Thesis (Ph.D (Faculty of Engineering, Built Environment and Information Technology)) -- Univerersity of Pretoria, 2008. / Includes bibliographical references.

Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2004. / Includes bibliographical references (leaves 27). Also available in electronic version. Access restricted to campus users.

Thesis (Ph. D.)--Florida State University, 2005. / Advisor: Dr. Ralph S. Brower, Florida State University, College of Social Sciences. Reuben O'D. Askew School of Public Administration and Policy. Title and description from dissertation home page (viewed Sept. 13, 2005). Document formatted into pages; contains xi, 174 pages. Includes bibliographical references.

Thesis (Ph. D.)--University of Texas at Austin, 2005. / Vita. Includes bibliographical references.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1976. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 143-148).

Thesis (Ph.D.)--McMaster University, 2002 / Includes bibliographical references (leaves 92-101). Also available via World Wide Web.

<p>This project report defines network robustness and discusses capability indicators that could be used to analyze network robustness. Growing dependence on telecommunication networks and recent network outages have focused attention on network robustness. The National Communications System (NCS), a confederation of 23 Federal departments and agencies, has been concerned with network robustness since its formation in 1963. The NCS is developing and implementing systems and services that enhance the capability of the public switched networks to support critical Government communication requirements during times of crisis or emergency. Quantitative indicators of network robustness are needed to analyze the benefits of these enhancements. This project proposes a set of candidate capability indicators that could be used by the NCS in future analyses of the public switched networks with and without network enhancements.</p> / Master of Science

Location-allocation problems arise in many practical settings and may be generically stated as follows: Given the location or distribution of a set of customers and their associated demands, simultaneously determine an optimal location of a number of supply facilities and their allocation of products or services to the customers, so as to minimize total location and transportation costs. This study is concerned with the development, convergence analysis, and testing of exact and heuristic algorithms for location-allocation problems in which demands can occur continuously over regions according to some probability density functions. In this context, minisum location problems on undirected networks are considered in which demands can occur on links with uniform probability distributions. Three types of networks are considered. The first type is a chain graph. It is shown that except for the 1-median case, the problem is generally nonconvex. However, for the p-median case, it is shown that all local and global trd.nima to the problem may be discovered by solving a series of linear programming problems. This analysis forms the basis for similar problems on trees and graphs with isolated cycles. These problems are then extended to multiperiod versions in which demands may change dynamically over time periods and at mostly one facility can be located per time period. Chain and tree graphs are considered in conjunction with three optimization strategies: myopic, long-range, and discounted present worth. It is hoped that the exact methods developed for these special networks will lead to at least effective heuristics for problems on more general networks. Finally, location-allocation problems are considered in which the region to be served is a convex polygon having a uniform demand distribution. Both single and multifacility formulations are considered. For the single facility problem, an algorithm is developed which is shown to converge to a global optimal solution. This analysis is extended to the nonconvex multifacility case, and although optimality is not guaranteed, an algorithm is presented for finding a good starting solution which increases the likelihood of finding an optimal solution. Extensions of the above problems to include discrete demand points and computational experience are also provided. / Ph. D.

published_or_final_version / abstract / Transport Policy and Planning / Master / Master of Arts in Transport Policy and Planning

by Kenneth Wing Chung Tang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 64-65). / LIST OF TABLES --- p.vi / LIST OF FIGURES --- p.vii / Chapter 1. --- INTRODUCTION --- p.1 / Chapter 1.1 --- Staff Scheduling Overview --- p.2 / Chapter 1.1.1 --- Days-off scheduling --- p.7 / Chapter 1.1.2 --- Shift Scheduling --- p.8 / Chapter 1.1.3 --- Tour Scheduling --- p.9 / Chapter 1.2 --- Outline of The Work of The Thesis --- p.11 / Chapter 2. --- NETWORK MODEL FOR STAFF SCHEDULING --- p.13 / Chapter 2.1 --- The Basic Network Model --- p.13 / Chapter 2.1.1 --- General Idea --- p.13 / Chapter 2.1.2 --- Modeling Precedent Relationship Constraints by Arcs --- p.15 / Chapter 2.1.3 --- Modeling Shift Stretch Constraints by Nodes --- p.16 / Chapter 2.1.4 --- Modeling to Handle Side Constraints --- p.17 / Chapter 2.1.5 --- Mathematical Model --- p.21 / Chapter 2.2 --- Solving The Network Model With Side Constraints --- p.23 / Chapter 2.2.1 --- Basis Partitioning Network Simplex method --- p.23 / Chapter 2.2.2 --- A Two-Phase Heuristic for Schedules Construction --- p.29 / Chapter 3. --- APPLICA TION IN AN AIR CARGO TERMINAL --- p.55 / Chapter 3.1 --- Background And Problem Statement --- p.35 / Chapter 3.2 --- Generation of Staff Requirement Patterns --- p.38 / Chapter 3.3 --- A Typical Setting of Parameters --- p.41 / Chapter 3.4 --- Case One: Staff Requirement for Each Shift Is Fixed --- p.43 / Chapter 3.4.1 --- Conversion of hourly requirements to shift requirements --- p.43 / Chapter 3.4.2 --- Network Modeling --- p.44 / Chapter 3.4.3 --- An Example --- p.47 / Chapter 3.4.4 --- Computational result on different staff requirements --- p.49 / Chapter 3.5 --- Case Two: Staff Requirement for Each Shift Is Changing --- p.50 / Chapter 3.5.1 --- Network modeling --- p.51 / Chapter 3.5.2 --- An Example --- p.52 / Chapter 3.5.2.1 --- Overlapping shifts with one kind of break times --- p.54 / Chapter 3.5.2.2 --- Overlapping shifts with two kinds of break times --- p.56 / Chapter 3.5.2.3 --- Overtime work --- p.57 / Chapter 3.5.3 --- Computational results on different staff requirement patterns --- p.60 / Chapter 4. --- CONCLUSION --- p.62 / Chapter 5. --- BIBLIOGRAPHY --- p.64 / Chapter 6. --- APPENDIX --- p.66 / Chapter 6.1 --- Applying the heuristic to complete the incomplete schedules --- p.66 / Chapter 6.2 --- List of Schedules --- p.68 / Chapter 6.2.1 --- Terminologies --- p.68 / Chapter 6.2.2 --- The Optimal Schedules for Case One --- p.69 / Chapter 6.2.3 --- The Optimal Schedules for Case Two --- p.70 / Chapter 6.2.4 --- The Optimal Schedules with One-hour Break in One Shift --- p.71 / Chapter 6.2.5 --- The Optimal Schedules with Breaks after 4 and 3 Hours of Work --- p.72 / Chapter 6.2.6 --- The Optimal Schedules with Overtime Shifts --- p.73