Integrating neighborhood delivery services into parcel delivery networks
Leveraging the developments in the crowd-sourced economy, several innovative delivery models have been adopted in the parcel delivery industry. One such innovation is the use of neighborhood delivery services, where local residents receive parcels and deliver them within their neighborhood. We study the integration of neighborhood delivery services into the parcel delivery network. We use a combination of distributionally robust optimization and continuous approximation to build a model that captures the interaction between demand volatility, delivery capacities of neighborhood delivery points and delivery performance of the parcel company. Efficient lower bounding procedures are developed for determining fleet requirements that guarantee service level targets for individual neighborhoods are met. An algorithm based on column generation resolves the problem on the network level. Analytical results show that not only average demand profiles, but also demand volatility levels and service level targets determine the ability of neighborhood delivery services in reducing fleet size and saving total network cost. Numerical analyses, with empirical data from a case study, emphasize the important role of capacity of the neighborhood delivery points. With modest capacity of 3.3% of the peak demand, the fleet size can be reduced by 4.0% and by 24.9% when capacity is at 33.3% of the peak demand. Interestingly, fleet size can be reduced while saving on total network cost by 1.3% and 8.6%, respectively. Managers have two key levers at their disposal to recruit and retain neighborhood delivery point operators and negotiate for higher capacities. Of those, making minimum compensation agreements is new to the literature and can be used more liberally than the other level: increasing unit outsourcing cost. Furthermore, we show how managers should consider the average demand, demand volatility and service level targets in recruiting neighborhood delivery point operators.
Meeting ID: 978 6985 0938