Yu Hongbo. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 130-138). / Abstracts in English and Chinese. / ABSTRACT IN ENGLISH --- p.i-ii / ABSTRACT IN CHINESE --- p.iii / ACKNOWLEDGEMENTS --- p.iv-v / TABLE OF CONTENTS --- p.vi-viii / LIST OF TABLES --- p.ix / LIST OF FIGURES --- p.x-xi / Chapter CHAPTER I --- INTRODUCTION / Chapter 1.1 --- Problem Statement --- p.1 / Chapter 1.2 --- Research Purpose --- p.5 / Chapter 1.3 --- Significance --- p.7 / Chapter 1.4 --- Methodology --- p.8 / Chapter 1.5 --- Outline of the Thesis --- p.9 / Chapter CHAPTER II --- LITERATURE REVIEW / Chapter 2.1 --- Introduction --- p.12 / Chapter 2.2 --- Origin of GIS --- p.12 / Chapter 2.3 --- Development of GIS-T --- p.15 / Chapter 2.4 --- Capabilities of GIS-T --- p.18 / Chapter 2.5 --- Structure of a GIS-T --- p.19 / Chapter 2.5.1 --- Data Models for GIS-T --- p.19 / Chapter 2.5.2 --- Relational DBMS and Dueker-Butler's Data Model for Transportation --- p.22 / Chapter 2.5.3 --- Objected-oriented Approach --- p.25 / Chapter 2.6 --- Main Techniques of GIS-T --- p.26 / Chapter 2.6.1 --- Linear Location Reference System --- p.26 / Chapter 2.6.2 --- Dynamic Segmentation --- p.27 / Chapter 2.6.3 --- Planar and Non-planar Networks --- p.28 / Chapter 2.6.4 --- Turn-table --- p.28 / Chapter 2.7 --- Algorithms for Finding Shortest Paths on a Network --- p.29 / Chapter 2.7.1 --- Overview of Routing Algorithms --- p.29 / Chapter 2.7.2 --- Dijkstra's Algorithm --- p.31 / Chapter 2.7.3 --- Routing Models for the Multi-modal Network --- p.32 / Chapter 2.8 --- Recent Researches on GIS Data Models for the Multi-modal Transportation System --- p.33 / Chapter 2.9 --- Main Software Packages for GIS-T --- p.36 / Chapter 2.10 --- Summary --- p.37 / Chapter CHAPTER III --- MODELING THE MULTI-MODAL PUBLIC TRANSPORTATION SYSTEM / Chapter 3.1 --- Introduction --- p.40 / Chapter 3.2 --- Elaborated Stages and Methods for GIS Modeling --- p.40 / Chapter 3.3 --- Application Domain: The Multi-modal Public Transportation System --- p.43 / Chapter 3.3.1 --- Definition of a Multi-modal Public Transportation System --- p.43 / Chapter 3.3.2 --- Descriptions of the Multi-modal Public transportation System --- p.44 / Chapter 3.3.3 --- Objective of the Modeling Work --- p.46 / Chapter 3.4 --- A Layer-cake Based Application Domain Model for the Multi- modal Public Transportation System --- p.46 / Chapter 3.5 --- A Conceptual Model for the Multi-modal Public Transportation System --- p.49 / Chapter 3.6 --- Logical and Physical Implementation of the Data Model for the Multi-modal Public Transportation System --- p.54 / Chapter 3.7 --- Criteria for Routing on the Multi-modal Public Transportation System --- p.57 / Chapter 3.7.1 --- Least-time Routing --- p.58 / Chapter 3.7.2 --- Least-fare Routing --- p.60 / Chapter 3.7.3 --- Least-transfer Routing --- p.60 / Chapter 3.8 --- Summary --- p.61 / Chapter CHAPTER IV --- DATA PREPARATION FOR THE STUDY AREA / Chapter 4.1 --- Introduction --- p.53 / Chapter 4.2 --- The Study Area: Hong Kong Island --- p.63 / Chapter 4.2.1 --- General Information of the Transportation System on Hong Kong Island --- p.63 / Chapter 4.2.2 --- Reasons for Choosing Hong Kong Island as the Study Area --- p.66 / Chapter 4.2.3 --- Mass Transit Routes Selected for the Prototype --- p.67 / Chapter 4.3 --- Data Source and Data Collection --- p.67 / Chapter 4.4 --- Geographical Data Preparation --- p.71 / Chapter 4.4.1 --- Data Conversion --- p.73 / Chapter 4.4.2 --- Geographical Data Input --- p.79 / Chapter 4.5 --- Attribute Data Input --- p.86 / Chapter 4.6 --- Summary --- p.88 / Chapter CHAPTER V --- IMPLEMENTATION OF THE PROTOTYPE / Chapter 5.1 --- Introduction --- p.89 / Chapter 5.2 --- Construction of the Route Service Network --- p.89 / Chapter 5.2.1 --- Generation of the Geographical Network --- p.90 / Chapter 5.2.2 --- Setting Attribute Data for the Route Service Network --- p.95 / Chapter 5.3 --- A GIS-T Prototype for the Study Area --- p.102 / Chapter 5.4 --- General GIS Functions of the Prototype --- p.104 / Chapter 5.4.1 --- Information Retrieve --- p.104 / Chapter 5.4.2 --- Display --- p.105 / Chapter 5.4.3 --- Data Query --- p.105 / Chapter 5.5 --- Routing in the Prototype --- p.105 / Chapter 5.5.1 --- Routing Procedure --- p.108 / Chapter 5.5.2 --- Examples and Results --- p.110 / Chapter 5.5.3 --- Comparison and Analysis --- p.113 / Chapter 5.6 --- Summary --- p.118 / Chapter CHAPTER VI --- CONCLUSION / Chapter 6.1 --- Research Findings --- p.123 / Chapter 6.2 --- Research Limitations --- p.126 / Chapter 6.3 --- Direction of Further Studies --- p.128 / BIBLIOGRAPHY --- p.130

