Staged-deployment optimization for expansion planning of large scale complex systems

The needs of staged-deployment optimization for the expansion planning of large scale complex systems have been acknowledged extensively in various contexts. The design methods for adaptability or evolvability and their valuation have also been studied from both qualitative and quantitative perspectives. The flexibility gained by incorporating evolutionary design options has been analyzed using real options in projects and architecture options. Real options literature has typically tackled the flexible design problem by discretizing the time-variant uncertainties into scenarios and consider the flexible decision variables in each scenario separately. Given the discretized scenarios, stochastic optimization with either direct formulation or decision-rule based formulation, can be used to find the flexible designs. The importance of considering staged decisions is studied and the benefit of the model is evaluated in cases where the stochasticity of the parameters decreases with time. The impact of considering staged deployment for highly stochastic, large scale systems is investigated through a numerical case study. The case study presented in this paper investigates a multidisciplinary design problem that is typical in expansion planning for large scale complex systems. The importance of considering staged deployment for multi-disciplinary systems that have decreasing variability of their parameters with time is highlighted and demonstrated through the results of a numerical case study.