1st Edition

# Business Analytics for Decision Making

330 Pages 109 B/W Illustrations
by Chapman & Hall

330 Pages 109 B/W Illustrations
by Chapman & Hall

Also available as eBook on:

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.

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

Constrained Optimization Models: Introduction and Concepts
Constrained Optimization
Classification of Models
Solution Concepts
Computational Complexity and Solution Methods
Metaheuristics
Discussion
For Exploration

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

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
Discussion
For Exploration

Assignment Problems
Introduction
The Generalized Assignment Problem
Case Example: GAP 1-c5-15-1
Using Decisions from Evolutionary Computation
Discussion
For Exploration

The Traveling Salesman Problem
Introduction
Problem Definition
Solution Approaches
Discussion
For Exploration

Vehicle Routing Problems
Introduction
Problem Definition
Solution Approaches
Extensions of VRP
For Exploration

Resource-Constrained Scheduling
Introduction
Formal Definition
Solution Approaches
Extensions of RCPSP
For Exploration

Location Analysis
Introduction
Locating One Service Center
A Naїve Greedy Heuristic for Locating n Centers
Using a Greedy Hill Climbing Heuristic
Discussion
For Exploration

Two-Sided Matching
Quick Introduction: Two-Sided Matching Problems
Narrative Description of Two-Sided Matching Problems
Representing the Problem
Stable Matches and the Deferred Acceptance Algorithm
Once More, in More Depth
Generalization: Matching in Centralized Markets
Discussion: Complications

III: METAHEURISTIC SOLUTION METHODS

Local Search Metaheuristics
Introduction
Greedy Hill Climbing
Simulated Annealing
Running the Simulated Annealer Code
Threshold Accepting Algorithms
Tabu Search
For Exploration

Evolutionary Algorithms
Introduction
EPs: Evolutionary Programs
The Basic Genetic Algorithm (GA)
For Exploration

Identifying and Collecting Decisions of Interest
Kinds of Decisions of Interest (DoIs)
The FI2-Pop GA
Discussion
For Exploration

IV: POST-SOLUTION ANALYSIS OF OPTIMIZATION MODELS

Decision Sweeping
Introduction
Decision Sweeping with the GAP 1-c5-15-1 Model
Deliberating with the Results of a Decision Sweep
Discussion
For Exploration

Parameter Sweeping
Introduction: Reminders on Solution Pluralism and Parameter Sweeping
Parameter Sweeping: Post-Solution Analysis by Model Re-Solution
Parameter Sweeping with Decision Sweeping
Discussion
For Exploration

Multiattribute Utility Modeling
Introduction
Single Attribute Utility Modeling
Multiattribute Utility Models
Discussion
For Exploration

Data Envelopment Analysis
Introduction
Implementation
Demonstration of DEA Concept
Discussion
For Exploration

Redistricting: A Case Study in Zone Design
Introduction
The Basic Redistricting Formulation
Representing and Formulating the Problem
Initial Forays for Discovering Good Districting Plans
Solving a Related Solution Pluralism Problem
Discussion
For Exploration

V: CONCLUSION

Conclusion
Looking Back
Revisiting Post-Solution Analysis
Looking Forward

Resources
A.1 Resources on the Web

Bibliography

Index

### Biography

Steven Orla Kimbrough, The Wharton School, University of Pennsylvania, Philadelphia, USA

Hoong Chuin Lau, School of Information Systems, Singapore Management University, Singapore