Optimization for the Multiday Urban Personalized Trip Design Problem With Time Windows and Transportation Mode Recommendations

Abstract

The urban tourism market has been increasingly dominated by the demand for personalized experiences. This paper focuses on the multiday urban personalized trip design problem considering the time windows and transportation mode recommendations (MUPTDP-TW-TMR). A two-stage methodology is proposed to solve this problem. In the first stage, a linear programming model for the multiday urban personalized trip design problem with time windows (MUPTDP-TW) is proposed. A modified memetic algorithm (MMA) is devised to solve it, containing efficient operators. In the second stage, a novel transportation mode recommendation model is developed, which considers the diversity of tourist preferences. A modified label-setting algorithm is applied to solve the model. The data from Chongqing, the most popular city in Southwest China, are collected to evaluate the performance of our approach. The case study based on tourist and popular preferences is conducted. Compared with the state-of-the-art algorithms, the experimental results show that our approach can design more reasonable and personalized itineraries for tourists.