[en] A key aim of the European Union is to double freight rail traffic by 2050 in order to reduce pollution emissions and alleviate congestion by shifting traffic from roads to rail networks. To accomplish this objective, it is crucial to minimize emissions and high costs associated with shunting yard operations while maintaining an acceptable level of service. This research paper introduces a new MINLP model that optimizes the shunt-in and shunt-out (SISO) operations of wagons in a shunting yard that handles full train load services. Additionally, an efficient Multi-Objective Dijkstra Algorithm (MDA) is proposed to handle simultaneous shunt-out operations in a multi-train scenario. The MINLP model takes a mesoscopic approach, aiming to minimize the number of SISO operations while satisfying strategic and tactical objectives such as wagon fleet size, operational costs, and shunting locomotive emissions. Several versions of the mathematical model are described, each employing different Shunt-In (SI) policies with varying criteria for wagon selection and strong goal orientation. The Multi-Objective Dijkstra Algorithm determines the Multi-Objective Shortest Path between mandatory shunts, considering both the time required for shunting and clustering costs. It provides information on the clusters of wagons that need to be shunted out. To assess the effectiveness of the MINLP model, real train timetables for freight trains in the Bettembourg Eurohub Sud Terminal (Luxembourg) are used, and various KPIs related to tactical and strategic objectives are evaluated. Furthermore, the performance of the Multi-Objective Dijkstra Algorithm is compared to the Shunt-Out sub-model, considering average computation time and solution quality in relation to the MINLP model. The computational results demonstrate that the criteria for wagon selection have a significant impact on the analyzed KPIs.
Disciplines :
Ingénierie, informatique & technologie: Multidisciplinaire, généralités & autres
Auteur, co-auteur :
BOSI, Tommaso ; University of Luxembourg ; Department of Civil, Computer Science and Aeronautical Technologies Engineering, Roma Tre University, Rome, Italy
BIGI, Federico ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
D'Ariano, Andrea; Department of Civil, Computer Science and Aeronautical Technologies Engineering, Roma Tre University, Rome, Italy
VITI, Francesco ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
Pineda-Jaramillo, Juan ; Faculty of Science, Technology and Medicine (FSTM), University of Luxembourg, Esch-Sur-Alzette, Luxembourg
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Optimal management of full train load services in the shunting yard: A comprehensive study on Shunt-In Shunt-Out policies
This research was supported by the Société’s nationale des chemins de fer luxembourgeois (CFL), Roma Tre and Luxembourg Universities. We thank supervisors and colleagues from our respective Engineering Departments who provided insight and expertise that greatly assisted the research.
Agency, E.E., Greenhouse gas emissions from transport in Europe. 2021, European Environment Agency.
Commission, E., Shift2Rail strategic master plan. 2015, European Commission.
Kearns, S., Albrecht, H., D'Ariano, A., Franco, G., Tzanidaki, J., Network and traffic management systems. 2016, Strategic Transport Research and Innovation Agenda (STRIA).
Pagand, I., Carr, C., Doppelbauer, J., Fostering the railway sector through the european green deal. 2020, European Union Agency For Railways.
Boysen, N., Emde, S., Fliedner, M., The basic train makeup problem in shunting yards. OR Spectrum 38 (2016), 207–233.
Bohlin, M., Gestrelius, S., Dahms, F., Mihalak, M., Flier, H., Optimization methods for multistage freight train formation. Transportation Science 50 (2016), 823–840.
Guglielminetti, P., Lagraulet, M.F., Artuso, D., Lunadei, E., Musso, A., Fusco, G., Licciardello, R., Piccioni, C., Seno, F., Study on single wagonload traffic in Europe – Challenges, prospects and policy options. European Commission, 2015.
Bohlin, M., and Hansmann, R., (2018). Optimization of Railway Freight Shunting, Handbook of Optimization in the Railway Industry, pp. 181–212.
Deleplanque, S., Hosteins, P., Pellegrini, P., Rodriguez, J., Train management in freight shunting yards: Formalisation and literature review. IET Intelligent Transport Systems 16 (2022), 1286–1305.
Meinert, M., Prenleloup, P., Schmid, S., Palacin, R., Energy storage technologies and hybrid architectures for specific diesel-driven rail duty cycles: Design and system integration aspects. Applied Energy 157 (2015), 619–629.
