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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

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

Thesis--PlanB (M.S.)--University of Wisconsin--Stout, 2002. / Includes bibliographical references.

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Assembly Sequence Planning (ASP) is part of Assembly Planning. The assembly sequence is the most important part of an assembly plan. Assembly has an important share in both lead time and cost of a product. Therefore, its optimisation is necessary to ensure the competitivity of manufactured goods. The aim of this thesis is the optimisation of assembly sequences for mechanical products, for real/realistic problems and constraints. This thesis represents an integrated approach in assembly sequence planning and optimisation. It tackles real problems by building the generality in the models. / Thesis (PhD)--University of South Australia, 2003.