Bob Lenglet

Engineering student (IG2I Centrale Lille) specializing in Operations Research and Data Science, I work on the algorithmic resolution of complex optimization problems.

My approach is pragmatic: when faced with an NP-hard problem, I first prioritize implementing exact solvers to guarantee a mathematically optimal solution, before exploring heuristics (particularly through Machine Learning) to drastically reduce algorithmic computation times if the instance size requires it.

My application areas:

-> Logistics Optimization (VRP): Constraint modeling, calculation of real distance matrices via OSRM, and resolution (Google OR-Tools) to reduce vehicle fleet mileage.

-> Industrial Scheduling (JSSP): Mathematical modeling to minimize production times (Makespan) and eliminate bottlenecks.

-> Forecasting and Supply Chain: Development of hybrid Python models (Linear Regression + XGBoost) for dynamic calculation of reorder points and safety stocks.

My main technical stack: Python (Pandas, Numpy), Google OR-Tools, OSRM, XGBoost, Scikit-learn.

Accustomed to English-speaking environments (currently interning abroad), I work autonomously. Let's talk about your data and your business constraints.

--- ENGLISH VERSION ---

Operations Research & Data Science engineering student (Centrale Lille). I solve complex optimization problems (NP-hard) by prioritizing exact solvers before exploring ML heuristics.

Core expertise:

VRP: Routing optimization using OSRM & Google OR-Tools.

JSSP: Mathematical modeling for makespan minimization.

Supply Chain: Hybrid forecasting models (Linear Regression + XGBoost) for dynamic safety stocks.

Tech: Python, Google OR-Tools, OSRM, Scikit-learn. Currently working in an English-speaking environment.