This thesis presents mathematical framework for solving transport planning problems in manufacturing supply chain. The work is motivated by a real-world use case involving the collection of goods from a network of suppliers to a central production facility. Rather than focusing only on optimizing a specific scenario, the goal is to develop a generalizable solution method capable of adapting to a variety of similar logistics contexts. To this end, the thesis introduces both an exact optimization model and a heuristic algorithm. The exact model is based on a variant of the Capacitated Vehicle Routing Problem (CVRP), namely the Split-Delivery Vehicle Routing Problem (SDVRP) and is implemented using Python and Google OR- Tools on the Azure Databricks platform.