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Limited Re-Sequencing for Mixed-Models with Multiple Objectives, Part II: A Permutation Approach  [PDF]
Patrick R. McMullen
American Journal of Operations Research (AJOR) , 2012, DOI: 10.4236/ajor.2012.21002
Abstract: This research presents an approach to solving the limited re-sequencing problem for a JIT system when two objectives are considered for multiple processes. One objective is to minimize the number of setups; the other is to minimize the material usage rate [1]. For this research effort, each unique permutation of the problem’s demand structure is noted, and used as a mechanism for finding subsequent sequences. Two variants of this permutation approach are used: one employs a Monte-Carlo simulation, while the other employs a modification of Ant-Colony Optimization to find sequences satisfying the objectives of interest. Problem sets from the literature are used for assessment, and experimentation shows that the methodology presented here outperforms methodology from an earlier research effort [3].
Online Stochastic Optimization with Multiple Objectives  [PDF]
Mehrdad Mahdavi,Tianbao Yang,Rong Jin
Mathematics , 2012,
Abstract: In this paper we propose a general framework to characterize and solve the stochastic optimization problems with multiple objectives underlying many real world learning applications. We first propose a projection based algorithm which attains an $O(T^{-1/3})$ convergence rate. Then, by leveraging on the theory of Lagrangian in constrained optimization, we devise a novel primal-dual stochastic approximation algorithm which attains the optimal convergence rate of $O(T^{-1/2})$ for general Lipschitz continuous objectives.
A Genetic Algorithm for Multiple Inspections with Multiple Objectives  [PDF]
Patrick R. McMullen
American Journal of Operations Research (AJOR) , 2013, DOI: 10.4236/ajor.2013.36045
Abstract:  This research presents a genetic algorithm to address the problem where multiple inspections are done to test conformity of multiple product characteristics. The genetic algorithm is employed to find an inspection plan where the multiple inspections are carried out, motivated to optimize two objectives: minimization of the total cost associated with the inspection; and maximization of probability of accepting conforming units. The genetic algorithm includes a constraint to induce variety into the characteristics being tested, so that the inspections are not dominated by “specialized” product characteristics. The resulting solutions are compared to optimal solutions, and it is determined that formidable solutions are found via the Genetic Algorithm approach.
Microarray-based resequencing of multiple Bacillus anthracis isolates
Michael E Zwick, Farrell Mcafee, David J Cutler, Timothy D Read, Jacques Ravel, Gregory R Bowman, Darrell R Galloway, Alfred Mateczun
Genome Biology , 2004, DOI: 10.1186/gb-2004-6-1-r10
Abstract: Population genomics, the study of genome-wide patterns of genetic variation in a large number of organisms, is emerging as a vigorous new field of study [1-3]. Rapid, accurate and inexpensive resequencing could enable a variety of potential applications and studies. For the biowarfare (BW) pathogen, Bacillus anthracis, genomic sequences from multiple strains and non-pathogenic close relatives could aid studies that definitively identify B. anthracis in environmental and clinical samples, determine forensic attribution and phylogenetic relationships of strains, and uncover the genetic basis of phenotypic variation in traits such as mammalian virulence. Moreover, first recognizing the presence of a novel pathogen, and then attempting the difficult task of discerning between novel naturally occurring pathogenic organisms (for instance Bacillus cereus G9241 [4]) and artificially enhanced bacterial pathogens, requires a thorough knowledge of extant patterns and levels of genetic variation in natural populations. Unusual patterns of genetic variation may serve as evidence aiding the detection of these unusual types of pathogens.The current technological model for genome sequencing employs high-throughput shotgun sequencing at large centers. This highly successful enterprise has completed about 200 bacterial genomes with more than 500 ongoing as of July 2004 [5]. The genome sequences of the B. anthracis Ames chromosome (5.2 Mb, NC_003997) and plasmids pXO1 (181.6 kilobases (kb), NC_001496) and pXO2 (96.2 kb, NC_002146) have been determined [6-8], as have the genomes of three near neighbors, B. cereus ATCC 14579 [9], B. cereus ATCC 10987 [10] and B. cereus G9241 [4]. A strain of B. anthracis Ames strain isolated from a victim of the autumn 2001 bioterror attack in Florida was also sequenced to a high level of coverage using the random shotgun method and compared to the Ames sequence to identify 60 new markers that included single nucleotide polymorphisms (SNPs), inserted or
Layout design of user interface components with multiple objectives  [PDF]
Peer S.K.,Sharma Dinesh K.,Ravindranath K.,Naidu M.M.
Yugoslav Journal of Operations Research , 2004, DOI: 10.2298/yjor0402171p
Abstract: A multi-goal layout problem may be formulated as a Quadratic Assignment model, considering multiple goals (or factors), both qualitative and quantitative in the objective function. The facilities layout problem, in general, varies from the location and layout of facilities in manufacturing plant to the location and layout of textual and graphical user interface components in the human–computer interface. In this paper, we propose two alternate mathematical approaches to the single-objective layout model. The first one presents a multi-goal user interface component layout problem, considering the distance-weighted sum of congruent objectives of closeness relationships and the interactions. The second one considers the distance-weighted sum of congruent objectives of normalized weighted closeness relationships and normalized weighted interactions. The results of first approach are compared with that of an existing single objective model for example task under consideration. Then, the results of first approach and second approach of the proposed model are compared for the example task under consideration.
