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ILOG Solver Directions Laurent Perron ILOG SA. Outline Constraint Programming, a powerful technology The CP suite in ILOG CP faces new challenges Recent.

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Presentation on theme: "ILOG Solver Directions Laurent Perron ILOG SA. Outline Constraint Programming, a powerful technology The CP suite in ILOG CP faces new challenges Recent."— Presentation transcript:

1 ILOG Solver Directions Laurent Perron ILOG SA

2 Outline Constraint Programming, a powerful technology The CP suite in ILOG CP faces new challenges Recent Technical Advances

3 Constraint Programming, a Powerful Technology I hope this will be demonstrated by the workshop CP is a robust technology with a past of successful applications  Some famous academics success in the OR community (10 teams problem closed easily)  A large collection of deployed industrial applications based on Constraint Programming

4 CP Strengths A high level modeling language allows for a rich and accurate representation of the model Domain expertise is captured through a rich search language Special techniques (decomposition, LNS, repair…) allows the tackling of large and difficult problems “A clever tool for clever people”

5 CP weaknesses A high level modeling language allows for a rich and accurate representation of the model Domain expertise is captured through a rich search language Special techniques (decomposition, LNS, repair…) allows the tackling of large and difficult problems You need clever people to use this clever tool

6 ILOG CP Suite ILOG Solver  Generic CP System ILOG Scheduler  Detailed Scheduling Specialization ILOG Dispatcher  Vehicule Routing and Disptaching Specialization ILOG Configurator  Configuration System

7 ILOG CP Market ILOG CP Market is defined by  ILOG Consultants  Big ISV  Specialized Solution Vendors  Technical Consulting Companies  Big Companies with Dedicated R&D This is not a huge market We want to enlarge our market

8 CP Meets New Challenges To reach our goals, CP should be improved At the evaluation phase  Easier to use tools  Rapid results  Low coding effort At the implementation phase  Low technology profile At the maintenance phase  The application should improve with time

9 New Rules for Tools Evaluation People are not very technical  Maybe one week of training They may have a limited OR background  The know basic OR rules about modeling They may not be Computer Science experts  The cp system should be easily integrated/documented  Maybe not keen with compilers and library

10 News Rules for Evaluation (2) Evaluation Phase is Limited in time IT is made against other competing techniques  And sometimes against internal tools We need to achieve something soon  Even if the problem is over-constrained  Even if the data are dirty  Even if the model is naive

11 New Rules for Application Development The IT guy is not the OR Expert The code will not evolve The time devoted to Optimization is usually limited Users will need guidance  Why this doesn’t work? Users expect performance to improve with time  Without code evolution

12 Usability To be effective, a CP solution consists of  A good model  A clever search part We would like to remove the need for search part At least for small problems, typically the one encountered in the evaluation phase

13 Robustness Different logically equivalent formulations can lead to different runtime performances  Expand a global constraint into smaller subparts This imply that getting a good model is an art We do not believe this cannot be The CP Solver should detect these cases and reformulate the model Users do not know about different level of propagation

14 Evolution of the Code In the past, to use a new technology, a new constraint or a new search construct was implemented Users had to rewrite their code in order to benefit from it And new users had to learn more each time

15 Interactivity Users want Explanation  And useful explanations Users want Solutions  Even with over-constrained problems  Along with explanations of why some constraints are not part of the solution

16 ILOG Solver Directions Default Search Model Reinforcement Constraint Aggregation Various work on robustness  Better constraints (without filtering levels)  No pathological cases  No slow propagation Explanations and Solver Anyway

17 ILOG CP Directions There is a new suite of optimization tools in ILOG ILOG Plant Power Ops for production planning and scheduling ILOG Transport Power Ops for routing and dispatching ILOG Fab Power Ops for semi conductor industry


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