A multi-objective optimization model for green demand responsive airport shuttle scheduling with a stop location problem

We proposed a multi-objective optimization framework for green demand responsive airport shuttle scheduling, which simultaneously aims at assigning demand points to selected stops and routing airport shuttles to visit these stops in their overlapping time windows to transport all passengers from their homes or workplaces to the airport. Our objectives were to minimize total travel time for passengers, the punishment expense of violating the time-window as well as carbon emissions for all shuttles. Since such issues belongs to the NP-problem, a two-stage Multi-objective ant lion optimizer (MOALO)-based algorithm incorporating dynamic programming search method was developed to acquire the optimal scheduling schemes. Finally, a case study of airport shuttle service in Tianjin Airport, China, was used to demonstrate the validity of the model and algorithm.