Genetic Programming Hyper-Heuristic for Stochastic Team Orienteering Problem with Time Windows

This paper investigates the stochastic team orienteering problem with time windows, which is a well known problem to model personalised tourist trip design. Specifically, we consider the stochastic visit duration, which may make preplanned trip infeasible. Existing studies focus on optimising robust solutions in advance, which is not effective in adjusting the subsequent trip in real time. Decision making policies, on the other hand, are effective heuristics to this end. However, it is very challenging to manually design effective policies. In this paper, we investigate automatically evolving policies for the stochastic team orienteering problem with time windows by genetic programming hyper-heuristics. We designed novel problem-specific features for the terminal set, and a meta-algorithm for fitness evaluation. Furthermore, we developed two look-ahead features that can provide more fruitful information than the basic features for real-time decision making. The experimental studies showed that the proposed genetic programming hyper-heuristic can evolve policies that are much better than the manually designed policies. In addition, it seems that the look-ahead features are not so effective when directly included in the terminals. This suggests the requirement of more intelligent ways of incorporating lookahead information.