This dissertation focuses on the material transport system design problem (MTSDP), integrating decisions of technology selection and flow network design. This research is motivated by the design of material transport systems (MTS) in manufacturing plants. The objective is to design a MTS with minimum lifetime costs, subject to service requirements, flow network restrictions, and limited resources. We characterize the MTSDP from the perspectives of task requirements, transport technology, and space utilization. A classification is proposed for transport technologies such that instances in the same class share the same properties, and a decision framework is proposed to emphasize the inter-relationships of three major decisions: task clustering, network connecting, and technology selection. We consider fixed and variable costs, arc capacities, and empty travel in our formulations. We propose two solution approaches for the MTSDP. The first is the compact formulation (CF) approach where the three major decisions are handled by a mixed integer non-linear programming (MINLP) formulation. Relaxation techniques are applied to linearize the model. The solution of the resulting linear formulation (MILP) provides a lower bound to that of MINLP. A tightened formulation reduces the computational time by a factor of 3.85. The experiment also shows that when control system costs are significant, designs with multiple-task clusters are more economical than those restricted to single-task clusters. The other approach is clustering/set partition (CSP), where the three decisions are decomposed and solved sequentially. In an example MTS design problem, three methods are compared: CSP, a GREEDY approach from the literature, and enumeration. CSP finds the optimal solution, while GREEDY results in 31% greater costs. A similar comparison with another example is made for the CF and CSP approaches. We apply the CSP approach in a case problem, using data from an auto parts manufacturer. We include flow path crossing constraints and perform experiments to determine solution quality over a range of small problem sizes. The largest difference from optimality is 3.34%, and the average is 0.98%. More importantly, based on these experiments, it seems there is no evidence that the difference percentage grows with an increase in the number of tasks.

Indiana University-Purdue University Indianapolis (IUPUI) / In recent decades, Petri nets (PNs) have been used to model traffic networks for different purposes, such as signal phase control, routing, and traffic flow estimation, etc. Because of the complex nature of traffic networks where both discrete and continuous dynamics come into play, the Hybrid Petri net (HPN) model becomes an important tool for the modeling and analysis of traffic networks. In Chapter 1 a brief historical summery about traffic systems control and then related work is mentioned followed by the major contributions in this research. Chapter 2 provides a theoretical background on Petri nets. In Chapter 3, we develop a HPN model for a single signalized intersection first, then we extend this model to study a simple traffic network that consists of two successive intersections. Time delays between different points of network are also considered in order to make the model suitable for analysis and simulation. In addition to HPN models, we also consider discrete Petri nets where their modeling simplicity enables the characterization of the occurrences of all events in the system. This discrete PN is particularly useful to give a higher-level representation of the traffic network and study its event occurrences and correlations. In Chapter 4, we build a discrete PN model to represent a traffic network with two successive intersections. However, we find that the model leads to unbounded places which cannot accurately reflect the dynamics of the traffic in terms of event occurrences. Hence, we introduce the Modified Binary Petri nets (MBPN) to overcome the limitation and resolve the confliction problem when we design our controllers. This MBPN model is a powerful tool and can be useful for the modeling and analysis of many other applications in traffic networks. Chapter 5 gives a summary for each chapter, provides conclusion and discusses future work for both discrete and hybrid Petri nets.