Some Metaheuristics for Tourist Trip Design Problem

Tourist Trip Design Problem (TTDP) is fundamental in improving tourists' travel experiences and urban development. This study introduces a recommender engine to create a tour trip plan. The output of the system is a detailed trip itinerary for the tourist. It allows the tourist to determine the places to visit, the length of stay, and the entire route. The system's core is an optimizer for the combinatorial multi-objective optimization problem (MOP). There, users specify time and budget conditions as a query for the system. We have proposed a combination of Compromise Programming (CP) and Metaheuristics for this multi-objective optimization problem. Our method can handle situations where decision-makers cannot assign preferences to each goal and different decision-making scenarios. We have built two metaheuristic algorithms based on the proposed approach, which are Genetic Algorithm (GA) and another is Ant Colony Optimization (ACO). The objective was to examine how the influence of different search strategies affects the quality of the solution. The results show that ACO's swarm search strategy allows for finding slightly better-quality solutions than GA. However, it must trade-off with CPU time. We also compared the proposed method with the Posteriority approach to MOP. The results show that CP-based algorithms are superior to NSGA-II in finding a Pareto frontier.