DOI: https://doi.org/10.15802/stp2018/145470

INTELLIGENT CONTROL OF MARSHALLING YARDS AT TRANSPORTATION OF DANGEROUS GOODS BASED ON MULTIOBJECTIVE OPTIMIZATION

T. V. Butko, V. M. Prokhorov, D. M. Chekhunov

Abstract


Purpose. The scientific paper involves formalizing the process of building a plan for the operational work of the marshalling yard in the conditions of processing carloads with dangerous goods. The developed mathematical model is implemented in the form of an intelligent planning system that will minimize both operational costs and technological risks during the work of the marshalling yard. Methodology. Based on the analysis of modern approaches to the management of transport systems under risk conditions, a mathematical model has been formed that includes the objective function of technological costs associated with all the main technological operations that are performed at the marshalling yard: reception, disbanding, form and departure of trains, accumulation of cars, processing of trains containing cars with dangerous goods, operations with local cars. In addition, the model also contains an objective function of the risk exposure, which also requires minimization in order to minimize the risk of accidents and their consequences when operating cars with dangerous goods. The model should be optimized under certain conditions that correspond to the technological features of the marshalling yard and which were formalized as a system of constraints. Optimization of the model is proposed to be carried out using methods of multiobjective optimization based on a genetic algorithm of a special type. Findings. A mathematical model is created that allows in an automated mode to build an operational plan for a marshalling yard operation with simultaneous consideration of two criteria: operational costs and risk exposure. The model was implemented as part of the created software product with the use of which the simulation was carried out. Originality. An intelligent planning technology has been developed that uses multiobjective optimization methods and allows finding a compromise solution while taking into account both the criterion of operational expenses and the risk exposure one in the conditions of handling carloads with dangerous goods. Practical value. During the simulation it was revealed that the effectiveness of the proposed technology of intelligent planning based on the developed model in comparison with the traditional planning technology is about 6.5% by the criterion of operating costs and about 8% by the criterion of the risk exposure.


Keywords


marshalling yards intelligent control; multiobjective optimization; processing of cars flows with dangerous goods

References


Butko, T. V., Prokhorov, V. M., & Chekhunov, D. M. (2018). Formalizatsiia tekhnolohii pererobky vahonopotokiv iz nebezpechnymy vantazhamy na sortuvalnii stantsii na osnovi ekspozytsii ryzyku Informatsiino-keruiuchi systemy na zaliznychnomu transporti, (2), 18-22. (in Ukranian)

Muzykin, M. I., & Nesterenko, G. I. (2014). Influence of maintenance windows on the working capacity of railway route. Science and Transport Progress, 3(51), 24-33. doi: 10.15802/stp2014/25797 (in Ukranian)

Muzykina, S. I., Muzykin, M. I., & Nesterenko, G. I. (2016). Study of working capacity of the marshalling yard. Science and Transport Progress, 2(62), 47-60. doi: 10.15802/stp2016/67289 (in Ukranian)

Chekhunov, D. M. (2018). Formuvannia modeli otsinky ryzykiv na sortuvalnii stantsii pry operuvanni vahonamy z nebezpechnymy vantazhamy iz vykorystanniam matematychnykh aparativ nechitkoi lohiky ta Baiiesovykh merezh. Informatsiino-keruiuchi systemy na zaliznychnomu transporti, (1), 35-41. (in Ukranian)

Budish, E. B., Cachon, G., Kessler, J. B., & Othman, A. (2017). Course Match: A Large-Scale Implementation of Approximate Competitive Equilibrium from Equal Incomes for Combinatorial Allocation. Operations Research, 65(2), 314-336. doi: 10.1287/opre.2016.1544 (in English)

Khishtandar, S., & Zandieh, M. (2017). Comparisons of some improving strategies on NSGA-II for multi-objective inventory system. Journal of Industrial and Production Engineering, 34(1), 61-69. doi: 10.1080/21681015.2016.1210681 (in English)

Lin, E., & Cheng, C. (2009). YardSim: A rail yard simulation framework and its implementation in a major railroad in the U.S. Proceedings of the 2009 Winter Simulation Conference (WSC) (pp. 2532-2541). Austin. doi: 10.1109/wsc.2009.5429654 (in English)

Jacob, R., Márton, P., Maue, J., & Nunkesser, M. (2010). Multistage methods for freight train classifi-cation. Networks, 57(1), 87-105. doi: 10.1002/net.20385 (in English)

Bohlin, M., Dahms, H. W., Flier, M. H., & Gestrelius, S. (2012). Optimal freight train classification using column generation. Proc. of the 12th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (pp. 10-22). Ljubljana. (in English)

Belošević, I., Ivić, M., Kosijer, M., Pavlović, N., & Aćimović, S. (2016). Railroad transshipment yards: layouts and rail operation. Horizons Series B. 3, 559-569. Retrived from https://goo.gl/jLUPw3 (in English)

Bohlin, M., Flier, H., Maue, J., & Mihalák, M. (2011). Track allocation in freight-train classification with mixed tracks. Proc. of the 11th Workshop on Algorithmic Approaches for Transportation Mo-delling, Optimization, and Systems (pp. 38-51). Saarbrücken. (in English)

Nesterenko, G. I., Muzykin, M. I., Horobets, V. L., & Muzykina, S. I. (2016). Study of car traffic flow structure on arrival and departure at the marshalling yard x. Science and Transport Progress, 1(61), 85-99. doi: 10.15802/stp2016/60986 (in English)


GOST Style Citations


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