Resequencing: A Method for Conforming to Conventions for Sharing Credits Among Multiple Authors  [PDF]
Ash Mohammad Abbas
Computer Science , 2011,
Abstract: Devising an appropriate scheme that assigns the weights to share credits among multiple authors of a paper is a challenging task. This challenge comes from the fact that different types of conventions might be followed among different research discipline or research groups. In this paper, we discuss that for the purpose of evaluating the quality of research produced by authors, one can resequence either authors or weights and can apply a weight assignment policy which the evaluator deems fit for the particular research discipline or research group.
Project Management for a Country with Multiple Objectives  [PDF]
Willem K. M. Brauers
AUCO Czech Economic Review , 2012,
Abstract: This paper proposes project management for a national economy in search for new projects, even with competition between projects. Traditional Cost-Benefit does not respond to this purpose. Indeed Cost-Benefit is only interested in one specific project and not in a competition between projects. In addition all goals (objectives) have to be translated into money terms, leading sometimes to immoral consequences. On the contrary Multi-Objective Optimization takes care of different objectives, whereas the objectives keep their own units. However different methods exist for the application of Multi-Objective Optimization. The author tested them after their robustness resulting in seven necessary conditions for acceptance. Nevertheless these seven conditions concern only Discrete Optimization and not Continuous Optimization or Interactive Multi-Objective Methods. MOORA (Multi-Objective Optimization by Ratio Analysis coupled with Reference Point Theory) and MULTIMOORA (MOORA plus the Full Multiplicative Form), assisted by Ameliorated Nominal Group and Delphi Techniques, satisfy the seven conditions, although in a theoretical way. A simulation exercise illustrates the use of these methods, ideals to be strived for as much as possible.
Bin Packing Under Multiple Objectives - a Heuristic Approximation Approach  [PDF]
Martin Josef Geiger
Computer Science , 2008,
Abstract: The article proposes a heuristic approximation approach to the bin packing problem under multiple objectives. In addition to the traditional objective of minimizing the number of bins, the heterogeneousness of the elements in each bin is minimized, leading to a biobjective formulation of the problem with a tradeoff between the number of bins and their heterogeneousness. An extension of the Best-Fit approximation algorithm is presented to solve the problem. Experimental investigations have been carried out on benchmark instances of different size, ranging from 100 to 1000 items. Encouraging results have been obtained, showing the applicability of the heuristic approach to the described problem.
A Flexible Approach for Highly Multiplexed Candidate Gene Targeted Resequencing  [PDF]
Georges Natsoulis,John M. Bell,Hua Xu,Jason D. Buenrostro,Heather Ordonez,Susan Grimes,Daniel Newburger,Michael Jensen,Jacob M. Zahn,Nancy Zhang,Hanlee P. Ji
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0021088
Abstract: We have developed an integrated strategy for targeted resequencing and analysis of gene subsets from the human exome for variants. Our capture technology is geared towards resequencing gene subsets substantially larger than can be done efficiently with simplex or multiplex PCR but smaller in scale than exome sequencing. We describe all the steps from the initial capture assay to single nucleotide variant (SNV) discovery. The capture methodology uses in-solution 80-mer oligonucleotides. To provide optimal flexibility in choosing human gene targets, we designed an in silico set of oligonucleotides, the Human OligoExome, that covers the gene exons annotated by the Consensus Coding Sequencing Project (CCDS). This resource is openly available as an Internet accessible database where one can download capture oligonucleotides sequences for any CCDS gene and design custom capture assays. Using this resource, we demonstrated the flexibility of this assay by custom designing capture assays ranging from 10 to over 100 gene targets with total capture sizes from over 100 Kilobases to nearly one Megabase. We established a method to reduce capture variability and incorporated indexing schemes to increase sample throughput. Our approach has multiple applications that include but are not limited to population targeted resequencing studies of specific gene subsets, validation of variants discovered in whole genome sequencing surveys and possible diagnostic analysis of disease gene subsets. We also present a cost analysis demonstrating its cost-effectiveness for large population studies.
Markov Decision Processes with Multiple Long-run Average Objectives  [PDF]
Tomá? Brázdil,Václav Bro?ek,Krishnendu Chatterjee,Vojtěch Forejt,Antonín Ku?era
Computer Science , 2011, DOI: 10.2168/LMCS-10(1:13)2014
Abstract: We study Markov decision processes (MDPs) with multiple limit-average (or mean-payoff) functions. We consider two different objectives, namely, expectation and satisfaction objectives. Given an MDP with k limit-average functions, in the expectation objective the goal is to maximize the expected limit-average value, and in the satisfaction objective the goal is to maximize the probability of runs such that the limit-average value stays above a given vector. We show that under the expectation objective, in contrast to the case of one limit-average function, both randomization and memory are necessary for strategies even for epsilon-approximation, and that finite-memory randomized strategies are sufficient for achieving Pareto optimal values. Under the satisfaction objective, in contrast to the case of one limit-average function, infinite memory is necessary for strategies achieving a specific value (i.e. randomized finite-memory strategies are not sufficient), whereas memoryless randomized strategies are sufficient for epsilon-approximation, for all epsilon>0. We further prove that the decision problems for both expectation and satisfaction objectives can be solved in polynomial time and the trade-off curve (Pareto curve) can be epsilon-approximated in time polynomial in the size of the MDP and 1/epsilon, and exponential in the number of limit-average functions, for all epsilon>0. Our analysis also reveals flaws in previous work for MDPs with multiple mean-payoff functions under the expectation objective, corrects the flaws, and allows us to obtain improved results.
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