Denari, R., Derossi, M., An innovative maintenance methodology for freight wagons combining proportional hazard model and life cycle cost. Polimi, 2019.
Van Den Broek, R., Hoogeveen, H., Van Den Akker, M., Huisman, B., A local search algorithm for train unit shunting with service scheduling. Transportation Science 56 (2021), 141–161.
Lin, B., Liu, S., Lin, R., Wang, J., Sun, M., Wang, X., Liu, C., Wu, J., Xiao, J., The location-allocation model for multi-classification-yard location problem. Transportation Research Part E: Logistics and Transportation Review 122 (2019), 283–308.
Zhang, Y., Song, R., He, S., Li, H., Guo, X., Optimization of classification track assignment considering block sequence at train marshalling yard. Journal of Advanced Transportation, 2018.
Boysen, N., Scholl, J., Stephan, K., When road trains supply freight trains: Scheduling the container loading process by gantry crane between multi-trailer trucks and freight trains. OR Spectrum 39 (2017), 137–164.
Murali, P., Ordóñez, F., Dessouky, M.M., Modeling strategies for effectively routing freight trains through complex networks. Transportation Research Part C: Emerging Technologies 70 (2016), 197–213.
Falsafain, H., Tamannaei, M., A novel dynamic programming approach to the train marshalling problem. IEEE Transactions on Intelligent Transportation Systems 21 (2019), 701–710.
Xu, X., Dessouky, M.M., Train shunting with service scheduling in a high-speed railway depot. Transportation Research Part C: Emerging Technologies, 143, 2022, 103819.
Jaehn, F., Rieder, J., Wiehl, A., Minimizing delays in a shunting yard. OR Spectrum 37 (2015), 407–429.
Lin, B. and Lin, R., (2017). An approach to the high-level maintenance planning for emu trains based on simulated annealing, arXiv.
Luan, X., Miao, J., Meng, L., Corman, F., Lodewjiks, G., Integrated optimization on train scheduling and preventive maintenance time slots planning. Transportation Research Part C: Emerging Technologies 80 (2017), 329–359.
Herr, N., Nicod, J., Varnier, C., Zerhouni, N., Herr, N., Nicod, J., Varnier, C., Zerhouni, N., Cherif, M., Joint optimization of train assignment and predictive maintenance scheduling. International Conference on Railway Operations Modelling and Analysis, 2017.
Gerum, P.C.L., Altay, A., Baykal-Gürsoy, M., Data-driven predictive maintenance scheduling policies for railways. Transportation Research Part C: Emerging Technologies 107 (2019), 137–154.
Zagorskikh, A.A., Alsova, O.K., Gavrilov, A.V., A simulation model of the process of organizing the movement and maintenance of trains at a railway station. Journal of Physics: Conference Series, 1661, 2020.
Chuijiang, G., Optimization model and algorithm for the placing-in and taking-out of wagons in railway stations with branch-shaped goods operating sites. International Journal of Rail Transportation 9 (2021), 579–594.
Adlbrecht, J., Hüttler, B., Zazgornik, J., Gronalt, M., (2015). The train marshalling by a single shunting engine problem, Transportation Research Part C: Emerging Technologies, vol. 58, pt. A, pp. 56-72.
Di Loreto, S., Malikova, L., Tomekova, I., Mirkovis, T., Ohlin, M., Maliacek, J., Overview of priority rules in operation. RailNetEurope, 2018.
Cabral, M.O., Shires, J., Wardman, M., Teklu, F., Harris, N., The use of recovery time in timetables: Rail passengers’ preferences and valuation relative to travel time and delays. Transportation 48 (2021), 337–368.
Jovanovic, P., Kecman, P., Bojovic, N., Mandic, D., Optimal allocation of buffer times to increase train schedule robustness. European Journal of Operational Research 256 (2017), 44–54.
Corman, F., D'Ariano, A., Marra, A., Pacciarelli, D., Samà, M., Integrating train scheduling and delay management in real-time railway traffic control. Transportation Research Part E: Logistics and Transportation Review 105 (2017), 213–239.
Islam, D.M.Z., Laparidou, K., Burgess, A., Cost effective future derailment mitigation techniques for rail freight traffic management in Europe. Transportation Research Part C: Emerging Technologies 70 (2016), 185–196.
Navajas Cawood, E., Rotoli, F., Soria, A., Capacity assessment of railway infrastructure: Tools, methodologies and policy relevance in the eu context. 2016, Publications Office.
