7 research outputs found
Π ΠΎΠ·ΡΠΎΠ±ΠΊΠ° ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΌΠΎΠ΄Π΅Π»ΡΠ²Π°Π½Π½Ρ ΡΠΎΠ·ΠΏΠΎΠ²ΡΡΠ΄ΠΆΠ΅Π½Π½Ρ Π·Π°ΡΡΠΈΠΌΠΊΠΈ Ρ Π½Π΅ΡΠΈΠΊΠ»ΡΡΠ½ΠΎΠΌΡ Π³ΡΠ°ΡΡΠΊΡ ΡΡΡ Ρ ΠΏΠΎΡΠ·Π΄ΡΠ² Π½Π° Π·Π°Π»ΡΠ·Π½ΠΈΡΡΡ Π·ΠΌΡΡΠ°Π½ΠΎΠ³ΠΎ ΡΡΡ Ρ
The main goal of present study is to develop a method for modeling delay propagation in non-cyclic train scheduling on a railroad network with mixed traffic. This will make it possible to explore the dynamics of delay transfer between trains and to identify the most vulnerable points in the timetable of trains. We have devised a method for modeling delay propagation in non-cyclic train scheduling for the rail networks with mixed traffic. It is proposed to apply as a basis of the developed method a mathematical model for the construction of a non-cyclic train timetable. A distinctive feature of the objective function of the mathematical model is taking into consideration the patterns of building a non-cyclic train timetable under conditions of mixed traffic of passenger and heavy-weight or multi-car freight trains, for which it is important to minimize the cost of stopping during motion. The proposed mathematical model was solved based on the multiagent optimization. To account for delay propagation on the railroad network of great dimensionality, we devised a procedure for connecting interdependent sections, which makes it possible to decompose the general problem based on the construction of schedule of trains for separate estimated sections taking into consideration the network effect. We performed an analysis of the dynamics of propagation of secondary delays in non-cyclic train scheduling with detailed patterns of changes in all parameters in time and space. We obtained dependences of the number and duration of delayed trains on the point of occurrence in the timetable of trains along the estimated line of the Ukrainian railroad network. The approach proposed allows the automatization of determining a time reserve in the standard non-cyclic train scheduling based on forecasting the consequences of train delays.ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ ΠΌΠ΅ΡΠΎΠ΄ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½ΠΈΡ Π·Π°Π΄Π΅ΡΠΆΠΊΠΈ Π² Π½Π΅ΡΠΈΠΊΠ»ΠΈΡΠ΅ΡΠΊΠΎΠΌ Π³ΡΠ°ΡΠΈΠΊΠ΅ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ ΠΏΠΎΠ΅Π·Π΄ΠΎΠ² Ρ ΡΡΠ΅ΡΠΎΠΌ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΎΡΠΎΠ±Π΅Π½ΠΎΡΡΠ΅ΠΉ ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡΠΎΠΆΠ½ΠΎΠΉ ΡΠ΅ΡΠΈ ΡΠΌΠ΅ΡΠ°Π½Π½ΠΎΠ³ΠΎ ΠΏΠ°ΡΡΠ°ΠΆΠΈΡΡΠΊΠΎΠ³ΠΎ ΠΈ Π³ΡΡΠ·ΠΎΠ²ΠΎΠ³ΠΎ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ. Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π° ΠΏΡΠΎΡΠ΅Π΄ΡΡΠ° ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π²Π»ΠΈΡΠ½ΠΈΡ Π·Π°Π΄Π΅ΡΠΆΠΊΠΈ ΠΏΠΎΠ΅Π·Π΄ΠΎΠ² Π² Π½ΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΠΎΠΌ Π³ΡΠ°ΡΠΈΠΊΠ΅ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ ΠΏΠΎΠ΅Π·Π΄ΠΎΠ² Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΎΠ½Π½ΠΎΠΉ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ Π³ΡΠ°ΡΠΈΠΊΠ° Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ ΠΏΠΎΠ΅Π·Π΄ΠΎΠ² Ρ ΡΡΠ΅ΡΠΎΠΌ Π·Π°Π΄Π°Π½ΠΎΠΉ ΠΏΠ΅ΡΠ²ΠΈΡΠ½ΠΎΠΉ Π·Π°Π΄Π΅ΡΠΆΠΊΠΈ. ΠΡΠΎΠ²Π΅Π΄Π΅Π½Π½ΡΠ΅ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½ΠΈΡ Π·Π°Π΄Π΅ΡΠΆΠΊΠΈ Π² Π½ΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΠΎΠΌ Π³ΡΠ°ΡΠΈΠΊΠ΅ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ ΠΏΠΎΠ΅Π·Π΄ΠΎΠ² Π½Π° ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡΠΎΠΆΠ½ΠΎΠΉ Π»ΠΈΠ½ΠΈΠΈ Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ ΠΏΡΠΎΡΡΠΆΠ΅Π½Π½ΠΎΡΡΠΈΠΠ°ΠΏΡΠΎΠΏΠΎΠ½ΠΎΠ²Π°Π½ΠΎ ΠΌΠ΅ΡΠΎΠ΄ ΠΌΠΎΠ΄Π΅Π»ΡΠ²Π°Π½Π½Ρ ΡΠΎΠ·ΠΏΠΎΠ²ΡΡΠ΄ΠΆΠ΅Π½Π½Ρ Π·Π°ΡΡΠΈΠΌΠΊΠΈ Ρ Π½Π΅ΡΠΈΠΊΠ»ΡΡΠ½ΠΎΠΌΡ Π³ΡΠ°ΡΡΠΊΡ ΡΡΡ
Ρ ΠΏΠΎΡΠ·Π΄ΡΠ² Π· ΡΡΠ°Ρ
ΡΠ²Π°Π½Π½ΡΠΌ ΡΠ΅Ρ
Π½ΡΡΠ½ΠΈΡ
ΡΠ° ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΡΡΠ½ΠΈΡ
ΠΎΡΠΎΠ±Π»ΠΈΠ²ΠΎΡΡΠ΅ΠΉ Π·Π°Π»ΡΠ·Π½ΠΈΡΠ½ΠΎΡ ΠΌΠ΅ΡΠ΅ΠΆΡ Π·ΠΌΡΡΠ°Π½ΠΎΠ³ΠΎ ΡΡΡ
Ρ ΠΏΠ°ΡΠ°ΠΆΠΈΡΡΡΠΊΠΈΡ
Ρ Π²Π°Π½ΡΠ°ΠΆΠ½ΠΈΡ
ΠΏΠΎΡΠ·Π΄ΡΠ². Π ΠΎΠ·ΡΠΎΠ±Π»Π΅Π½ΠΎ ΠΏΡΠΎΡΠ΅Π΄ΡΡΡ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½Ρ Π²ΠΏΠ»ΠΈΠ²Ρ Π·Π°ΡΡΠΈΠΌΠΊΠΈ ΠΏΠΎΡΠ·Π΄ΡΠ² Ρ Π½ΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΠΎΠΌΡ Π³ΡΠ°ΡΡΠΊΡ ΡΡΡ
Ρ ΠΏΠΎΡΠ·Π΄ΡΠ² Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ ΠΎΠΏΡΠΈΠΌΡΠ·Π°ΡΡΠΉΠ½ΠΎΡ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ½ΠΎΡ ΠΌΠΎΠ΄Π΅Π»Ρ ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²ΠΈ Π³ΡΠ°ΡΡΠΊΡ ΡΡΡ
Ρ ΠΏΠΎΡΠ·Π΄ΡΠ² Π· ΡΡΠ°Ρ
ΡΠ²Π°Π½Π½Ρ Π·Π°Π΄Π°Π½ΠΎΡ ΠΏΠ΅ΡΠ²ΠΈΠ½Π½ΠΎΡ Π·Π°ΡΡΠΈΠΌΠΊΠΈ. ΠΡΠΎΠ²Π΅Π΄Π΅Π½Ρ Π΅ΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½Ρ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½Ρ ΠΌΠΎΠ΄Π΅Π»ΡΠ²Π°Π½Π½Ρ ΠΏΠΎΡΠΈΡΠ΅Π½Π½Ρ Π·Π°ΡΡΠΈΠΌΠΊΠΈ ΠΏΠΎΡΠ·Π΄ΡΠ² Ρ Π½ΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΠΎΠΌΡ Π³ΡΠ°ΡΡΠΊΡ ΡΡΡ
Ρ ΠΏΠΎΡΠ·Π΄ΡΠ² Π· ΡΡΠ°Ρ
ΡΠ²Π°Π½Π½ΡΠΌ Π²Π·Π°ΡΠΌΠΎΡΠ²βΡΠ·ΠΊΠΈ Π·Π°Π»ΡΠ·Π½ΠΈΡΠ½ΠΈΡ
Π΄ΡΠ»ΡΠ½ΠΈΡ
Π€ΠΎΡΠΌΡΠ²Π°Π½Π½Ρ ΠΌΠΎΠ΄Π΅Π»Ρ ΠΏΠ»Π°Π½ΡΠ²Π°Π½Π½Ρ ΡΠ²ΠΈΠ΄ΠΊΠΎΡ Π΄ΠΎΡΡΠ°Π²ΠΊΠΈ ΠΊΠΎΠ½ΡΠ΅ΠΉΠ½Π΅ΡΡΠ² Π·Π°Π»ΡΠ·Π½ΠΈΡΠ΅Ρ Π² ΡΠΌΠΎΠ²Π°Ρ ΡΠ½ΡΠ΅ΡΠΌΠΎΠ΄Π°Π»ΡΠ½ΠΈΡ ΠΏΠ΅ΡΠ΅Π²Π΅Π·Π΅Π½Ρ Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ ΡΠΎΠ±Π°ΡΡΠ½ΠΎΡ ΠΎΠΏΡΠΈΠΌΡΠ·Π°ΡΡΡ
