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This document discusses the emergence of online-to-offline logistics services, such as attended home delivery, bike sharing, and ride-hailing, which have become integral to modern on-demand lifestyles. The management of these services requires considering the behavior of customers and drivers in decision-making processes. The document highlights a special issue of OR Spectrum that focuses on data-driven operations research methods for online-to-offline logistics, covering topics such as parcel delivery management, attended home delivery, same-day delivery, vehicle sharing systems, and airline revenue management. The papers in the special issue explore various approaches, including predictive and prescriptive analytics, clustering and forecasting, and simulation, to address the challenges of managing these services effectively. [Extracted from the article]

Time-stamped data for transportation and logistics are essential for estimating times on transportation legs and times between successive stages in logistic processes. Often these data are subject to recording errors and omissions. Matches must then be inferred from the time stamps alone because identifying keys are unavailable, suppressed to preserve confidentiality, or ambiguous because of missing observations. We present an integer programming (IP) model developed for matching successive events in such situations and illustrate its application in three problem settings involving (a) airline operations at an airport, (b) taxi service between an airport and a train station, and (c) taxi services from an airport. With data from the third setting (where a matching key was available), we illustrate the robustness of estimates for median and mean times between events under different random rates for “failure to record”, different screening criteria for outliers, and different target times used in the IP objective. The IP model proves to be a tractable and informative tool for data matching and data cleaning, with a wide range of potential applications. [ABSTRACT FROM AUTHOR]

• Complete information flow to consider travel time uncertainty in city logistics vehicle routing. • An innovative approach for robust routing in city logistics. • Applicability of interval travel times in conjunction with min–max regret evaluation. • Comprehensive case study based on real-world data for a representative city logistics vehicle routing setting. • Computational results show alternatives that are more robust, but also cost efficient. We propose a consistent information flow to integrate Interval Travel Times (ITT) into a city logistics vehicle routing problem with satellites. The consistent information flow encompasses the derivation of ITT, the usage of ITT in robust shortest path computation and the integration of ITT into robust vehicle routing. Based on the results of the information flow, robust meeting times of first-tier and second-tier vehicles are created. We evaluate the approach with large real-world traffic data sets. We observe that the integration of ITT leads to first-tier routes that are reasonably efficient and robust. [ABSTRACT FROM AUTHOR]