In last-mile routing, the optimization is often framed as a Traveling Salesman Problem to minimize travel time and associated cost. However, solutions stemming from this approach do not match the realized paths as drivers deviate due to navigational considerations and preferences. To prescribe routes that incorporate this tacit knowledge, a data-driven model is proposed that aligns well with the hierarchical structure of delivery data wherein each stop belongs to a zone-a geographical area. First, on the global level, a zone sequence is found as a result of a minimization over a cost matrix which balances historical information and distances between zones. Second, within zones, a sequence of stops is determined, such that integrated with the predetermined zone sequence a full solution is obtained.The methodology allows for several enrichments, such as replacing distances by travel times, different cost structures, and intuition while traversing from one zone to another ensuring a seamless connection.These directions are particularly promising as they propel the approach in the top-tier of submissions to the Last-Mile Routing Research Challenge while maintaining the elegant decomposition that ensures a feasible implementation into practice. From a cognitive perspective, the concurrence between prescribed and realized routes reveals that delivery drivers adopt a hierarchical breakdown of the full problem combining intuition and locally optimal decisions when navigating. Furthermore, experimenting with the trade-off between historical information and travel times exposes that drivers rely more on their intuition at the start, whereas at the end, when returning to the station, only travel time is the concern.

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This paper considers appointment scheduling in a setting in which at every client arrival the schedule of all future clients can be adapted. Starting our analysis with an explicit treatment of the case of exponentially distributed service times, we then develop a phase-typebased approach to also cover cases in which the service times' squared coefficient of variation differs from 1. The approach relies on dynamic programming, with the state information being the number of clients waiting, the elapsed service time of the client in service, and the number of clients still to be scheduled. Numerical evaluation of the dynamic programming procedure poses computational challenges; we point out how we have succeeded in overcoming these. The use of dynamic schedules is illustrated through a set of numerical experiments, showing (i) the effect of wrongly assuming exponentially distributed service times, and (ii) the gains (over static schedules, that is) achieved by rescheduling.

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