Probabilistic Bounds on the k-Traveling Salesman Problem and the Traveling Repairman Problem

The $k\text{-}$traveling salesman problem (k-TSP) seeks a tour of minimal length that visits a subset of $k\le n$ points. The traveling repairman problem (TRP) seeks a complete tour with minimal latency. This paper provides constant-factor probabilistic approximations of both problems. We first show that the optimal length of the k-TSP path grows at a rate of $\mathrm{\Theta }\left(k/n^{k/(2(k-1))}\right)$. The proof provides a constant-factor approximation scheme, which solves a TSP in a high-concentration zone, leveraging large deviations of local concentrations. Then, we show that the optimal TRP latency grows at a rate of $\mathrm{\Theta }(n\sqrt{n})$. This result extends the classic Beardwood–Halton–Hammersley theorem to the TRP. Again, the proof provides a constant-factor approximation scheme, which visits zones by decreasing order of probability density. We discuss practical implications of this result in the design of transportation and logistics systems. Finally, we propose dedicated notions of fairness—randomized population-based fairness for the k-TSP and geographic fairness for the TRP—and give algorithms to balance efficiency and fairness.

Funding: This work was partly supported by [Grant ONR N00014-18-1-2122] and the Singapore National Research Foundation through the Singapore–Massachusetts Institute of Technology Alliance for Research and Technology Centre for Future Urban Mobility.

Supplemental Material: The e-companion is available at https://doi.org/10.1287/moor.2021.0286.