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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/optimization-for-decision-making
课程评论：没有评论
课程名称：决策优化 课程概述：在这个数据驱动的世界中，企业通常希望知道在给定数据的情况下，最佳的行动方案是什么。例如，制造商需要决定根据预计需求和原材料可用性生产多少单位的产品？他们应该全部在内部生产，还是从第三方购买一些以满足需求？处方分析是能够回答这些问题的分析分支，它用于基于数据的决策。处方分析工具箱中最重要的方法是优化。本课程将向学生介绍线性优化的基本原理，以支持决策制定。通过实际案例，本课程教授如何将问题场景转化为数学模型，从而解决并获得最佳商业成果。我们将学习识别决策变量、目标函数和约束条件，并利用这些元素来构造和解决优化问题，使用Excel求解器和电子表格进行分析。 课程大纲： 1. 模块1：线性规划简介 描述：处方分析是商业分析的一部分，旨在为决策问题提供解决方案。最重要的建模技术是优化。在本模块中，我们将学习如何识别可以应用优化的背景，并了解线性优化的基本概念。 2. 模块2：求解线性程序 描述：为了求解线性优化问题（即线性程序），我们可以使用图形方法解决基本示例问题。对于高维问题，我们将在课程后期使用Excel求解器等工具。使用图形方法的好处在于，它给我们提供了对这些问题解决方案的直观理解。 3. 模块3：线性优化中的替代规格与特殊情况 描述：本模块将探讨当模型参数更改时会发生什么。此外，我们将研究线性优化问题的特殊案例。 4. 模块4：在Excel中建模和求解线性问题 描述：在学习了如何构造线性优化问题和使用图形方法求解后，我们现在将开始使用Excel求解器解决更大规模的问题。本模块提供了如何设置和解决这些决策问题的概述。 通过本课程，学习者将能够有效应用线性优化技术，以提升决策制定能力。
Name:Module 1: Introduction to Linear Programming
Description:Prescriptive analytics is a part of business analytics that is aimed at prescribing solutions to decision problems. The most important modeling technique within prescriptive analytics is optimization. In this module, we will learn how to recognize contexts where it can be applied and get introduced to the basics of linear optimization.
Name:Module 2: Solving Linear Programs
Description:In order to solve linear optimization problems (i.e., linear programs), we can use graphical methods for basic example problems. For higher dimensional problems, we will use tools like Excel Solver later in the course. The benefit of using graphical methods is that it gives us an intuition into how these problems can be solved.
Name:Module 3: Alternative Specifications & Special Cases in Linear Optimization
Description:In this module we will explore what happens when the model parameters are changed. We will also look at special cases of linear optimization problems.
Name:Module 4: Modeling & Solving Linear Problems in Excel
Description:Having learned how to formulate linear optimization problem and the graphical methods for solving them, we are now going to start solving larger problems using Excel Solver. This module provides an overview of how to set up and solve these decision problems using Excel.
In this data-driven world, companies are often interested in knowing what is the "best" course of action, given the data. For example, manufacturers need to decide how many units of a product to produce given the estimated demand and raw material availability? Should they make all the products in-house or buy some from a third-party to meet the demand? Prescriptive Analytics is the branch of analytics that can provide answers to these questions. It is used for prescribing data-based decisions. The most important method in the prescriptive analytics toolbox is optimization. This course will introduce students to the basic principles of linear optimization for decision-making. Using practical examples, this course teaches how to convert a problem scenario into a mathematical model that can be solved to get the best business outcome. We will learn to identify decision variables, objective function, and constraints of a problem, and use them to formulate and solve an optimization problem using Excel solver and spreadsheet.
