
Several industry-led initiatives have shown that pooling resources may help ameliorate the last-mile logistics and the related operations, while at the same time satisfying customers’ expectations for fast, error-free and on-time delivery. The constant effort of Logistics Service Providers and local authorities to minimise travel times, costs and CO2 emissions of last-mile urban operations has conceived novel concepts in facility location strategies. Given that the capacities of the vehicles which conduct last-mile operations are limited, each one of them performs multiple trips from inventory facilities and back; at the same time, large depots are usually located in a considerable distance from the centre of cities, which concentrates the majority of demand.
To address the challenges of this problem, advanced location-routing variants focus on mobile depots, which integrate regular routing with the flexibility of a mobile depot. Vehicles begin at the depot, pick up parcels, deliver them, and return to reload or drop off unserved parcels. A mobile depot, positioned near demand clusters, reduces vehicle travel time. However, its effectiveness is limited by the restricted time it can stay in one location, requiring all operations to be completed within a tight window. Mobile depots also support innovative last-mile delivery methods, such as drones, public transport, and crowd shipping models.
OptEngine:
Impact & Offerings
OptEngine is activated either in a periodic manner or whenever an unexpected event occurs, e.g., new urgent delivery requests, unavailable vehicles, receiving as input, candidate locations (demand nodes, consolidation hubs), vehicles, orders (pickup or delivery requests). optEngine proposes a sequence of stops for each vehicle, indicating their arrival/departure times together with the involved orders, while minimising over total travel time, transportation costs, CO2 emissions, and possible violations of time windows.
The results of optEngine are under validation on real datasets from courier companies in different EU cities. Initial tests on benchmark datasets have proved significant reductions in transportation and energy costs, and travel times.
