Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Neuro-Dynamic Programming
Options in the Real World: Lessons Learned in Evaluating Oil and Gas Investments
Operations Research
Pricing American Options: A Duality Approach
Operations Research
Valuation of Commodity-Based Swing Options
Management Science
Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)
A Semi-Lagrangian Approach for Natural Gas Storage Valuation and Optimal Operation
SIAM Journal on Scientific Computing
An Analysis of the Control-Algorithm Re-solving Issue in Inventory and Revenue Management
Manufacturing & Service Operations Management
Optimal Commodity Trading with a Capacitated Storage Asset
Management Science
Information Relaxations and Duality in Stochastic Dynamic Programs
Operations Research
Valuation of Storage at a Liquefied Natural Gas Terminal
Operations Research
Integrated Optimization of Procurement, Processing, and Trade of Commodities
Operations Research
Manufacturing & Service Operations Management
Manufacturing & Service Operations Management
Pathwise Optimization for Optimal Stopping Problems
Management Science
A simulation-and-regression approach for stochastic dynamic programs with endogenous state variables
Computers and Operations Research
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The valuation of the real option to store natural gas is a practically important problem that entails dynamic optimization of inventory trading decisions with capacity constraints in the face of uncertain natural gas price dynamics. Stochastic dynamic programming is a natural approach to this valuation problem, but it does not seem to be widely used in practice because it is at odds with the high-dimensional natural gas price evolution models that are widespread among traders. According to the practice-based literature, practitioners typically value natural gas storage heuristically. The effectiveness of the heuristics discussed in this literature is currently unknown because good upper bounds on the value of storage are not available. We develop a novel and tractable approximate dynamic programming method that, coupled with Monte Carlo simulation, computes lower and upper bounds on the value of storage, which we use to benchmark these heuristics on a set of realistic instances. We find that these heuristics are extremely fast to execute but significantly suboptimal compared to our upper bound, which appears to be fairly tight and much tighter than a simpler perfect information upper bound; computing our lower bound takes more time than using these heuristics, but our lower bound substantially outperforms them in terms of valuation. Moreover, with periodic reoptimizations embedded in Monte Carlo simulation, the practice-based heuristics become nearly optimal, with one exception, at the expense of higher computational effort. Our lower bound with reoptimization is also nearly optimal, but exhibits a higher computational requirement than these heuristics. Besides natural gas storage, our results are potentially relevant for the valuation of the real option to store other commodities, such as metals, oil, and petroleum products.