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作者： Omer Selvi BILKENT UNIVERSITY

学位级别：博士

One of the key questions that engineers face in ‡ow shop systems is the service time control, i. e., how long jobs should be processed at each machine. This is an important question because processing times can have great impacts on the cost e¢ciency of the ‡ow shop systems. In order to meet job completion deadlines and to decrease inventory costs, one may set the service times as small as possible; however, this usually comes at the expense of reduced tool life increasing service costs. In this thesis, we study the ‡ow shop systems under such trade-o¤s. We consider the service time optimization of deterministic ‡ow shop systems process- ing identical jobs that arrive at the system at known times and are processed in the order they arrive within deadlines. The cost function to be minimized con- sists of service costs at machines and regular completion-time costs of jobs. The decision variables are the service times that are controllable within constraints. We... rst consider the... xed service time ‡ow shop systems formed of initially controllable machines, where the service times are set only once at the start up time and cannot be altered between processes, and uncontrollable machines, where the service times are... xed and known in advance. For such systems, we formulate a non-convex and non-di¤erentiable optimization problem with a stan- dard solution procedure based on the linearization of the constraints allowing for a convex optimization problem with high memory requirements. Regardless of the cost function, we present a set of waiting and completion time characteristics in such ‡ow shop systems and employ them to derive a simpler equivalent convex optimization problem which improves solution times and alleviates the memory requirements enabling solutions for larger systems. However, the resulting sim- pli... ed convex optimization problem still needs the use of a convex optimization solver which may not be available at some of the manufacturing companies. To

关键词： Deterministic flow shop systems Optimal control Controllable service times Controllable/Uncontrollable machines Convex programming subgradient algorithm

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Lifetime Maximization in Wireless Sensor Networks with Network Coding

Lifetime Maximization in Wireless Sensor Networks with Netwo...

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3rd International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2007)

作者： Hong, Yongfa Xu, Juan Jiang, Changjun Tongji Univ Coll Elect & Informat Engn Shanghai 200092 Peoples R China Shandong Univ Sci & Technol Coll Informat Sci & Technol Qingdao Peoples R China

ISBN: (纸本)9781424413119

The lifetime is a critical parameter for wireless sensor networks, which is defined as the maximum time of delivering certain data to the sink node before sensor node runs out of energy under an initial energy is given. In this paper, we propose a distributed algorithm for data gathering in wireless sensor networks with the assistance of network coding, such that the network lifetime is maximized. We prove rigorously that the proposed algorithm converge to the optimal solution of regularized problem.

关键词： network lifetime wireless sensor networks subgradient algorithm distributed algorithm network coding

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On ascending Vickrey auctions for heterogeneous objects

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JOURNAL OF ECONOMIC THEORY 2007年第1期132卷 95-118页

作者： de Vries, Sven Schummer, James Vohra, Rakesh V. Northwestern Univ JL Kellogg Grad Sch Management Dept Managerial Econ & Decis Sci Evanston IL 60208 USA Tech Univ Munich Zentrum Math D-85747 Garching Germany

We construct an ascending auction for heterogeneous objects by applying a primal-dual algorithm to a linear program that represents the efficient-allocation problem for this setting. The auction assigns personalized prices to bundles, and asks bidders to report their preferred bundles in each round. A bidder's prices are increased when he belongs to a "minimally undersupplied" set of bidders. This concept generalizes the notion of "overdemanded" sets of objects introduced by Demange, Gale, and Sotomayor for the one-to-one assignment problem. Under a submodularity condition, the auction implements the Vickrey-Clarke-Groves outcome;we show that this type of condition is somewhat necessary to do so. When classifying the ascending-auction literature in terms of their underlying algorithms, our auction fills a gap in that literature. We relate our results to various ascending auctions in the literature. (c) 2005 Elsevier Inc. All rights reserved.

关键词： Vickrey auctions combinatorial auctions duality primal-dual algorithm subgradient algorithm

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Lifetime Maximization in Wireless Sensor Networks with Network Coding

Lifetime Maximization in Wireless Sensor Networks with Netwo...

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第三届IEEE无线通讯、网络技术暨移动计算国际会议

作者： Yongfa Hong Juan Xu Changjun Jiang College of Electronic and Information Engineering Tongji University Shanghai China College of Infor College of Electronic and Information Engineering Tongji University Shanghai China

The lifetime is a critical parameter for wireless sensor networks，which is defined as the maximum time of delivering certain data to the sink node before sensor node runs out of energy under an initial energy is given. In this paper, we propose a distributed algorithm for data gathering in wireless sensor networks with the assistance of network coding, such that the network lifetime is maximized. We prove rigorously that the proposed algorithm converge to the optimal solution of regularized problem.

关键词： network lifetime wireless sensor networks subgradient algorithm distributed algorithm network coding

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Lagrangean heuristics combined with reoptimization for the 0-1 bidimensional knapsack problem

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DISCRETE APPLIED MATHEMATICS 2006年第15期154卷 2200-2211页

作者： Thiongane, Babacar Nagih, Anass Plateau, Gerard Univ Paris 13 Lab Informat Paris Nord UMR 7030 CNRS F-93430 Villetaneuse France

First, this paper deals with lagrangean heuristics for the 0-1 bidimensional knapsack problem. A projected subgradient algorithm is performed for solving a lagrangean dual of the problem, to improve the convergence of the classical subgradient algorithm. Secondly, a local search is introduced to improve the lower bound on the value of the biknapsack produced by lagrangean heuristics. Thirdly, a variable fixing phase is embedded in the process. Finally, the sequence of 0-1 one-dimensional knapsack instances obtained from the algorithm are solved by using reoptimization techniques in order to reduce the total computational time effort. Computational results are presented. (c) 2006 Elsevier B.V. All rights reserved.

