Fleet planning and distribution optimization methods are mature topics from a research perspective, and they are implemented in a variety of transport management solutions available on the market. In the projected 2030 scenario, however, current centralized information management and decision support systems appear unable to support the optimization of globally distributed and heterogeneous logistic networks. The provision of multi-modal door-to-door services, as well as of low-carbon freight transport services maximising transport resources utilization, relies on the ability to calculate, compare and dynamically update transport plans involving a variety of actors (shippers, logistics services providers, carriers, infrastructure operators).
Support is needed for intermodal end-to-end transport planning in distributed and cooperative scenarios. The required innovative planning methods shall be able to match demand, from single or multiple shippers, with available transport services from different modes and service providers. To support cooperation, planning engines shall be able to interrogate transport resources availability and status, and to take into account multiple constraints coming from different stakeholders, for example by simultaneously interrogating and booking sea, in-land waterways and last mile distribution services.
Planning shall be multi-criteria, i.e., able to optimize both environmental and logistics performances. Besides matching delivery terms and fleet schedules, advanced optimization criteria for CO2.
Furthermore, planning engines with self-regulation capabilities will be introduced, to deal with the complexity of highly distributed and dynamic logistic networks. This will allow, for example, rescheduling vehicles or terminal operations as changes or delays affect the overall plan. Plans will be automatically updated based on real-time information, through propagation of changes on the schedules of all affected resources. Local optimization decisions, not impacting on higher-level plans, shall be taken autonomously by connected vehicle and cargo interacting with the local environment. To a certain degree, this will make logistics systems self-regulating. This would allow, for example, a delayed truck to re-route itself to a nearby terminal, in case it cannot match the departing ship schedule. A similar decision might not be taken in time if escalated to a centralized planning system. calculation will be applied, taking into account, e.g.: underlying network data as calculation basis, e.g. road, barge or train classes, capacity targets (e.g., comparing emissions per taxable cargo unit carried), emissions on cargo unit level as well as at consignment level, topological issues (slope, curvature), traffic conditions, alternative fuel and propulsion technology.
Standard emissions calculation methods from ISO, CEN will be applied on door-to-door level, following a supply chain carbon foot printing approach.
As summarised in the Table below, the horizon for research and development results is longer than for other ICT challenges. The reason is that, although planning and optimization methods are a complex but largely studied subject, the expected cooperative and dynamic solutions rely on other challenges to be met first. In particular, platforms and standards for supply chain carbon foot printing are needed. By 2018 convincing industrial tests should be available, as well as tested business models showing cooperation at work. Actions aimed at fostering innovation on the market should focus on adoption by ICT leaders in the transportation management field, and on promotion of cooperative models even through appropriate incentives (e.g., to get small carriers involved).