Lim Ka-Lai. / Thesis submitted in: December 2003. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 76-78). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgment --- p.iii / List of Figures --- p.vii / List of Tables --- p.viii / Chapter Part A --- Optimal Acceptance Solution for an Electricity Auction Problem / Chapter Chapter A1 --- Introduction --- p.1 / Chapter A1.1 --- Global Electricity Auction Market --- p.1 / Chapter A1.2 --- Electricity Buy-Back Program --- p.5 / Chapter A1.3 --- Motivation --- p.9 / Chapter A1.4 --- Literatures Review --- p.11 / Chapter A1.5 --- Organization of Thesis Part A --- p.14 / Chapter Chapter A2 --- Deterministic Models --- p.15 / Chapter A2.1 --- Models Descriptions and Notations --- p.15 / Chapter A2.2 --- Assumptions --- p.16 / Chapter A2.3 --- Mathematical Models --- p.16 / Chapter A2.4 --- Transforming and Re-writing the Models --- p.18 / Chapter A2.5 --- 0-1 Knapsack Problem --- p.20 / Chapter A2.6 --- Solving the Models by Dynamic Programming --- p.23 / Chapter A2.7 --- Numerical Results --- p.24 / Chapter Chapter A3 --- Stochastic Models --- p.31 / Chapter A3.1 --- Models with Uncertain Extra Demand Q --- p.31 / Chapter A3.2 --- Models with Uncertain Spot Market Price pa --- p.34 / Chapter A3.3 --- Models with Uncertain Extra Demand Q and Uncertain Spot Market Price pa --- p.36 / Chapter Chapter A4 --- Heuristics Algorithm --- p.45 / Chapter A4.1 --- Model Analysis --- p.45 / Chapter A4.2 --- Heuristics Algorithm --- p.47 / Chapter A4.3 --- Computational Complexity --- p.49 / Chapter Chapter A5 --- Conclusions --- p.50 / Chapter A5.1 --- Summary and Findings --- p.50 / Chapter A5.2 --- Future Research --- p.51 / Chapter Part B --- A Study on the Plastics Industry in Hong Kong / Chapter Chapter B1 --- Introduction --- p.53 / Chapter B1.1 --- Overview of the Plastics Industry in Hong Kong --- p.53 / Chapter B1.2 --- Literatures Review --- p.56 / Chapter B1.3 --- Scope and Limitation --- p.59 / Chapter B1.4 --- Motivation --- p.60 / Chapter B1.5 --- Organization of Thesis Part B --- p.60 / Chapter Chapter B2 --- Research Methodology --- p.61 / Chapter B2.1 --- Research Design --- p.61 / Chapter B2.2 --- Propositions --- p.62 / Chapter B2.3 --- Overview of the Plastics Raw Material Industry in Hong Kong --- p.63 / Chapter Chapter B3 --- Findings and Analysis --- p.66 / Chapter B3.1 --- Introduction of the Case Study --- p.66 / Chapter B3.2 --- Proposition B21 --- p.70 / Chapter B3.3 --- Proposition B22 --- p.71 / Chapter B3.4 --- Proposition B23 --- p.72 / Chapter Chapter B4 --- Conclusions --- p.74 / References --- p.75 / Appendix --- p.79

Thesis (M.B.A.)--University of Hong Kong, 1980.

Thesis (M.B.A.)--University of Hong Kong, 1980.

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

The primary purpose of this study was to identify the skills and knowledge required of employees in the plastics industry in the Dallas Metropolitan area. An instrument was utilized to obtain data in order to identify various skills and knowledge. This study was limited to fifteen instruments returned by fifteen participating firms in the Dallas Metropolitan area. A comparison was made of the industrial arts plastics course offerings in the Dallas Metropolitan area schools with the requirements of the plastic industry in order to ascertain the degree of importance the course offerings were to the plastics industry.

