Issue |
MATEC Web Conf.
Volume 239, 2018
Siberian Transport Forum - TransSiberia 2018
|
|
---|---|---|
Article Number | 04001 | |
Number of page(s) | 8 | |
Section | Planning and Managing Resources of Transport Enterprises | |
DOI | https://doi.org/10.1051/matecconf/201823904001 | |
Published online | 27 November 2018 |
Optimization of work performance of snow removal work trains on the basis of linear programming
1 Siberian Transport University, Dusi Kovalchuk st., 630049, Novosibirsk, Russia
2 Russian University of Transport, Obrazcova Str., 9b9, 127994, Moscow
* Corresponding author: asi@stu.ru
The introduction of monitoring systems for the work performance of special rolling stock and monitoring the load of snow removal work trains revealed a number of shortcomings in the planning, organization, and recording of work performed by snow removal work trains. The elimination of the identified problems is possible on the basis of optimization of work performance, the implementation of which can be achieved on the basis of existing systems with appropriate additional functionality. For this purpose, in the framework of the theoretical studies presented in the paper, a methodology for optimization of work performance of snow removal work trains has been developed. Linear programming is adopted as a method of solving the optimization problem. On the basis of the algorithm for solving the transport problem, the problem of minimizing the cost of snow removal from various sections of the track by the existing park of work trains is formulated, for which a mathematical model is constructed that includes the objective function and the corresponding restrictions. The results of the study show that the widespread use of work planning on the basis of the presented optimization methodology will make it possible to make the most efficient use of snow removal equipment and, as a result, to reduce the cost of this type of railway track maintenance work.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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