Sustainable decisions in a ridesharing system with a tri-objective optimization approach

Over the past years, the concept of ridesharing started receiving more attention to improve the sustainability of transportation systems. Although this concept has a lot of potential due to effective utilization of vehicles, there are still some challenges associated with routing and scheduling of the available vehicles, emissions produced by vehicles, and excessive delays in reaching the final destination. Therefore, this study proposes a novel tri-objective optimization model for routing and scheduling decisions within a ridesharing system, aiming to minimize the total travel time, the total carbon dioxide emissions produced throughout the transportation process, and the total delay in reaching the designated destination. A Multi-Objective Red Deer Algorithm is developed to find efficient Pareto solutions intelligently. The computational experiments confirm the superiority of the proposed algorithm against the alternative methods. Furthermore, the conducted sensitivity analyses reveal some important managerial insights that could be used for intelligent planning of ridesharing systems.