S2-4.3 - Intelligent logistics (Freight)

This article presents the global optimization routing system implemented in LOGISTAR . LOGISTAR is an H2020 project funded by the European Commission which seeks for the effective planning and optimization of transport operations in the supply chain by taking advantage of horizontal collaboration. During the development of the system, several particular goals were planned, one of them being the review and update of the state of the art about models and techniques for the optimization of the freight transport network for horizontal collaboration, as well as the optimization of transhipment planning and scheduling in hubs. The reason for the popularity and the importance of such a domain is two-fold: the social and economical interest they generate, and their inherent scientific interest. On the one hand, vehicle route optimization solutions designed to deal with real-world situations related to transport or logistics entail profits for logistics companies. Nevertheless, most of the problems arising in this field have a high computational complexity that algorithms must deal with, making the resolution of such problems a major challenge for the scientific community, and usually, a testbed for the design of new methods in the field of combinatorial optimization.

In this paper, we describe a data processing procedure to enrich an automated big data collection of naturalistic truck diaries with missing information about the stop location of the vehicles. The data collection provides information on the commercial vehicle operation, the shipments carried, and the route it followed (loading and unloading location). Information on the type of stop location or the sender and receiver of the shipments is not registered. A data fusion procedure was developed in which the load- and unload locations in the truck diaries are linked to a firm database with distribution centres, transhipment terminals and individual firms. The multi-level data fusion framework is presented and descriptive statistics from the final database are discussed. The database contains transport- and firm data for 2,065,404 observed freight transports and provides a rich basis for studies into logistic behaviour.