Academic literature on the topic 'U-shaped line assembly balancing'

This research introduces parallel U-shaped assembly lines with crossover points. Crossover points are connecting points between two parallel U-shaped lines making the lines interdependent. The assembly lines can be employed to manufacture a variety of products belonging to the same product family. This is achieved by utilizing the concepts of crossover points, multi-line stations, and regular stations. The binary programming formulation presented in this research can be employed for any scenario (e.g. task times, cycle times, and the number of tasks) in the configuration that includes a crossover point. The comparison of numerical problem solutions based on the proposed heuristic approach with the traditional approach highlights the possible reduction in the quantity of workers required. The conclusion from this research is that a wider variety of products can be manufactured at the same capital expense using parallel U-shaped assembly lines with crossover points, leading to a reduction in the total number of workers.<br>M. S.

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

碩士<br>大葉大學<br>工業工程研究所<br>89<br>U-shaped production line means that production line lays out in U-shape and each worker can serve more than one machine in a specified cycle time. By this kind of production line, we can not only reduce the number of workers but also make the production line more flexible. Traditionally, the addressed problem is solved with the assumption that the path of workers cannot be crossed. The objective is to minimize the total number of worker in the production system. Unlike the past researches, in this research, we discuss the problem in two phases that are number of workers required and balancing delay in the system. However, the path of workers might be crossed. The computational results shown that the production system can obtain better solutions in the case of crossing the path of workers is permitted.