and particle filter) require a predetermined kinematic model of the target. Moreover, tracking an unknown and changing number of targets within a certain search area requires complex mathematical association filters to identify the number of targets and associate measurements to different target tracks. As the number of false detections increases, the computational complexity of conventional tracking system grows introducing further challenges for real-time target tracking situations. The methodology presented in this thesis provides a rapid and reliable tracking system capable of tracking multiple targets without depending on a kinematic model of the target movement. In this algorithm, Self Organizing Maps, Dynamic Programming and the Hough transform are combined to produce tracks of possible targets’ paths and estimate of targets’ locations. Evaluation of the performance of the tracking algorithm is performed using three types of simulations and a set of real data obtained from a sea trial. This research documents the results of experimental testing and analysis of the tracking system. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2009-08-07 13:21:06.869

Top-percentile traffic routing problem

Yang, Xinan January 2012 (has links)
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 providers divide the charging period into several fixed length time intervals and calculate their cost according to the traffic volume that has been shipped during the θ-th highest time interval. Unlike traditional pricing regimes, the network design under top-percentile pricing has not been fully studied. This paper investigates the optimal routing strategy in case where network providers charge ISPs according to top-percentile pricing. We call this problem the Top-percentile Traffic Routing Problem (TpTRP). As the ISP cannot predict next time interval’s traffic volume in real world application, in our setting up the TpTRP is a multi-stage stochastic optimisation problem. Routing decisions should be made at the beginning of every time period before knowing the amount of traffic that is to be sent. The stochastic nature of the TpTRP forms the critical difficulty of this study. In this paper several approaches are investigated in either the modelling or solving steps of the problem. We begin by exploring several simplifications of the original TpTRP to get an insight of the features of the problem. Some of these allow analytical solutions which lead to bounds on the achievable optimal solution. We also establish bounds by investigating several “naive” routing policies. In the second part of this work, we build the multi-stage stochastic programming model of the TpTRP, which is hard to solve due to the integer variables introduced in the calculation of the top-percentile traffic. A lift-and-project based cutting plane method is investigated in solving the SMIP for very small examples of TpTRP. Nevertheless it is too inefficient to be applicable on large sized instances. As an alternative, we explore the solution of the TpTRP as a Stochastic Dynamic Programming (SDP) problem by a discretization of the state space. This SDP model gives us achievable routing policies on small size instances of the TpTRP, which of course improve the naive routing policies. However, the solution approach based on SDP suffers from the curse of dimensionality which restricts its applicability. To overcome this we suggest using Approximate Dynamic Programming (ADP) which largely avoids the curse of dimensionality by exploiting the structure of the problem to construct parameterized approximations of the value function in SDP and train the model iteratively to get a converged set of parameters. The resulting ADP model with discrete parameter for every time interval works well for medium size instances of TpTRP, though it still requires too long to be trained for large size instances. To make the realistically sized TpTRP problem solvable, we improve on the ADP model by using Bezier Curves/Surfaces to do the aggregation over time. This modification accelerates the efficiency of parameter training in the solution of the ADP model, which makes the realistically sized TpTRP tractable.

Portfolio Optimization under Partial Information with Expert Opinions

Frey, Rüdiger, Gabih, Abdelali, Wunderlich, Ralf January 2012 (has links) (PDF)
This paper investigates optimal portfolio strategies in a market with partial information on the drift. The drift is modelled as a function of a continuous-time Markov chain with finitely many states which is not directly observable. Information on the drift is obtained from the observation of stock prices. Moreover, expert opinions in the form of signals at random discrete time points are included in the analysis. We derive the filtering equation for the return process and incorporate the filter into the state variables of the optimization problem. This problem is studied with dynamic programming methods. In particular, we propose a policy improvement method to obtain computable approximations of the optimal strategy. Numerical results are presented at the end. (author's abstract)

Look-ahead Control of Heavy Trucks utilizing Road Topography

Hellström, Erik January 2007 (has links)
The power to mass ratio of a heavy truck causes even moderate slopes to have a significant influence on the motion. The velocity will inevitable vary within an interval that is primarily determined by the ratio and the road topography. If further variations are actuated by a controller, there is a potential to lower the fuel consumption by taking the upcoming topography into account. This possibility is explored through theoretical and simulation studies as well as experiments in this work. Look-ahead control is a predictive strategy that repeatedly solves an optimization problem online by means of a tailored dynamic programming algorithm. The scenario in this work is a drive mission for a heavy diesel truck where the route is known. It is assumed that there is road data on-board and that the current heading is known. A look-ahead controller is then developed to minimize fuel consumption and trip time. The look-ahead control is realized and evaluated in a demonstrator vehicle and further studied in simulations. In the prototype demonstration, information about the road slope ahead is extracted from an on-board database in combination with a GPS unit. The algorithm calculates the optimal velocity trajectory online and feeds the conventional cruise controller with new set points. The results from the experiments and simulations confirm that look-ahead control reduces the fuel consumption without increasing the travel time. Also, the number of gear shifts is reduced. Drivers and passengers that have participated in tests and demonstrations have perceived the vehicle behavior as comfortable and natural. / Report code: LIU-TEK-LIC-2007:28.

Optimal Control of Perimeter Patrol Using Reinforcement Learning

Walton, Zachary 2011 May 1900 (has links)
Unmanned Aerial Vehicles (UAVs) are being used more frequently in surveillance scenarios for both civilian and military applications. One such application addresses a UAV patrolling a perimeter, where certain stations can receive alerts at random intervals. Once the UAV arrives at an alert site it can take two actions: 1. Loiter and gain information about the site. 2. Move on around the perimeter. The information that is gained is transmitted to an operator to allow him to classify the alert. The information is a function of the amount of time the UAV is at the alert site, also called the dwell time, and the maximum delay. The goal of the optimization is to classify the alert so as to maximize the expected discounted information gained by the UAV's actions at a station about an alert. This optimization problem can be readily solved using Dynamic Programming. Even though this approach generates feasible solutions, there are reasons to experiment with different approaches. A complication for Dynamic Programming arises when the perimeter patrol problem is expanded. This is that the number of states increases rapidly when one adds additional stations, nodes, or UAVs to the perimeter. This in effect greatly increases the computation time making the determination of the solution intractable. The following attempts to alleviate this problem by implementing a Reinforcement Learning technique to obtain the optimal solution, more specifically Q-Learning. Reinforcement Learning is a simulation-based version of Dynamic Programming and requires lesser information to compute sub-optimal solutions. The effectiveness of the policies generated using Reinforcement Learning for the perimeter patrol problem have been corroborated numerically in this thesis.

The Longest Common Subsequence Problem with a Gapped Constraint

Cheng, Kai-Yuan 12 September 2012 (has links)
This thesis considers a variant of the classical problem for finding the longest common subsequence (LCS) called longest common subsequence problem with a gapped constraint (LCSGC). Given two sequences A, B, and a constrained sequence C, which is accomplished with a corresponding gapped constraint for each symbol, whose lengths are m, n, and r, respectively, the LCSGC problem is to find an LCS of A and B, such that C is also a subsequence of this LCS and the gapped constraints corresponding to C are satisfied. In this thesis, two algorithms with time complexities O(m2n2r) and O(mnr ¡Ñ min(m, n)) are proposed based on the dynamic programming technique for solving the LCSGC problem.

Statistical modeling of the value function in high-dimensional, continuous-state SDP

Tsai, Julia Chia-Chieh 08 1900 (has links)
No description available.

A feedback dynamics model of the development of DeKalb County, Georgia

Wright, John Halpin 05 1900 (has links)
No description available.

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