Resource sharing is a common collaborative strategy used in practice. It has the potential to create synergistic value and leads to higher system efficiency. However, realizing this synergistic value can be challenging given the prevalence of decentralization in practice, where individual operators manage resources based on their own benefits. Hence, optimizing a decentralized system requires understanding not only the optimal operational strategy in terms of the overall system efficiency, but also the implementation of the strategy through proper management of individual incentives. However, traditional network optimization approaches typically assume a centralized perspective. The classic game theory framework, on the other hand, addresses incentive issues of decentralized decision makers, but mainly takes a high-level, economic perspective that does not fully capture the operational complexity involved in optimizing systems with resource sharing.
The purpose of this thesis is to bridge this gap between practice and theory by studying the design of tools to manage and optimize the operations in decentralized systems with resource sharing using approaches that combine optimization and game theory. In particular, we focus on decentralized network systems and analyze two research streams in two application domains: (i) implementation of environmental legislation, and (ii) managing collaborative transportation systems. These applications are characterized by their decentralized multi-stakeholder nature where the conflicts and tension between the heterogeneous individual perspectives make system management very challenging. The main methodology used in this thesis is to adopt game theory models where individual decisions are endogenized as the solutions to network optimization problems that reflect their incentives. Such an approach allows us to capture the connection between the operational features of the system (e.g., capacity configuration, network structure, synergy level from resource sharing) and the individual incentives thus the effectiveness of the management tools, which is a main research contribution of this thesis.
In the first research stream, we consider designing effective, efficient and practical implementation of electronic waste take-back legislation based on the widely-adopted Extended Producer Responsibility (EPR) concept that mandates the financial responsibility of post-use treatment of their products. Typical implementations of EPR are collective, and allocate the resulting operating cost to involved producers. In this thesis, we demonstrate the complexity of collective EPR implementation due to the tension among different stakeholder perspectives, based on a case analysis of the Washington implementation. We then perform analytical studies of the two prominent challenges identified in current implementations: (i) developing cost allocation mechanisms that induce the voluntary participation of all producers in a collective system, thus promoting implementation efficiency; and (ii) designing collective EPR so as to encourage environmentally-friendly product design, thus promoting implementation effectiveness. Specifically, we prescribe new cost allocation methods to address the first challenge, and demonstrate the practicality and economic impact of the results using implementation data from the state of Washington. We then analyze the tensions between design incentives, efficiency and the effectiveness of the cost allocation to induce voluntary participation under collective EPR implementation. We show there exists a tradeoff among the three dimensions, driven by the network effects inherent in a collective system. The main contribution of this research stream is to demonstrate how the implementation outcomes of an environmental policy is influenced by the way that the policy ``filters' through operational-level factors, and to propose novel and implementation mechanisms to achieve efficient and effective EPR implementation. Hence, our study has the potential to provide guidance for practice and influence policy-making.
In the second research stream, motivated by the practice of transportation alliances, we focus on a decentralized network setting where the individual entities make independent decisions regarding the routing of their own demand and the management of their own capacity, driven by their own benefits. We study the use of market-based exchange mechanisms to motivate and regulate capacity sharing so as to achieve the optimal overall routing efficiency in a general multicommodity network. We focus on the design of capacity pricing strategies in the presence of several practical operational complexities, including multiple ownership of the same capacity, uncertainty in network specifications, and information asymmetry between the central coordinator and individual operators. Our study in this research stream produces two sets of results. First, we demonstrate the impact of the underlying network structure on the effectiveness of using market-based exchange mechanisms to coordinate resource sharing and to allocate the resulting synergistic benefit, and characterize the network properties that matter. Second, we propose efficient and effective pricing policies and other mechanism design strategies to address different operational complexities. Specifically, we develop duality-based pricing algorithms, and evaluate different pricing strategies such as commodity-based price discrimination, which is shown to have an advantage in coordinating networks under uncertainty.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/47707 |
| Date | 08 April 2013 |
| Creators | Gui, Luyi |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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