Trip Itinerary Planning: A Bio-inspired Metaheuristic Approach
Jingkai Jia, Yuli Chen, Yuxuan Liu, Alaa M. Khamis · 2022
Trip itinerary planning plays an important role in the tourism industry and in our daily lives. In this paper, trip itinerary planning problem is modelled as Team Orienteering Problem with Time Window (TOPTW) with travel distance as a soft constraint. Three bio-inspired meta-heuristic algorithms, namely, genetic algorithm, adaptive genetic algorithm and artificial bee colony algorithm are considered to solve this problem. These solvers are evaluated in terms of algorithms' execution time, optimality, and coverage time using real data from City of Toronto. The experiment results show that adaptive genetic algorithm outperforms the other algorithms in terms of optimality and robustness.