Food delivery services are gaining popularity. To deliver products, horizontal cooperation, e.g. multiple competing restaurants share delivery resources, enables one to reduce costs, and increase logistics performance and service quality. Consequently, numerous logistics providers like Deliveroo, and UberEATS are entering the market. Such delivery services, however, require considerable logistical efforts to guarantee a fresh and timely delivery at low costs. In urban areas, planning is further complicated as lead times are dependent on current traffic situations and as vehicle restrictions exist.
Cargo bikes and micro hubs
A potential strategy to improve operations is the combination of cargo bikes and micro hubs. Cargo bikes are used to deliver goods to clients, enabling shorter delivery routes by driving in areas with vehicle restrictions and being less impacted by traffic conditions.
Additionally, micro hubs, i.e. transshipment points where goods are transferred from one vehicle to another, are operated to consolidate multiple shipments. Consequently, larger vehicles bring goods close to the city centre while the last-mile distribution in congested or restricted areas is performed by cargo bikes, potentially reducing delivery times and costs.
Potential locations for transshipment points are manifold and include large parking areas, loading zones as well as existing infrastructure of the provider.
Decisions support systems
A paper by Christian Fika, Patrick Hirsch and Manfred Gronalt presents a decision support system (DSS) to facilitate efficient urban last-mile distribution. Orders are collected and delivered by a fleet of both conventional vehicles owned by a logistics provider and cargo bikes operated by freelancers. Additionally, micro-hubs are operated to perform transshipments between multiple vehicles.
To investigate the corresponding problem setting, an agent-based simulation was developed, which uses dynamic optimisation procedures to generate and select vehicle routes and transshipment points. Experiments motivated by dynamic real-world urban restaurant delivery services in Vienna investigated the impact of cargo-bikes, urban consolidation and guaranteed delivery times.
The decision support system supports both accuracy and velocity of decision-making by enabling the investigation of different problem settings and various impacts of facilitating cargo bikes and consolidation in an urban food delivery context. It enables decision-makers to design, test and adapt urban last-mile distribution concepts for highly dynamic settings in a flexible and risk-free environment. This is achieved by the combination of an agent-based simulation with dynamic optimisation procedures. The former models dynamic demand and uncertainty in the system, while the latter enables one to derive order assignments to vehicles, driving routes and transshipment points.
Results indicate the potential of facilitating micro-hubs and the importance of having a sufficient number of cargo-bikes available to guarantee timely deliveries.
Future research
Future research fields to enhance the DSS include the implementation of real-time traffic data, backhauls as well as the development of interfaces to online ordering and inventory management systems. Therefore, an integration of the delivery strategies with picking, handling or processing processes at sources and hubs is necessary to jointly optimise operations. Furthermore, this enables one to put a focus on total costs.
Studies investigating policy implications and legal frameworks to use idle urban space for consolidation as well as cost-utility analysis support future real-world implementations. To extend the scope of the DSS to related fields such as e-groceries, various specific challenges of food logistics operations such as recommended temperature ranges and food safety consideration have to be included.
Additionally, while the DSS optimises routing decisions, it does not consider the willingness of cargo bike drivers to follow such instructions. Consequently, incorporating various behavioural factors of drivers to study the impact of incentives is worth further investigation.
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