Last-mile logistics is an essential yet highly expensive component of city logistics responsible for many nuisances in urban areas. Mobile access hubs are flexible consolidation and transshipment points aiming at creating more sustainable city logistics systems by dynamically using urban space as logistics facilities. In this paper, we examine the potential of mobile access hub deployments for urban parcel logistics by identifying the impact of design parameters on economic and environmental performance. We propose a mathematical modeling framework and an integer program to assess the performance of mobile access hub deployments, and study the impact of a set of design parameters through synthetic cases and an illustrative case inspired from a large parcel express carrier’s operations. Results indicate design flexibility relative to the location of hubs and pronounced advantages in highly variable environments. The illustrative case shows significant savings potential in terms of cost and time efficiency as well as environmental sustainability. It emphasizes a trade-off between operational efficiency and environmental sustainability that can be balanced to achieve global sustainability goals while being economically sound.

In this article, we consider how to increase trust in development processes in which there is a risk for adversarial manipulation, and the adversary's objectives and resources are either ill-specified, imprecisely specified, or unknown. In such problems, we must hedge against the risk of misapprehension of attacker objectives and resources, which is further complicated in the absence of adversarial training data. We show how to model dynamic agent interaction, on the basis of partially observed or noise-corrupted data, using a partially observable Markov game (POMG) framework. We then propose a threefold heuristic solution procedure that: 1) uses the POMG to generate potential adversarial policies; 2) explicitly incorporates these adversarial policies in the construction of a robust defender policy by solving a robust dynamic program (DP); and 3) employs a probability matching heuristic in partially observable environments.