This paper considers the possibility of devising a technology of fast railroad communication for the transportation of containers between the port and customer enterprises in the course of intermodal transportation. The purpose of technology development is to reduce the share of the use of trucks on intermodal routes and thus solve a number of related environmental, transport, municipal, and economic problems. The devised technology is based on the principles of bringing the railroad as close as possible to the end points of the route, minimizing the number of intermediate modes of transport, and enabling the maximum speed of movement of containers by rail. For this purpose, the use of MetroCargoβ’ freight terminals and CargoSprinter modular trains is proposed. In the course of the study, the task to reliably plan the operation of the fleet of such trains for the delivery of containers between the port and enterprises under the conditions of "noisy" initial data was set and solved. To this end, the problem was formalized in the form of a model of mixed programming, based on the principles of robust optimization. To optimize the model taking into consideration the principles of robustness, a procedure was proposed that uses a two-circuit genetic algorithm. As a result of the simulation, it was found that the resulting plan was only 6.5 % inferior to the objective criterion of the plan, which was compiled without taking into consideration robustness. It was proved that the devised model makes it possible to build an operational plan for the delivery of containers by rail, which is close to optimal. At the same time, the plan is implemented even in the case of the most unfavorable set of circumstances in the form of delays, shifts in the time windows of the cargo fronts, etc., that is, under the actual conditions of the transport processΠΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½ΠΎ ΠΌΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ ΡΡΠ²ΠΎΡΠ΅Π½Π½Ρ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΡΡ ΡΠ²ΠΈΠ΄ΠΊΠΎΠ³ΠΎ Π·Π°Π»ΡΠ·Π½ΠΈΡΠ½ΠΎΠ³ΠΎ ΡΠΏΠΎΠ»ΡΡΠ΅Π½Π½Ρ Π΄Π»Ρ ΡΡΠ°Π½ΡΠΏΠΎΡΡΡΠ²Π°Π½Π½Ρ ΠΊΠΎΠ½ΡΠ΅ΠΉΠ½Π΅ΡΡΠ² ΠΌΡΠΆ ΠΏΠΎΡΡΠΎΠΌ ΡΠ° ΠΏΡΠ΄ΠΏΡΠΈΡΠΌΡΡΠ²Π°ΠΌΠΈ-ΠΊΠ»ΡΡΠ½ΡΠ°ΠΌΠΈ Π² Ρ
ΠΎΠ΄Ρ Π·Π΄ΡΠΉΡΠ½Π΅Π½Π½Ρ ΡΠ½ΡΠ΅ΡΠΌΠΎΠ΄Π°Π»ΡΠ½ΠΈΡ
ΠΏΠ΅ΡΠ΅Π²Π΅Π·Π΅Π½Ρ. ΠΠ΅ΡΠΎΡ ΡΠΎΠ·ΡΠΎΠ±ΠΊΠΈ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΡΡ Ρ Π·ΠΌΠ΅Π½ΡΠ΅Π½Π½Ρ ΡΠ°ΡΡΠΊΠΈ Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½Ρ Π°Π²ΡΠΎΡΡΠ°Π½ΡΠΏΠΎΡΡΡ Π½Π° ΡΠ½ΡΠ΅ΡΠΌΠΎΠ΄Π°Π»ΡΠ½ΠΈΡ
ΠΌΠ°ΡΡΡΡΡΠ°Ρ
ΡΠ° Π²ΠΈΡΡΡΠ΅Π½Π½Ρ ΡΠ°ΠΊΠΈΠΌ ΡΠΈΠ½ΠΎΠΌ ΡΡΠ»ΠΎΡ Π½ΠΈΠ·ΠΊΠΈ ΠΏΠΎΠ²'ΡΠ·Π°Π½ΠΈΡ
Π· Π½ΠΈΠΌ Π΅ΠΊΠΎΠ»ΠΎΠ³ΡΡΠ½ΠΈΡ
, ΡΡΠ°Π½ΡΠΏΠΎΡΡΠ½ΠΈΡ
, ΠΌΡΠ½ΡΡΠΈΠΏΠ°Π»ΡΠ½ΠΈΡ
ΡΠ° Π΅ΠΊΠΎΠ½ΠΎΠΌΡΡΠ½ΠΈΡ
