Drivers are the foundation blocks of the transportation and logistics industries. Any damage to the foundation can topple the entire structure. Therefore, the well-being of drivers is of utmost priority and unfortunately, often overlooked. The availability of extensive literature on driver burnout and technological advancements in natural language programming (NLP) techniques motivated the development of the idea presented here. Multiple research articles on driver behaviour, driver burnout and driver fatigue are analyzed using NLP on Python to cluster the factors responsible for driver burnout. A burnout function is created using the controllable factors derived from NLP. This function is used as an objective in a multiobjective fuzzy vehicle routing problem (VRP). The problem is coded and solved using a genetic algorithm (GA) to derive trade-off solutions for the VRP and the well-being of the drivers. Incorporating a driver burnout/well-being function in a fuzzy VRP, where the burnout function is created using factors derived from text analytics forms the novelty of the approach proposed hereafter.