Refining Urban Logistics Practices
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The delivery sector literally exploded during the pandemic and trends show no signs of slowing down. New phenomena like take-out orders from fancy restaurants pose additional logistical constraints to ensure just-in-time deliveries. Traffic issues in some neighbourhoods mean traffic jams and parking problems for delivery trucks. What is more, the large number of delivery vehicles contribute to greenhouse gas emissions and affect the safety and tranquility of residents. One solution could be decentralized fleets of smaller electric trucks, cargo bikes, electric bikes or even semi-autonomous mobile robots, each serving the population and local businesses in a specific neighbourhood. This is the solution explored by a group of researchers at ÉTS to limit the number of trucks on the road, reduce congestion, cut delivery costs and lower environmental impacts.
A Comprehensive Research Program
The objective of the proposed program is to create theoretical and conceptual architectures, as well as decision support tools, mathematical models, and optimization algorithms matching fleets of independent drivers with merchants, without long-term contracts. The program has four components.
Logistics Models for Neighbourhood Fleet Management
Electrification of long-haul freight transport is challenging due to the limited range of electric vehicles, their charge time, and the few charging stations available. These constraints are significantly lower for neighbourhood deliveries that involve much shorter distances. Fleets of small electric vehicles could be modelled with charging periods after a certain number of kilometers.
Logistics Models for Social Inclusion and Diversity
The last mile of the delivery route is often handled by people with job insecurity. Major players in the field have been able to connect user information with available drivers, but without focusing on sustainability. A decentralized fleet of drivers, serving a specific neighbourhood and with information aggregated on a common platform, would simplify logistics, reduce pollution, and promote social inclusion and diversity. Mobility as a Service (MaaS) and automotive connectivity could pave the way to decentralized, non-monopolistic decision-making.
Small Fleets Offering Compensation Measures
Several solutions can be explored to support logistics service providers: open outsourcing of pickups and deliveries, part-time drivers, and simultaneous inventory decision-making. However, many other options are possible, such as flexible delivery times, compensation when the fleet is not available or stuck in traffic and other delays.
Artificial Intelligence Algorithms to Determine Routes
Machine learning will simplify the decision-making rules, which will be based on characterization and grouping of commonly used logistical solutions, but involving complex mathematical calculations.
A Step Closer to Smart Cities
Transport of goods must meet the new demands in this last leg of the pandemic while embracing sustainability to reduce congestion, noise and costs, and keep our neighbourhoods safe. To achieve this, we must review current logistics practices and implement new decision-making models.
