Simulation Model to Optimize Turnover Schedule for Train Locomotives

This work relates to a suggested simulation model to optimize turnover schedule for train locomotives operating in train flow service. It is first time the problem was formulated as a strict optimization model - a dynamic transportation problem. On the functional level, the main goal was to minimize...

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Main Authors: P. A. Kozlov, S. P. Vakulenko
Format: Article
Language:Russian
Published: Joint Stock Company «Railway Scientific and Research Institute» 2015-04-01
Series:Вестник Научно-исследовательского института железнодорожного транспорта
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Online Access:https://www.journal-vniizht.ru/jour/article/view/23
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author P. A. Kozlov
S. P. Vakulenko
author_facet P. A. Kozlov
S. P. Vakulenko
author_sort P. A. Kozlov
collection DOAJ
description This work relates to a suggested simulation model to optimize turnover schedule for train locomotives operating in train flow service. It is first time the problem was formulated as a strict optimization model - a dynamic transportation problem. On the functional level, the main goal was to minimize overall costs which are directly related to locomotives operation and to losses due to train delays. At present the problem has been solved through various heuristic procedures, which fail to find the optimum alternative due to a huge amount of multivariate solutions. The dynamic traffic problem was taken as a starting point for simulation model development. In the process of calculations the dynamic problem is reduced to the static one by use of time multiplication method. The model helps to calculate routes for each locomotive with due account for train flow pattern, location of trains at the start of calculations, and specified traffic schedule. The lowest total costs of locomotives use and losses for train delays can be taken as a criterion of optimality. The output reflects the complete nature of productive locomotive operations, train operation dynamics and resultant delays. Additionally, each locomotive’s operation mode and each train’s timetable can be seen. For calculation tests there was selected an existing main line section between stations Druzhinino and Vekovka including 9 stations; traffic data were chosen close to reality. Actually, the above sub-division employs 180 locomotives. Various experiments were held. The minimum number of locomotives necessary to secure faultless operation (195 units) was calculated. The model generates different results, which characterize train operations. Output includes a schedule for each locomotive, timetable for each train, average operating results for locomotives, and train delays data. For a selected criterion the model suggests calculations of an optimal number of locomotives and their best locations at the start of calculations. In order to register depot orders to locomotives, a multistage procedure for solving dynamic transportation problems was proposed. In order to check the feasibility of generated results, another imitational model was built which may reconsider many of non-definable factors. Operating principles for a two-level model were reviewed by the authors in their earlier publications. The model can be used as an optimizing unit in the Locomotive Computer-Assisted Management System - in sphere of day-to-day management and for calculating the optimum turnover modes. Automated management of locomotive fleet operations throughout the railway network will deliver significant technological and economical effects.
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id doaj-art-b52b35def04e4d208ad31bd3a7058d6e
institution Kabale University
issn 2223-9731
2713-2560
language Russian
publishDate 2015-04-01
publisher Joint Stock Company «Railway Scientific and Research Institute»
record_format Article
series Вестник Научно-исследовательского института железнодорожного транспорта
spelling doaj-art-b52b35def04e4d208ad31bd3a7058d6e2025-08-20T03:37:38ZrusJoint Stock Company «Railway Scientific and Research Institute»Вестник Научно-исследовательского института железнодорожного транспорта2223-97312713-25602015-04-0102152010.21780/2223-9731-2015-0-2-15-2023Simulation Model to Optimize Turnover Schedule for Train LocomotivesP. A. Kozlov0S. P. Vakulenko1Research & Production Holding STRATEGInstitute for Management and Information Technologies at Moscow State University of Railway Engineering (MIIT)This work relates to a suggested simulation model to optimize turnover schedule for train locomotives operating in train flow service. It is first time the problem was formulated as a strict optimization model - a dynamic transportation problem. On the functional level, the main goal was to minimize overall costs which are directly related to locomotives operation and to losses due to train delays. At present the problem has been solved through various heuristic procedures, which fail to find the optimum alternative due to a huge amount of multivariate solutions. The dynamic traffic problem was taken as a starting point for simulation model development. In the process of calculations the dynamic problem is reduced to the static one by use of time multiplication method. The model helps to calculate routes for each locomotive with due account for train flow pattern, location of trains at the start of calculations, and specified traffic schedule. The lowest total costs of locomotives use and losses for train delays can be taken as a criterion of optimality. The output reflects the complete nature of productive locomotive operations, train operation dynamics and resultant delays. Additionally, each locomotive’s operation mode and each train’s timetable can be seen. For calculation tests there was selected an existing main line section between stations Druzhinino and Vekovka including 9 stations; traffic data were chosen close to reality. Actually, the above sub-division employs 180 locomotives. Various experiments were held. The minimum number of locomotives necessary to secure faultless operation (195 units) was calculated. The model generates different results, which characterize train operations. Output includes a schedule for each locomotive, timetable for each train, average operating results for locomotives, and train delays data. For a selected criterion the model suggests calculations of an optimal number of locomotives and their best locations at the start of calculations. In order to register depot orders to locomotives, a multistage procedure for solving dynamic transportation problems was proposed. In order to check the feasibility of generated results, another imitational model was built which may reconsider many of non-definable factors. Operating principles for a two-level model were reviewed by the authors in their earlier publications. The model can be used as an optimizing unit in the Locomotive Computer-Assisted Management System - in sphere of day-to-day management and for calculating the optimum turnover modes. Automated management of locomotive fleet operations throughout the railway network will deliver significant technological and economical effects.https://www.journal-vniizht.ru/jour/article/view/23model locomotivetraffic flow of trainsoptimizationoperation mode
spellingShingle P. A. Kozlov
S. P. Vakulenko
Simulation Model to Optimize Turnover Schedule for Train Locomotives
Вестник Научно-исследовательского института железнодорожного транспорта
model locomotive
traffic flow of trains
optimization
operation mode
title Simulation Model to Optimize Turnover Schedule for Train Locomotives
title_full Simulation Model to Optimize Turnover Schedule for Train Locomotives
title_fullStr Simulation Model to Optimize Turnover Schedule for Train Locomotives
title_full_unstemmed Simulation Model to Optimize Turnover Schedule for Train Locomotives
title_short Simulation Model to Optimize Turnover Schedule for Train Locomotives
title_sort simulation model to optimize turnover schedule for train locomotives
topic model locomotive
traffic flow of trains
optimization
operation mode
url https://www.journal-vniizht.ru/jour/article/view/23
work_keys_str_mv AT pakozlov simulationmodeltooptimizeturnoverschedulefortrainlocomotives
AT spvakulenko simulationmodeltooptimizeturnoverschedulefortrainlocomotives