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 that cannot be investigated with conventional analytical techniques. Conventional techniques e.g. include static calculations, mathematical […]
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 putting them closer to each other when assigning them to a set of pre-determined positions. […]
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) based on different prices, and now wants to know the optimal price to set for […]
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 paradigms are supervised, unsupervised, and reinforcement learning. Supervised machine learning algorithms Supervised machine learning algorithms […]
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 for predictive analytics. Mathematical definition of a neuron In a machine learning model, a neuron, […]