Multiobjective Vehicle-type Scheduling in Urban Public Transport

In this paper, we study the problem of vehicle scheduling in urban public transport systems taking into account the vehicle-type (different capacity and operating cost) known as VTSP. It is modeled as a multiobjective optimization problem (MOP). We propose a heuristic based on MOCell (Multi-Objective Cellular evolutionary algorithm) to solve the problem considering restrictions of government agencies in context of smart cities to improve the Intelligent Transportation Systems (ITS). A set of non-dominated solutions represents different assignments of vehicles to cover trips of a specific route. The conflicting objectives of provider and users (passenger) are to minimize the total operating cost, and maximize the quality of service, reducing the waiting time and congestion in buses. We present experimental analysis and conclude that the proposed heuristic provides a good performance and competitive results in terms of convergence and diversity of the solutions along the Pareto front.