Business Analytics for Decision Making, the first complete text suitable for use in introductory Business Analytics courses, establishes a national syllabus for an emerging first course at an MBA or upper undergraduate level. This timely text is mainly about model analytics, particularly analytics for constrained optimization. It uses implementations that allow students to explore models and data for the sake of discovery, understanding, and decision making.
Business analytics is about using data and models to solve various kinds of decision problems. There are three aspects for those who want to make the most of their analytics: encoding, solution design, and post-solution analysis. This textbook addresses all three. Emphasizing the use of constrained optimization models for decision making, the book concentrates on post-solution analysis of models.
The text focuses on computationally challenging problems that commonly arise in business environments. Unique among business analytics texts, it emphasizes using heuristics for solving difficult optimization problems important in business practice by making best use of methods from Computer Science and Operations Research. Furthermore, case studies and examples illustrate the real-world applications of these methods.
The authors supply examples in Excel®, GAMS, MATLAB®, and OPL. The metaheuristics code is also made available at the book's website in a documented library of Python modules, along with data and material for homework exercises. From the beginning, the authors emphasize analytics and de-emphasize representation and encoding so students will have plenty to sink their teeth into regardless of their computer programming experience.
Table of Contents
Part I: Starters
Chapter 1: Introduction
Chapter 2: Constrained Optimization Models: Introduction and Concepts
Chapter 3: Linear Programming
Part II: Optimization Modeling
Chapter 4: Simple Knapsack Problems
Chapter 5: Assignment Problems
Chapter 6: The Traveling Salesman Problem
Chapter 7: Vehicle Routing Problems
Chapter 8: Resource-Constrained Scheduling
Chapter 9: Location Analysis
Chapter 10: Two-Sided Matching
Part III: Metaheuristic Solution Methods
Chapter 11: Local Search Metaheuristics
Chapter 12: Evolutionary Algorithms
Chapter 13: Identifying and Collecting Decisions of Interest
Part IV: Post-Solution Analysis of Optimization Models
Chapter 14: Decision Sweeping
Chapter 15: Parameter Sweeping
Chapter 16: Multiattribute Utility Modeling
Chapter 17: Data Envelopment Analysis
Chapter 18: Redistricting: A Case Study in Zone Design
Part V: Conclusion
Chapter 19: Conclusion
Appendix A: Resources
