Transportation cost optimization plays a vital role in the operations of sand manufacturing companies that depend on third-party logistics for the efficient movement of their products. This research focuses on reducing the total transportation costs for a sand manufacturing company by selecting the best logistics supplier using the PuLP library, a popular open-source Linear Programming tool in Python. The objective is to minimize the total transportation cost while meeting the demand requirements of customers by determining the optimal allocation of sand shipment from the company's multiple warehouses to various customers while considering factors such as distance, freight rates, and capacity constraints. To address this transportation problem, the PuLP library is employed. PuLP provides a powerful interface for formulating and solving solver capabilities to facilitate finding the optimal solution that minimizes the total transportation cost. By leveraging the Pulp library, sand manufacturing companies can optimize their transportation operations and reduce the total transportation cost associated. The project aims to provide valuable insights into supplier selection for improved cost efficiency, enhanced customer service, and increased operational effectiveness.

Keywords : Transportation Cost Reduction, Logistics Management, Python Programming, Prescriptive Analytics, Microsoft Solver, Linear Programming.