Business analytics for decision making / Steven Orla Kimbrough, the Wharton School University of Pennsylvania Philadelphia, USA, Hoong Chuin Lau, School of Information Systems Singapore Management University Singapore.

Format:

Book

Author/Creator:

Kimbrough, Steve, author.

Lau, Hoong Chuin, author.

Contributor:

Taylor & Francis eBooks.

Lippincott Library Book Endowment Fund.

Language:

English

Subjects (All):

Decision making--Statistical methods.

Decision making.

Decision making--Data processing.

Management--Statistical methods.

Management.

Physical Description:

1 online resource (xxii, 307 pages) : illustrations

Place of Publication:

Boca Raton : CRC Press, Taylor & Francis Group, [2016]

System Details:

text file

Summary:

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 key 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. Book jacket.

Contents:

Starters

Optimization modeling

Metaheuristic solution methods

Post-solution analysis of optimization models

Conclusion.

Notes:

Includes bibliographical references (pages 291-301) and index.

Electronic reproduction. London Available via World Wide Web.

Description based on print version record.

Local Notes:

Acquired for the Penn Libraries with assistance from the Lippincott Library Book Endowment Fund.

ISBN:

9781315372426

1315372428

Publisher Number:

99984934682

Access Restriction:

Restricted for use by site license.