HHere are the main advances beyond the state of the art:
Artificial intelligence focused on prediction
Predictive models were developed which can predict arrival times for different legs in logistics transportation, between specific locations, for specific vehicle types, at different hours of the day, days or the week, or times of the year. These were augmented with predictions of turnaround times at the destinations, which are again location, time and season specific. Finally, models for acquiring the preferences of specific companies and logistics staff in those companies for balancing the trade-offs between important objectives such as time, distance, cost and reliability of logistics components have been developed.
Global optimisation planning
Research on mathematical models and optimization meta-heuristics focused on the reduction of logistic costs in different real scenarios have been done. Based on this analysis, fouroptimization modules were deployed and integrated: twofor collaborative optimization, where 2 or more companies can share resources for better use and improvement of efficiency. A thirdone for multimodal optimization, considering the return of different solutions for selection by stakeholder, and a fourthone for the planning of the use of resources at the distribution center.
Automated negotiation and planning re-optimisation
Research on models of automated negotiation agents that propose the exchange of tasks. Every agent has a set of tasks (deliveries to be done) but can propose to other agents to perform part of these tasks on the basis of a pre-fixed compensation (according to the distance of the delivery). The agents can automatically suggest exchanges for those tasks that are expensive for them, detecting other agents for which they are cheaper, and who could accept them. This implies that every agent knows their own set of tasks, can optimize them, but also knows (part) of the task of other agents.
LOGISTAR delivered two services based on the advanced processing techniques:
• A control and decision-making tool for logistics operations capable of monitoring goods through the whole logistics procedure, allowing an integrated planning of resources and providing dynamic routing relying on synchromodality and horizontal collaboration among agents.
• Real time information of freight transport will be delivered by means of a website, where the position of the goods in real time in the various means of transport will be shown.
The project results contributed to enhance different aspects related to logistics and freight transportation. LOGISTAR improved the logistic infrastructure by means of the use of cutting-edge ICT technologies. These technologies were thoroughly tested in four different living labs and showed benefits in every single use case.
The LOGISTAR system calculated logistic improvements, showed collaborative cost savings, a decrease in empty kilometres and increase in vehicle fill rate. Additionally the emerging technologies used provided visibility on the logistic flow.
And while there are still difficulties in achieving these results from a practical standpoint, due to the fact that it is sometimes difficult to grasp a full human planning system by a single algorithm, there is a common understanding that the LOGISTAR platform can grow into a decision support tool for numerous use-cases in the transport and logistics sector.
