Data-Driven Optimization and Knowledge Discovery for an Enterprise Information System

This book provides a comprehensive set of optimization and prediction techniques for an enterprise information system. Readers with a background in operations research, system engineering, statistics, or data analytics can use this book as a reference to derive insight from data and use this knowledge as guidance for production management. The authors identify the key challenges in enterprise information management and present results that have emerged from leading-edge research in this domain. Coverage includes topics ranging from task scheduling and resource allocation, to workflow optimization, process time and status prediction, order admission policies optimization, and enterprise service-level performance analysis and prediction. With its emphasis on the above topics, this book provides an in-depth look at enterprise information management solutions that are needed for greater automation and reconfigurability-based fault tolerance, as well as to obtain data-driven recommendations for effective decision-making.

Addresses system complexity by studying the information system as a mass-customization enterprise Provides practical engineering solutions for real-time applications and data-driven prediction Uses real data and an industry-strength simulation platform that mimics the features of a real enterprise Offers a technology-synthesis platform, combining different techniques such as simulation, optimization, statistical methods and machine-learning algorithms

Autorentext
Qing Duan is a data scientist at Paypal, Inc. Krishnendu Chakrabarty is a Professor in the Department of Electrical and Computer Engineering at Duke University. Jun Zeng is a principal researcher at Hewlett-Packard Labs.

Inhalt
Introduction.- Production Simulation Platform.- Production Workflow Optimizations.- Predictions of Process-Execution Time and Process-Execution Status.- Optimization of Order-Admission Policies.- Conclusion.