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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/advanced-models-for-decision-making
课程评论：没有评论
课程名称：高级决策模型 概述：商业分析师需要能够为问题提供最佳解决方案。然而，许多分析课程往往集中于数据分析和可视化的培训，而不是帮助学员如何将可用数据与正确的数学模型相结合以制定解决方案。本课程旨在将数据和模型与制造、供应链、金融和人力资源管理等现实决策场景相连接。特别地，我们将了解如何使用线性优化——一种处方分析方法——来形成决策问题并提供基于数据的最佳解决方案。课程将涵盖不同行业的应用问题，例如： (a) 财务决策：投资经理应该如何创建最佳投资组合，以最大化净回报，同时在各种投资中控制风险？ (b) 生产决策：在预测需求、原材料供应和运输成本的情况下，在哪些工厂位置制造产品的最佳数量是多少？ (c) 人力资源决策：在规划期限内需要雇佣或解雇多少员工，以在满足公司运营需求的同时最小化成本？ (d) 制造业：在考虑原材料可用性和客户需求的情况下，应该生产哪种利润最大化的产品组合？ 我们将学习如何将这些问题公式化为数学模型，并使用Excel电子表格解决它们。 课程大纲： 1. 模块1：金融决策的线性规划 描述：本模块将通过案例研究线性优化在金融中的应用，特别是投资组合优化和多期现金流管理问题的公式化学习。 2. 模块2：供应链决策的线性规划 描述：本模块探讨线性优化在供应链决策中的应用，特别是在库存运输或物流问题以及生产和库存管理中的应用。 3. 模块3：人员安排决策的线性规划 描述：本模块探讨人力资源经理如何利用优化作为处方分析工具来规划员工安排、房间分配和劳动力规模管理。 4. 模块4：生产决策的线性规划 描述：线性优化在制造业决策过程中发挥着重要作用。本模块将探讨如何使用优化来制定产品组合和混合决策。
Name:Module 1: LPs for Financial Decisions
Description:In this module we will look at examples illustrating the application of linear optimization in finance. In particular, we will learn to formulate problems in investment portfolio optimization and multi-period cash flow management.
Name:Module 2: LP for Supply Chain Decisions
Description:This module explores the use of linear optimization in supply chain decisions, particularly in the context of inventory transportation or logistics problems as well as in production and inventory management.
Name:Module 3: LP for Staffing Decisions
Description:This module explores how human resource managers can use optimization as a prescriptive analytics tool to plan staff schedules, room allocation, and workforce size management.
Name:Module 4: LP for Production Decisions
Description:Linear optimization plays an important role in the decision making process in the manufacturing sector. This module explores how optimization can be used to prescribe product mix and blending decisions.
Business analysts need to be able to prescribe optimal solution to problems. But analytics courses are often focused on training students in data analysis and visualization, not so much in helping them figure out how to take the available data and pair that with the right mathematical model to formulate a solution. This course is designed to connect data and models to real world decision-making scenarios in manufacturing, supply chain, finance, human resource management, etc. In particular, we understand how linear optimization - a prescriptive analytics method - can be used to formulate decision problems and provide data-based optimal solutions. Throughout this course we will work on applied problems in different industries, such as: (a) Finance Decisions: How should an investment manager create an optimal portfolio that maximizes net returns while not taking too much risks across various investments? (b) Production Decisions: Given projected demand, supply of raw materials, and transportation costs, what would be the optimal volume of products to manufacture at different plant locations? (c) HR Decisions: How many workers need to be hired or terminated over a planning horizon to minimize cost while meeting operational needs of a company? (c) Manufacturing: What would be the profit maximizing product mix that should be produced, given the raw material availability and customer demand? We will learn how to formulate these problems as mathematical models and solve them using Excel spreadsheet.
