In this thesis we deal with the problem of optimizing the logistics network of a Third-Party Logistics (3PL) company. The goal is to minimize the cost of storage and transport operations, in the case of multiple warehouses, multiple suppliers, multiple clients, multiple products and multiple types of transportation vehicles.

We model this problem by a new Mixed Integer Linear Program (MILP). The related decisions include selection of (a) the warehouse(s) to store each product (SKU), (b) the inventory level per SKU per warehouse, (c) the warehouse(s) to serve each customer and (d) the appropriate vehicles to transport the products from the suppliers to the warehouses, and from the latter to the final customers.

We implemented the model in Python Pulp and solved it using Gurobi Optimizer 9.1.2. Multiple validation tests were performed to confirm the correct structure, and completeness of the model. Subsequently the model was applied in case study of a 3PL company in Thessaloniki Greece. The company’s network consists of: (a) three warehouses located in the industrial area of Thessaloniki, (b) 23 suppliers throughout Greece that ship one or more products, 53 customers, the majority of which are located in northern Greece. The company manages 41 dry products classified into 13 product families. All operations are performed in pallets. The transport fleet comprises commercial vehicles. All information was provided directly from the company.

The proposed method was fully capable to model this complex practical environment, Furthermore, the results obtained were very encouraging, since overall warehousing and distribution costs were lowered by 10.84% compared to the way the company operates currently.