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6TECHNICAL SCIENCES
SYSTEM ANALYSIS OF UNIFORMALITY OF OPERATION OF SORTING STATION "CH" IN
DAILY MODE
Butunov D.
PhD, Docent of the Department "Organization of transport movement" Tashkent state transport university (Uzbekistan)
Akhmedova M.
Senior lecturer of the Department "Organization of transport movement", Tashkent State Transport University (Uzbekistan)
Buriyev Sh.
Assistant of the Department "Organization of transport movement", Tashkent state transport university (Uzbekistan),
Abstract
The article uses the methods of system analysis, statistical data processing and comparison of indicators of the sorting station. The procedure for the arrival of trains and wagons for processing at the station is considered. The dynamics of changes in the daily processing of wagons on the hump are studied. The dynamics of changes in the average daily spent time spent by transit wagons in the fleets and the station as a whole and by piece-by-rate analysis in a daily mode by the elements of the time spent by wagons are considered. Such a systematic analysis makes it possible to form statistical patterns of non-fulfillment of the norms for the time spent by wagons at the station.
Keywords: Sorting station, system analysis, unevenness, train flows, wagon flows.
The main task of the sorting station is to process wagon flows in an optimal way, so that the presence of a wagon at the station is minimal [1-7].
In this regard, the most important indicator of the station is the time spent by wagons [1-4, 6, 8, 10].
At present, the values of this indicator are most often normalized by building a daily schedule of the station's operation [1, 4]. According to the daily schedule, the cost of wagon-hours is determined for calculating the main indicators of the station's work. Certain daily average values are averaged at a monthly level and reflected in the reporting form on the operation of the DO-24VTs sorting station [9].
Based on the report, the fulfillment of the norms, the time spent by wagons at the sorting station is analyzed in a monthly period. However, a monthly analysis of the performance of a qualitative indicator of the station's performance is currently insufficient. Because in the monthly analysis, the factors influencing the time spent by wagons at the station is obtained as a general one and does not take into account its influence on individual elements (securing and fencing the train, waiting for processing, processing the train, etc.) and the place of origin [1, 4, 6].
In this case, it is necessary to analyze the operation of the station in a shift-daily mode for individual elements of the time spent by wagons in order to identify the cause of losses. Shift-daily element-by-element analysis allows timely identification and reduction of the causes of losses of the entire process with a quanti-
tative assessment of their impact on the value of the residence time, and also allows you to fulfill the established daily tasks and monthly technological norms. In addition, it allows you to objectively assess the time spent by wagons at the sorting station and timely and reasonably develop measures to reduce it.
Analysis of the time spent by wagons at the station, especially transit wagons with processing (hereinafter referred to as transit wagons) plays an important role in the development of measures to improve technological processes and the organization of the entire complex for processing wagon flows [1, 4].
The actual location of transit wagons at the sorting station depends on the volume of processing of wagons and their uneven arrival for processing.
The order of arrival of trains and wagons for processing affects the operation of the station. Uniform supply creates the best conditions for the use of all sorting devices, and ensures the shortest time spent wagons during processing [3, 4, 9]. However, for a number of well-known reasons, the actual arrival of trains and wagons is uneven.
Irregularities in the arrival of trains and wagons for processing significantly affect the quality of the station, lead to an increase in the time spent by transit wagons [4, 5, 7].
Let us consider the daily irregularities in the arrival of trains and wagons for processing at station "Ch" for a month (fig. 1).
Day
Figure 1. Dynamics of changes in the daily number of arrivals of dismantling trains and wagons for processing
Fig. 1 shows that the average daily arrival of dismantling trains is 21 trains for the current month and fluctuates on average within 4-5 trains, and the number of wagons is 1079 wagons and ranges from 820 to 1318 wagons.
Such fluctuations in the uneven arrival of dismantling trains and wagon flows at the station in a daily 2250
mode lead to an increase in the actual location of wagons [4].
