Dynamic Pooled Capacity Deployment for Urban Parcel Logistics

TL;DR

This paper introduces a stochastic optimization model for dynamically deploying pooled storage in urban parcel logistics, significantly reducing last-mile costs and increasing capacity efficiency in dense city environments.

Contribution

It presents a novel two-stage stochastic model and a rolling horizon solution approach for dynamic capacity deployment in urban parcel networks under uncertainty.

Findings

Achieves up to 28% cost savings in last-mile delivery.

Realizes 26% capacity savings with dynamic deployment.

Demonstrates computational efficiency on large-scale urban instances.

Abstract

Last-mile logistics is regarded as an essential yet highly expensive component of parcel logistics. In dense urban environments, this is partially caused by inherent inefficiencies due to traffic congestion and the disparity and accessibility of customer locations. In parcel logistics, access hubs are facilities supporting relay-based last-mile activities by offering temporary storage locations enabling the decoupling of last-mile activities from the rest of the urban distribution chain. This paper focuses on a novel tactical problem: the geographically dynamic deployment of pooled relocatable storage capacity modules in an urban parcel network operating under space-time uncertainty. In particular, it proposes a two-stage stochastic optimization model for the access hub dynamic pooled capacity deployment problem with synchronization of underlying operations through travel time estimates, and a solution approach based on a rolling horizon algorithm with lookahead and a benders decomposition able to solve large scale instances of a real-sized megacity. Numerical results, inspired by the case of a large parcel express carrier, are provided to evaluate the computational performance of the proposed approach and suggest up to 28% last-mile cost savings and 26% capacity savings compared to a static capacity deployment strategy.