Abstract
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.
Highlights
The strategy for the development of the railway industry involves the development of fundamentally new systems for diagnosing and monitoring of infrastructure and rolling stock that use algorithms of artificial intelligence in their work.One of such systems was developed at the center “Automated systems for diagnostics and monitoring of rolling stock and tracks” of JSC “VNIIZhT” (Railway Research Institute)
The monitoring system for loading snow removal work trains contains four ultrasonic sensors installed above the conveyor belt, which continuously measure the height and speed of the snow mass and automatically transfer them to cubic meters
Analysis of existing optimization methods allowed us to focus on linear programming as the most suitable for estimating the work performance of snow removal work trains
Summary
The strategy for the development of the railway industry involves the development of fundamentally new systems for diagnosing and monitoring of infrastructure and rolling stock that use algorithms of artificial intelligence in their work. One of such systems was developed at the center “Automated systems for diagnostics and monitoring of rolling stock and tracks” of JSC “VNIIZhT” (Railway Research Institute). Https://doi.org/10.10 51/matecconf /201823904001 satellite navigation system and GSM/GPRS data channels [1, 2] This system is being implemented and is being tested. The current main task is to develop a methodology for optimizing the work performance of snow removal work trains. The main goal of optimization, which is set for the employees of the track complex, is a reduction of operating costs of up to 30%, as well as an increase in productivity due to the intensification of snow removal works
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