In this article I discuss machine learning and discrete-event simulation. I will introduce machine learning as a supportive technology for making discrete-event simulation more resource efficient and effective. Discrete-event simulation is a technique used in manufacturing and logistics for problems…
In this blog post I model a support vector machine in Python. Previously, I modeled and solved the quadratic assignment problem (QAP) in Python using Pyomo (+). I described that the “similarity” of two facilities could be a reason for…
How can we decide on a product or service price using observed data over time? For example, a retailer may have changed the price for a specific product in multiple time slots of a week, tested the demanded quantity (sales)…
In previous posts, we have introduced exemplary machine learning algorithms for common real-world problems. We e.g. introduced optimization for shallow learning and deep learning models. In this article, we want to clarify the three main paradigms of machine learning. These…
In this post, I explain how supervised learning models in machine learning use single or multiple neurons in a single layer (shallow learning) or multiple neurons in multiple layers (deep learning) to generate a real, binary, or integer value beneficial…