ΠΏΡΠΎΠ±Π»Π΅ΠΌ. Π‘ΡΠΎΡΠΌΠΎΠ²Π°Π½Π° ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΡΡ Π±Π°Π·ΡΡΡΡΡΡ Π½Π° ΠΏΡΠΈΠ½ΡΠΈΠΏΠ°Ρ
ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠ³ΠΎ Π½Π°Π±Π»ΠΈΠΆΠ΅Π½Π½Ρ Π·Π°Π»ΡΠ·Π½ΠΈΡΡ Π΄ΠΎ ΠΊΡΠ½ΡΠ΅Π²ΠΈΡ
ΠΏΡΠ½ΠΊΡΡΠ² ΠΌΠ°ΡΡΡΡΡΡ, ΠΌΡΠ½ΡΠΌΡΠ·Π°ΡΡΡ ΠΊΡΠ»ΡΠΊΠΎΡΡΡ ΠΏΡΠΎΠΌΡΠΆΠ½ΠΈΡ
Π²ΠΈΠ΄ΡΠ² ΡΡΠ°Π½ΡΠΏΠΎΡΡΡ ΡΠ° Π·Π°Π±Π΅Π·ΠΏΠ΅ΡΠ΅Π½Π½Ρ ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΡ ΡΠ²ΠΈΠ΄ΠΊΠΎΡΡΡ ΠΏΡΠΎΡΡΠ²Π°Π½Π½Ρ ΠΊΠΎΠ½ΡΠ΅ΠΉΠ½Π΅ΡΡΠ² Π·Π°Π»ΡΠ·Π½ΠΈΡΠ΅Ρ. Π ΡΡΡΡ ΠΌΠ΅ΡΠΎΡ Π·Π°ΠΏΡΠΎΠΏΠΎΠ½ΠΎΠ²Π°Π½Π΅ Π·Π°ΡΡΠΎΡΡΠ²Π°Π½Π½Ρ Π²Π°Π½ΡΠ°ΠΆΠ½ΠΈΡ
ΡΠ΅ΡΠΌΡΠ½Π°Π»ΡΠ² MetroCargoβ’ ΡΠ° ΠΌΠΎΠ΄ΡΠ»ΡΠ½ΠΈΡ
ΠΏΠΎΡΠ·Π΄ΡΠ² CargoSprinter. Π Ρ
ΠΎΠ΄Ρ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½Ρ Π±ΡΠ»Π° ΠΏΠΎΡΡΠ°Π²Π»Π΅Π½Π° Ρ Π²ΠΈΡΡΡΠ΅Π½Π° Π·Π°Π΄Π°ΡΠ° Π½Π°Π΄ΡΠΉΠ½ΠΎΠ³ΠΎ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΠΏΠ»Π°Π½ΡΠ²Π°Π½Π½Ρ ΡΠΎΠ±ΠΎΡΠΈ ΠΏΠ°ΡΠΊΡ ΡΠ°ΠΊΠΈΡ
ΠΏΠΎΡΠ·Π΄ΡΠ² Π΄Π»Ρ Π·Π΄ΡΠΉΡΠ½Π΅Π½Π½Ρ Π΄ΠΎΡΡΠ°Π²ΠΊΠΈ ΠΊΠΎΠ½ΡΠ΅ΠΉΠ½Π΅ΡΡΠ² ΠΌΡΠΆ ΠΏΠΎΡΡΠΎΠΌ Ρ ΠΏΡΠ΄ΠΏΡΠΈΡΠΌΡΡΠ²Π°ΠΌΠΈ Π² ΡΠΌΠΎΠ²Π°Ρ
"Π·Π°Π±ΡΡΠ΄Π½Π΅Π½ΠΈΡ
" Π²ΠΈΡ
ΡΠ΄Π½ΠΈΡ
Π΄Π°Π½ΠΈΡ
. Π ΡΡΡΡ ΠΌΠ΅ΡΠΎΡ Π·Π°Π΄Π°ΡΠ° ΡΠΎΡΠΌΠ°Π»ΡΠ·ΠΎΠ²Π°Π½Π° Ρ Π²ΠΈΠ³Π»ΡΠ΄Ρ ΠΌΠΎΠ΄Π΅Π»Ρ Π·ΠΌΡΡΠ°Π½ΠΎΠ³ΠΎ ΠΏΡΠΎΠ³ΡΠ°ΠΌΡΠ²Π°Π½Π½Ρ, ΡΠΎ Π±Π°Π·ΡΡΡΡΡΡ Π½Π° ΠΏΡΠΈΠ½ΡΠΈΠΏΠ°Ρ
ΡΠΎΠ±Π°ΡΡΠ½ΠΎΡ ΠΎΠΏΡΠΈΠΌΡΠ·Π°ΡΡΡ. ΠΠ»Ρ Π·Π΄ΡΠΉΡΠ½Π΅Π½Π½Ρ ΠΎΠΏΡΠΈΠΌΡΠ·Π°ΡΡΡ ΠΌΠΎΠ΄Π΅Π»Ρ ΡΠ· ΡΡΠ°Ρ
ΡΠ²Π°Π½Π½ΡΠΌ ΠΏΡΠΈΠ½ΡΠΈΠΏΡΠ² ΡΠΎΠ±Π°ΡΡΠ½ΠΎΡΡΡ Π±ΡΠ»Π° Π·Π°ΠΏΡΠΎΠΏΠΎΠ½ΠΎΠ²Π°Π½Π° ΠΏΡΠΎΡΠ΅Π΄ΡΡΠ°, ΡΠΊΠ° Π²ΠΈΠΊΠΎΡΠΈΡΡΠΎΠ²ΡΡ Π΄Π²ΠΎΠΊΠΎΠ½ΡΡΡΠ½ΠΈΠΉ Π³Π΅Π½Π΅ΡΠΈΡΠ½ΠΈΠΉ Π°Π»Π³ΠΎΡΠΈΡΠΌ. Π ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΌΠΎΠ΄Π΅Π»ΡΠ²Π°Π½Π½Ρ Π±ΡΠ»ΠΎ Π²ΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΎ, ΡΠΎ ΠΎΡΡΠΈΠΌΠ°Π½ΠΈΠΉ ΠΏΠ»Π°Π½ Π»ΠΈΡΠ΅ Π½Π° 6,5 % ΠΏΠΎΡΡΡΠΏΠ°ΡΡΡΡΡ Π·Π° ΡΡΠ»ΡΠΎΠ²ΠΈΠΌ ΠΊΡΠΈΡΠ΅ΡΡΡΠΌ ΠΏΠ»Π°Π½Ρ, ΡΠΊΠΈΠΉ Π±ΡΠ»ΠΎ ΠΎΡΡΠΈΠΌΠ°Π½ΠΎ Π±Π΅Π· ΡΡΠ°Ρ
ΡΠ²Π°Π½Π½Ρ ΡΠΎΠ±Π°ΡΡΠ½ΠΎΡΡΡ. ΠΡΠ»ΠΎ Π΄ΠΎΠ²Π΅Π΄Π΅Π½ΠΎ, ΡΠΎ ΡΠΎΠ·ΡΠΎΠ±Π»Π΅Π½Π° ΠΌΠΎΠ΄Π΅Π»Ρ Π΄ΠΎΠ·Π²ΠΎΠ»ΡΡ ΠΎΡΡΠΈΠΌΡΠ²Π°ΡΠΈ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠ²Π½ΠΈΠΉ ΠΏΠ»Π°Π½ Π΄ΠΎΡΡΠ°Π²ΠΊΠΈ ΠΊΠΎΠ½ΡΠ΅ΠΉΠ½Π΅ΡΡΠ² Π·Π°Π»ΡΠ·Π½ΠΈΡΠ΅Ρ, ΡΠΊΠΈΠΉ Ρ Π±Π»ΠΈΠ·ΡΠΊΠΈΠΌ Π΄ΠΎ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠ³ΠΎ. Π ΡΠΎΠΉ ΠΆΠ΅ ΡΠ°Ρ, ΠΏΠ»Π°Π½ Ρ ΡΠ΅Π°Π»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΌ Π½Π°Π²ΡΡΡ Ρ Π²ΠΈΠΏΠ°Π΄ΠΊΡ Π½Π°ΠΉΠ±ΡΠ»ΡΡ Π½Π΅ΡΠΏΡΠΈΡΡΠ»ΠΈΠ²ΠΎΠ³ΠΎ Π·Π±ΡΠ³Ρ ΠΎΠ±ΡΡΠ°Π²ΠΈΠ½ Ρ Π²ΠΈΠ³Π»ΡΠ΄Ρ Π·Π°ΡΡΠΈΠΌΠΎΠΊ, Π·ΡΡΠ²Ρ ΡΠ°ΡΠΎΠ²ΠΈΡ
Π²ΡΠΊΠΎΠ½ ΡΠΎΠ±ΠΎΡΠΈ Π²Π°Π½ΡΠ°ΠΆΠ½ΠΈΡ
ΡΡΠΎΠ½ΡΡΠ² ΡΠΎΡΠΎ, ΡΠΎΠ±ΡΠΎ Π² ΡΠ΅Π°Π»ΡΠ½ΠΈΡ
ΡΠΌΠΎΠ²Π°Ρ
ΡΡΠ°Π½ΡΠΏΠΎΡΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΡΠ΅Ρ
Π ΠΎΠ·ΡΠΎΠ±ΠΊΠ° ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΌΠΎΠ΄Π΅Π»ΡΠ²Π°Π½Π½Ρ ΡΠΎΠ·ΠΏΠΎΠ²ΡΡΠ΄ΠΆΠ΅Π½Π½Ρ Π·Π°ΡΡΠΈΠΌΠΊΠΈ Ρ Π½Π΅ΡΠΈΠΊΠ»ΡΡΠ½ΠΎΠΌΡ Π³ΡΠ°ΡΡΠΊΡ ΡΡΡ Ρ ΠΏΠΎΡΠ·Π΄ΡΠ² Π½Π° Π·Π°Π»ΡΠ·Π½ΠΈΡΡΡ Π·ΠΌΡΡΠ°Π½ΠΎΠ³ΠΎ ΡΡΡ Ρ