Lau Hau Yu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 79-83). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgements --- p.iv / Table of Contents --- p.vi / Chapter Chapter 1: --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Objective --- p.4 / Chapter Chapter 2: --- Literature Survey --- p.6 / Chapter 2.1 --- Plastic Injection Molding Process --- p.6 / Chapter 2.2 --- Monitoring and Diagnosis Methods --- p.10 / Chapter 2.3 --- Remote Monitoring --- p.12 / Chapter Chapter 3: --- Monitoring Methods --- p.15 / Chapter 3.1 --- Predict nozzle pressure and part weight using the Radial Basis Function Neural Network --- p.15 / Chapter 3.1.1 --- Motivation --- p.15 / Chapter 3.1.2 --- Background --- p.15 / Chapter 3.1.3 --- Hybrid RBF neural network --- p.17 / Chapter 3.1.4 --- Estimation of nozzle pressure --- p.21 / Chapter 3.1.5 --- Estimation of part weight: The two steps and one step methods --- p.22 / Chapter 3.2 --- Short shot Monitoring using Similarity --- p.25 / Chapter 3.2.1 --- Background --- p.25 / Chapter 3.2.2 --- The Dissimilarity Approach --- p.26 / Chapter 3.3 --- Parameter Resetting using Support Vector Machine (SVM) and Virtual Search Method (VSM) --- p.27 / Chapter 3.3.1 --- Background --- p.27 / Chapter 3.3.2 --- Support Vector Regression --- p.27 / Chapter 3.3.3 --- SVM Parameters Resetting using Virtual Search Method (VSM) --- p.31 / Chapter 3.4 --- Experiments and Results --- p.33 / Chapter 3.4.1 --- Introduction to Design of Experiment (DOE) --- p.33 / Chapter 3.4.2 --- Set-points selection based on Design of Experiment (DOE) --- p.34 / Chapter 3.4.3 --- Nozzle pressure estimation --- p.40 / Chapter 3.4.4 --- Part weight prediction using the One Step Method --- p.47 / Chapter 3.4.5 --- Similarity Monitoring using estimated nozzle pressure --- p.49 / Chapter 3.4.6 --- Similarity Monitoring using ram position --- p.54 / Chapter 3.4.7 --- Parameter Resetting using SVM and VSM --- p.61 / Chapter Chapter 4: --- The Remote Monitoring and Diagnosis System (RMDS) --- p.63 / Chapter 4.1 --- Introduction to the Remote Monitoring and Diagnosis System --- p.63 / Chapter 4.2 --- Starting Use of the Software --- p.65 / Chapter 4.3 --- Properties and Channel Settings --- p.66 / Chapter 4.3.1 --- Statistic Process Control (SPC) --- p.69 / Chapter 4.3.2 --- Settings --- p.71 / Chapter 4.3.3 --- Viewing the signals --- p.72 / Chapter 4.3.4 --- Short shot monitoring --- p.73 / Chapter 4.3.5 --- Data management --- p.73 / Chapter Chapter 5: --- Coeclusions and Future Works --- p.76 / References --- p.79 / Appendix A: Machine settings in the experiment --- p.84 / Appendix B: Measured part weight in the part weight prediction experiment --- p.86 / Appendix C: Measured part weight in the similarity monitoring experiment --- p.87 / Appendix D: Results of Parameters Resetting Experiment --- p.88 / Appendix E: List of figures --- p.89 / Appendix F: List of tables --- p.91

Thesis (Ph. D.)--Ohio State University, 2008. / Title from first page of PDF file. Includes bibliographical references (p. 155-161).

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / At present, companies in the Peruvian plastic sector have an average capacity utilization indicator of 71.45%. However, in Peru many SMEs are with 50% well below this indicator. After a study performed in a representative company of the Peruvian plastic sector, the causes of low efficiency in resource utilization were determined through a problems tree and a Pareto analysis. These causes are failures, reprocesses and Setup times in the extrusion machines, taking around 1008 h a year to solve these problems. This article proposes a model to improve efficiency, integrating Lean Manufacturing tools such as 5s, SMED, and autonomous maintenance. The first one was used as a support tool, while SMED was used to reduce the configuration time and autonomous maintenance to reduce the failures number and the reprocesses number. The model was validated through a case study, obtaining as results the reduction of the setup time by 50%, breakdowns by 50% and reprocesses by 60%.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / Nowadays, SME industries in the plastics sector fail to work with their maximum installed capacity, among the main factors are lack of market, normal or inevitable and conventional or technical stops, will be dealt with in this investigation. The problem is evident in 71.45%, the average percentage of capacity used in the plastics industries in Peru. Therefore, this article proposes the development of a three-phase production model and complements the Planned Maintenance pillar with Lean tools that seek to improve the capacity used by 17% in industries in the sector. The model starts with the 5S as a basis and support to standardize the ordering and cleaning habits to continue with SMED and Planned Maintenance of TPM. The model was validated with an implementation, an 18% increase in the production capacity used, so it can be concluded that the proposal for improvement presented serves as a reference for future research.

Thesis (M.B.A.)--University of Hong Kong, 1987.