Increasingly, cities aim at improving the sustainability and efficiency of their urban supply chains, by restricting the use of certain vehicles or improving collaboration among supply chain actors. Additionally, new technologies and trends are emerging, e.g., micro consolidation centers, sidewalk robots, autonomous vehicles, synchromodality, etc. These trends generate a myriad of difficult intertwined strategic and operational decision problems. Currently, however, the optimization and simulation tools required to support decision-making in such a complex environment are (1) disjointed and scattered across the scientific literature, or (2) not mature enough to adequately handle emerging urban logistic trends. To bridge this gap in the literature, and to aid cities and supply chain actors in modelling and optimizing the supply chains, we propose to develop a comprehensive and complementary suite of urban logistics optimization algorithms for “the” city. These problems of optimizing urban supply chains are typically large-scale, multi-level and multi-objective. In order to appropriately model and solve these problems, significant advances in operations research techniques are required. They will ultimately contribute to improving the understanding of the impact of new technologies, trends, and policies for all urban logistics stakeholders, and support their decisions towards a more sustainable city.
