Course Code: MAV558
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Synopsis

MAV558 Prescriptive Analytics for Decision Making equips students with the knowledge and skills to leverage prescriptive analytics for enhancing decision-making through optimisation models. In this course, students will learn to build and apply these models to improve key business metrics such as profitability, cost reduction, and revenue optimisation. The course covers linear and integer programming and introduces decision trees for making decisions under uncertainty. Students will gain hands-on experience with open-source tools like Excel Solver, OpenSolver and Python. By integrating optimisation, simulation, and decision trees, learners will be equipped to tackle complex business problems and drive optimal outcomes, improving operational efficiency and strategic planning within their organisations. MAV558决策分析与优化旨在使学生掌握利用规范性分析和优化模型来增强决策制定的知识和技能。在本课程中，学生将学习如何构建和应用这些模型，以提升关键业务指标，如盈利能力、成本降低和收入优化。课程涵盖线性规划和整数规划，并介绍了在不确定性情况下进行决策的决策树。学生将通过Excel Solver、OpenSolver和Python等开源工具获得实践经验。通过整合优化、模拟和决策树，学生将能够解决复杂的业务问题，推动最佳成果，提高组织的运营效率和战略规划能力。
Level: 5
Credit Units: 5
Presentation Pattern: EVERY JULY

Topics

  • Probability Theory 概率理论
  • Decision Analysis with Decision Trees 使用决策树进行决策分析
  • Linear Optimisation 线性优化
  • Integer Optimisation 整数优化
  • Spreadsheet Modelling with Excel Solver 使用Excel Solver进行电子表格建模
  • Optimisation Techniques 优化技术
  • Software Tools for Prescriptive Analytics 决策分析的软件工具
  • Simulation Modelling I 模拟建模 I
  • Simulation Modelling II 模拟建模 II
  • Integration of Analytics Techniques 分析技术的整合
  • Decision Making under Uncertainty 不确定性条件下的决策
  • Real-World Applications of Prescriptive Analytics 规范性分析的实际应用

Learning Outcome

  • Critique the role of decision trees in making decisions under uncertainty 批判性地评价决策树在不确定性条件下决策中的作用
  • Evaluate the fundamental principles and methodologies of linear and integer programming and their applications in prescriptive analytics 评估线性和整数规划的基本原理和方法及其在规范性分析中的应用
  • Assess various open-source tools such as Excel Solver, OpenSolver, and Python for developing and solving optimisation models 评估Excel Solver、OpenSolver和Python等各种开源工具在开发和解决优化模型中的应用
  • Design how prescriptive analytics integrates with descriptive and predictive analytics to form a comprehensive decision-making framework 设计规范性分析如何与描述性和预测性分析相结合，形成一个全面的决策框架
  • Appraise how to apply prescriptive analytics to various business contexts 评价规范性分析在各种业务环境中的应用
  • Construct and implement optimisation models to solve real-world business problems 构建和实施优化模型以解决实际业务问题
  • Prioritise complex data sets to derive actionable insights and optimal strategies 优先处理复杂数据集以得出可操作的见解和最优策略
  • Formulate solutions by applying prescriptive analytics techniques to address business challenges in uncertain environments 通过应用规范性分析技术来应对不确定环境中的业务挑战，制定解决方案
  • Apply strategic decision-making skills by leveraging prescriptive analytics to identify and execute the best of action for business success 通过利用规范性分析，应用战略决策技能来识别并执行实现业务成功的最佳行动方案
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