The main goal of present study is to develop a method for modeling delay propagation in non-cyclic train scheduling on a railroad network with mixed traffic. This will make it possible to explore the dynamics of delay transfer between trains and to identify the most vulnerable points in the timetable of trains. We have devised a method for modeling delay propagation in non-cyclic train scheduling for the rail networks with mixed traffic. It is proposed to apply as a basis of the developed method a mathematical model for the construction of a non-cyclic train timetable. A distinctive feature of the objective function of the mathematical model is taking into consideration the patterns of building a non-cyclic train timetable under conditions of mixed traffic of passenger and heavy-weight or multi-car freight trains, for which it is important to minimize the cost of stopping during motion. The proposed mathematical model was solved based on the multiagent optimization. To account for delay propagation on the railroad network of great dimensionality, we devised a procedure for connecting interdependent sections, which makes it possible to decompose the general problem based on the construction of schedule of trains for separate estimated sections taking into consideration the network effect. We performed an analysis of the dynamics of propagation of secondary delays in non-cyclic train scheduling with detailed patterns of changes in all parameters in time and space. We obtained dependences of the number and duration of delayed trains on the point of occurrence in the timetable of trains along the estimated line of the Ukrainian railroad network. The approach proposed allows the automatization of determining a time reserve in the standard non-cyclic train scheduling based on forecasting the consequences of train delays.ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ ΠΌΠ΅ΡΠΎΠ΄ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½ΠΈΡ Π·Π°Π΄Π΅ΡΠΆΠΊΠΈ Π² Π½Π΅ΡΠΈΠΊΠ»ΠΈΡΠ΅ΡΠΊΠΎΠΌ Π³ΡΠ°ΡΠΈΠΊΠ΅ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ ΠΏΠΎΠ΅Π·Π΄ΠΎΠ² Ρ ΡΡΠ΅ΡΠΎΠΌ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΎΡΠΎΠ±Π΅Π½ΠΎΡΡΠ΅ΠΉ ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡΠΎΠΆΠ½ΠΎΠΉ ΡΠ΅ΡΠΈ ΡΠΌΠ΅ΡΠ°Π½Π½ΠΎΠ³ΠΎ ΠΏΠ°ΡΡΠ°ΠΆΠΈΡΡΠΊΠΎΠ³ΠΎ ΠΈ Π³ΡΡΠ·ΠΎΠ²ΠΎΠ³ΠΎ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ. Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π° ΠΏΡΠΎΡΠ΅Π΄ΡΡΠ° ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π²Π»ΠΈΡΠ½ΠΈΡ Π·Π°Π΄Π΅ΡΠΆΠΊΠΈ ΠΏΠΎΠ΅Π·Π΄ΠΎΠ² Π² Π½ΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΠΎΠΌ Π³ΡΠ°ΡΠΈΠΊΠ΅ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ ΠΏΠΎΠ΅Π·Π΄ΠΎΠ² Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΎΠ½Π½ΠΎΠΉ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ Π³ΡΠ°ΡΠΈΠΊΠ° Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ ΠΏΠΎΠ΅Π·Π΄ΠΎΠ² Ρ ΡΡΠ΅ΡΠΎΠΌ Π·Π°Π΄Π°Π½ΠΎΠΉ ΠΏΠ΅ΡΠ²ΠΈΡΠ½ΠΎΠΉ Π·Π°Π΄Π΅ΡΠΆΠΊΠΈ. ΠΡΠΎΠ²Π΅Π΄Π΅Π½Π½ΡΠ΅ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½ΠΈΡ Π·Π°Π΄Π΅ΡΠΆΠΊΠΈ Π² Π½ΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΠΎΠΌ Π³ΡΠ°ΡΠΈΠΊΠ΅ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ ΠΏΠΎΠ΅Π·Π΄ΠΎΠ² Π½Π° ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡΠΎΠΆΠ½ΠΎΠΉ Π»ΠΈΠ½ΠΈΠΈ Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ ΠΏΡΠΎΡΡΠΆΠ΅Π½Π½ΠΎΡΡΠΈΠΠ°ΠΏΡΠΎΠΏΠΎΠ½ΠΎΠ²Π°Π½ΠΎ ΠΌΠ΅ΡΠΎΠ΄ ΠΌΠΎΠ΄Π΅Π»ΡΠ²Π°Π½Π½Ρ ΡΠΎΠ·ΠΏΠΎΠ²ΡΡΠ΄ΠΆΠ΅Π½Π½Ρ Π·Π°ΡΡΠΈΠΌΠΊΠΈ Ρ Π½Π΅ΡΠΈΠΊΠ»ΡΡΠ½ΠΎΠΌΡ Π³ΡΠ°ΡΡΠΊΡ ΡΡΡ
Ρ ΠΏΠΎΡΠ·Π΄ΡΠ² Π· ΡΡΠ°Ρ
ΡΠ²Π°Π½Π½ΡΠΌ ΡΠ΅Ρ
Π½ΡΡΠ½ΠΈΡ
ΡΠ° ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΡΡΠ½ΠΈΡ
ΠΎΡΠΎΠ±Π»ΠΈΠ²ΠΎΡΡΠ΅ΠΉ Π·Π°Π»ΡΠ·Π½ΠΈΡΠ½ΠΎΡ ΠΌΠ΅ΡΠ΅ΠΆΡ Π·ΠΌΡΡΠ°Π½ΠΎΠ³ΠΎ ΡΡΡ
Ρ ΠΏΠ°ΡΠ°ΠΆΠΈΡΡΡΠΊΠΈΡ
Ρ Π²Π°Π½ΡΠ°ΠΆΠ½ΠΈΡ
ΠΏΠΎΡΠ·Π΄ΡΠ². Π ΠΎΠ·ΡΠΎΠ±Π»Π΅Π½ΠΎ ΠΏΡΠΎΡΠ΅Π΄ΡΡΡ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½Ρ Π²ΠΏΠ»ΠΈΠ²Ρ Π·Π°ΡΡΠΈΠΌΠΊΠΈ ΠΏΠΎΡΠ·Π΄ΡΠ² Ρ Π½ΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΠΎΠΌΡ Π³ΡΠ°ΡΡΠΊΡ ΡΡΡ
Ρ ΠΏΠΎΡΠ·Π΄ΡΠ² Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ ΠΎΠΏΡΠΈΠΌΡΠ·Π°ΡΡΠΉΠ½ΠΎΡ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ½ΠΎΡ ΠΌΠΎΠ΄Π΅Π»Ρ ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²ΠΈ Π³ΡΠ°ΡΡΠΊΡ ΡΡΡ
Ρ ΠΏΠΎΡΠ·Π΄ΡΠ² Π· ΡΡΠ°Ρ
ΡΠ²Π°Π½Π½Ρ Π·Π°Π΄Π°Π½ΠΎΡ ΠΏΠ΅ΡΠ²ΠΈΠ½Π½ΠΎΡ Π·Π°ΡΡΠΈΠΌΠΊΠΈ. ΠΡΠΎΠ²Π΅Π΄Π΅Π½Ρ Π΅ΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½Ρ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½Ρ ΠΌΠΎΠ΄Π΅Π»ΡΠ²Π°Π½Π½Ρ ΠΏΠΎΡΠΈΡΠ΅Π½Π½Ρ Π·Π°ΡΡΠΈΠΌΠΊΠΈ ΠΏΠΎΡΠ·Π΄ΡΠ² Ρ Π½ΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΠΎΠΌΡ Π³ΡΠ°ΡΡΠΊΡ ΡΡΡ
Ρ ΠΏΠΎΡΠ·Π΄ΡΠ² Π· ΡΡΠ°Ρ
ΡΠ²Π°Π½Π½ΡΠΌ Π²Π·Π°ΡΠΌΠΎΡΠ²βΡΠ·ΠΊΠΈ Π·Π°Π»ΡΠ·Π½ΠΈΡΠ½ΠΈΡ
Π΄ΡΠ»ΡΠ½ΠΈΡ
Constructing A Model for the Automated Operative Planning of Local Operations at Railroad Technical Stations
This paper has investigated the technology of forwarding local wagons at railroad technical stations and established the need to improve it given the extra downtime of local wagons. The main issue relates to the considerable combinatorial complexity of the tasks of operational planning. Another problem is that as part of the conventional approach, planning a station operation and planning a local operation at it is considered separately. Another planning issue