Cao, Y., Zhang, Z., Cheng, F., Su, S., Trajectory optimization for high-speed trains via a mixed integer linear programming approach. IEEE Transactions on Intelligent Transportation Systems 23 (2022), 17666–17676.
Wang, P., Goverde, R.M.P., van Luipen, J., A connected driver advisory system framework for merging freight trains. Transportation Research Part C: Emerging Technologies 105 (2019), 203–221.
Ursavas, E., Zhu, S.X., Integrated passenger and freight train planning on shared-use corridors. Transportation Science 6 (2017), 1376–1390.
Kurhan, M. and Kurhan, D., (2018.) Problems of providing international railway transport, MATEC – Web of Conferences, vol. 230, 2018.
P. Daszkiewicz and M. Andrzejewski, “Preliminary analyzes in terms of the possibility of reducing energy consumption by the sm42 locomotive used in track works,” MATEC Web of Conferences, vol. 118, 2017.
Feo-Valero, M., Garcia-Menendez, L., del Saz-Salazar, S., Rail freight transport and demand requirements: An analysis of attribute cut-offs through a stated preference experiment. Transportation 43 (2016), 101–122.
Mo, P., Yang, L., D'Ariano, A., Yin, J., Yao, Y., Gao, Z., Energy-efficient train scheduling and rolling stock circulation planning in a metro line: A linear programming approach. IEEE Transactions on Intelligent Transportation Systems 21:9 (2020), 3621–3633.
Merkisz, J., Fuc, P., Lijewski, P., Ziolkowski, A., Galant, M., Siedlecki, M., Analysis of an increase in the efficiency of a spark ignition engine through the application of an automotive thermoelectric generator. Journal of Electronic Materials 45 (2016), 4028–4037.
Rymaniak, L., Ziolkowski, A., Gallas, D., (2017). Particle number and particulate mass emissions of heavy duty vehicles in real operating conditions, MATEC Web of Conferences, vol. 118.
Wang, D., Zhao, J., Peng, Q., Optimizing the loaded train combination problem at a heavy-haul marshalling station. Transportation Research Part E: Logistics and Transportation Review, 162, 2022, 102717.
Giacco, G.L., Carillo, D., D'Ariano, A., Pacciarelli, D., Marìn, G., Short-term rail rolling stock rostering and maintenance scheduling. Transportation Research Procedia 3 (2014), 651–659.
Patro, S., Sahu, K.,(2015). Normalization: A preprocessing stage, arXiv.
Wang, H., Mao, W., Eriksson, L., A three-dimensional dijkstra's algorithm for multi-objective ship voyage optimization. Ocean Engineering, 186, 2019.
Casas, P., Kraus, L., Sedeno-Noda, A., Borndorfer, R., (2021). Targeted multiobjective dijkstra algorithm, arXiv.
Birol, F., Net zero by 2050 – A roadmap for the global energy sector. 2021, International Energy Agency.
Wang, X., Disney, S.M., The bullwhip effect: Progress, trends and directions. European Journal of Operational Research 250 (2016), 691–701.
Barbour, W., Martinez Mori, J.C., Kuppa, S., Work, D.B., Prediction of arrival times of freight traffic on US railroads using support vector regression. Transportation Research Part C: Emerging Technologies 93 (2018), 211–227.
Pineda-Jaramillo, J., Viti, F., Identifying the rail operating features associated to intermodal freight rail operation delays. Transportation Research Part C: Emerging Technologies, 147, 2023, 103993.
Rymaniak, L., Daszkiewicz, P., Merkisz, J., Bolzhelarskyi, Y., Method of determining the locomotive engine specific fuel consumption based on its operating conditions. AIP Conference Proceedings, vol, 2019, 2078.
Biemann, K., Notter, B., Environmental Methodology and Data. EcoTransIT World (EWI), 2023.
F. Kamenga, P. Pellegrini, J. Rodriguez, and B. Merabet, “Solution algorithms for the generalized train unit shunting problem,” EURO Journal on Transportation and Logistics, vol. 10, 2021.
Asghari, M., Fathollahi-Fard, A.M., Al-e-hashem, S.M.J., Dulebenets, M.A., Transformation and linearization techniques in optimization: A state-of-the-art survey. Mathematics, 10, 2022, 283.
