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Ho Kin Chuen Matthew. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 153-157). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.15 / Chapter 1.1 --- The U-line Balancing Problem --- p.15 / Chapter 1.2 --- Configuration of an U-line --- p.17 / Chapter 1.3 --- Feasible subsets and sequences --- p.19 / Chapter 1.4 --- Assignment of tasks to stations --- p.21 / Chapter 1.5 --- Costs --- p.22 / Chapter 1.6 --- Formulation of The U-line Balancing Problem --- p.23 / Chapter 1.7 --- Design of computational study --- p.25 / Chapter 1.7.1 --- Input parameters --- p.25 / Chapter 1.7.2 --- Output variables --- p.26 / Chapter 1.7.3 --- Problems solved --- p.27 / Chapter 1.7.3.1 --- Problem Set One --- p.28 / Chapter 1.7.3.2 --- Problem Set Two --- p.28 / Chapter 1.7.3.3 --- Problem Set Three --- p.29 / Chapter 1.7.3.4 --- Problem Set Four --- p.29 / Chapter 1.8 --- Contributions --- p.29 / Chapter 1.9 --- Organization of thesis --- p.30 / Chapter 2 --- Literature Review --- p.31 / Chapter 2.1 --- Introduction --- p.31 / Chapter 2.2 --- The Straight-line Balancing Problem --- p.32 / Chapter 2.2.1 --- Single-model Assembly Line Balancing with deterministic task time (SMD) --- p.34 / Chapter 2.2.2 --- Single-model Assembly Line Balancing with stochastic task times (SMS) --- p.36 / Chapter 2.2.3 --- Multi/Mixed-model Assemble Line Balancing with deterministic task times (MMD) --- p.37 / Chapter 2.2.4 --- Multi/Mixed-model Assembly Line Balancing with stochastic task times (MMS) --- p.38 / Chapter 2.3 --- The U-line Balancing Problem --- p.39 / Chapter 2.4 --- Conclusions --- p.45 / Chapter 3 --- Heuristic Methods --- p.47 / Chapter 3.1 --- Introduction --- p.47 / Chapter 3.2 --- Single-pass heuristic methods --- p.47 / Chapter 3.3 --- Computational results --- p.50 / Chapter 3.3.1 --- Problem Set One --- p.50 / Chapter 3.3.2 --- Problem Set Two --- p.52 / Chapter 3.3.3 --- Problem Set Three --- p.54 / Chapter 3.3.4 --- Problem Set Four --- p.55 / Chapter 3.4 --- Discussions --- p.57 / Chapter 3.5 --- Conclusions --- p.59 / Chapter 4 --- Genetic Algorithm --- p.60 / Chapter 4.1 --- Introduction --- p.60 / Chapter 4.2 --- Application of GA to The Straight-line Balancing Problem --- p.61 / Chapter 4.3 --- Application of GA to The U-line Balancing Problem --- p.62 / Chapter 4.3.1 --- Coding scheme --- p.63 / Chapter 4.3.2 --- Initial population --- p.64 / Chapter 4.3.3 --- Fitness function --- p.65 / Chapter 4.3.4 --- Selection scheme --- p.66 / Chapter 4.3.5 --- Reproduction --- p.67 / Chapter 4.3.6 --- Replacement scheme --- p.68 / Chapter 4.3.7 --- Elitism --- p.68 / Chapter 4.3.8 --- Termination criteria --- p.68 / Chapter 4.4 --- Repair method --- p.69 / Chapter 4.5 --- Crossover operators --- p.71 / Chapter 4.5.1 --- Sequence and configuration infeasible crossover operators --- p.72 / Chapter 4.5.1.1 --- Partially Mapped Crossover (PMX) --- p.72 / Chapter 4.5.1.2 --- Order Crossover #1 (ORD#l) --- p.74 / Chapter 4.5.1.3 --- Order Crossover #2 (ORD#2) --- p.74 / Chapter 4.5.1.4 --- Position Based Crossover (POS) --- p.75 / Chapter 4.5.1.5 --- Cycle Crossover (CYC) --- p.76 / Chapter 4.5.1.6 --- Edge Recombination Crossover (EDG) --- p.77 / Chapter 4.5.1.7 --- Enhanced Edge Recombination Crossover (EEDG) --- p.80 / Chapter 4.5.1.8 --- Uniform-order Based Crossover (UOX) --- p.81 / Chapter 4.5.2 --- Sequence feasible but configuration infeasible crossover operators --- p.82 / Chapter 4.5.2.1 --- One-point Crossover (1PX) --- p.82 / Chapter 4.5.2.2 --- Two-point Crossover (2PX) --- p.84 / Chapter 4.5.2.3 --- Uniform Crossover (UX) --- p.85 / Chapter 4.6 --- Mutation operators --- p.86 / Chapter 4.6.1 --- Sequence infeasible mutation operators --- p.87 / Chapter 4.6.1.1 --- Inversion (INV) --- p.87 / Chapter 4.6.1.2 --- Insertion (INS) --- p.87 / Chapter 4.6.1.3 --- Displacement (DIS) --- p.88 / Chapter 4.6.1.4 --- Reciprocal Exchange (RE) --- p.88 / Chapter 4.6.2 --- Sequence and configuration feasible mutation operators --- p.89 / Chapter 4.6.2.1 --- Scramble Mutation (SCR) --- p.89 / Chapter 4.6.2.2 --- Feasible Insertion (FINS) --- p.90 / Chapter 4.7 --- Computational study --- p.91 / Chapter 4.7.1 --- Comparison of crossover operators --- p.91 / Chapter 4.7.2 --- Comparison of mutation operators --- p.95 / Chapter 4.7.2.1 --- Order crossover#2 and mutation operators --- p.95 / Chapter 4.7.2.2 --- Position based crossover and mutation operators --- p.97 / Chapter 4.7.3 --- Parameters setting --- p.99 / Chapter 4.7.4 --- Computational