Now let us consider the daily processing of wagons on the sorting hump (fig. 2).
2200
2150
2100
o
S: 2050
s
Z
2000
1950
A
A \ 1\ ♦ A / r
y n f \ A f 1 A V V A \ A- J
V V A v 1 V f V f J \ V
V 1 \l 1
V
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
Day
Figure 2. Dynamics of changes in the daily number of wagons processed on the sorting hump, taking into
account the repeated
From fig. 2, it can be seen that the average daily plan for the processing of wagons on the humps is 2050 wagons, but in fact it is 2105 and the daily fluctuations are from 1965 to 2205 wagons.
Let us consider the dynamics of changes in the average daily time spent by transit wagons in the parks and the station as a whole (fig. 3, a, b).
a) 11,0 10,0 9,0 8,0 7,0 6,0 5,0 4,0 3,0 2,0
r u o h
« S
H
b)
r u o h
a?
S
H
123456789 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
Reception park
Day Sorting park
Departure park
21 20,5 20 19,5 19 18,5 18 17,5 17 16,5 16
m ■ * I.
■ i \ / > ™ u. ' ; * ■ 1
/ V f m i 1 \ à / » / \
/ \ / \ É V i \ tt \ l . • i \
i i \ / [ i \ / ■ l i
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k
-Plan Fact
123456789 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
Day
Figure 3. Dynamics of changes in the average daily time spent on processing a transit wagon by fleets (a) and in
general (b) stations
According to the dynamics of the change in the value of the time spent by transit wagons, the following conclusion can be made:
- the time spent on processing transit wagons in the station (a) parks shows that the departure and sorting parks play the dominant role here;
- in general (b) the station, from 1 to 31, there is a stable excess of the actual value from the planned one and the average daily deviation is 1,89 hours (11%).
Thus, the uneven arrival of trains and wagons has a significant impact on the operation of the sorting station. Fluctuations in the daily work volume significantly affect the quality of the station's work and, as a result, lead to an increase in the time spent by transit wagons with and without processing and local wagons at the sorting station at the "waiting for technological operation" element.
Regularly carrying out such an analysis makes it possible to determine the "bottlenecks" in the work of the sorting station and find out the state of individual
technological operations in order to timely develop action plans to reduce them.
It is advisable to establish the standard of time spent by wagon categories at a sorting station taking into account the results of such a detailed system analysis.
References
1. Dilmurod B. Butunov. Estimation of inefficient losses in railroad yard operation / D.B. Butunov, A.G. Kotenko // Emperor Alexander I St. Petersburg State Transport University, 2018, Volume 15, Issue 4, Pages 498-512. (https://cyberleninka.ru/article/n/otsenka-neproizvoditelnyh-poter-v-rabote-sortirovochnoy-stantsii)
2. Suyunbayev, Sh.M. and Butunov, D.B. (2019) "Development of classification of the reasons of losses in the work sorting stations" Journal of Tashkent Institute of Railway Engineers: Vol. 15: Iss. 2, Article 23. Available at: (https://uzjournals.edu.uz/tashiit/vol15/iss2/23)
3. Butunov, D.B. (2019) "A study of the implementation of standards-time of wagons at sorting station" Journal of Tashkent Institute of Railway Engineers: Vol. 15: Iss. 3, Article 23. Available at: (https://uzjournals.edu.uz/tashiit/vol15/iss3/23)
4. Butunov D.B. Improvement of technical experimental methods for organization of wagon flows and management evaluation at sorting stations. Dis. ... doc. Phil. (PhD). Tashkent: TashIIT. - 2019. - 187 p.
5. Romanova P.B. The formation of trains of various masses and lengths / P.B. Romanova, S.A. Tsy-ganov // Bulletin of the Volga region. - 2016. - No. 6. - Page. 71-76.
