Business analytics for decision making

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.