(summary)
CONTEXT
There are very significant inefficiencies in this type of operation: very often a container is dropped in a terminal and the truck returns empty. Additionally, truck routes are not optimized, and they frequently have to travel significant distances without carrying any cargo. Utilization of Big Data Analytics to devise demand data analytics and demand/forecast supply methods to the logistics network can enable optimization algorithms to unleash substantial savings in truck.kms. This will lead to cost savings for the hauliers and reduction of GHG.
Main components to develop: - Portal for transport operators, including registration of means, and all the components necessary to manage the process by them. - Digital twin of transport equipment, including not only technical aspects of capacity and restrictions and emission profile, but also a predictive model of availability in terms of capacity and location. - Algorithms for allocating means to services, taking advantage of the data provided by the control tower regarding transport needs and data from the digital twins of the means of transport. - Freight cost calculation algorithms for allocated services. - Administrative management portal, customer support, fracturing, etc.
This task has the following subtasks: