Abstract
Shared transportation involves vehicles, drivers, and customers, the interactions among which could have potential long-term impacts on the business. Machine learning techniques, and their integration with existing models, have proved to significantly improve results. Availability of extensive unstructured textual data has fostered research in text generation and mining. Cognizance and analysis of such data has become crucial for modern commercial applications. Thus, in this article, sentiment analysis, using natural language processing, is used to quantify raw customer feedback, to obtain drivers' ratings and perform driver selection. Selection of the best drivers for ferrying riders is desired and modeled accordingly. An integrated vehicle routing problem with generalized fuzzy travel durations, and uncertain pick-up and drop demands, is modeled and solved using a hybrid genetic algorithm. Fuzzy simulations in a credibilistic environment are employed to evaluate the cost function. Performance of selected drivers is used to update driver ratings for the subsequent run, and the process is repeated multiple times. The results obtained authenticate the purpose of this article, and comparative analysis is performed to further corroborate the model's capability. An additional case of triangular fuzzy ratings is also illustrated, and its impact on the model discussed. Suggestions for driver classification are also provided for personnel management.