We propose a novel approach to modeling fairness in the Vehicle Routing Problem (VRP) by introducing objective functions based on ordering route lengths, capturing both monotonic and non-monotonic equity measures. Our method ensures allocations that are efficient, capacity-feasible, and equitable according to criteria like min-max, range, Gini, variance, or absolute deviations. To prevent biased or inefficient routes, we enforce per-vehicle TSP-optimality, making each route the shortest for its assigned customers. This creates a combinatorial dependency between routing and assignment, modeled via a bilevel optimization framework. We develop mixed-integer linear programming formulations embedding TSP subproblems using value function reformulations, allowing exact solutions. Computational experiments show that enforcing TSP-optimality is crucial for equitable routing under non-monotonic objectives and highlight the structural impact of different fairness criteria.