Integration of Rules and Optimization in Plant PowerOps

Plant PowerOps (PPO) [9] is a new ILOG product, based on business rules and optimization technology, dedicated to production planning and detailed scheduling for manufacturing. This paper describes how PPO integrates a rule based system with the optimization engines and the graphical user interface....

Full description

Saved in:
Bibliographic Details
Published inIntegration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems pp. 1 - 15
Main Authors Bousonville, Thomas, Focacci, Filippo, Le Pape, Claude, Nuijten, Wim, Paulin, Frederic, Puget, Jean-Francois, Robert, Anna, Sadeghin, Alireza
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2005
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Artificial intelligence
Business Policy
Business Rule
Computer science; control theory; systems
Customer Order
Enterprise Resource Planning
Exact sciences and technology
Learning and adaptive systems
Logistics
Mathematical programming
Operational research and scientific management
Operational research. Management science
Scheduling, sequencing
Supply Chain
New product
Expert system
Knowledge base
Scheduling
Combinatorial optimization
Constrained optimization
Production management
Behavioral analysis
User interface
Production planning
Graphical interface
Artificial intelligence
Logistics
Mathematical programming
Online AccessGet full text

Cover

More Information
Summary:Plant PowerOps (PPO) [9] is a new ILOG product, based on business rules and optimization technology, dedicated to production planning and detailed scheduling for manufacturing. This paper describes how PPO integrates a rule based system with the optimization engines and the graphical user interface. The integration proposed is motivated by the need to allow business users to manage unexpected changes in their environment. It provides a flexible interface for configuring, maintaining and tuning the system and for managing optimization scenarios. The proposed approach is discussed via several use cases we encountered in practice in supply chain management. Nevertheless, we believe that most of the ideas described in this paper apply in almost any area of optimization application.
ISBN:9783540261520
3540261524
ISSN:0302-9743
1611-3349
DOI:10.1007/11493853_1