Adaptive Model Predictive Control in the Inventory Control Problem

Paris Pennesi

ISBN 10: 3843379599 / ISBN 13: 9783843379595

Published by LAP LAMBERT Academic Publishing Feb 2011, 2011

About this Item

Neuware -The present thesis aims to develop and to analyze methods and algorithms to control uncertain systems. The methods developed are based on control and optimization theory. Model Predictive Control, Large Deviations, Approximate Dynamic Programming and Robust Optimization are applied, extended and combined to face the challenges presented in the application of such methods in real-world problems. In particular, the algorithms developed have been applied to supply chain systems. A supply chain is composed by several business units working together to match the market demand of a product. Despite several economic and cultural changes, (i.e. low production cost, international outsourcing.) the main goal of a supply chain networks is to procure raw materials and transform them into final products. This involves the automation of several processes: material and information flows, and relationships between supplier and customers. To reach these objective the management of a supply chain has to take several decisions and to supervise and to control several facilities.Books on Demand GmbH, Überseering 33, 22297 Hamburg 80 pp. Englisch.

Seller Inventory # 9783843379595

Bibliographic Details

• Title: Adaptive Model Predictive Control in the Inventory Control Problem
• Publisher: LAP LAMBERT Academic Publishing Feb 2011
• Publication Date: 2011
• Language: English
• ISBN 10: 3843379599
• ISBN 13: 9783843379595
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 5D millimeters 220 millimeters width by 150 millimeters height by 5 millimeters depth
• Weight: 137 grams

About this title

Synopsis

The present thesis aims to develop and to analyze methods and algorithms to control uncertain systems. The methods developed are based on control and optimization theory. Model Predictive Control, Large Deviations, Approximate Dynamic Programming and Robust Optimization are applied, extended and combined to face the challenges presented in the application of such methods in real-world problems. In particular, the algorithms developed have been applied to supply chain systems. A supply chain is composed by several business units working together to match the market demand of a product. Despite several economic and cultural changes, (i.e. low production cost, international outsourcing...) the main goal of a supply chain networks is to procure raw materials and transform them into final products. This involves the automation of several processes: material and information flows, and relationships between supplier and customers. To reach these objective the management of a supply chain has to take several decisions and to supervise and to control several facilities.

About the Author

He is a Quant Strategist at JP Morgan Chase & Co. He also is Visiting Professor of Operations Research at the Free University of Bozen. Previously, he has been a researcher at University of Cambridge. He holds a PhD in Artificial Intelligence from the Universita' Politecnica delle Marche. He has been a Visiting Scholar at Boston University.