Real Option Management of Commodity and Energy Conversion Assets

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

Managing commodity and energy conversion assets is difficult because it involves managing operational activities in the face of notoriously volatile commodity and energy prices. These activities include the production, processing, refining, transportation, storage, distribution, and physical trading of commodities and energy. The management of these activities is naturally modeled as the exercise of complex real options on commodity and energy prices. This gives rise to computationally intractable stochastic dynamic programming models. The goal of this award is to develop and analyze novel real options models of fundamental commodity and energy conversion assets, and to design effective and efficient novel approximate dynamic programming methods to compute near optimal operating policies and optimality bounds on their performance. This research will leverage in novel ways operations management models and analyses of operating policies, emerging operations research approaches to approximate dynamic programming based on math programming and Monte Carlo simulation techniques, and financial engineering models of the stochastic evolution of commodity and energy prices. If successful, this research will generate quantitative models and methods to support managers in maximizing the market value of commodity and energy infrastructure. This has the potential to benefit society at large because commodities and energy are used in virtually every manufacturing and service process, and commodity and energy conversion assets are typically managed heuristically in practice. This research will also benefit operations management, operations research, and financial engineering education by generating novel material for potential adoption in current and/or future courses on real options, providing an operations perspective on real options.