Hybrid Approach to the Cost Estimation of External-Fleet Full Truckload Contracts

Abstract

Freight forwarding and transportation are the backbone of the modern economy. There are thousands of transportation companies on the market whose sole purpose is to deliver ordered goods from pickup to delivery. Transportation can be carried out by two types of fleets. A company can have its own trucks, or it can use third-party companies. This transportation can be carried out in a variety of formulas, with full truckload being the most common for long routes. The shipper must be aware of the potential cost of such a service during the process of selecting a particular transport. The presented solution addresses this exact issue. There are many approaches, ranging from detailed cost calculators to machine learning solutions. The present study uses a dedicated hybrid algorithm that combines different techniques, spanning clustering algorithms, regression and kNN (k Nearest Neighbors) estimators. The resulting solution was tested on real shipping data covering multi-year contract data from several shipping companies operating in the European market. The obtained results proved so successful that they were implemented in a commercial solution used by freight forwarding companies on a daily basis.

Keywords

• kNN
• clustering
• regression
• cost estimation
• goods shipping
• full truckloads