is the lack of high-quality models for the preparation of initial data, in particular, data on the duration of technological operations, such as, for example, shunting operations involving local wagons forwarding. To resolve these issues, a new approach has been proposed, under which the tasks of operative planning of a technical station's operation and its subsystem of local operations are tackled simultaneously, based on a single model. To this end, a mathematical model of vector combinatoric optimization has been built, which uses the criteria of total operating costs and wagon-hours spent at a station when forwarding local wagon flows, in the form of separate objective functions. Within this model, a predictive model was constructed in the form of a fuzzy inference system. This model is designed to determine the duration of shunting half-runs when executing the spotting/picking operations for delivering local wagons to enterprises' goods sheds. The model provides for the accuracy level that would suffice at planning, in contrast to classical methods. A procedure has been devised for optimizing the planning model, which employs the modern genetic algorithm of vector optimization NSGA-III. This procedure is implemented in the form of software that makes it possible to build a rational operative plan for the operation of a technical station, including a subsystem of local operations, in graphic form, thereby reducing the operating costs by 5 % and the duration of maintenance of a local wagon by 8 %. The resulting effect could reduce the turnover time of a freight car in general on the railroad network, speed up the delivery of goods, and reduce the cost of transportatio
Constructing A Model for the Automated Operative Planning of Local Operations at Railroad Technical Stations
This paper has investigated the technology of forwarding local wagons at railroad technical stations and established the need to improve it given the extra downtime of local wagons. The main issue relates to the considerable combinatorial complexity of the tasks of operational planning. Another problem is that as part of the conventional approach, planning a station operation and planning a local operation at it is considered separately. Another planning issue is the lack of high-quality models for the preparation of initial data, in particular, data on the duration of technological operations, such as, for example, shunting operations involving local wagons forwarding. To resolve these issues, a new approach has been proposed, under which the tasks of operative planning of a technical station's