6. Butunov, D.B. (2020) "Substantiation of the input of the parameters of the unprofitable loss of time when norming the time of the duration of the wagons on the sorting station" Journal of Tashkent Institute of Railway Engineers: Vol. 16: Iss. 3, Article 16. (https://uzjournals.edu.uz/cgi/viewcontent.cgi?article= 1191 &context=tashiit)
7. Korol A.A. Determination of losses arising at the marshalling yard during the period of the "Window" in the areas adjacent to the station / A.A. Korol //
Science and Education in Transport: Proceedings of the IX International Scientific and Practical Conference. SamGUPS. - 2016. - p. 106-108.
8. Abdukodirov Sardor, Dilmurod Butunov, Mafratkhon Tukhakhodjaeva, Shukhrat Buriev, Utkir Khusenov. (2021). Administration of Technological Procedures at Intermediate Stations. Design Engineering, 14531-14540. Retrieved from http://thedesignengineering.com/index.php/DE/article/ view/6581
9. Instructional directions on the order of automated accounting of in-house statistical reporting form DO-24 CC "A report on operation of sorting stations" and DO-24 a CC "A report on operation of station yards". Moscow, OAO "RZhD" Publ., 2016, 45 p. (In Russian)
10. Butunov, Dilmurod Baxodirovich; Aripov, Nodir Kodirovich; and Bashirova, Alfiya Mirkhatimovna (2020) "Systematization of factors influencing during processing of wagons at the sorting station" Journal of Tashkent Institute of Railway Engineers: Vol. 16: Iss. 2, Article 10. (https ://uzj ournals.edu. uz/tashiit/vol16/iss2/10/)
DATA WITH PARTIAL MULTICOLLINEARITY HELPS TO RESOLVE OVERFIT PROBLEM IN
LINEAR MODELS
Solovei O.
Candidate of Technical Sciences (PhD) ORCID: 0000-0001-8 774- 7243 Kyiv University of Civil Building and Architecture Kyiv, Povitroflotsky Avenue, 31, 03680
Abstract
Linear regression models are built on raw data which is supposed to have linear relation between predictors and target and no multicollinearity between predictors [1]. However, multicollinearity can be complete or partial and the second type of multicollinearity may be successfully utilized in Ridge regression algorithms to solve overfit problem.
Keywords: overfit, multicollinearity, Ridge regression, MSE, determination coefficient
Overfit term in machine learning refers to a problem when a model fits its purpose only for a certain set of data and fails for laid out data set. The overfit problem is visible when mean square error (MSE) on data used to train the model (train data) is less then MSE on data to test the model (test data); and determination coefficient R2 on train data is bigger then R2 on test data. Overfit for linear regression may happen when dataset has either small number of informative variables or small number of samples. In the first case, data is shrunk to the smaller dimension with maximum information preserved [2]; in the second case, new features are constructed as polynomial combinations of the existing predictors with a certain degree [3]. After data is pre-processed the mentioned ways, a linear model is built with ridge (1) or lasso (2) regularization term added to cost function of the linear regression [4].
¿£miyi-*w||2 + a||w||2^min (1)
1Xi=i Wyi-Xwf + a\\w\\^min (2)
where ji - a model's prediction for a sample with index i in dataset; X- a matrix with features' values; w - a vector of linear equation's model coefficient; a- a constant which provides the balance for model's coefficients adjustments and model's fit to data.
Linear regression analysis to be performed demands a raw data doesn't have multicollinearity between predictors. However, when new features are added to raw data as existing features' combinations then partial multicollinearity is introduced in dataset and to follow the mentioned linear regression standards the new features couldn't be included in the model. Current research is created to demonstrate that partial multicollinearity among predictors when correlation coefficient is not equal -1 or 1 can be used with ridge regression to resolve overfit problem.
Let consider a construction of linear regression model for dataset with 50 samples; 3 independent features xl, x2, x3 and target y. In raw data there are no samples with empty values and a linear relation exists only between predictor x3 and target y so linear model
