Ng Chi To. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 169-173). / Acknowledgements --- p.i / Abstract --- p.ii / Chapter Chapter 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- Problem Formulation and Motivation --- p.1 / Chapter 1.2 --- Past Research Works --- p.3 / Chapter 1.3 --- Results of the Study --- p.5 / Chapter 1.4 --- Organization of the Thesis --- p.7 / Chapter Part I --- THE CTV PROBLEM --- p.9 / Chapter Chapter 2 --- A GENERALIZATION OF SCHRAGE'S CONJEC- TURE --- p.10 / Chapter 2.1 --- Schrage's Conjecture --- p.10 / Chapter 2.2 --- Generalization --- p.13 / Chapter Chapter 3 --- ASYMPTOTIC OPTIMALITY --- p.15 / Chapter 3.1 --- Optimal Sequences under a Symmetric Structure --- p.17 / Chapter 3.2 --- An Upper Bound for the Relative Error --- p.21 / Chapter 3.3 --- Asymptotical Probabilistic Analysis --- p.25 / Chapter Chapter 4 --- ADDITIONAL FINDINGS --- p.37 / Chapter Chapter 5 --- THE BEST V-SHAPED SEQUENCE --- p.46 / Chapter 5.1 --- Transformation of the CTV Problem to a Boolean Optimization Problem --- p.47 / Chapter 5.2 --- Minimization of the Expected CTV among All the V-shaped Fixed Sequences --- p.48 / Chapter Chapter 6 --- THE WORST CASE ANALYSIS --- p.65 / Chapter 6.1 --- A Lower Bound for the CTV Problem --- p.66 / Chapter 6.2 --- A Worst Case Bound --- p.71 / Chapter Part II --- EXTENSIONS --- p.75 / Chapter Chapter 7 --- A MORE GENERAL MODEL --- p.76 / Chapter 7.1 --- Some Basic Concepts --- p.76 / Chapter 7.2 --- Problem Description --- p.78 / Chapter 7.3 --- Applications and Difficulties --- p.80 / Chapter Chapter 8 --- THE ZERO STARTING PROBLEM --- p.83 / Chapter 8.1 --- Problem Transformation --- p.85 / Chapter 8.2 --- Properties --- p.88 / Chapter 8.3 --- Algorithm A and Promising Solutions --- p.93 / Chapter 8.4 --- Time Complexity of Algorithm A --- p.94 / Chapter Chapter 9 --- PROBABILISTIC ANALYSIS OF PROMISING SO- LUTIONS --- p.95 / Chapter 9.1 --- Promising Solutions under a Symmetric Structure --- p.95 / Chapter 9.2 --- An Upper Bound for the Relative Error of Promising Solutions --- p.100 / Chapter 9.3 --- Probabilistic Analysis on the Relative Error of Promising Solutions --- p.106 / Chapter Chapter 10 --- CONCLUDING REMARKS AND FUTURE RESEARCH WORK --- p.118 / Appendix A Preliminary Results for Analysis --- p.122 / Appendix B Proofs of Some Lemmas --- p.127 / Appendix C Proofs of Some Theorems --- p.149 / Appendix D Proofs of Some Properties --- p.160 / Appendix E An Alternative to Completion Time Variance --- p.167 / Bibliography --- p.169

by Cheung Chit-Cheung, Gavin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 75-77). / Abstract also in Chinese. / Abstract --- p.i / Acknowledgment --- p.ii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.2 / Chapter 1.2 --- Problem Description --- p.5 / Chapter 1.3 --- Contributions --- p.6 / Chapter 1.4 --- Thesis Organization --- p.6 / Chapter 2 --- Problem Formulation and Solution Approaches --- p.8 / Chapter 2.1 --- Mathematical Modeling --- p.8 / Chapter 2.2 --- Transformation of Problem --- p.11 / Chapter 2.3 --- Problem Analysis --- p.12 / Chapter 2.3.1 --- Optimality Criteria --- p.13 / Chapter 2.3.2 --- Heuristic Solutions --- p.15 / Chapter 2.4 --- Literatures Review on Single-Machine Scheduling --- p.18 / Chapter 3 --- Discussion of Some Special Cases --- p.21 / Chapter 3.1 --- Two Operations --- p.22 / Chapter 3.2 --- Identical Distributions --- p.24 / Chapter 3.2.1 --- Error Bound of LPTF - Maximum Distribution Approach --- p.27 / Chapter 3.3 --- Large Initial Time and Special Processing Times Structure --- p.29 / Chapter 3.3.1 --- Application of SVF to Exponential Distribution --- p.34 / Chapter 3.3.2 --- Error Bound of SVF 一 Switching Processing Times Approach --- p.37 / Chapter 3.3.3 --- Extended Error Bound Analysis --- p.41 / Chapter 4 --- Heuristics to Solve the General Problems --- p.47 / Chapter 4.1 --- Level 1 - PIPF and LPTF Rules --- p.48 / Chapter 4.2 --- Level 2 - Adjacent Pair wise Interchange --- p.51 / Chapter 4.3 --- Computational Complexity --- p.53 / Chapter 5 --- Experimental Results --- p.54 / Chapter 5.1 --- Design of Experiments --- p.54 / Chapter 5.1.1 --- Design of Problem Parameters --- p.55 / Chapter 5.1.2 --- Evaluation Methods --- p.57 / Chapter 5.2 --- Results Analysis --- p.59 / Chapter 5.2.1 --- Evaluation for Problems with Small Size --- p.60 / Chapter 5.2.2 --- Evaluation for Problems with Large Size --- p.63 / Chapter 6 --- Conclusion --- p.67 / Chapter 6.1 --- Summary --- p.67 / Chapter 6.2 --- Future Extension --- p.68 / Appendix --- p.69 / Chapter A --- Crossing Point of Normal Density Functions --- p.69 / Chapter B --- Probaiblity Distributions --- p.73 / Chapter B.1 --- Uniform Distribution --- p.73 / Chapter B.2 --- Exponential Distribution --- p.74 / Chapter B.3 --- Normal Distribution --- p.74 / Bibliography --- p.75

published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

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

published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

No description available.

