As we inch closer to Black Friday and the start of the holiday buying extravaganza, retailers are putting the final touches on the demand forecasts they're using to predict the mix of goods they'll carry this winter. There are lot of variables to juggle, including COVID, the economy, and the weather. It seems like a perfect use case for the increasingly sophisticated machine learning models that are in vogue in the industry. But can they trust their predictions? Over the past decade, retailers and other companies in the consumer goods supply chain have started upgrading their demand forecasting systems in hopes of gaining ground in this super competitive industry. Forward-looking retailers, in particular, are replacing the largely deterministic approaches that were favored in the past–which used simple linear regression models based on historical data with relatively static assumptions about the state of the world–with probabilistic approaches that bring more data into the equation and rely on more sophisticated machine learning algorithms, like neural nets and XGBoost, to generate more detailed forecast ranges.

In this work, we consider the weekly home health care scheduling problem with time windows, continuity of care, workload fairness, and inter-visit temporal dependency, and service/travel time uncertainties. We formulate the problem as a chance constrained mathematical model. We further apply Lagrangian relaxation, exploit the separable structure of the problem, and handle the uncertainties by employing a sampling-based strategy. Experiments have been conducted on a real-world dataset to demonstrate the effectiveness and efficiency of our proposed approaches.

Artificial Intelligence (AI) and robotics are both very popular topics in the media today, which comes as no surprise. Recent developments in AI and robotic technology have encouraged an increase in adoption of automation tools rates across the world. With all this coverage, it's common to hear myths and hyperbolic statements about how this new technology will impact our lives and careers. So in order to help you keep the facts straight, here are some of the most common myths about robotics and automation that we've heard recently. This seems to be a recurring headline in the newspapers.

It seems today that Machine Learning has become synonymous with Artificial Intelligence. While this is the result of good marketing, it isn't rooted in reality. The reality is that Machine Learning is just one approach to AI (in fact it's called the statistic approach). In the field of AI, there are two main schools of thought when it comes to programming how to make a machine mimic a human business process: statistic (sometimes called probabilistic) and deterministic. Neither of these approaches are "superior" to the other, they are just suited better to different use cases.