Solving tourist trip planning problem via a simulated annealing algorithm

Nowadays, by help of tourist trip planners, in many travel destinations, the trip itinerary could be prepared automatically. The trip planners enable customization of trip itinerary based on tourist preferences, available time and budget, where the itinerary is optimized by using optimization techniques from the field of metaheuristics. In this paper, we present a new approach based on simulated annealing algorithm for solving the tourist trip planning problem. The hard constraints include limited trip duration, working hours of points of interest (POIs) and tourist budget, while tourist preferences and traveling time comprise the soft constraints, which take part in a two component function for evaluation of quality of solutions. The search space is explored by using three types of operators, namely insert, swap and shake. The algorithm is tested against some real test data for the city of Prishtina in Kosova. In addition to fine tuning the values of algorithm parameters, further experiments are made for exploring different approaches of construction of initial solution, time duration of algorithm execution for various trip lengths, and influence of shake operator into the solution quality.