Multitasking Genetic Programming for Stochastic Team Orienteering Problem with Time Windows

Deepak Karunakaran, Yi Mei, Mengjie Zhang

Abstract: The tourism industry is witnessing high growth in recent years leading to a large number of options for a tourist. Personalised tourist trip design is faced with many places of interests, different tourist preferences and uncertainty in visit duration. In this paper, we study the stochastic Team Orienteering Problem with Time Windows (TOPTW) that well models the personalised tourist trip design. Under an uncertain environment, determining a robust solution in advance is not very effective due to frequent changes in the trip. Reactive decision-making policies have shown to be effective alternatives. Genetic programming-based hyper-heuristic (GPHH) approaches have been explored to automatically design policies. However, GPHH is computationally intensive. Considering a large number of trip design scenarios (e.g. cities), evolving a policy for each of these scenarios individually is difficult and time consuming. In this work, we propose a multitasking GPHH approach based on island model to evolve a set of policies which are effective across multiple trip design scenarios. The experimental studies show that our multitasking approach which needs only a single run to evolve policies for all problem instances is both efficient and effective when compared with the standard GPHH approach which requires a separate population for each TOPTW instance and runs sequentially.