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Online grocers typically let customers choose a delivery time slot to receive their goods. To ensure reliable service, the retailer may want to close time slots as capacity fills up. The number of customers that can be served per slot largely depends on the specific order sizes and delivery locations. Conceptually, checking whether it is possible to serve a certain customer in a certain time slot given a set of already accepted customer orders involves solving a vehicle routing problem with time windows. This is challenging in practice as there is little time available and not all relevant information is known in advance. We explore the use of machine learning to support time slot decisions in this context. Our results on realistic instances using a commercial route solver suggest that machine learning can be a promising way to assess the feasibility of customer insertions. On large-scale routing problems it performs better than insertion heuristics. History: This paper has been accepted for the Transportation Science Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems. Funding: This research was funded by the Netherlands Organization for Scientific Research under the City Logistics Living Laboratory project [Grant 439.18.424]. [ABSTRACT FROM AUTHOR]

Abstract. The trend toward shorter delivery lead times reduces operational efficiency and increases transportation costs for Internet retailers. However, mobile technology creates new opportunities to organize the last mile. In [...]

An introduction is presented in which the editor discusses articles in the issue on topics including Mathematical Models, Computational Algorithms, Data-Driven Methodologies, Emerging Opportunities, and other topics.

The transportation science research community has contributed to numerous practical and intellectual innovations and improvements over the last decades. Technological advancements have broadened and amplified the potential impacts of our field. At the same time, the world and its communities are facing greater and more serious challenges than ever before. In this paper, we call upon the transportation science research community to work on a research agenda that addresses some of the most important of these challenges. This agenda is guided by the sustainable development goals outlined by the United Nations and organized into three areas: (1) well‐being, (2) infrastructure, and, (3) natural environment. For each area, we identify current and future challenges as well as research directions to address those challenges. [ABSTRACT FROM AUTHOR]