The COVID-19 pandemic brings many unexpected disruptions, such as frequently shifting markets and limited human workforce, to manufacturers. To stay competitive, flexible and real-time manufacturing decision-making strategies are needed to deal with such highly dynamic manufacturing environments. One essential problem is dynamic resource allocation to complete production tasks, especially when a resource disruption (e.g., machine breakdown) occurs. Though multi-agent methods have been proposed to solve the problem in a flexible and agile manner, the agent internal decision-making process and resource uncertainties have rarely been studied. This work introduces a model-based resource agent (RA) architecture that enables effective agent coordination and dynamic agent decision-making. Based on the RA architecture, a rescheduling strategy that incorporates risk assessment via a clustering agent coordination strategy is also proposed. A simulation-based case study is implemented to demonstrate dynamic rescheduling using the proposed multi-agent framework. The results show that the proposed method reduces the computational efforts while losing some throughput optimality compared to the centralized method. Furthermore, the case study illustrates that incorporating risk assessment into rescheduling decision-making improves the throughput.

This article is reposted from the Parity Substack. Follow us there for updates. Welcome back to Parity, now coming from the brand new team. We took over for Dr. Rumman Chowdhury while she's off solving some of the world's most pressing (and challenging) algorithmic issues at Twitter. Rumman's still with us as our lead investor and board member, helping us immensely as we grow.

Short Term Load Forecast (STLF) is necessary for effective scheduling, operation optimization trading, and decision-making for electricity consumers. Modern and efficient machine learning methods are recalled nowadays to manage complicated structural big datasets, which are characterized by having a nonlinear temporal dependence structure. We propose different statistical nonlinear models to manage these challenges of hard type datasets and forecast 15-min frequency electricity load up to 2-days ahead. We show that the Long-short Term Memory (LSTM) and the Gated Recurrent Unit (GRU) models applied to the production line of a chemical production facility outperform several other predictive models in terms of out-of-sample forecasting accuracy by the Diebold-Mariano (DM) test with several metrics. The predictive information is fundamental for the risk and production management of electricity consumers.

When you hear about Data Science, Big Data, Analytics, Artificial Intelligence, Machine Learning, or Deep Learning, you may end up feeling a bit confused about what these terms mean. And it doesn't help reduce the confusion when every tech vendor rebrands their products as AI. So, what do these terms really mean? What are overlaps and differences? And most importantly, what can this do for your business?

In my last post on the future of cutting tools, I discussed a vision and roadmap document that I created and refined over the years. This roadmap was created by imagining what perfection (or utopia) looked like utilizing what we know to be technically possible today. It was a glimpse into a futuristic system for handling cutting tools in machining operations including robotic automation, copious amounts of data, and artificial intelligence in the form of machine learning. Today, I'm going to describe a vision for a futuristic production management system where data silos do not exist, predictive analytics provide glimpses into the future, and algorithms optimize throughput to balance costs and demand. Frequently, production decisions are made with partial information and what may seem like the "optimal" solution on a local-level creates costly disturbances on the macro-level.