A 20-hour course about LocalSolver©: The objective of this module is to discover LocalSolver© by relying on examples of increasing complexity.



The objective of this module is to discover LocalSolver© by relying on examples of increasing complexity. The problems will come from the classic Operations Research library, namely Routing, Scheduling, Packing, Assignment, Clustering, ...
Through concrete examples shared through PDF documents, we will gradually discover the relational and logical operators as well as the technical features allowing to fix its constraints, to launch its resolution, to check the execution status, to fix an initial solution, ... We will also address the issues of inconsistency and infeasibility.
We will use the Python programming language for this module.
The sessions will be in the form of Practical Work sessions with questions / answers and development assistance on the students' computer.
Depending on the level of each participant, the problems will be completed over the course of the session, by adding attributes that make the modeling and the implementation more complex.We will explain the different optimization techniques, exact and heuristic, used by the LocalSolver©

• - Session 1 (4h): Introduction & Installation of LocalSolver©. Getting started with the environment and first calls via Python - 1 or 2 examples with Knapsack, Smallest circle, ...
  
  
• - Session 2 (4h): Problem of planning TV commercials (Multidimensional Knapsack)
  
  
• - Session 3 (4h): 'Scheduling' problem
  
  
• - Session 4 (4h): 'Routing TSP' problem then 'Pricing Collecting TSP' problem
  
  
• - Session 5 (4h): 'Clustering' problem
  
  

This module can also be issued on request within your organization. Do not hesitate to contact us for more information.

Antoine Jeanjean, contact@opt2a.com

Antoine Jeanjean (PhD)