An introduction to LocalSolver© in Python for Beginners (FR & EN) - 24h

Antoine Jeanjean, contact@opt2a.com

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

An introduction to LocalSolver© in Python for Beginners (FR & EN)


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 will be available in videos. It can also be issued on request within your organization. Do not hesitate to contact us for more information.

Antoine Jeanjean, contact@opt2a.com

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Antoine Jeanjean (PhD)
Antoine Jeanjean has more than 15 years of experience in the field of Optimization and Augmented Analysis. He is a computer engineer from ISIMA and the University of Oklahoma with a doctorate in computer science from École Polytechnique. His thesis work aimed to prove the efficiency of local search algorithms applied to industrial problems.
He first worked as a consulting research engineer for the Bouygues Group within the LocalSolver e-lab team... Read more
Bordeaux FRANCE
contact@opt2a.com




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