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Developing models and algorithms to design a robust inland waterway transportation network under uncertaintyNur, Farjana 07 August 2020 (has links)
This dissertation develops mathematical models to efficiently manage the inland waterway port operations while minimizing the overall supply chain cost. In the first part, a capacitated, multi-commodity, multi-period mixed-integer linear programming model is proposed capturing diversified inland waterway transportation network related properties. We developed an accelerated Benders decomposition algorithm to solve this challenging NP-hard problem. The next study develops a two-stage stochastic mixed-integer nonlinear programming model to manage congestion in an inland waterway transportation network under stochastic commodity supply and water-level fluctuation scenarios. The model also jointly optimizes trip-wise towboat and barge assignment decisions and different supply chain decisions (e.g., inventory management, transportation decisions) in such a way that the overall system cost can be minimized. We develop a parallelized hybrid decomposition algorithm, combining Constraint Generation algorithm, Sample Average Approximation (SAA), and an enhanced variant of the L-shaped algorithm, to effectively solve our proposed optimization model in a timely fashion. While the first two parts develop models from the supply chain network design viewpoint, the next two parts propose mathematical models to emphasize the port and waterway transportation related operations. Two two-stage, stochastic, mixed-integer linear programming (MILP) models are proposed under stochastic commodity supply and water level fluctuations scenarios. The last one puts the specific focus in modeling perishable inventories. To solve the third model we propose to develop a highly customized parallelized hybrid decomposition algorithm that combines SAA with an enhanced Progressive Hedging and Nested Decomposition algorithm. Similarly, to solve the last mathematical model we propose a hybrid decomposition algorithm combining the enhanced Benders decomposition algorithm and SAA to solve the large size of test instances of this complex, NP-hard problem. Both proposed approaches are highly efficient in solving the real-life test instances of the model to desired quality within a reasonable time frame. All the four developed models are validated a real-life case study focusing on the inland waterway transportation network along the Mississippi River. A number of managerial insights are drawn for different key input parameters that impact port operations. These insights will essentially help decisions makers to effectively and efficiently manage an inland waterway-based transportation network.
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Mathematical methods for portfolio managementOndo, Guy-Roger Abessolo 08 1900 (has links)
Portfolio Management is the process of allocating an investor's wealth to in
vestment opportunities over a given planning period. Not only should Portfolio
Management be treated within a multi-period framework, but one should also take into consideration
the stochastic nature of related parameters.
After a short review of key concepts from Finance Theory, e.g. utility function, risk attitude,
Value-at-rusk estimation methods, a.nd mean-variance efficiency, this work describes a framework
for the formulation of the Portfolio Management problem in a Stochastic Programming setting.
Classical solution techniques for the resolution of the resulting Stochastic Programs (e.g.
L-shaped Decompo sition, Approximation of the probability function) are presented. These are
discussed within both the two-stage and the multi-stage case with a special em phasis on the
former. A description of how Importance Sampling and EVPI are used to improve the efficiency of
classical methods is presented. Postoptimality Analysis, a sensitivity analysis method, is also
described. / Statistics / M. Sc. (Operations Research)
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Mathematical methods for portfolio managementOndo, Guy-Roger Abessolo 08 1900 (has links)
Portfolio Management is the process of allocating an investor's wealth to in
vestment opportunities over a given planning period. Not only should Portfolio
Management be treated within a multi-period framework, but one should also take into consideration
the stochastic nature of related parameters.
After a short review of key concepts from Finance Theory, e.g. utility function, risk attitude,
Value-at-rusk estimation methods, a.nd mean-variance efficiency, this work describes a framework
for the formulation of the Portfolio Management problem in a Stochastic Programming setting.
Classical solution techniques for the resolution of the resulting Stochastic Programs (e.g.
L-shaped Decompo sition, Approximation of the probability function) are presented. These are
discussed within both the two-stage and the multi-stage case with a special em phasis on the
former. A description of how Importance Sampling and EVPI are used to improve the efficiency of
classical methods is presented. Postoptimality Analysis, a sensitivity analysis method, is also
described. / Statistics / M. Sc. (Operations Research)
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