Business analytics for decision making
Kimbrough, Steve
Business analytics for decision making - Boca Raton CRC Press 2016 - xxii, 307 p.
Table of Contents
I: STARTERS
Introduction
The Computational Problem Solving Cycle
Example: Simple Knapsack Models
An Example: The Eilon Simple Knapsack Model
Scoping Out Post-Solution Analysis
Parameter Sweeping: A Method for Post-Solution Analysis
Decision Sweeping
Summary of Vocabulary and Main Points
For Exploration
For More Information
Constrained Optimization Models: Introduction and Concepts
Constrained Optimization
Classification of Models
Solution Concepts
Computational Complexity and Solution Methods
Metaheuristics
Discussion
For Exploration
For More Information
Linear Programming
Introduction
Wagner Diet Problem
Solving an LP
Post-Solution Analysis of LPs
More than One at a Time: The 100% Rule
For Exploration
For More Information
II: OPTIMIZATION MODELING
Simple Knapsack Problems
Introduction
Solving a Simple Knapsack in Excel
The Bang-for-Buck Heuristic
Post-Solution Analytics with the Simple Knapsack
Creating Simple Knapsack Test Models
Description
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.
9781482221763
Management--Statistical methods
Decision making--Data processing
Decision making--Statistical methods
658.4032 / KIM
Business analytics for decision making - Boca Raton CRC Press 2016 - xxii, 307 p.
Table of Contents
I: STARTERS
Introduction
The Computational Problem Solving Cycle
Example: Simple Knapsack Models
An Example: The Eilon Simple Knapsack Model
Scoping Out Post-Solution Analysis
Parameter Sweeping: A Method for Post-Solution Analysis
Decision Sweeping
Summary of Vocabulary and Main Points
For Exploration
For More Information
Constrained Optimization Models: Introduction and Concepts
Constrained Optimization
Classification of Models
Solution Concepts
Computational Complexity and Solution Methods
Metaheuristics
Discussion
For Exploration
For More Information
Linear Programming
Introduction
Wagner Diet Problem
Solving an LP
Post-Solution Analysis of LPs
More than One at a Time: The 100% Rule
For Exploration
For More Information
II: OPTIMIZATION MODELING
Simple Knapsack Problems
Introduction
Solving a Simple Knapsack in Excel
The Bang-for-Buck Heuristic
Post-Solution Analytics with the Simple Knapsack
Creating Simple Knapsack Test Models
Description
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.
9781482221763
Management--Statistical methods
Decision making--Data processing
Decision making--Statistical methods
658.4032 / KIM