Optimizing a Worldwide-Scale Shipper Transportation Planning in a Carmaker Inbound Supply Chain

We study the shipper-side design of large-scale inbound transportation networks, motivated by the global supply chain of the carmaker Renault. We formalize the Shipper Transportation Planning Problem (STPP), which integrates discrete flow consolidation via explicit bin-packing, time-expanded routing, and operational regularity constraints. To solve this high-complexity combinatorial problem at an industrial scale, we propose a tailored Iterated Local Search (ILS) metaheuristic. The algorithm combines large-neighborhood search with MILP-based perturbations and leverages bundle-specific decompositions to obtain scalable lower bounds and effective search guidance. Computational experiments on real industrial data involving more than 700,000 commodities and 1.2 million arcs demonstrate that the ILS achieves an average gap of 7.9% to the best available lower bound. The results reveal a 23.2% cost-reduction potential compared to legacy planning benchmarks. Most significantly, the proposed framework is currently deployed in production at Renault, where it supports weekly strategic decisions and generates realized cost savings estimated at approximately 20 million euros per year. Our analysis yields key managerial insights: we demonstrate that explicit 1D bin-packing is a critical step forward for realistic consolidation modeling, that transport regularity offers a robust balance between cost and stability, and that high-volume global networks benefit significantly from in-house strategic planning over third-party outsourcing. To the best of our knowledge, this is the first work to successfully solve a shipper-side transportation design problem at this magnitude.