Business Analytics for Decision Making: 1st Edition (e-Book) book cover

Business Analytics for Decision Making

1st Edition

By Steven Orla Kimbrough, Hoong Chuin Lau

Chapman and Hall/CRC

308 pages | 109 B/W Illus.

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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.

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

Discussion

For Exploration

For More Information

Assignment Problems

Introduction

The Generalized Assignment Problem

Case Example: GAP 1-c5-15-1

Using Decisions from Evolutionary Computation

Discussion

For Exploration

For More Information

The Traveling Salesman Problem

Introduction

Problem Definition

Solution Approaches

Discussion

For Exploration

For More Information

Vehicle Routing Problems

Introduction

Problem Definition

Solution Approaches

Extensions of VRP

For Exploration

For More Information

Resource-Constrained Scheduling

Introduction

Formal Definition

Solution Approaches

Extensions of RCPSP

For Exploration

For More Information

Location Analysis

Introduction

Locating One Service Center

A Naїve Greedy Heuristic for Locating n Centers

Using a Greedy Hill Climbing Heuristic

Discussion

For Exploration

For More Information

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

For More Information

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

For More Information

Evolutionary Algorithms

Introduction

EPs: Evolutionary Programs

The Basic Genetic Algorithm (GA)

For Exploration

For More Information

Identifying and Collecting Decisions of Interest

Kinds of Decisions of Interest (DoIs)

The FI2-Pop GA

Discussion

For Exploration

For More Information

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

For More Information

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

For More Information

Multiattribute Utility Modeling

Introduction

Single Attribute Utility Modeling

Multiattribute Utility Models

Discussion

For Exploration

For More Information

Data Envelopment Analysis

Introduction

Implementation

Demonstration of DEA Concept

Discussion

For Exploration

For More Information

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

For More Information

V: CONCLUSION

Conclusion

Looking Back

Revisiting Post-Solution Analysis

Looking Forward

Resources

A.1 Resources on the Web

Bibliography

Index

About the Authors

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

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

Subject Categories

BISAC Subject Codes/Headings:
BUS042000
BUSINESS & ECONOMICS / Management Science
BUS049000
BUSINESS & ECONOMICS / Operations Research
COM021030
COMPUTERS / Database Management / Data Mining