Удосконалення методів визначення схем обігу локомотивів з урахуванням технологічних особливостей вагонопотоків

This paper focuses on new analytical solutions in the area of building locomotives’ circulation plans to handle individual applications for route transportation of freight. Such a domain has been little researched for the railway network of Ukraine, whereas the present study provides a basis for automating the planning process. The main aim is to improve the methods of determining the schemes of locomotives’ turnover in the railway network of Ukraine under the condition of an accelerated handling of individual railcar traffic and with regard to technological peculiarities. The developed mathematical model simultaneously makes it possible to determine the weight of trains on the routes they follow, to outline the circuity of locomotives with regard to deploying various series of locomotives within the network, and to regulate the system of locomotive crews’ operations in view of the existing technical and technological features of locomotive facilities and the railway infrastructure. The suggested mathematical model is processed in the study through the use of an integer genetic algorithm with its own system of coding the solution. The results have confirmed the adequacy of the developed mathematical model. The use of the suggested mathematical model on the basis of the genetic algorithm can help automate the complex process of determining the schemes of locomotives’ circulation with regard to the technological peculiarities of railcar traffic and, consequently, improve the accuracy and speed of decision-making for servicing individual applications for route transportation of freight.Предложено совершенствование методов определения схем обращения локомотивов с учетом технологических особенностей вагонопотоков. Разработана математическая модель, которая позволяет найти массу поездов на маршрутах их следования, схемы обращения локомотивов и работы бригад с учетом дислокации парка с различными сериями на полигоне сети. Для решения данной математической модели применен целочисленный генетический алгоритм с собственной схемой кодирования решенияЗапропоновано удосконалення методів визначення схем обігу локомотивів з урахуванням технологічних особливостей вагонопотоків. Розроблено математичну модель, яка дозволяє знайти масу поїздів на маршрутах їх слідування, схеми обігу локомотивів та роботи бригад з врахуванням дислокації парку з різними серіями на полігоні мережі. Для рішення даної математичної моделі застосовано цілочисельний генетичний алгоритм з власною схемою кодування рішенн

Optimization of Locomotive Management and Fuel Consumption in Rail Freight Transport

For the enormous capital investment and high operation expense of locomotives, the locomotive management/assignment and fuel consumption are two of the most important areas for railway industry, especially in freight train transportation. Several algorithms have been developed for the Locomotive Assignment Problem (LAP), including exact mathematics models, approximate dynamic programming and heuristics. These previously published optimization algorithms suffer from scalability or solution accuracy issues. In addition, each of the optimization models lacks part of the constraints that are necessary in real-world train/locomotive operation, e.g., maintenance/shop constraints or consist busting avoidance. Furthermore, there are rarely research works for the reduction of total train energy consumption on the locomotive assignment level. The thesis is organized around our three main contributions. Firstly we propose a “consist travel plan” based LAP optimization model, which covers all the required meaningful constraints and which can efficiently be solved using large scale optimization techniques, namely column generation (CG) decomposition. Our key contribution is that our LAP model can evaluate the occurrence of consist busting using the number of consist travel plans, and allows locomotive status transformation in flow conservation constraints. In addition, a new column generation acceleration architecture is developed, that allows the subproblem, i.e., column generator to create multiple columns in each iteration, that each is an optimal solution for a reduced sub-network. This new CG architecture reduces computational time greatly comparing to our original LAP model. For train fuel consumption, we derive, linearize and integrate a train fuel consumption model into our LAP model. In addition, we establish a conflict-free pre-process for time windows for train rescheduling without touching train-meet time and position. The new LAP-fuel consumption model works fine for the optimization of the train energy exhaustion on the locomotive assignment level. For the optimization models above, the numerical results are conducted on the railway network infrastructure of Canada Pacific Railway (CPR), with up to 1,750 trains and 9 types of locomotives over a two-week time period in the entire CPR railway network