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Title: Robust global supply chain planning under uncertainty
Author: Wu, Yue
ISNI:       0000 0004 2680 2917
Awarding Body: London School of Economics and Political Science
Current Institution: London School of Economics and Political Science (University of London)
Date of Award: 2009
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The New World Economy presents business organizations with some special challenges that they have never met before, when they manage their activities in the global supply chain network. Business managers find that traditional managerial approaches, techniques and principles are no longer effective in dealing with these challenges. This dissertation is a study of how to solve new problems emerging in the global supply chain network. Three main issues identified in the global supply chain network are: production loading problems for global manufacturing, logistics problems for global road transport and container loading problems for global air transport. These problems involve a higher level of uncertainty and risk. Three types of dual-response strategies have been developed to hedge the uncertainty and short lead time in the above three problems. These strategies are: a dual-response production loading strategy for global manufacturing, a dual-response logistics strategy for global road transport and a dual-response container loading strategy for global air transport. In order to implement these strategies, the two-stage stochastic recourse programming models have been formulated. The computational results show that the two-stage stochastic recourse models have an advantage in comparison to the corresponding deterministic models for the three issues. However, the two-stage stochastic recourse models lack the ability of handling risk, which is particularly important in today's highly-competitive environment. We thus develop a robust optimization framework for dealing with uncertainty and risk. The robust optimization framework consists of a robust optimization model with solution robustness, a robust optimisation model with model robustness and a robust optimization model with trade-off between solution robustness and model robustness. Each type of the robust optimization models represents a different measure of performance in terms of risk and cost. A series of experiments demonstrate that the robust optimization models can create a global supply chain planning system with more flexibility, reliability, agility, responsiveness and lower risk.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available