results --- p.104 / Chapter 4.7.5 --- Comparative results --- p.105 / Chapter 4.7.5.1 --- Problem Set One --- p.105 / Chapter 4.7.5.2 --- Problem Set Two --- p.105 / Chapter 4.7.5.3 --- Problem Set Three --- p.107 / Chapter 4.7.5.4 --- Problem Set Four --- p.107 / Chapter 4.8 --- Conclusions --- p.109 / Chapter 5 --- Dynamic Programming and Lower Bounds --- p.110 / Chapter 5.1 --- Dynamic Programming (DP) --- p.110 / Chapter 5.1.1 --- Introduction --- p.110 / Chapter 5.1.2 --- Modified Dynamic Programming algorithm --- p.112 / Chapter 5.1.3 --- Comparison between optimal solution and heuristics --- p.120 / Chapter 5.1.4 --- Comparison between optimal solution and the GA --- p.123 / Chapter 5.2 --- Lower Bounds --- p.123 / Chapter 5.2.1 --- Introduction --- p.123 / Chapter 5.2.2 --- The U-line Balancing Problem and The Bin Packing Problem --- p.127 / Chapter 5.2.3 --- Martello and Toth's lower bounds for The BPP --- p.128 / Chapter 5.2.3.1 --- Bound L1 --- p.128 / Chapter 5.2.3.2 --- Bound L2 --- p.128 / Chapter 5.2.3.3 --- Dominances and reductions --- p.129 / Chapter 5.2.3.3.1 --- Dominance criterion --- p.129 / Chapter 5.2.3.3.2 --- Reduction procedure --- p.130 / Chapter 5.2.3.4 --- Lower Bound LR --- p.131 / Chapter 5.2.4 --- Chen and Srivastava's lower bounds for The BPP --- p.131 / Chapter 5.2.4.1 --- A unified lower bound --- p.132 / Chapter 5.2.4.2 --- Improving Lm --- p.133 / Chapter 5.2.4.3 --- "Computing a lower bound on N(1/4,1]" --- p.134 / Chapter 5.2.5 --- Lower bounds for The U-line Balancing Problem --- p.137 / Chapter 5.2.5.1 --- Lower bounds on number of stations required --- p.137 / Chapter 5.2.5.2 --- Lower bounds on total cost --- p.139 / Chapter 5.2.6 --- Computational results --- p.140 / Chapter 5.2.6.1 --- Results for different Problem Sets --- p.140 / Chapter 5.2.6.2 --- Comparison between lower bounds and optimal solutions --- p.143 / Chapter 5.2.6.3 --- Comparison between lower bounds and heuristics --- p.145 / Chapter 5.2.6.4 --- Comparison between lower bounds and GA --- p.147 / Chapter 5.3 --- Conclusions --- p.149 / Chapter 6 --- Conclusions --- p.150 / Chapter 6.1 --- Summary of achievements --- p.150 / Chapter 6.2 --- Future works --- p.151

Submitted by Gizele Lima (gizele.lima@inpa.gov.br) on 2016-09-22T14:31:54Z No. of bitstreams: 2 Dissertação Dayane.pdf: 20358939 bytes, checksum: d0ddbe8f452b69b381be809c7baeff64 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2016-09-22T14:31:54Z (GMT). No. of bitstreams: 2 Dissertação Dayane.pdf: 20358939 bytes, checksum: d0ddbe8f452b69b381be809c7baeff64 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2015-06-29 / Fundação de Amparo à Pesquisa do Estado do Amazonas - FAPEAM / Saguinus is the largest and most complex genus of the subfamily Callitrichinae, with 23 species. They are distributed from Southern of Central America to northern of South America. Saguinus bicolor have very limited geographic distribuition, affected by demographic expansion of the city Manaus. In contrast, Saguinus midas have largest geographic distribuition among the Saguinus. They share the same characteristics general and overlap the north of Manaus. Cytogenetics studies with Saguinus described a karyotypic macrostructure conserved, with 2n=46 and patterns of similar bands. However, mapping studies of repetitive sequence are incipient. Repetitive sequence in tandem: telomere and rDNA; and repetitive sequence dispersed include the transposable elements were searched in the work. Analysis were made on S. midas and two populations of S. bicolor. The classical cytogenetics confirmed macrostructure of 2n=46, but differed in morphology of chromosomes, classified into: 8 metacentrics; 10 submetacentrics; 10 subtelocentrics and 6 acrocentrics. The patterns bands were similar, but showed variations among individuals of the same species. The G-bands patterns suggest the fourth pair as cytogenetic markers that show differences among two species and identify natural hybrids in contact zone. The NOR’s were detected in pairs 17 and 18, agreeing with the localization of sequences of rDNA 18S in region pericentromeric of long arms of chromosomes 17, 18 and 19, located in heterochomatic region. LINE–1 was found in regions: euchromatics – having an impact on the organization and function of genome, and; heterochromatics - particularly in centromeric heterochromatin. Accumulation in sex chromosomes are associated with inactivation of one chromosome X in females to promote the gene silencing and ensure gene dosage of sex pair when compared with