Abstract

Resumen: Este trabajo se enfoca en dar solución a un problema real de planificación de transportes o ruteo de vehículos, definido gracias al aporte de una empresa colombiana del sector de servicios de transporte por carretera. El problema consiste en realizar la programación de operaciones de transporte internacional entre dos países, Colombia y Venezuela, para un horizonte de tiempo con múltiples variantes; entre ellas podemos resaltar: una flota heterogénea de vehículos y tráileres, múltiples depots (clientes), restricciones de ventanas de tiempo en los depots, diversas modalidades de servicio, entre otras. El objetivo es obtener una programación de recursos (vehículos y tráileres) para la atención de la demanda de solicitudes de servicios de transportes en un horizonte de tiempo, minimizando la distancia recorrida en estado vacío de los vehículos para el periodo. La solución se realizó a través de la implementación de la metaheurística Recocido Simulado (SA, Simulated Annealing), la cual se probó iniciando con soluciones factibles generadas por dos algoritmos heurísticos; el primero se basa en la heurística clásica conocida como el vecino más cercano y el segundo genera soluciones de forma aleatoria. Los resultados del Recocido Simulado usando las soluciones generadas con el primer algoritmo, no mostraron una mejora frente a dicha solución inicial, contrario a lo hallado con el uso de las soluciones del segundo método, donde se alcanzó hasta cerca de un 50% de mejora Abstract: This work focuses on finding a solution to a real problem of transport planning or vehicle routing, which has been defined with the collaboration of a Colombian company from the road transport service industry. The problem consists of scheduling the international transport operations between two countries, Colombia and Venezuela, over a time horizon, with multiple variants; such as: a heterogeneous fleet of vehicles and trailers, multiple depots (customers), time windows constraints in the depots, various service modalities, among others. The goal is to schedule the resources (vehicles and trailers) in order to meet the demand for transport service requests on a given time horizon, minimizing the distance traveled by the vehicles while empty. A solution to the problem is given by a Simulated Annealing (SA) Metaheuristic, which was tested by using two types of heuristics to generate the initial feasible solution; the first one is based on the classic “Nearest Neighbor” heuristic, while the second one generates random feasible solutions. The results of the Simulated Annealing using the first algorithm to generate the initial solution did not show any improvement over it, contrary to what was found by using the second method, which obtained up to around 50% of improvemen

Abstract

a new solution to estimate OD-M of transport and to design tailored bus routes, the project B_us (commercial"br" name of the project FitYourBus, funded by the European Commision H2020 programme frontierCities) proposes"br" a new way of collecting and treating mobility pattern data in order to reduce about 36% the cost of data"br" acquisition and 41% the cost of exploiting data, allowing the deployment of user-driven transport services. The"br" proposed methodology includes the following stages: 1) Platform. Deployment of a back-end service and its"br" administration interfaces. The data collection set-up is based on a client-server architecture using J2EE and"br" Docker technologies; 2) Data collection. Users provide their basic commuting data –origin, destination, work"br" hours, etc– using our cross-platform smartphone app, which communicates with the back-end service; 3) Data"br" treatment. The collected data stored in a database is converted into a proper OD-M through an algorithm that"br" combines Dijkstra's and A*algorithms, running as a MapReduce job on a Big Data Apache Hadoop engine."br" Single citizen objective optimization algorithm influences the development of the multi-objective optimization"br" branches in the problem (maximizing the overall time savings for the participants at the same time as maximizes"br" the number of passengers per bus)."br" To test the methodology and validate the correct implementation of the algorithm, a pilot project has taken place"br" in coordination with EMT, the main bus public company in the city of Madrid (Spain). The trial consisted in"br" deploying employees’ bus routes to reach to and to go from one of their operation centres (involving about 1,300"br" workers, including drivers, mechanical technicians, and other workers). Mobility patterns data of 30.8% of them"br" were obtained. After running the algorithm, the result was a set of vectors (one from each user), which was"br" exported to a GIS platform to plot the first “draft corridors” surrounding the routes that go through the most"br" repeated nodes. These corridors were particularized for the conditions of circulation of the buses and according"br" to the schedules of the daytime and night-time of the rest of employees’ routes of EMT and the current public"br" transport services in the metropolitan area. Results show that operation times of the two current employees’"br" routes have been reduced between 1.2% (but improving spatial coverage and frequencies) and 44.1% while has"br" been increased the fleet utilization ratio because the service passes to be used by workers who previously did not"br" use it (with a majority change from the car to the bus).