This research deals with production scheduling of manufacturing systems that predominantly consist of hybrid flowshops. Hybrid Flowshop Scheduling (HFS) problems are common in metal working industries. Their solution has significant inferences on company performance in a globally competitive market in terms of production cycle time, delivery dates, warehouse and work-in-process inventory management. HFS problems have attracted considerable research efforts on examining their scientific complexity and practical solution algorithms. In conventional HFS systems, an individual job goes through the flowshop with its own processing route, which has no influence on other jobs. However, in many metal working HFS systems, jobs have interdependent relationships during the process. This thesis focuses on addressing two classes of HFS problems with job interdependence that have been motivated by real-life industrial problems observed from our collaborating companies. The first class of HFS problems with job interdependence are faced by manufacturers of typically standard metal components where jobs are organized in families according to their machine settings and tools. Family setup times arise when a machine shifts from processing one job family to another. This problem is compounded by the challenges that the formation of job families is different in different stages and only a limited number of jobs can be processed within one setup. This class of problems is defined as HFS with family setup and inconsistent family formation. The second class of HFS problems with job interdependence is typically faced in a production process consisting of divergent operations where a single input item is converted into multiple output items. Two important challenges have been investigated. One is that one product can be produced following different process routes. The other is that the total inventory capacity is very limited in the company in the sense that the inventory spaces are commonly shared by raw materials, work-in-process items and finished products. This class of problems is defined as HFS with divergent production and common inventory. The aim is to analyze the general characteristics of HFS with job interdependence and develop effective and practical methodologies that can tackle real-world constraints and reduce the scheduling effort in daily production. This research has made the following contributions: (1) A V-A-X structural classification has been proposed to represent the divergent (V), convergent (A) and mixed (X) job interdependent relations during the production. (2) A genetic algorithm based approach and a particle swarm optimization based approach have been developed to solve two classes of HFS problems with job interdependence, respectively. The computational results based on actual production data have shown that the proposed solutions are robust, efficient and advantageous for solving the practical problems. (3) A waiting factor approach and delay timetable approach have been developed to extend the solutions space of two classes of HFS problems by inserting intentional idle times into original schedules. The computational results have indicated that better schedules can be obtained in the extended solution spaces. / published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

Manufacturing companies must constantly improve productivity to respond to dynamic changes in customer demand in order to maintain their competitiveness and marketshares. This requires manufacturers to adopt more efficient methodologies to design and control their manufacturing systems. In recent decades, virtual cellular manufacturing (VCM), as an advanced manufacturing concept, has attracted increasing attention in the research community, because traditional cellular manufacturing is inadequate when operating in a highly dynamic manufacturing environment. Virtual cellular manufacturing temporarily and dynamically groups production resources to form virtual cells according to production requirements, thus enjoying high production efficiency and flexibility simultaneously. The objective of this research is to develop cost-effective methodologies for manufacturing cell formation and production scheduling in virtual cellular manufacturing systems (VCMSs), operating in single-period/multi-period, and dynamic manufacturing environments. In this research, two mathematical models are developed to describe the characteristics of VCMSs operating under a single-period and a multi-period manufacturing environment respectively. These models aim to develop production schedules to minimize the total manufacturing cost incurred in manufacturing products for the entire planning horizon, taking into consideration many practical constraints such as workforce requirements, effective capacities of production resources, and delivery due dates of orders. In the multi-period case, worker training is also considered and factors affecting worker training are analyzed in detail. This research also develops a novel hybrid algorithm to solve complex production scheduling problems optimally for VCMSs. The hybrid algorithm is based on the techniques of discrete particle swarm optimization, ant colony system and constraint programming. Its framework is discrete particle swarm optimization which can locate good production schedules quickly. To prevent the optimization process being trapped into a local optimum, concepts of ant colony system and constraint programming are incorporated into the framework to greatly enhance the exploration and exploitation of the solution space, thus ensuring better quality production schedules. Sensitivity analyses of the key parameters of the hybrid algorithm are also conducted in detail to provide a theoretical foundation which shows that the developed hybrid algorithm is indeed an excellent optimization tool for production scheduling in VCMSs. In practice, the occurrence of unpredictable events such as breakdown of machines, change in the status of orders and absenteeism of workers will make the current production schedule infeasible. A new feasible production schedule may therefore need to be generated rapidly to ensure smooth manufacturing operations. This research develops several cost-effective production rescheduling strategies for VCMSs operating under different dynamic manufacturing environments. These strategies facilitates the determination of when-to and how-to take rescheduling actions. To further enhance the performance of such strategies in generating new production schedules, especially for large-scale manufacturing systems, a parallel approach is established to implement the developed hybrid algorithm on GPU with compute unified device architecture. The convergence characteristics of the proposed hybrid algorithm are also studied theoretically by using probability theory and Markov chain model. The analysis results show that the optimization process will eventually converge to the global optimal solution. / published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

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

No description available.