published_or_final_version / Urban Planning / Master / Master of Science in Urban Planning

published_or_final_version / Geography and Geology / Master / Master of Arts

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

published_or_final_version / Urban Planning / Master / Master of Science in Urban Planning

published_or_final_version / Urban Planning / Master / Master of Science in Urban Planning

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

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

In the past two decades, as regional free trade agreements prevailed between states and governments, there is an imperative need for the concerned trade control authorities to minimise non-tariff trade barriers. Trade facilitation policy becomes one of the essential tools to enhance the competitive strength of a state in the global market. As the common yardstick adopted to assess policy effectiveness, the global trade efficiency indicators seem to reflect that state-level trade facilitation policies may not be generating the expected results. Not only the validity of such indicators have been challenged by some scholars, their application in port-level studies are also questionable. This project develops an evaluation framework that consists of a qualitative and a quantitative assessment tool to evaluate port-based trade facilitation policies. The qualitative analytical instrument examines how trade facilitation measures affect port-based supply chain. The quantitative survey tool measures the extent of these policies may impact on the supply chain activities of port-related firm, infrastructure, and institutional stakeholders. The evaluation framework is applied to investigate the effects of trade facilitation policies on hub ports of the Pearl River Delta (PRD) region. Such framework not only provides an instrument to measure policy impact, but also illustrates how a trade facilitation programme may affect the competitiveness of port supply chain. The qualitative tool by adopting multiple supply chain perspectives, contributes a consistent and comprehensive assessment method for trade facilitation studies to extend the research scope to port level. The quantitative instrument provides a mechanism that could facilitate an accurate measurement of the trade facilitation policy impacts not only in a single port but also in a networked ports' environment.

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

Cheung, Kwan Wing. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (p. 115-124). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Contributions --- p.7 / Chapter 1.3 --- Findings --- p.8 / Chapter 1.4 --- Structure of the Thesis --- p.10 / Chapter 2 --- Beer Logistics in China --- p.12 / Chapter 2.1 --- The Logistics and Supply Chain in China --- p.12 / Chapter 2.2 --- Beer in China --- p.22 / Chapter 2.2.1 --- The Expanding Market --- p.22 / Chapter 2.2.2 --- The Multi-tiered Supply Chain --- p.22 / Chapter 2.2.3 --- Reverse Logistics --- p.25 / Chapter 2.2.4 --- Manual Demand and Inventory Manage- ment --- p.26 / Chapter 2.2.5 --- Retail Fees and Value Chains --- p.28 / Chapter 2.2.6 --- Packaging --- p.30 / Chapter 2.3 --- The Wholesaler --- p.31 / Chapter 2.3.1 --- High Service Quality under Fierce Com- petition --- p.31 / Chapter 2.3.2 --- Use of Vans --- p.33 / Chapter 2.3.3 --- Delivery Problem of Wholesalers --- p.35 / Chapter 3 --- Literature Review --- p.37 / Chapter 3.1 --- Beer Logistics in China --- p.37 / Chapter 3.2 --- Modelling Delivery Problems --- p.39 / Chapter 3.3 --- Applications of the Vehicle Routing Problem --- p.42 / Chapter 3.4 --- Heuristics and Metaheuristics --- p.43 / Chapter 3.5 --- Round up --- p.45 / Chapter 4 --- Problem Definition --- p.48 / Chapter 4.1 --- Problem Definition --- p.48 / Chapter 4.2 --- Assumptions --- p.53 / Chapter 5 --- Problem Formulation --- p.55 / Chapter 5.1 --- Introduction --- p.55 / Chapter 5.2 --- Notations --- p.58 / Chapter 5.2.1 --- Indices --- p.58 / Chapter 5.2.2 --- Parameters --- p.58 / Chapter 5.2.3 --- Decision Variables --- p.59 / Chapter 5.3 --- Mixed Integer Programming Model --- p.60 / Chapter 5.3.1 --- The Model --- p.60 / Chapter 5.3.2 --- Descriptions --- p.61 / Chapter 5.3.3 --- Complexity and Polynomial Number of Con- straints --- p.66 / Chapter 6 --- Solution Methodology --- p.69 / Chapter 6.1 --- Input Parameters --- p.69 / Chapter 6.2 --- Finding the Optimal Solution --- p.70 / Chapter 6.2.1 --- By CPLEX Optimization Package --- p.70 / Chapter 6.2.2 --- Problems of Using Optimization Packages --- p.72 / Chapter 6.2.3 --- Observations of Some Optimal Solutions --- p.74 / Chapter 6.3 --- Heuristics Development --- p.77 / Chapter 6.3.1 --- Solution Strategies --- p.77 / Chapter 6.3.2 --- Evaluation of the Strategy Combinations --- p.84 / Chapter 6.3.3 --- Best Combinations --- p.90 / Chapter 6.3.4 --- Final Heuristic --- p.92 / Chapter 7 --- Computational Results --- p.94 / Chapter 7.1 --- Methodology --- p.94 / Chapter 7.2 --- Results of Using the Final Heuristic --- p.95 / Chapter 7.3 --- Computational Time --- p.99 / Chapter 8 --- Managerial Insights --- p.102 / Chapter 8.1 --- Practical Issues --- p.102 / Chapter 8.2 --- Managerial Insights --- p.105 / Chapter 9 --- Future Work and Conclusion --- p.109 / Chapter 9.1 --- Future Work --- p.109 / Chapter 9.2 --- Conclusion --- p.112 / Bibliography --- p.115