Stochastic programming-approaches for integrating operational and financial decisions

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The objective of the research is to develop mathematical programming models and solution methods for important problems in production and logistics that integrate both operational and financial perspectives. With increasing globalization of markets and re-structuring of industries e.g. power industry new opportunities arise for modeling problems where operational and financial decisions need to be jointly considered. Furthermore, the increasing liberalization of markets and industries has created an unprecedented increase in uncertainty in prices and quantities for vital resources in modern society. Traditional models treat the financial and operational decisions separately and independently. Decisions from these typical models can be vastly irrelevant in modern environments. To this end, we aim to develop unique stochastic programming models for problems in energy and transportation where financial instruments/mechanisms or real option decisions are prevalent. Solution methods based on mathematical programming decomposition and heuristics will be developed that exploit the specific structure of the models. We develop two classes of stochastic models. The first class of problems will focus on optimization-based frameworks to evaluate portfolios of energy contracts and other derivatives/contracts for assets that are difficult to evaluate under standard methods. Another class of models and methods will focus on transportation procurement under uncertainty where financial frameworks such as (combinatorial) auctions are used to allocate business to competing transportation providers. These new classes of models and their solution methods will enable greatly improved decision support in many vital areas of society such as the power and transportation sectors.