Thesis (M.S.)--Oregon State University, 1971. / Typescript (photocopy). Includes bibliographical references. Also available online.

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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 (M.S.)--Virginia Polytechnic Institute and State University, 1992. / Vita. Abstract. Includes bibliographical references (leaves 63-66). Also available via the Internet.

Thesis (Ph.D.) -- McMaster University, 1997. / Includes bibliographical references. Also available via World Wide Web.

This study is focused on two-sided assembly line balancing problems of type-I and type-II. This problem is encountered in production environments where a two-sided assembly line is used to produce physically large products. For type-I problems, there is a specified production target for a fixed time interval and the objective is to reach this production capacity with the minimum assembly line length used. On the other hand, type-II problem focuses on reaching the maximum production level using a fixed assembly line and workforce. Two different mathematical models for each problem type are developed to optimally solve the problems. Since the quality of the solutions by mathematical models decreases for large-sized problems due to time and memory limitations, two heuristic approaches are presented for solving large-sized type-I problem. The validity of all formulations is verified with the small-sized literature problems and the performances of the methods introduced are tested with large-sized literature problems.

A methodology for the solution of single-model, stochastic assembly line balancing problem is developed for the objective of minimizing the total labor cost (dictated by the number of stations on the line) and expected incompletion cost arising from tasks not completed within the prespecified cycle time. The proposed procedure is an approximation procedure that divides the problem into subproblems. For each subproblem, an approximate solution is obtained using the dynamic programming procedure developed for the problem. This procedure is incorporated with a special bounding strategy to overcome the rapidly increasing storage and computational requirements as the size of the problem increases. These approximate solutions are further improved by a branch-and-bound type of procedure called the improvement procedure. This procedure uses approximate costs, instead of lower bounds, to fathom the nodes of the enumeration tree constructed; thus, it is not, in the true sense of the word, the branch-and-bound technique. Consequently, the procedure is not guaranteed to result in the optimal solution; however, it is shown to generate solutions within (1 + ε) of the optimal solution. The improvement procedure either improves the approximate solutions obtained using the dynamic programming procedure or determines that they are quite close to the optimal ones. The improved solutions of the subproblems are then appended to each other to produce the solution of the original problem. Some dominance properties that contribute to the effectiveness of the improvement procedure and help in reducing the size of the enumeration tree are developed. Some sequencing and scheduling problems related to the node evaluation scheme of the improvement procedure are also investigated. A single-machine sequencing procedure is developed for the objective of minimizing the expected incompletion cost with tasks having a common due date and stochastic processing times. This procedure is extended to construct a schedule on M parallel machines. In these procedures, in-completion costs of the tasks are independent of their expected performance times; it can be interpreted as relaxing the precedence relations among the tasks. Solution procedures are also developed for the above sequencing and scheduling problems for the case in which the incompletion costs of the tasks are proportional to their expected performance times. Computational results and analyses made indicate that these procedures result in almost optimal solutions. / Ph. D.

published_or_final_version / Industrial Engineering / Master / Master of Science in Engineering

Thesis (M.S.)--Massachusetts Institute of Technology, Alfred P. Sloan School of Management, 1980. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND DEWEY. / Bibliography: leaves 128-129. / by Bruce William Lamar. / M.S.

A fascinating feature of bucket brigade assembly lines is that work load on workers is balanced spontaneously as workers follow some simple rules in the assembly process. This self-organizing property significantly reduces the management effort on an assembly line. We generalize this idea in several directions. These include an adapted bucket brigade protocol for complex assembly networks, a generalized model that permits chaotic behavior, and a more detailed model for a flow line in which jobs arrive arbitrarily in time and are introduced into the system at several points on the line.