Python Tutorial : Basics of optimization

Want to learn more? Take the full course at https://learn.datacamp.com/courses/supply-chain-analytics-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Welcome to this course on Supply Chain Analytics. In this first lesson, we will talk about the Basics of Optimization. This course will focus on Supply Chain Optimization so, let us briefly define what a Supply Chain is. A Supply Chain consists of all the parties involved directly or indirectly, in fulfilling a customer's request. That includes external Suppliers, Manufacturing, Production Planning and more. When fulfilling a customer's request, there are often multiple routes through the Supply Chain. Supply Chain optimization attempts to find the best path to achieve an objective based on constraints. For example, a production plan is limited by the production capacity available or a logistics plan might be limited by how much truck capacity is available. Supply chain optimization attempts to use the different resources that are available to achieve an objective. That objective could be focused on delivering the lowest cost, or the highest service. Okay, here is a crash course in Linear Programing, or LP. LP is a powerful tool for modeling decisions for optimization. It is an optimization method using a mathematical model whose requirements are represented by linear relationships. There are three basic components of LP modeling. First are the decision variables, or the things that you can control. Next, the objective function, which describes the goal as a mathematical expression. It is what we want to maximize or minimize, such as profit or costs. Finally, because we live in the real world there are constraints that limit our possible solutions, for example, manufacturing capacity. To provide more context let's start with an example. Imagine that you are deciding on an exercise routine. In this situation, you only have 10 minutes