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.

For any business, surviving in a competitive market while maintaining all the operational performance indices up to mark is very crucial. There are several theories and techniques to improve the efficiency of the operational performances. Line balancing is one of those well practiced techniques used daily in most of the industries. Line balancing helps balance the assembly lines with regards to man, machine, takt times, etc. This thesis research was done with Electrolux laundry systems, Ljungby in Sweden. With the varying customer demands the case company was balancing its line manually using basic methods. As a part of lean development schemes, Electrolux Ljungby, wanted to transform the line balancing techniques from manual to a fully automated software. The purpose of this research is to determine the company-specific line balancing criteria which should be considered before performing line balancing. This research furthermore lays out a guideline to follow a smooth transition from the manual system of LB to an automated software by concluding the features the software must handle to perform the LB according to required objectives. A case study approach was utilized to collect all the required data to achieve the results. Using the data collection techniques such as interviews, observations and historical analysis we arrived at the data required to design the guidelines with regards to line balancing software features.  The findings suggest that the desired line balancing constraints which are very important in the multi model single sided straight-line balancing problems are flow of materials, assembly precedence, physical constraints, product demand, bill of materials, restricted processes, man power and desired line balancing objectives. Keeping these constraints into consideration the features which are desired in an onlooking line balancing software are the Integration of data and documents/ maximum control, mixed model and option intelligence and analysis, multiple resources, smart variant management, scenario management, yamazumi chart, constraints and reporting tabs. The findings of this thesis can be used as guidelines by any manufacturing industry while they consider buying a new software which can handle Line balancing problems. This research is one of its kind which talks purely about the constraints and desired features only in a specific line balancing scenario. Practitioners can use this as a base for conducting further research on constraints and features pertaining to it, for different line balancing scenarios.

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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.

In this study, we deal with the profit oriented partial disassembly line balancing problem which seeks a feasible assignment of selected disassembly tasks to stations such that the precedence relations among the tasks are satisfied and the profit is maximized. We consider two versions of this problem. In the profit maximization per cycle problem (PC), we maximize the profit for a single disassembly cycle given the task times and costs, part revenues and demands and station costs. We propose a heuristic solution approach for PC based on the liner programming relaxation of our mixed integer programming formulation. In the profit maximization over the planning horizon problem (PH), the planning horizon is divided into time zones each of which may have a different disassembly rate and a different line balance. We also incorporate other issues such as finite supply of discarded product, subassembly and released part inventories availability, and smoothing of the number of stations across the zones. PH is decomposed into a number of successive per cycle problems, which are solved by a similar heuristic approach. Computational analysis is conducted for both problems and results are reported.

The focus of this research is to establish control and planning management in the sewing production process of lingerie clothing to better prepare companies for demand growth. The lack of improvement tools in this sector, the lack of staff training and a lack of quality culture has led to companies, especially MYPES, not being able to meet the established delivery times and non-fulfillment of orders with the customers, which represents 80% of dissatisfied orders due to the limited production capacity and non-productive time in the plant. This problem is due to limited production capacity, deficient production planning, and lack of materials. In order to solve this problem, industrial engineering tools were used. The application of these tools improved production from 79% to 95%.