10 accepted papers
Recent advances in time series forecasting (TSF) leverage large language models (LLMs) to provide semantic priors, enabling more robust forecasting under limited training data. However, directly fusing semantic signals into temporal features often induces modality entanglement and obscures local tem
The multi-commodity flow (MCF) problem is a fundamental topic in network flow and combinatorial optimization, with broad applications in transportation, communication, and logistics, etc. Nowadays, the rapid expansion of allocation systems has posed challenges for existing optimization engines in ba…
Diffusion models have exhibited state-of-the-art performance in generative tasks across various domains. A few recent works leveraged the powerful modeling ability of the diffusion model to time-series forecasting, leading to a significant breakthrough. However, all these works perform the forecasti…
The increasing demand for efficient last-mile delivery in smart logistics underscores the role of autonomous robots in enhancing operational efficiency and reducing costs. Traditional navigation methods, which depend on highprecision maps, are resource-intensive, while learning-based approaches ofte
Denoising diffusion probabilistic models (DDPMs) are becoming the leading paradigm for generative models. It has recently shown breakthroughs in audio synthesis, time series imputation and forecasting. In this paper, we propose Diffusion-TS, a novel diffusion-based framework that generates multivari…
In semiconductor manufacturing, to improve the throughput of cluster tools, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">process modules</i> (PMs) are designed to have multiple spaces such that more than one wafers can be processed in a PM concurr
Unsupervised anomaly detection for multivariate time series (MTS) is a challenging task due to the difficulties of precisely learning the complex data patterns of MTS. The recent progress in sample generation achieved by diffusion models (DMs) motivates us to leverage the powerful learning ability o…
To satisfy the requirements of high-mix integrated-circuit chip production, multiple wafer types are grouped into a lot to be fabricated concurrently in a multi-cluster tool system. However, the existing studies do not solve the scheduling problem of multi-cluster tools with multiple wafer types bei
Semiconductor manufacturing industry has adopted multi-cluster tools as wafer fabrication equipment that is extraordinarily pricey but highly attractive owing to their higher productivity than single cluster tools can achieve. A challenging issue is how to schedule these tools. It is especially diff…