Tree Search for the Sequential Ordering Problem

We study several generic tree search techniques applied to the Sequential Ordering Problem. This study enables us to propose a simple yet competitive tree search. It consists of an iterative beam search that favors search over inference and integrates prunings that are inspired by dynamic programming. The resulting method proves optimality on half of the SOPLIB instances, 10 to 100 times faster than other existing methods. Furthermore, it finds new best-known solutions on 6 among 7 open instances of the benchmark in a small amount of time. These results highlight that there is a category of problems (containing at least SOP) where an anytime tree search is extremely efficient (compared to classical meta-heuristics) but was underestimated.