A survey article by Mor Kaspi, Tal Raviv, and Marlin W. Ulmer provides an overview of future directions for research in urban mobility and city logistics. It sets a focus on three severe changes in the business models:
Service fleets might fully or partially be autonomous in the future, which brings new operational opportunities and challenges. The capability of cars, trucks, busses, drones, and delivery robots to travel fully autonomously within the urban environment transforms how people and goods are moving in cities and beyond. Fully autonomous vehicles introduce opportunities to revolutionize current transit and logistics services.
In many business models, crowdsourcing jobs are already familiar. While this might save costs, it also leads to uncertainty in the available workforce and their behavior. In addition, the nature of crowdsourced logistics is that the service is not conducted by company-employed drivers but by independent individuals. Thus, the service providers cannot (fully) control the availability and acceptance of jobs by drivers.
Finally, micro-consolidation centers enable smaller, cheaper, and emission-friendlier vehicles, leading to more complex planning and operations. Logistics networks are traditionally built as hierarchical graphs, where goods or parcels are initially transported from the supplier to the upper tiers of the network and down to consolidation centers located near the recipient. However, a modern design of service networks introduces more flexibility, referred to as Physical Internet (PI). In a PI, each node in the network can serve as the entrance or exit point or as an intermediate transshipment point.
The changes in the urban mobility and logistics field bring substantial new opportunities and challenges to companies and the operations research and transportation science and logistics community. Optimization models become more complex with various new decisions to make, objectives to integrate, and constraints to consider. For an increasing number of problems, vast amounts of data are available, allowing and requiring a detailed consideration within the optimization, for example, via data-driven decision making and machine learning.
For more and more problems, information changes stochastically over time leading to the need for robust or dynamic decision making, often in real-time. All this while problem sizes and complexity grow. In addition, optimization needs to capture larger fleets, increasingly heterogeneous, and customer demand for individually tailored services. Thus, there is ample area for new models, methods, and analyses.
Source: Mor Kaspi, Tal Raviv, Marlin W. Ulmer, Preface: Special issue on the future of city logistics and urban mobility, Networks, 10.1002/net.22093, 79, 3, (251-252), (2022).Wiley Online Library