COURSE OBJECTIVES

Mastering the principles of hybridization and decomposition methods in order to solve difficult problems of large size.

TARGET AUDIENCE

Engineers, economists, scientists and developers interested in modeling decision problems and implementing optimization algorithms.

Hybridization techniques

• Linear programming / Constraint programming hybridization. Mixed modeling, common search trees, dialogue among branching schemes. Using reduced cost.

• Constraint programming / Local search hybridization. Description of neighborhoods as constrained neighborhoods. Under constraints’ neighborhood exploration.

Decomposition techniques

• Principles of Price Decomposition and Resource Decomposition. Types of information exchanges. Elementary examples.

• Lagrangian. Duality. Definition and economic interpretation of the dual function. Duality gap. Convex and Non-Convex cases.

• Price Decomposition: coordination algorithms and Non-differentiable Optimization.

• Benders decomposition: principles and implementation.

Applications

• Joint gas and electricity assets optimization: introduction, Benders and price decomposition.

• Example of constraint programming and local search hybridization: timetables scheduling, frequency assignment.

• Decomposition and hybridization for maintenance scheduling.