male. It is possible to observe his presence in regions of negatives G-bands (light bands) implying that deposition in genome of S. bicolor and S. midas is recent in evolutionary time. Differences of sinalization of LINE-1 among populations of S. bicolor were detected, possibly due to isolation the two populations. / Saguinus é o maior e mais complexo gênero da subfamília Callitrichinae, com 23 espécies. Eles estão distribuídos do sul da América Central ao norte da América do Sul. Saguinus bicolor possui uma distribuição geográfica muito limitada, afetada pela expansão demográfica da cidade de Manaus. Ao contrário, Saguinus midas possui a maior distribuição geográfica dentre os Saguinus. São próximas filogeneticamente, compartilham das mesmas características gerais e se sobrepõem ao norte de Manaus. Estudos citogenéticos dos Saguinus descrevem uma macroestrutura cariotípica conservada, com 2n=46 e padrões de bandas similares. Porém, estudos com mapeamento de sequências repetitivas são incipientes. Sequências repetidas em tandem: telômericas e DNAr, e; sequências repetitivas dispersas que englobam os elementos transponíveis são pesquisadas neste trabalho. Análises citogenéticas foram feitas em S. midas e duas populações de S. bicolor. A citogenética clássica confirmou a macroestrutura das duas espécies em 2n=46, porém diferiu na morfologia dos cromossomos quando comparadas com estudos anteriores, sendo aqui, classificados em: 8 M; 10 SM; 20 ST e 6 A. O padrão de banda G apresentou variações entre as espécies, sugerindo o quarto par como marcador citogenético que diferenciaria as duas espécies e identificaria híbridos naturais de 1a geração, em zona de contato. As RON’s foram detectadas nos pares 17 e 18, sendo confirmadas pela localização da sequência de DNAr 18S na região pericentromérica dos braços longos nos cromossomos 17, 18 e 19, localizada em regiões próximas as heterocromatinas. A sequência LINE-1 foi encontrada nas regiões: eucromáticas – podendo influenciar na organização e função do genoma, e; heterocromáticas - particularmente na heterocromatina centromérica. O acúmulo de LINE-1 nos cromossomos sexuais está relacionado com a inativação de um dos cromossomos X nas fêmeas garantindo a dosagem gênica do par sexual quando comparado com os machos. A presença de LINE- 1 nas regiões de bandas G negativas (bandas claras) indica que sua deposição no genoma de S. bicolor e S. midas é recente no tempo evolutivo. Diferenças de sinalização do LINE-1 entre populações de S. bicolor foram detectadas, possivelmente devido ao isolamento das duas populações.

A Product Line Family contains similar applications that differ only in the sets of sup-ported features from the family. To properly engineer these product lines, programmers design a common code base used by all members of the product line. The structure of this common code base is often an Object-Oriented (OO) framework, designed to contain the detailed domain-specific knowledge needed to implement these applications. However, these frameworks are often quite complex and implement detailed dynamic behavior with complex coordination among their classes. Extending an OO framework to realize a single product line instance is a unique exercise in OO programming. The ultimate goal is to develop a consistent approach, for managing all instances, which relies on configuration rather than programming. In this thesis, we show the novel application of Combinatory Logic to automatically syn-thesize correct product line members using higher-level code fragments specified by means of combinators. Using the same starting point of an OO framework, we show how to design a repository of combinators using FeatureIDE, an extensible framework for Feature-Oriented Software Development. We demonstrate a proof of concept using two different Java-based frameworks: a card solitaire framework and a multi-objective optimization algorithms framework. These case studies rely on LaunchPad, an Eclipse plugin developed at WPI that extends FeatureIDE. The broader impact of this work is that it enables framework designers to formally en-code the complex functional structure of an OO framework. Once this task is accomplished, then, generating product line instances becomes primarily a configuration process, which enables correct code to be generated by construction based on the combinatory logic.

Thesis. 1979. M.S. cn--Massachusetts Institute of Technology. Dept. of Mechanical Engineering. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / by Craig C. Selvage. / M.S.cn