Solving the ridesharing problem with Non-homogeneous vehicles by using an improved genetic algorithm and the social preferences of the users

Most existing ridesharing systems perform travel planning based only on two criteria of spatial and temporal similarity of travelers. In general, neglecting the social preferences caused to reduce users' willingness to use ridesharing services. To achieve this purpose a system should be designed and implemented not just based on two necessary conditions of spatial and temporal similarities, but also based on the similarities between users in terms of their social and personal preferences to plan a travel. This study aims to create and implement a suitable model for ridesharing systems by using vehicles with different capacities, using an advanced genetic algorithm, and considering users' social preferences to determine similar passengers. In this study, two innovative mutation operators and two local search algorithms have been applied to improve the genetic algorithm in this particular case. In this model, a mechanism has been designed to help users to share feedbacks based on their travel experience on a hypothetical social network with other users. In this way, users' opinions on each other to use or not to use this system and why it can be analyzed and examined. The results obtained from the analysis and evaluations of the implemented model indicate efficiency and success.