This dissertation studies two important problems in the field of biomass supply chain network. In the first part of the dissertation, we study the pre-disaster planning problem that seeks to strengthen the links between the multi-modal facilities of a biomass supply chain network. A mixed-integer nonlinear programming model is developed to determine the optimal locations for multi-modal facilities and bio-refineries, offer suggestions on reliability improvement at vulnerable links, production at bio-refineries, and make transportation decision under both normal and disrupted scenarios. The aim is to assist investors in determining which links’ reliability can be improved under specific budget limitations so that the biouel supply chain network can prevent possible losses when transportation links are disrupted because of natural disasters. We used states Mississippi and Alabama as a testing ground for our model. As part of numerical experimentation, some realistic hurricane scenarios are presented to determine the potential impact that pre-investing may have on improving the bio-mass supply chain network’s reliability on vulnerable transportation links considering limited budget availability. In the second part of the dissertation, we study the impact of feedstock supply uncertainty on the design and management of an inbound biomass coiring supply chain network. A two-stage stochastic mixed integer linear programming model is developed to determine the optimal use of multi-modal facilities, biomass storage and processing plants, and shipment routes for delivering biomass to coal plants under feedstock supply uncertainty while considering congestion into account. To represent a more realistic case, we generated a scenario tree based on the prediction errors obtained from historical and forecasted feedstock supply availability. We linearized the nonlinear problem and solved with high quality and in a time efficient manner by using a hybrid decomposition algorithm that connects a Constraint generation algorithm with Sample average approximation algorithm and enhanced Progressive hedging algorithm. We used states Mississippi and Alabama as a testing ground for our study and conducted thorough computational experiments to test our model and to draw managerial insights.

We propose a distributed, collaborative traffic congestion detection and dissemination system using VANET that makes efficient use of the communication channel, maintains location privacy, and provides drivers with real-time information on traffic congestions over long distances. The system uses vehicles themselves, equipped with simple inexpensive devices, as gatherers and distributors of information without the need for costly road infrastructure such as sensors, cameras or external communication equipment. Additionally, we present a flexible simulation and visualization framework we designed and developed to validate our system by showing its effectiveness in multiple scenarios and to aid in the research and development of this and future VANET applications. / by Francisco M. Padron. / Thesis (M.S.C.S.)--Florida Atlantic University, 2009. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2009. Mode of access: World Wide Web.

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