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The first-mile transportation system connects scattered requests in residential areas to mass transit networks and provides convenient and high-quality travelling services. The complicated road network and passenger mobility lead to potential multiple selections of passengers when choosing pick-up locations, which is ignored in the literature. This study develops a multi-objective optimisation model for the first-mile transportation system considering requests' preference ranks for multiple boarding stops. The objectives are to minimize the system cost and the number of unserved requests as a proxy for service quality, respectively. We devise a hybrid solution approach combining the fast elitist non-dominated sorting genetic algorithm (NSGA II) with a variable neighbourhood search (VNS) improvement method. The method is tested and compared with the classical NSGA II and an exact method on a number of instances. We also examine the effect of multiple stops and the  discount scheme on the system performance.

Multi-objective optimization for multi-depot heterogeneous first-mile transportation system considering requests’ preference ranks for pick-up stops