An evolutionary real options framework for the design and management of projects and systems with complex real options and exercising conditions

a r t i c l e i n f o To address the issue of decision support for designing and managing flexible projects and systems in the face of uncertainties, this paper integrates real options valuation, decision analysis techniques, Monte Carlo simulations and evolutionary algorithms in an evolutionary real options framework. The proposed evolutionary real options framework searches for an optimized portfolio of real options and makes adaptive plans to cope with uncertainties as the future unfolds. Exemplified through a test case, the evolutionary framework not only compares favorably with traditional fixed design approaches but also delivers considerable improvements over prevailing real options practices. Managers tend to adopt a single scenario for the future, come up with a fixed design, and compute a single performance measure for a project, thus ignoring the importance of embedding multiple real options in projects [2]. Nevertheless, embedding multiple real options and exercising them based on how the future unfolds is a very important means to deal with uncertainties in the design and management of projects and systems [43]. Decision making that involves the planning and exercising of multiple options under uncertainty is complex, and, due to the complexity, organizations often fail in practice to follow a well-structured, accountable and reproducible decision-making process for assessing and selecting a dynamic strategy to formulate a flexible design solution [24]. The difficulties to design and value a portfolio of real options under uncertainty are fundamentally caused by the complex structure of project pay-offs generated from the many possible paths of uncertainty and the interaction effects among the option portfolio, where each option may alter the boundary conditions of other options [30]. The value of a combination of real options is not the combined value of each option in isolation [44]. To value a portfolio of real options, Anand [1] analyzed the determinants to explain the portfolio effects, and Baldwin and Clark [4] calculated the option value of modules in a modular architecture theoretically. However, it remains unclear how to quantitatively assess a portfolio of real options when the number of interacting real options becomes large and the design space becomes non-convex. No conventional real options methodol-ogies are able to systematically and holistically value and select multiple interdependent real options and their exercising conditions in complex projects and systems. To deal with the issue, this paper proposes to use Evolutionary Algorithms (EA) as a part of an evolutionary …