The efficient management of energy research and development (R&D) projects is important to reduce the required the development time and cost. However, each development project is unique and innovational in nature and the duration of activities involved in an energy R&D project often cannot be predicted accurately. The uncertainty of activity duration may lead to incorrect scheduling decisions. The objective of this paper is to develop a fuzzy scheduling methodology to deal with these problems. Fuzzy variables theory is used to model the uncertain and flexible temporal information. A fuzzy scheduling algorithm with depth-first search is developed to find the possible critical paths based on fuzzy expected value simulation. A numerical example with solar photovoltaics development project is used to illustrate the effectiveness of the methodology.

A power R&D process can be regarded as a jump process of scientific knowledge full of exploration and complexity. Every jump represents a scientific breakthrough or a new discovery in this setting. In this paper, the inter-arrival times are treated as random variables observe arbitrary distributions. The ɑ-optimistic net return of project performance is proposed and a chance-constrained programming model is established to model the power R&D optimal stopping decision problem. Considering the complexity of the model, the stochastic simulation is designed to estimate the values of project return performance and the simultaneous perturbation stochastic approximation (SPSA) algorithm is employed to solve the proposed model. Finally, the effectiveness of the hybrid algorithm and the applicability of the model are illustrated by numerical examples.

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