关键词： 0-1 knapsack Lagrangean relaxation subgradient algorithm local search reoptimization

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Distributed algorithms for maximum lifetime routing in wireless sensor networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2006年第8期5卷 2185-2193页

作者： Madan, Ritesh Lall, Sanjay Stanford Univ Dept Elect Engn Stanford CA 94305 USA Stanford Univ Dept Aeronaut & Astronaut Stanford CA 94035 USA

A sensor network of nodes with wireless transceiver capabilities and limited energy is considered. We propose distributed algorithms to compute an optimal routing scheme that maximizes the time at which the first node in the network drains out of energy. The problem is formulated as a linear programming problem and subgradient algorithms are used to solve it in a distributed manner. The resulting algorithms have low computational complexity and are guaranteed to converge to an optimal routing scheme that maximizes the network lifetime. The algorithms are illustrated by an example in which an optimal flow is computed for a network of randomly distributed nodes. We also show how our approach can be used to obtain distributed algorithms for many different extensions to the problem. Finally, we extend our problem formulation to more general definitions of network lifetime to model realistic scenarios in sensor networks.

关键词： routing linear programming dual decomposition subgradient algorithm sensor networks network lifetime

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Distributed algorithms for maximum lifetime routing in wireless sensor networks

Distributed algorithms for maximum lifetime routing in wirel...

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IEEE Global Telecommunications Conference (GLOBECOM 04)

作者： Madan, R Lall, S Stanford Univ Dept Elect Engn Stanford CA 94305 USA

ISBN: (纸本)0780387945

关键词： linear programming dual decomposition subgradient algorithm sensor networks network lifetime

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On some difficult linear programs coming from set partitioning

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DISCRETE APPLIED MATHEMATICS 2002年第1-2期118卷 3-11页

作者： Barahona, F Anbil, R IBM Corp Thomas J Watson Res Ctr Yorktown Hts NY 10598 USA Caleb Technol Corp Austin TX 78759 USA

We deal with the linear programming relaxation of set partitioning problems arising in airline crew scheduling. Some of these linear programs have been extremely difficult to solve with the traditional algorithms, We have used an extension of the subgradient algorithm, the volume algorithm, to produce primal solutions that might violate the constraints by at most 2%, and that are within 1% of the lower bound. This method is fast, requires minimal storage, and can be parallelized in a straightforward way. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： subgradient algorithm volume algorithm large scale linear programming

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A weak-to-strong convergence principle for Fejer-monotone methods in Hilbert spaces

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MATHEMATICS OF OPERATIONS RESEARCH 2001年第2期26卷 248-264页

作者： Bauschke, HH Combettes, PL Okanagan Univ Coll Dept Math & Stat Kelowna BC V1V 1V7 Canada Univ Paris 06 Anal Numer Lab F-75005 Paris France

We consider a wide class of iterative methods arising in numerical mathematics and optimization that are known to converge only weakly. Exploiting an idea originally proposed by Haugazeau, we present a simple modification of these methods that makes them strongly convergent without additional assumptions. Several applications are discussed.

关键词： convex feasibility Fejer-monotonicity firmly nonexpansive mapping fixed point Haugazeau maximal monotone operator projection proximal point algorithm resolvent subgradient algorithm

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Limited memory space dilation and reduction algorithms

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2001年第1期19卷 55-77页

作者： Sherali, HD Choi, G Ansari, Z Virginia Polytech Inst & State Univ Dept Ind & Syst Engn 0118 Blacksburg VA 24061 USA Hanyang Univ Dept Ind Engn Seoul 133791 South Korea

In this paper, we present variants of Shor and Zhurbenko's r-algorithm, motivated by the memoryless and limited memory updates for differentiable quasi-Newton methods. This well known r-algorithm, which employs a space dilation strategy in the direction of the difference between two successive subgradients, is recognized as being one of the most effective procedures for solving nondifferentiable optimization problems. However, the method needs to store the space dilation matrix and update it at every iteration, resulting in a substantial computational burden for large-sized problems. To circumvent this difficulty, we first propose a memoryless update scheme, which under a suitable choice of parameters, yields a direction of motion that turns out to be a convex combination of two successive anti-subgradients. Moreover, in the space transformation sense, the new update scheme can be viewed as a combination of space dilation and reduction operations. We prove convergence of this new method, and demonstrate how it can be used in conjunction with a variable target value method that allows a practical, convergent implementation of the method. We also examine a memoryless variant that uses a fixed dilation parameter instead of varying degrees of dilation and/or reduction as in the former algorithm, as well as another variant that examines a two-step limited memory update. These variants are tested along with Shor's r-algorithm and also a modified version of a related algorithm due to Polyak that employs a projection onto a pair of Kelley's cutting planes. We use a set of standard test problems from the literature as well as randomly generated dual transportation and assignment problems in our computational experiments. The results exhibit that the proposed space dilation and reduction method and the modification of Polyak's method are competitive, and offer a substantial advantage over the r-algorithm and over the other tested limited memory variants with respect to accura

关键词： nondifferentiable optimization subgradient algorithm space dilation operator memoryless update limited memory update variable target value method

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