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

    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
    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

    Biography

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

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