operation and its subsystem of local operations are tackled simultaneously, based on a single model. To this end, a mathematical model of vector combinatoric optimization has been built, which uses the criteria of total operating costs and wagon-hours spent at a station when forwarding local wagon flows, in the form of separate objective functions. Within this model, a predictive model was constructed in the form of a fuzzy inference system. This model is designed to determine the duration of shunting half-runs when executing the spotting/picking operations for delivering local wagons to enterprises' goods sheds. The model provides for the accuracy level that would suffice at planning, in contrast to classical methods. A procedure has been devised for optimizing the planning model, which employs the modern genetic algorithm of vector optimization NSGA-III. This procedure is implemented in the form of software that makes it possible to build a rational operative plan for the operation of a technical station, including a subsystem of local operations, in graphic form, thereby reducing the operating costs by 5 % and the duration of maintenance of a local wagon by 8 %. The resulting effect could reduce the turnover time of a freight car in general on the railroad network, speed up the delivery of goods, and reduce the cost of transportatio
Molecular Typing of Ukrainian <i>Bacillus anthracis</i> Strains by Combining Whole-Genome Sequencing Techniques
Anthrax is a recurrent zoonosis in the Ukraine with outbreaks occurring repeatedly in certain areas. For determining whether several Bacillus anthracis genotypes are circulating in this region, four strains from various sources isolated from different regions of the Ukraine were investigated. By combining long- and short-read next-generation sequencing techniques, highly accurate genomes were reconstructed, enabling detailed in silico genotyping. Thus, the strains could be assigned to the Tsiankovskii subgroup of the βTransEurAsiaβ clade, which is commonly found in this region. Their high genetic similarity suggests that the four strains are members of the endemic population whose progenitor was once introduced in the Ukraine and bordering regions. This study provides information on B. anthracis strains from a region where there is little knowledge of the local population, thereby adding to the picture of global B. anthracis genotype distribution. We also emphasize the importance of surveillance and prevention methods regarding anthrax outbreaks, as other studies predicted a higher number of cases in the future due to global warming