SIMDIFF: Modeling and Generation of Stochastic Discrete-Event Simulation Inputs via Denoising Probabilistic Diffusion Process

This paper introduces Simulation using Diffusion process (SimDiff), a novel framework for automated modeling and generation of stochastic discrete-event simulation (DES) input distributions, addressing the high entry barrier and challenges associated with obtaining accurate input data. Traditional DES models often rely on simplifying assumptions, such as Poisson and Exponential distributions, which may not fully capture the complexity of real-world systems. We propose SimDiff to overcome these limitations by utilizing the denoising probabilistic diffusion model, a generative neural network capable of learning complex statistical distributions and efficiently sampling from them. Additionally, we introduce SimDiff-ConvTrans, an extension that incorporates Transformer and Convolution components for simulating non-i.i.d inputs. Our experiments demonstrate the effectiveness of SimDiff in handling simple and hybrid data distributions, as well as empirical datasets. SimDiff represents a significant advancement in simplifying the stochastic simulation process, making it more accessible and efficient for users across various expertise levels.