Yau Man-Kuen. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 222-234). / Abstracts in English and Chinese. / Chapter Chapter 0 --- Introduction --- p.1 / Chapter PART A: --- Logistics in Hong Kong 一 Overview and Prospects / Chapter A.1 --- Study Objectives --- p.3 / Chapter A.2 --- Methodology --- p.4 / Chapter A.3 --- What is Logistics? --- p.4 / Chapter A.3.1 --- Major Trends --- p.6 / Chapter A.4 --- Key Features of the Logistics in Hong Kong & China --- p.8 / Chapter A.4.1 --- China Industry --- p.8 / Chapter A.4.2 --- National Developments in China --- p.13 / Chapter A.4.3 --- Hong Kong Industry --- p.16 / Chapter A.5 --- Growth Trends & Statistics for Hong Kong --- p.25 / Chapter A.6 --- Competitive Analysis for Hong Kong as a Logistics Hub --- p.45 / Chapter A.6.1 --- Current Industry Strengths --- p.45 / Chapter A.6.2 --- Current Industry Weaknesses --- p.46 / Chapter A.6.3 --- Competitiveness Challenges --- p.47 / Chapter A.6.4 --- Future Opportunities --- p.51 / Chapter A.7 --- Changing Conditions and Infrastructure Needs --- p.54 / Chapter A.7.1 --- Trade --- p.54 / Chapter A.7.2 --- Technology --- p.56 / Chapter A.7.3 --- Investment --- p.56 / Chapter A.7.4 --- Human Resources --- p.57 / Chapter A.7.5 --- Government and Regulation --- p.58 / Chapter A.8 --- Recommendations --- p.61 / Chapter A.9 --- Conclusions --- p.64 / Chapter A.10 --- Future Work --- p.65 / Chapter PART B: --- Inventory Management with Advance Ordering / Chapter Chapter B.1 --- Introduction --- p.66 / Chapter B.1.1 --- Overview --- p.66 / Chapter B.1.2 --- Literature Review --- p.69 / Chapter Chapter B.2 --- Model Formulation --- p.72 / Chapter B.2.1 --- Introduction --- p.72 / Chapter B.2.2 --- Mathematical Model --- p.74 / Chapter B.2.3 --- Preliminaries --- p.76 / Chapter B.2.4 --- Table of variables --- p.77 / Chapter Chapter B.3 --- Study of Window Size0 --- p.79 / Chapter B.3.1 --- Introduction --- p.79 / Chapter B.3.2 --- Mathematical Model --- p.79 / Chapter B.3.3 --- Proof of Window Size0 --- p.81 / Chapter Chapter B.4 --- Study of Window Size1 --- p.94 / Chapter B.4.1 --- Introduction --- p.94 / Chapter B.4.2 --- Mathematical Model --- p.95 / Chapter B.4.3 --- Optimal Ordering Policy for Window Size1 --- p.95 / Chapter B.4.4 --- Special Case of Uniformly Distributed Demand --- p.109 / Chapter B.4.5 --- Discussion of Fukuda's Paper --- p.114 / Chapter Chapter B.5 --- Simulation Study of Window Size1 --- p.120 / Chapter B.5.1 --- Simulation Models --- p.120 / Chapter B.5.2 --- Simulation Program Structure --- p.126 / Chapter B.5.3 --- Simulation Numerical Analysis --- p.131 / Chapter Chapter B.6 --- Simulation Study of Window Size K --- p.172 / Chapter B.6.1 --- Simulation Models --- p.172 / Chapter B.6.2 --- Simulation Program Structure --- p.179 / Chapter B.6.3 --- Simulation Numerical Analysis --- p.181 / Chapter Chapter B.7 --- Conclusion and Further Studies --- p.201 / Appendix (PART A) --- p.204 / Appendix (PART B) --- p.208 / Bibliography (PART A) --- p.222 / Bibliography (PART B) --- p.229

This research focuses on large-scale manufacturing systems having a number of stations with multiple tools and product types with different and deterministic processing steps. The objective is to determine the production quantities of multiple products and the tool requirements of each station that maximizes net profit while satisfying strategic constraints such as cycle times, required throughputs, and investment. The formulation of the problem, named OptiProfit, is a mixed-integer nonlinear programming (MINLP) with the stochastic issues addressed by mean-value analysis (MVA) and queuing network models. Observing that OptiProfit is an NP-complete, nonconvex, and nonmonotonic problem, the research develops a heuristic method, Differential Coefficient Based Search (DCBS). It also performs an upper-bound analysis and a performance comparison with six variations of Greedy Ascent Procedure (GAP) heuristics and Modified Simulated Annealing (MSA) in a number of randomized cases. An example problem based on a semiconductor manufacturing minifab is modeled as an OptiProfit problem and numerically analyzed. The proposed methodology provides a very good quality solution for the high-level design and operation of manufacturing facilities.

A concept for the mathematical modeling of multiple repairable equipment and logistic systems (MREAL systems) is developed; These systems consist of multiple populations of repairable equipment, and their associated design, procurement, maintenance, and supply support. MREAL systems present management and design problems which parallel the·management and design of multiple, consumable item inventory systems. However, the MREAL system is more complex since it has a repair component. The MREAL system concept is described in a classification hierarchy which attempts to categorize the components of such systems. A specific mathematical model (MREAL1) is developed for a subset of these components. Included in MREAL1 are representations of the equipment reliability and maintainability design problem, the maintenance capacity problem, the retirement age problem, and the population size problem, for each of the multiple populations. MREAL1 models the steady state stochastic behavior of the equipment repair facilities using an approximation which is based upon the finite source, multiple server queuing system. System performance measures included in MREAL1 are: the expected MREAL total system life cycle cost (including a shortage cost penalty); the steady state expected number of shortages; the probability of catastrophic failure in each equipment population; and two budget based measures of effectiveness. Two optimization methods are described for a test problem developed for MREAL1. The first method computes values of the objective function and the constraints for a specified subset of the solution space. The best feasible solution found is recorded. This method can also examine all possible solutions, or can be used in a manual search. The second optimization method performs an exhaustive enumeration. of the combinatorial programming portion of MREAL1, which represents equipment design. For each enumerated design combination, an attempt is made to find the optimal solution to the remaining nonlinear discrete programming problem. A sequential unconstrained minimization technique is used which is based on an augmented Lagrangian penalty function adapted to the integer nature of MREAL1. The unconstrained minimization is performed by a combination of Rosenbrock's search technique, the steepest descent method, and Fibonacci line searches, adapted to the integer nature of the search. Since the model contains many discrete local minima, the sequential unconstrained minimization is repeated from different starting solutions, based upon a heuristic selection procedure. A gradient projection method provides the termination criteria for each unconstrained minimization. / Ph. D.

Thesis (MSc (Applied Mathematics))--University of Stellenbosch, 2005. / The process of modelling the fruit export infrastructure capacity of South Africa formed part of a larger project called the \Fruit Logistics Infrastructure Capacity Optimisation Study," which was coordinated by the Transportek division of the CSIR in Stellenbosch during the period August 2002 to March 2004. The aim of this project was to create efficiencies for, and enhance the competitiveness of, the South African fruit industry by improved usage of, and investment in, shared logistics infrastructure. After putting the size of the fruit industry into perspective, numerous aspects of the export process are considered in this thesis so as to be able to perform a comprehensive cost analysis of the export of fruit, including the cost of handling, cooling and transportation. The capacities of packhouses, cold stores and terminals are found and presented. This information, combined with fruit export volumes of 2003, then allow an estimation of the current utilisation of the South African ports with respect to fruit export.