We address the problem of optimal route planning for competitive orien-
teering on real terrain. A Geographic Information System (GIS) pipeline
transforms orienteering map data and digital terrain models into a fully
asymmetric cost matrix that captures directional slope costs (via the Minetti
metabolic model) and cumulative athlete fatigue. The resulting problem, the
Asymmetric Orienteering Problem with Fatigue (AOPF), is a new variant
of the Orienteering Problem in which travel cost depends on both direc-
tion and elapsed effort. We propose two mixed-integer linear programming
formulations: a Miller–Tucker–Zemlin (MTZ) model with McCormick lin-
earisation and a single-commodity flow model where the fatigue budget is
natively linear and a Branch-and-Cut solver strengthened with tailored valid
inequalities, including a novel family based on the assignment relaxation. We
validate the framework on two Spanish terrains (14 instances) and release
a benchmark suite of 21 synthetic instances. The flow-based solver certifies
global optimality on 9 of 14 real-terrain instances within 15 minutes, re-
ducing the mean optimality gap from 16.0% to 5.4% compared to the MTZ
formulation.