xSeil VRP

xSeil was deployed in 2016-2017 to plan passenger transport for an integrated tourism operation in the Mexican Caribbean. A tourism sale made upstream became a transport commitment before the complete daily demand, fleet state, and network interactions were known. The resulting problem combined hard pickup promises, indivisible passenger groups, a heterogeneous owned-and-rented fleet, multiple trips per unit, direct and multi-leg service, typed transfer points, destination-capacity externalities, and a rigid planning deadline. This paper presents a single self-contained account of the operation, the vehicle-routing problem, and the deployed solution architecture.

The implementation did not solve a conventional static VRP by repeatedly constructing routes from scratch. It converted idle computation into operational latency reserve: feasible stop sequences, compatibility relations, and scenario-specific candidate structures were built and persisted offline; online planning retrieved those structures, added state-dependent scarcity and isolation prices, committed one passenger-group coalition at a time, repriced the remaining candidates, and invoked rollback or bounded search widening only on evidence of insufficiency. The code further shows that passenger groups were atomic rather than split-delivery quantities, that transfers could occur at typed non-central points, and that human scenario weights controlled both objective level and penalty curvature.

The paper makes four contributions. First, it classifies xSeil as an industrial rich-VRP with heterogeneous multi-trip routing, hard time commitments, atomic group demand, synchronized transfers, and destination resources. Second, it provides a code-auditable reconstruction while correcting earlier overstatements: the route builder is bounded level-wise enumeration rather than demonstrated dynamic programming; the allocator lacks a global optimality guarantee but does not necessarily produce a suboptimal solution; and route-library coverage is bounded by the configured topology and policies. Third, it proposes a modernization that preserves the offline-online doctrine while replacing exhaustive construction with modern candidate generation and bounded improvement. Fourth, because real demand days can be recovered, it specifies a paired historical replay comparing legacy-faithful, semantically repaired, modern, and hybrid solvers under the same time limits, scenarios, and raw KPI vectors. No superiority claim is made before that replay.

Keywords: rich vehicle routing; multi-trip VRP; time windows; synchronized transfers; passenger transport; atomic demand; route pools; human-in-the-loop optimization; historical replay; xSeil.