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

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Abstract

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.

Introduction

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 methodologies 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 framework, which values and selects portfolios of real options and formulates an overall dynamic option exercising plan.

EA has been widely applied to solve difficult optimization problems. It is a generic multi-objective population-based metaheuristic optimization method, inspired by biological evolution [51]. One of the most popular techniques in the field of EA is Genetic Algorithm (GA). GA is suitable to solve very large, path dependent and non-convex problems [23]. GA relaxes the conventional requirements that variables be independent and identically distributed. Instead, only the objectives and the environment of a problem need to be formulated, and GA applies the genetic principles of selection, crossover and mutation repeatedly to a set (i.e. population) of solutions, until a satisfying set of solutions is found.

An important advantage of using evolutionary algorithms for complex problems is that they overcome some significant challenges that are difficult to study analytically or by conventional techniques. Dias [20] and Lazo et al. [27] proposed using GA to find the exercise regions (price and time) of real options in oil field developments, and recently Hassan et al. [24] have used it in aircraft design. This paper develops a decision support framework that uses evolutionary algorithms to select portfolios of real options in systems and projects and formulate flexible design solutions.

The proposed framework has the potential to take into account a larger pool of real options, a wide variety of exercising conditions, and an increased degree of interactions. We illustrate the proposed framework by an example of Maritime Domain Protection (MDP) system, created at the Naval Postgraduate School (NPS) [11].

Section snippets

Real options analysis

Multiple sources of flexibility exist in the design and management of systems and projects. Such flexibilities are specifically known as “real options”. The term real options was coined by Myers [33] to describe the choices embedded in physical systems and projects, much like the choices made available to investors by financial options. Formally, the definition of both financial and real options is a right but not an obligation to take certain actions at or within a specific time2

A numerical example applicaton

Terrorism threats in the Strait of Malacca, one of the world's most important shipping lanes, require an appropriate Maritime Domain Protection (MDP) system. The Naval Postgraduate School (NPS), Monterrey, CA, developed an MDP system based on which we will illustrate our proposed framework. NPS applied a widely adopted classical system engineering approach and architected a modular MDP system [34]. The MDP system consists of five different subsystems (sensors, C3I, force, land and sea

Results

Before comparing the resulting design solutions from different approaches (based on the agents that choose the options and exercising conditions), we first take a look at their respective design spaces (Table 2). In the MDP case study, a fixed design is chosen from the design space of 109 alternative system configurations, and, as the design is fixed, neither options nor exercising conditions are applicable. The conventional real options approach is called hand picking real options approach. It

Discussions

This study confirms the important benefits as well as the difficulties of using multiple real options [45], [46]. As demonstrated in the MDP case study, the framework makes possible the task of evaluating and selecting portfolios of real options as well as their exercising conditions in a large design space. The resulting design solutions are better than those from the conventional real options approaches and the traditional system engineering approach. Evolutionary algorithms have also

Conclusions

Extant literature has long recognized both the importance and the difficulties of incorporating multiple interacting real options [45], [46]. The proposed evolutionary real options framework, with evolutionary algorithms as the engine of its extended optimization step, is able to value and select flexible solutions with portfolios of real options. It delivers a roadmap for deploying those options under uncertainty. Moreover, the proposed framework opens up the design space, frees the range of

Acknowledgements

The authors gratefully acknowledge the support and contribution from Singapore-Delft Water Alliance (SDWA)R-264-001-001-272. For more information, please visit http://www.sdwa.nus.edu.sg. The authors also thank Defense Science and Technology Agency of Singapore, who funded the case used in this paper. Finally, the authors acknowledge the support by Academic Research Fund under grant “Data Assimilation and Data-Driven Knowledge Discovery” R-264-000-199-133/112.

Stephen Zhang is an assistant professor in the Department of Industrial and System Engineering in the Pontifical Catholic University of Chile (Pontificia Universidad Católica de Chile). His PhD is in the area of using real options in new technological projects, from the Department of Engineering and Technology Management at National University of Singapore. Prior to become a professor, Stephen had engineering experiences in STMicroelectronics and Siemens AG, management consulting experiences in

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  • Cited by (0)

    Stephen Zhang is an assistant professor in the Department of Industrial and System Engineering in the Pontifical Catholic University of Chile (Pontificia Universidad Católica de Chile). His PhD is in the area of using real options in new technological projects, from the Department of Engineering and Technology Management at National University of Singapore. Prior to become a professor, Stephen had engineering experiences in STMicroelectronics and Siemens AG, management consulting experiences in Titan Group and Mikinsey, and research experiences in National University of Singapore and Singapore Delft Water Alliance (SDWA). Webpage: http://www.ing.puc.cl/ics/detalle.html?pr=szhang.

    Vladan Babovic is an associate professor at National University of Singapore and the founding Director of Singapore-Delft Water alliance, a multi-disciplinary research initiative involving NUS, PUB (Singapore) and Delft Hydraulics (The Netherlands).Dr Babovic obtained his PhD. degrees from both UNESCO-IHE and Delft University of Technology, The Netherlands in 1995. In 2001, he has obtained a business degree at IMD in Lausanne (Switzerland). Prior to joining NUS, he was Head of emerging Technologies at Danish Hydraulic Institute (1995–2002), Chief Technology Officer at Tetrasys, Switzerland (2002–2004) and Senior Research Scientist at Delft Hydraulics (2003–2005). Webpage: http://www.eng.nus.edu.sg/civil/people/cvebv/cvebv.html.

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