Optimal Investment Decision for Energy R&D Project Under Uncertainty

The investment decision for energy research and development (R&D) project is a complex and difficult task, since the energy R&D process is usually full of high uncertainties. Thus, the energy R&D investment process can be regarded as a jump diffusion process. The inter-arrival times between jumps are generally exponentially distributed, which is a common assumption in classical literature. Here, the inter-arrival times are assumed as random fuzzy variables which observe arbitrary distributions. A dependent-chance programming model is established to obtain optimal investment strategies by maximizing the chance function of return event. A hybrid algorithm is also designed based on the random fuzzy simulation and particle swarm optimization (PSO) algorithm to solve the model. Finally, a numerical example is presented to show the performance of this investment decision model and the effectiveness of the proposed algorithm.