published_or_final_version / abstract / Industrial and Manufacturing Systems Engineering / Master / Master of Philosophy

Lee Wai Hung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 108-115). / Chapter 1 --- Introduction --- p.8 / Chapter 1.1 --- Introduction to Group Technology --- p.8 / Chapter 1.2 --- Cell design --- p.9 / Chapter 1.3 --- Objectives of the research --- p.11 / Chapter 1.4 --- Organization of thesis --- p.11 / Chapter 2 --- Literature review --- p.13 / Chapter 2.1 --- Introduction --- p.13 / Chapter 2.2 --- Standard models --- p.14 / Chapter 2.2.1 --- Array-based methods --- p.16 / Chapter 2.2.2 --- Cluster identification --- p.16 / Chapter 2.2.3 --- Graph-based methods --- p.17 / Chapter 2.2.4 --- Integer programming --- p.17 / Chapter 2.2.5 --- Seed-based --- p.18 / Chapter 2.2.6 --- Similarity coefficient --- p.18 / Chapter 2.2.7 --- Artificial intelligence methods --- p.19 / Chapter 2.3 --- Generalized models --- p.19 / Chapter 2.3.1 --- Machine assignment models --- p.20 / Chapter 2.3.2 --- Part family models --- p.20 / Chapter 2.3.3 --- Cell formation models --- p.21 / Chapter 3 --- Genetic cell formation algorithm --- p.22 / Chapter 3.1 --- Introduction --- p.22 / Chapter 3.2 --- TSP formulation for a permutation of machines --- p.23 / Chapter 3.3 --- Genetic algorithms --- p.26 / Chapter 3.3.1 --- Representation and basic crossover operators --- p.27 / Chapter 3.3.2 --- Fitness function --- p.28 / Chapter 3.3.3 --- Initialization --- p.29 / Chapter 3.3.4 --- Parent selection strategies --- p.30 / Chapter 3.3.5 --- Crossover --- p.31 / Chapter 3.3.6 --- Mutation --- p.37 / Chapter 3.3.7 --- Replacement --- p.38 / Chapter 3.3.8 --- Termination --- p.38 / Chapter 3.4 --- Formation of machine cells and part families --- p.39 / Chapter 3.4.1 --- Objective functions --- p.39 / Chapter 3.4.2 --- Machine assignment --- p.42 / Chapter 3.4.3 --- Part assignment --- p.43 / Chapter 3.5 --- Implementation --- p.43 / Chapter 3.6 --- An illustrative example --- p.45 / Chapter 3.7 --- Comparative Study --- p.49 / Chapter 3.8 --- Conclusions --- p.50 / Chapter 4 --- A multi-chromosome GA for minimizing total intercell and intracell moves --- p.55 / Chapter 4.1 --- Introduction --- p.55 / Chapter 4.2 --- The model --- p.57 / Chapter 4.3 --- Solution techniques to the workload model --- p.61 / Chapter 4.3.1 --- Logendran's original approach --- p.62 / Chapter 4.3.2 --- Standard representation - the GA approach --- p.63 / Chapter 4.3.3 --- Multi-chromosome representation --- p.65 / Chapter 4.4 --- Comparative Study --- p.70 / Chapter 4.4.1 --- Problem 1 --- p.70 / Chapter 4.4.2 --- Problem 2 --- p.71 / Chapter 4.4.3 --- Problem 3 --- p.75 / Chapter 4.4.4 --- Problem 4 --- p.76 / Chapter 4.5 --- Bi-criteria Model --- p.79 / Chapter 4.5.1 --- Experimental results --- p.85 / Chapter 4.6 --- Conclusions --- p.85 / Chapter 5 --- Integrated design of cellular manufacturing systems in the presence of alternative process plans --- p.88 / Chapter 5.1 --- Introduction --- p.88 / Chapter 5.1.1 --- Literature review --- p.90 / Chapter 5.1.2 --- Motivation --- p.92 / Chapter 5.2 --- Mathematical models --- p.93 / Chapter 5.2.1 --- Notation --- p.93 / Chapter 5.2.2 --- Objective functions --- p.95 / Chapter 5.3 --- Our solution --- p.96 / Chapter 5.4 --- Illustrative example and analysis of results --- p.98 / Chapter 5.4.1 --- Solution for objective function 1 --- p.101 / Chapter 5.4.2 --- Solution for objective function 2 --- p.102 / Chapter 5.5 --- Conclusions --- p.103 / Chapter 6 --- Conclusions --- p.104 / Chapter 6.1 --- Summary of achievements --- p.104 / Chapter 6.2 --- Future works --- p.106

