Flexibility analysis helps improve the expected value of engineering systems under uncertainty (economic and/or social). Designing for flexibility, however, can be challenging as a large number of design variables, parameters, uncertainty drivers, decision making possibilities and metrics must be considered. Many available techniques either rely on assumptions that are not suitable for an engineering setting, or may be limited due to computational intractability. This paper makes the case for an increased integration of Machine Learning into flexibility and real options analysis in engineering systems design to complement existing design methods. Several synergies are found and discussed critically between the fields in order to explore better solutions that may exist by analyzing the data, which may not be intuitive to domain experts. Reinforcement Learning is particularly promising as a result of the theoretical common grounds with latest methodological developments e.g. decision-rule based real options analysis. Relevance to the field of computational creativity is examined, and potential avenues for further research are identified. The proposed concepts are illustrated through the design of an example infrastructure system.

Previous research shows that volatility in oil prices has tended to depress output, as measured by nonresidential investment, gross domestic product, and aggregated measures of industrial production in several countries. This paper investigates the effect of oil price volatility on disaggregated measures of industrial production. The disaggregated measures that we examine are the special aggregates by market groups as calculated by the Federal Reserve Board. Our results are reported for three categories of special aggregates: indexes for industrial production excluding two major industries (technology and motor vehicles), energy-related special aggregates, and non-energy-related special aggregates. Our results indicate that among energy-related market groups, the effects of oil price volatility are concentrated in activities related to primary energy generation and oil and gas drilling. Among non-energy-related market groups, oil price volatility affects a broad range of special aggregates, including aggregates sorted by consumer goods and business equipment.

We use the longest span data that have ever been studied before (from 1870 to 2014) to investigate the relationship between the price of oil and the level of economic activity in the United States. In the context of a bivariate (identified) structural generalized autoregressive conditional heteroscedasticity (GARCH)-in-Mean VAR in real output growth and the change in the real price of oil, we find that uncertainty about oil prices has had a negative and significant effect on real output. We also find that the responses of real output growth to positive and negative shocks are not very informative of whether they are symmetric or asymmetric, and that accounting for oil price uncertainty tends to amplify the negative dynamic response of real output growth to unfavorable (positive) oil price shocks.

This study analyses the value of a switching option in a flexible biorefinery plant that produces ethanol and sugar juice in a single plant using energy beets. A real-options approach is used to compute threshold prices and optimal switching decision rules for switching between sugar and ethanol production modes. The analysis shows that it is economically optimal to keep producing ethanol then switching to sugar juice, given the stochastic price parameters of the two products.

This study evaluates optimal investment decision rules for an energy beet ethanol firm to exercise the option to invest, mothball, reactivate, and exit the ethanol market, considering uncertainty and volatility in the market price of ethanol, feedstock, and irreversible investment. A real options framework is used to compute gross margins of ethanol that trigger entry into and exit from the ethanol market. Results show that volatility in ethanol gross margins has much greater effects on exit and entry decisions than investment costs, and it also causes firms to wait longer before entering the ethanol market and, once active, to wait longer before exiting.

The Dixit entry/exit real option model was applied to the entry/exit decisions of New York dairy farmers. For the cost structure of a 500-cow farm, the entry milk price is $17.52 per hundredweight (cwt) and the exit milk price is $10.84. For the 50-cow farm cost structure, the entry price is higher at $23.71 per cwt, and the exit price is also higher at $13.48. If infinite numbers of representative farms enter and exit at these prices, the price of milk should range between $13.48 and $17.52 per cwt.

Automatic, or robotic, milking systems have the potential to significantly change the way milk is produced on U.S. dairy farms. However, there is a high degree of uncertainty associated with adoption of this new technology. A real options approach is used to analyze the decision to replace an operational milking system with an automatic milking system. The most important source of uncertainty is shown to be the length of the technology's useful life. Under our assumptions, the automatic system is always an optimal investment if it is certain that it will last longer than the operational system being replaced.

Investing in a new perennial crop variety involves an irreversible commitment of capital and generates an uncertain return stream. As a result, the decision to adopt a new variety includes a significant real option value. Waiting for returns to rise above this real option causes a delay in adoption because of economic hysteresis. This study tests for hysteresis in the adoption of wine grape varieties using a sample of district-level data from the state of California. The empirical results show a significant hysteretic effect in wine grape investment, which might be reduced by activities that smooth earnings over time.

This paper considers the role of costless decisions relating to the extraction of a non-renewable resource in the presence of uncertainty. We begin by deriving a size scale of the extractable resource, above which the solution to the valuation and optimal control strategy can be described by analytic solutions; we produce solutions for a general form of operating cost function. Below this critical resource size level the valuation and optimal control strategy must be solved by numerical means; we present a robust numerical algorithm that can solve such a class of problem. We also allow for the embedding of an irreversible investment decision (abandonment) into the optimisation. Finally, we conduct experimentation for each of these two approaches (analytical and numerical), and show how they are consistent with one another when used appropriately. The extensions of this paper's techniques to renewable resources are explored.

This paper reports a use of the real-options valuation methodology to analyze wine grape vineyard investment under price and yield uncertainty. Threshold annual rates of revenue per hectare to trigger entry and exit, respectively, were calculated for three different sizes of wine grape vineyards in northwest Victoria, Australia. The modeling identified lower exit and higher entry triggers than would be indicated by a conventional approach that ignores the uncertainty underpinning adaptive investment decisions. Between these triggers is a relatively wide gap of estimated indeterminacy in vineyard investment that highlights the intertwined influence of numerous economic factors—cost structure, economies of scale, market volatility, transaction costs, and sunk and salvaged asset valuation. Drawing on these determinants of vineyard investment and disinvestment, the paper discusses the role of investment incentives in affecting industry transformation and the scope for policy intervention to assist structural adjustment of the wine grape sector. (JEL Classification: C61, G11, I25, Q12)

Previous research shows that volatility in oil prices has tended to depress output, as measured by nonresidential investment and GDP. This is interpreted as evidence in support of the theory of real options in capital budgeting decisions, which predicts that uncertainty about, for example, commodity prices will cause firms to delay production and investment. We continue that investigation by analyzing the effect of oil price uncertainty on monthly measures of U.S. firm production related to industries in mining, manufacturing, and utilities. We use a more general specification, an updated sample that includes the increased oil price volatility since 2008, and we control for other nonlinear measures of oil prices. We find additional empirical evidence in support of the predictions of real options theory, and our results indicate that the extreme volatility in oil prices observed in 2008 and 2009 contributed to the severity of the decline in manufacturing activity.

In this paper, we study the first instant when Brownian motion either spends consecutively more than a certain time above a certain level, or reaches another level. This stopping time generalizes the ‘Parisian’ stopping times that were introduced by Chesney et al. (1997). Using excursion theory, we derive the Laplace transform of this stopping time. We apply this result to the valuation of investment projects with a delay constraint, but with an alternative: pay a higher cost and get the project started immediately