Linear And Nonlinear Optimization Using Spreadsheets Examples for Prescriptive, Predictive and Descriptive Analytics – (PDF/EPUB Version)

The use of spreadsheets to obtain solutions to a diverse array of examples offers a reader-friendly way of addressing a topic (optimization) that can sometimes be viewed as intimidating. Many people are readily familiar with spreadsheets and how they work, yet are apt to be unaware of the incredible power of Excel for solving some rather complex optimization problems. A major goal of the book is to sell readers on why it is so important to understand optimization, and a large collection of examples for a wide range of business decision making areas (e.g., production planning and scheduling, workforce planning and scheduling, location and supply chain distribution, location of emergency services, assembly line balancing, vehicle routing, project scheduling, revenue management, advertising, product design, payout schedules, productivity measurement, investment portfolio management, sports league scheduling, ranking models, etc.) affords a practical mechanism for achieving that goal. Another important contribution of the book is that it provides coverage of the mechanics of some common yet sophisticated statistical methods (regression, logistic regression, discriminant analysis, factor analysis, and cluster analysis), which are often opaque to many users of such methods.

Contents:

• Introduction
• Optimization Examples in Prescriptive Analytics:
  • Production Planning
  • Workforce Planning
  • Continuous Facility Location
  • Discrete Facility Location
  • Routing Problems
  • Facility Layout
  • Project Scheduling
  • Marketing
  • Finance
  • Sports
• Optimization Examples for Multivariate Statistical Methods Used in Predictive and Descriptive Analytics:
  • Regression
  • Logistic Regression
  • Linear Discriminant Analysis
  • Factor Analysis
  • Cluster Analysis

Readership: Advanced undergraduate students, graduate students, and practitioners in the fields of business analytics, operations and supply-chain management, operations research, industrial engineering, and applied multivariate statistics.

Key Features:

• An organizational layout based primarily on areas of application, as opposed to the more traditional management science book that is based on methodology (linear programming, advanced linear programming, integer programming, nonlinear programming, etc.)
• Considerable breadth of examples spanning various business functions (location, product design, production planning, workforce scheduling, assignment problems, financial planning, data envelopment analysis, portfolio optimization, advertising, revenue management, product life cycle, sports applications, etc.)
• Coverage of multivariate statistical optimization problems (regression, logistic regression, discriminant analysis, factor analysts, cluster analysis) that are seldom covered in management science or optimization books