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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.
21

A dynamic programming approach to planning with decision networks

Sipper, Daniel 12 1900 (has links)
No description available.
22

Optimum deployment of countermeasures

Hans, Jerry Wayne 08 1900 (has links)
No description available.
23

Assembly line balancing by zero-one integer programming

Thangavelu, S. R. 12 1900 (has links)
No description available.
24

Combinatorial Bin Packing Problems

Nielsen, Torben Noerup January 1985 (has links)
In the past few years, there has been a strong and growing interest in evaluating the expected behavior of what we call combinatorial bin packing problems. A combinatorial bin packing problem consists of a number of items of various sizes and value ratios (value per unit of size) along with a collection of bins of fixed capacity into which the items are to be packed. The packing must be done in such a way that the sum of the sizes of the items into a given bin does not exceed the capacity of that bin. Moreover, an item must either be packed into a bin in its entirety or not at all: this "all or nothing" requirement is why these problems are characterized as being combinatorial. The objective of the packing is to optimize a given criterion Junction. Here optimize means either maximize or minimize, depending on the problem. We study two problems that fit into this framework: the Knapsack Problem and the Minimum Sum of Squares Problem. Both of these problems are known to be in the class of NP-hard problems and there is ample reason to suspect that these problems do not admit of efficient exact solution. We obtain results concerning the performance of heuristics under the assumption that the inputs are random samples from some distribution. For the Knapsack Problem, we develop four heuristics, two of which are on-line and two off-line. All four heuristics are shown to be asymptotically optimal in expectation when the item sizes and value ratios are assumed to be independent and uniform. One heuristic is shown to be asymptotically optimal in expectation when the item sizes are uniformly distributed and the value ratios are exponentially distributed. The amount of time required by these heuristics is no more than proportional to the amount of time required to sort the items in order of nonincreasing value ratios. For the Minimum Sum of Squares Problem, we develop two heuristics, both of which are off-line. Both of these heuristics are shown to be asymptotically optimal in expectation when the sizes of the items input are assumed uniformly distributed.
25

A multivariate control solution to the mixed species/diameter class thinning and final rotation problem /

Cousar, Paul K. January 1992 (has links)
Thesis (M.S.)--Oregon State University, 1993. / Typescript (photocopy). Includes bibliographical references (leaves 48-49). Also available on the World Wide Web.
26

Dynamic programming applied to a new formulation of the stochastic truckload routing problem /

Miori, Virginia Marie. Benson, Hande Y. January 2006 (has links)
Thesis (Ph. D.)--Drexel University, 2006. / Includes abstract and vita. Includes bibliographical references (leaves 92-97).
27

Dynamic Programming Methodologies in Very Large Scale Neighborhood Search Applied to the Traveling Salesman Problem

Ergun, Özlem, Orlin, James B. 02 April 2004 (has links)
We provide two different neighborhood construction techniques for creating exponentially large neighborhoods that are searchable in polynomial time using dynamic programming. We illustrate both of these approaches on very large scale neighborhood search techniques for the traveling salesman problem. Our approaches are intended both to unify previously known results as well as to offer schemas for generating additional exponential neighborhoods that are searchable in polynomial time. The first approach is to define the neighborhood recursively. In this approach, the dynamic programming recursion is a natural consequence of the recursion that defines the neighborhood. In particular, we show how to create the pyramidal tour neighborhood, the twisted sequences neighborhood, and dynasearch neighborhoods using this approach. In the second approach, we consider the standard dynamic program to solve the TSP. We then obtain exponentially large neighborhoods by selecting a polynomially bounded number of states, and restricting the dynamic program to those states only. We show how the Balas and Simonetti neighborhood and the insertion dynasearch neighborhood can be viewed in this manner. We also show that one of the dynasearch neighborhoods can be derived directly from the 2-exchange neighborhood using this approach.
28

Incomplete gene structure prediction with almost 100% specificity

Chin, See Loong 30 September 2004 (has links)
The goals of gene prediction using computational approaches are to determine gene location and the corresponding functionality of the coding region. A subset of gene prediction is the gene structure prediction problem, which is to define the exon-intron boundaries of a gene. Gene prediction follows two general approaches: statistical patterns identification and sequence similarity comparison. Similarity based approaches have gained increasing popularity with the recent vast increase in genomic data in GenBank. The proposed gene prediction algorithm is a similarity based algorithm which capitalizes on the fact that similar sequences bear similar functions. The proposed algorithm, like most other similarity based algorithms, is based on dynamic programming. Given a genomic DNA, X = x1 xn and a closely related cDNA, Y = y1 yn, these sequences are aligned with matching pairs stored in a data set. These indexes of matching sets contain a large jumble of all matching pairs, with a lot of cross over indexes. Dynamic programming alignment is again used to retrieve the longest common non-crossing subsequence from the collection of matching fragments in the data set. This algorithm was implemented in Java on the Unix platform. Statistical comparisons were made against other software programs in the field. Statistical evaluation at both the DNA and exonic level were made against Est2genome, Sim4, Spidey, and Fgenesh-C. The proposed gene structure prediction algorithm, by far, has the best performance in the specificity category. The resulting specificity was greater than 98%. The proposed algorithm also has on par results in terms of sensitivity and correlation coeffcient. The goal of developing an algorithm to predict exonic regions with a very high level of correctness was achieved.
29

Sur un problème de minimisation: localisation optimal d'une source

Solar-Behelak, Claudie January 1974 (has links)
No description available.
30

The Performance of Sequence Alignment Algorithms

Alimehr, Leila January 2013 (has links)
This thesis deals with sequence alignment algorithms. The sequence alignment is a mutual arrange of two or more sequences in order to study their similarity and dissimilarity. Four decades after the seminal work by Needleman and Wunsch in 1970, these methods still need more explorations. We start out with a review of a sequence alignment, and its generalization to multiple alignments, although the focus of this thesis is on the evaluation of the new alignment algorithms. The research presented here in has stepped into the different algorithms that are in terms of the dynamic programming. In the study of sequence alignment algorithms, two powerful techniques have been invented. According to the simulations, the new algorithms are shown to be extremely efficient for the comparing DNA sequences. All the sequence alignment algorithmsare compared in terms of the distance. We use the programming language R for the implementation and simulation of the algorithms discussed in this thesis.

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