Chu Pok Nang. / Thesis submitted on: October 2002. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 69-73). / Abstracts in English and Chinese. / Chapter 1. --- Introduction / Group Technology --- p.6 / Purposes of Research --- p.10 / The Outline of this Thesis --- p.13 / Chapter 2. --- Literature Review / Algorithms for Group Technology --- p.14 / Hierarchical Clustering Approaches --- p.17 / Sorting Based Approaches --- p.18 / Heuristic Exchange Approaches --- p.19 / Seed Based Approaches --- p.20 / Simulated Annealing Approaches --- p.20 / Tabu Search Approaches --- p.21 / Genetic Algorithm Approaches --- p.21 / Neural Network Approaches --- p.22 / Cluster Identification Approaches --- p.22 / Chapter 3. --- The Group Technology Problem / Representing a Manufacturing System --- p.25 / Machine-Part Incidence Matrix --- p.26 / Chapter 4. --- The Improved Cluster Identification Algorithm / Cluster Identification --- p.34 / Formulation --- p.35 / Branch-and-Bound Method --- p.37 / Original Cluster Identification Algorithm --- p.39 / Branching Rule --- p.44 / Chapter 5. --- Computational Studies / Plans for Comparative Studies --- p.49 / Comparison with Existing Cluster Identification Approaches --- p.51 / Solutions to Some Well-known Problems --- p.53 / Comparison with an Optimal Method --- p.60 / Chapter 6. --- Conclusion --- p.63 / Reference --- p.69

Thesis (M.S.)--Ohio University, August, 1992. / Title from PDF t.p.

Thesis (M. Phil.)--University of Hong Kong, 2005. / Title proper from title frame. Also available in printed format.

Cell formation in cellular manufacturing deals with the identification of machines which can be grouped to create manufacturing cells as well as the identification of part families processed within each cell. Manufacturing flexibility is the property of the system components that are integrally designed and linked to each other in order to allow the adaptation to various tasks. This research focuses on classifying and quantifying several types of cellular manufacturing flexibility. These types are defined in order to respond to internal and external changes. Based on these definitions, we link components of the cellular manufacturing systems (part families and machine groups) to develop a CM flexibility hierarchy. Several cellular flexibility and structural measures are developed at each level of the CM flexibility hierarchy and for each component of the CM systems. These measures can be used in order to evaluate and/or design cellular manufacturing systems. A new cell formation method (Flexible Cell Formation method), which has several unique features, is developed. This method incorporates the flexibility measures in designing cellular systems and can generate several alternative designs with different levels of flexibility. The method developed also uses a new similarity measure which incorporates machine processing capability. The proposed method is compared to selected methods of cell formation. Finally, this research concludes with a comprehensive experimental analysis to investigate the impact of several input parameters. The results are used to show how each parameter should be set by the user of the method to incorporate specific types of flexibility.

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Photocopy of typescript. / Digitized by Kansas Correctional Industries

Lean Manufacturing has proven to be a very successful strategy for achieving production efficiencies. The basic elements of lean manufacturing are flow and pull. The traditional methods for establishing flow and pull do not fit well in the realm of high variety low volume manufacturing systems. This thesis provides a general framework for establishing flow and pull in high variety low volume manufacturing systems, through the concept of hybrid flow layouts. The existing analytical procedure for forming hybrid flow layouts is described and a new heuristic procedure, that overcomes some of the limitations of the existing procedure, is proposed. The performance of the new procedure in comparison to the existing procedure is illustrated using a real world case study. Finally, certain practical implementation issues that affect the formation of hybrid flow layouts are provided.