ISYE 6501 Module 15 Reference Material

ISYE 6501 Module 15 Reference Material Slide 1 - Lesson 15.1 (M): Introduction to Optimization In quite a few of the lesson in this class, we've seen various models that I've noted can be solved using optimization. Y ...

ISYE 6501 Module 15 Reference Material Slide 1 - Lesson 15.1 (M): Introduction to Optimization In quite a few of the lesson in this class, we've seen various models that I've noted can be solved using optimization. You've probably heard me refer to optimization in lessons on classification. Clustering, regression, variable selection, time series analysis and more. The reason is that many, perhaps most, statistical and machine learning models involve optimization to find their answers. So optimization is a key underlying part of descriptive and predictive analytics, as we'll see a little bit more formally in this topic. But optimization is also a very important topic in analytics in its own right, especially for prescriptive analytics. The type of analytics that asks, given what I know and what I predict, what's the best course of action to take? Optimization can answer questions like, which airplane mechanic should be scheduled for each shift over the next week? To meet the expected maintenance requirements at O'Hare Airport at lowest cost. Making sure we don't violate any federal regulations or union contract requirements and accounting for unexpected breakdowns. How much crude oil should be send by tanker and by pipeline from each oil field to each refinery to meet demand and avoid oversupply while keeping costs low? What webpages should be optimized in a server farm? And how many copies of each should be stored to maximize the profit made by responding quickly to hits? How should a large machine shop sequence its production to get maximum throughput while meeting all clients' deadlines? Taking into account the reality that some output will fail inspection and need to be remained. And even the GPS routing problem, what's the shortest route from my house to the airport, given current and predicted traffic? In my own research and consulting, I've used optimization models to suggest how Army recruiters could most efficiently use their resources. Create a method for Major League Baseball teams to determine their draft priority. Plan worldwide delivery schedules and routes for giant oil tankers. And schedule electricity generation that accounts for specific effects of current and upcoming weather patterns on the atmospheric effects of pollution. As you can see, optimization can be a very powerful tool in analytics, sitting on top of descriptive and predictive analytics. It allows you to not only use analytics to report on things but also to direct your organization at strategic, operational and tactical levels. But optimization is also a more difficult tool to use than many of the models we've seen before. There's nice software that automates many of the models we've seen, both building them and solving them. In fact, you've had a chance to use some of that software in your homework assignments. But optimization software is different. There's good software for solving optimization models, but there isn't yet good software to build the models for you. Learning to build optimization models takes a lot of practice, more than we have time for in this course. I'll show you some examples and give a few general principles and pointers, but you'll have to practice on your own. Or even better, you can take Georgia Tech's excellent online elective course on Applied Optimization. Where we'll help you learn a lot more of the important details and advanced techniques. Before we go on and start learning about optimization models, I wanna highlight a 2013 study by Gartner. They surveyed lots of companies across a wide variety of industries to find out about their use of analytics. Only 3% reported the use of prescriptive analytics. It wasn't very common, primarily, I believe, because of the following difficulties. In addition to needing good data and descriptive and predictive analytics as a foundation. They need people with specialized training to build optimization models, and to a lesser extent, simulation models. But just three years later, Gartner was predicting growth to 35% by 2020, from 3% to 35% in just seven years. So my perspective is that it's not just worth learning about, it's necessary to learn about prescriptive analytics models. Because either the organization you join will already using them or because they'll want to be using them. And your knowledge can give you an important edge. So go ahead, cue up the next video and let's get started. Slide 2 - Lesson 15.2 (M): Elements of Optimization Models In a previous lesson we saw how optimization is both a key piece of other analytics models we've seen in this course. And also a powerful tool in its own right for answering prescriptive analytics questions. Given what we know and what we can predict, what are the best a