The pollution traveling salesman problem (PTSP) and the energy minimization traveling salesman problem (EMTSP) generalize the well‐known asymmetric traveling salesman problem by including environmental issues and the goal of reducing carbon emissions. Both problems call for determining a Hamiltonian tour that, in the PTSP, minimizes a function of fuel consumption and driver cost (where the fuel consumption depends on the distance traveled, the vehicle speed, and the vehicle load), while, in the EMTSP, minimizes a function depending on the vehicle load and the traveled distances. For both PTSP and EMTSP, we propose a matheuristic algorithm that uses the solution of the linear programming relaxation of a mixed integer linear programming model for the considered problem to determine good initial feasible solutions, applies a multioperator genetic algorithm to improve these solutions, and refines the best solution found through an iterated local search procedure. In order to evaluate the performance of the proposed matheuristics, we compare them with exact and heuristic algorithms from the literature on benchmark instances of both problems.

In this article, a hybrid algorithm is proposed to solve the Vehicle Scheduling Problem with Multiple Depots. The proposed methodology uses a genetic algorithm, initialized with three specialized constructive procedures. The solution generated by this first approach is then refined by means of a Set Partitioning (SP) model, whose variables (columns) correspond to the current itineraries of the final population. The SP approach possibly improves the incumbent solution which is then provided as an initial point to a well-known MDVSP model. Both the SP and MDVSP models are solved with the help of a mixed integer programming (MIP) solver. The algorithm is tested in benchmark instances consisting of 2, 3 and 5 depots, and a service load ranging from 100 to 500. The results obtained showed that the proposed algorithm was capable of finding the optimal solution in most cases when considering a time limit of 500 seconds. The methodology is also applied to solve a real-life instance that arises in the transportation system in Colombia (2 depots and 719 services), resulting in a decrease of the required fleet size and a balanced allocation of services, thus reducing deadhead trips.

This paper proposes a two-phase heuristic algorithm to solve the crew scheduling problem of the Megabus Bus Rapid Transit System. In the first stage, a division of the original schedules is performed using a recursive algorithm based on dynamic scheduling. In the second stage, workshift construction based on graph theory is performed using a pairing algorithm (i.e., matching). The method is validated by applying it to the mass transit system of the Central Western Metropolitan Area (AMCO), operated by Integra SA, which serves 11 routes for a daily total of 2899 trips. .

En este trabajo, se presenta una estimación de la elasticidad de la demanda en un sistema de transporte público masivo, con respecto a su tarifa. Para realizar este cálculo, primero se establecieron unos intervalos de confianza para los períodos observados, luego, con estos valores aplicados a fórmulas de la literatura especializada, se obtuvo el valor de la elasticidad de la demanda con respecto al cambio de la tarifa de viaje. Esta estimación permite determinar el impacto del cambio tarifario en un sistema de transporte público de pasajeros específico, o si existen otros factores que tengan el mismo efecto en el sistema. En el caso de este estudio, se encuentra que el sistema es inelástico para 3 períodos de 6 observados, teniendo en los últimos 3 períodos, una estimación alta cuando se analizan otras razones que hayan influido en la demanda del sistema.