A Genetic Algorithm for the Vehicle Routing Problem with Time Windows: Application to the NAFTAL Fuel Distribution Network in Algeria

This study applies a Genetic Algorithm (GA) to optimize the Vehicle Routing Problem with Time Windows (VRPTW) for NAFTAL, Algeria’s national fuel distribution company. The model minimizes both fleet size and total travel distance while achieving high compliance (۹۹.۳%) with customer time constraints. Using operational data from the Constantine regional network one depot serving ۱۴۸ service stations (۱۴۹  nodes in total ), the GA achieved optimal solutions deploying ۹۴ vehicles covering ۱۵,۴۱۵.۶۳ km. Results demonstrated exceptional convergence stability (σ = ۰.۰۰ across ۴۰ runs) and high computational efficiency (under ۶۰ seconds per optimization run). Sensitivity analyses confirmed the robustness of the calibrated configuration, highlighting its reliability and scalability for real-world logistics. The proposed framework provides NAFTAL with a cost-effective, consistent, and practical decision-support tool for optimizing fuel-delivery operations. Future research will focus on integrating machine learning for demand prediction, extending the model to multi-product and heterogeneous-fleet routing, and enabling adaptive real-time optimization to support smart and sustainable logistics.

Keywords:

Vehicle Routing Problem with Time Windows (VRPTW) , Genetic Algorithm (GA) , Optimization , Logistics , Fuel Distribution , Routing Optimization , MATLAB Implementation