Frederick Hillier, Mark Hillier

Introduction to Management Science ISE

• Broschiertes Buch

Produktdetails

• Verlag: McGraw-Hill Education
• 7 ed
• Seitenzahl: 770
• Erscheinungstermin: 15. November 2022
• Englisch
• Abmessung: 215mm x 275mm x 29mm
• Gewicht: 1366g
• ISBN-13: 9781265040055
• ISBN-10: 1265040052
• Artikelnr.: 63199278

Autorenporträt

Professor emeritus of operations research at Stanford University. Dr. Hillier is especially known for his classic, award-winning text, Introduction to Operations Research, co-authored with the late Gerald J. Lieberman, which has been translated into well over a dozen languages and is currently in its 8th edition. The 6th edition won honorable mention for the 1995 Lanchester Prize (best English-language publication of any kind in the field) and Dr. Hillier also was awarded the 2004 INFORMS Expository Writing Award for the 8th edition. His other books include The Evaluation of Risky Interrelated Investments, Queueing Tables and Graphs, Introduction to Stochastic Models in Operations Research, and Introduction to Mathematical Programming. He received his BS in industrial engineering and doctorate specializing in operations research and management science from Stanford University. The winner of many awards in high school and college for writing, mathematics, debate, and music, he ranked first in his undergraduate engineering class and was awarded three national fellowships (National Science Foundation, Tau Beta Pi, and Danforth) for graduate study. Dr. Hillier's research has extended into a variety of areas, including integer programming, queueing theory and its application, statistical quality control, and production and operations management. He also has won a major prize for research in capital budgeting.

Inhaltsangabe

PART 1 The Essence of Management Science and Business Analytics
1 Introduction
2 Overview of the Analysis Process
PART 2 Models for Predictive Analytics
3 Classification and Prediction Models for Predictive Analytics
4 Predictive Analytics Based on Traditional Forecasting Methods
PART 3 Using Linear Programming to Perform Prescriptive Analytics
5 Linear Programming: Basic Concepts
6 Linear Programming: Formulation and Applications
7 The Art of Modeling with Spreadsheets
8 What-If Analysis for Linear Programming
9 Network Optimization Problems
PART 4 Using Integer or Nonlinear Programming to Perform Prescriptive
Analytics
10 Integer Programming
11 Nonlinear Programming
PART 5 Traditional Uncertainty Models for Performing Predictive or
Prescriptive Analytics
12 Decision Analysis
13 Queueing Models
14 Computer Simulation: Basic Concepts
15 Computer Simulation with Analytic Solver
APPENDIXES
A Tips for Using Microsoft Excel for Modeling
B Partial Answers to Selected Problems