1 research outputs found
Multi-agent Optimization Modeling for Sharing Economy Operation
No full textThis dissertation investigates two interrelated challenges in complex, dynamic environments: optimizing resource allocation under information asymmetry and designing mechanisms to achieve desirable Nash Equilibrium solutions in the sharing economy. These challenges are particularly critical in disaster relief operations and sharing economy platforms, where efficient and equitable resource distribution is paramount. This thesis introduces the Multi-Agency Collaborative Prepositioning Method, a two-stage stochastic programming model that applies the principles of the sharing economy to disaster preparedness and response. This method fosters horizontal collaboration among agencies, emphasizing shared resource utilization and recognizing diverse roles beyond emergency response. By addressing total-cost differences between consumable and non-consumable relief supplies across varying disaster impact levels, the model aligns with the sharing economy’s goal of efficient resource allocation. Incorporating key cost components such as procurement, transportation, holding, penalty, and return costs, the method analyzes three capacity allocation strategies. A case study based on FEMA and partner agencies demonstrates how resource-sharing frameworks enhance disaster relief operations, minimizing redundancies and improving efficiency. The findings highlight the transformative potential of the sharing economy in optimizing resource distribution, offering practical insights for coordinated disaster response while illustrating the broader applicability of collaborative, shared-resource systems. Expanding this foundation, I introduce the concept of "mechanism optimization," a novel game-theoretic approach specifically designed for the sharing economy. While traditional mechanism design ensures Nash Equilibrium states where agents truthfully reveal their preferences, it often overlooks alignment with platform-specific objectives such as maximizing social welfare, revenue, or environmental sustainability. Mechanism optimization bridges this gap by creating adaptable game rules that not only maintain equilibrium but also achieve the market designer’s goals. By addressing pervasive information asymmetry, this framework enables more efficient resource allocation, improves decision-making among agents, and fosters trust in peer-to-peer interactions, ultimately enhancing platform effectiveness. By integrating insights from disaster relief and the sharing economy, this research provides interdisciplinary solutions to resource management under uncertainty. The combined use of stochastic optimization and mechanism optimization advances both theoretical understanding and practical applications. The findings enhance collaborative decision-making, optimize resource allocation, and improve operational effectiveness across industries shaped by shared resources and dynamic interactions
