7 research outputs found

    Π ΠΎΠ·Ρ€ΠΎΠ±ΠΊΠ° ΠΌΠ΅Ρ‚ΠΎΠ΄Ρƒ модСлювання Ρ€ΠΎΠ·ΠΏΠΎΠ²ΡΡŽΠ΄ΠΆΠ΅Π½Π½Ρ Π·Π°Ρ‚Ρ€ΠΈΠΌΠΊΠΈ Ρƒ Π½Π΅Ρ†ΠΈΠΊΠ»Ρ–Ρ‡Π½ΠΎΠΌΡƒ Π³Ρ€Π°Ρ„Ρ–ΠΊΡƒ Ρ€ΡƒΡ…Ρƒ ΠΏΠΎΡ—Π·Π΄Ρ–Π² Π½Π° залізницях Π·ΠΌΡ–ΡˆΠ°Π½ΠΎΠ³ΠΎ Ρ€ΡƒΡ…Ρƒ

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    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.ΠŸΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½ ΠΌΠ΅Ρ‚ΠΎΠ΄ модСлирования распространСния Π·Π°Π΄Π΅Ρ€ΠΆΠΊΠΈ Π² нСцикличСском Π³Ρ€Π°Ρ„ΠΈΠΊΠ΅ двиТСния ΠΏΠΎΠ΅Π·Π΄ΠΎΠ² с ΡƒΡ‡Π΅Ρ‚ΠΎΠΌ тСхничСских ΠΈ тСхнологичСских особСностСй ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡ€ΠΎΠΆΠ½ΠΎΠΉ сСти смСшанного пассаТирского ΠΈ Π³Ρ€ΡƒΠ·ΠΎΠ²ΠΎΠ³ΠΎ двиТСния. Π Π°Π·Ρ€Π°Π±ΠΎΡ‚Π°Π½Π° ΠΏΡ€ΠΎΡ†Π΅Π΄ΡƒΡ€Π° исслСдования влияния Π·Π°Π΄Π΅Ρ€ΠΆΠΊΠΈ ΠΏΠΎΠ΅Π·Π΄ΠΎΠ² Π² Π½ΠΎΡ€ΠΌΠ°Ρ‚ΠΈΠ²Π½ΠΎΠΌ Π³Ρ€Π°Ρ„ΠΈΠΊΠ΅ двиТСния ΠΏΠΎΠ΅Π·Π΄ΠΎΠ² Π½Π° основС ΠΎΠΏΡ‚ΠΈΠΌΠΈΠ·Π°Ρ†ΠΈΠΎΠ½Π½ΠΎΠΉ матСматичСской ΠΌΠΎΠ΄Π΅Π»ΠΈ построСния Π³Ρ€Π°Ρ„ΠΈΠΊΠ° двиТСния ΠΏΠΎΠ΅Π·Π΄ΠΎΠ² с ΡƒΡ‡Π΅Ρ‚ΠΎΠΌ Π·Π°Π΄Π°Π½ΠΎΠΉ ΠΏΠ΅Ρ€Π²ΠΈΡ‡Π½ΠΎΠΉ Π·Π°Π΄Π΅Ρ€ΠΆΠΊΠΈ. ΠŸΡ€ΠΎΠ²Π΅Π΄Π΅Π½Π½Ρ‹Π΅ ΡΠΊΡΠΏΠ΅Ρ€ΠΈΠΌΠ΅Π½Ρ‚Π°Π»ΡŒΠ½Ρ‹Π΅ исслСдования модСлирования распространСния Π·Π°Π΄Π΅Ρ€ΠΆΠΊΠΈ Π² Π½ΠΎΡ€ΠΌΠ°Ρ‚ΠΈΠ²Π½ΠΎΠΌ Π³Ρ€Π°Ρ„ΠΈΠΊΠ΅ двиТСния ΠΏΠΎΠ΅Π·Π΄ΠΎΠ² Π½Π° ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡ€ΠΎΠΆΠ½ΠΎΠΉ Π»ΠΈΠ½ΠΈΠΈ Π·Π½Π°Ρ‡ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΠΉ протяТСнностиЗапропоновано ΠΌΠ΅Ρ‚ΠΎΠ΄ модСлювання Ρ€ΠΎΠ·ΠΏΠΎΠ²ΡΡŽΠ΄ΠΆΠ΅Π½Π½Ρ Π·Π°Ρ‚Ρ€ΠΈΠΌΠΊΠΈ Ρƒ Π½Π΅Ρ†ΠΈΠΊΠ»Ρ–Ρ‡Π½ΠΎΠΌΡƒ Π³Ρ€Π°Ρ„Ρ–ΠΊΡƒ Ρ€ΡƒΡ…Ρƒ ΠΏΠΎΡ—Π·Π΄Ρ–Π² Π· урахуванням Ρ‚Π΅Ρ…Π½Ρ–Ρ‡Π½ΠΈΡ… Ρ‚Π° Ρ‚Π΅Ρ…Π½ΠΎΠ»ΠΎΠ³Ρ–Ρ‡Π½ΠΈΡ… особливостСй Π·Π°Π»Ρ–Π·Π½ΠΈΡ‡Π½ΠΎΡ— ΠΌΠ΅Ρ€Π΅ΠΆΡ– Π·ΠΌΡ–ΡˆΠ°Π½ΠΎΠ³ΠΎ Ρ€ΡƒΡ…Ρƒ ΠΏΠ°ΡΠ°ΠΆΠΈΡ€ΡΡŒΠΊΠΈΡ… Ρ– Π²Π°Π½Ρ‚Π°ΠΆΠ½ΠΈΡ… ΠΏΠΎΡ—Π·Π΄Ρ–Π². Π ΠΎΠ·Ρ€ΠΎΠ±Π»Π΅Π½ΠΎ ΠΏΡ€ΠΎΡ†Π΅Π΄ΡƒΡ€Ρƒ дослідТСння Π²ΠΏΠ»ΠΈΠ²Ρƒ Π·Π°Ρ‚Ρ€ΠΈΠΌΠΊΠΈ ΠΏΠΎΡ—Π·Π΄Ρ–Π² Ρƒ Π½ΠΎΡ€ΠΌΠ°Ρ‚ΠΈΠ²Π½ΠΎΠΌΡƒ Π³Ρ€Π°Ρ„Ρ–ΠΊΡƒ Ρ€ΡƒΡ…Ρƒ ΠΏΠΎΡ—Π·Π΄Ρ–Π² Π½Π° основі ΠΎΠΏΡ‚ΠΈΠΌΡ–Π·Π°Ρ†Ρ–ΠΉΠ½ΠΎΡ— ΠΌΠ°Ρ‚Π΅ΠΌΠ°Ρ‚ΠΈΡ‡Π½ΠΎΡ— ΠΌΠΎΠ΄Π΅Π»Ρ– ΠΏΠΎΠ±ΡƒΠ΄ΠΎΠ²ΠΈ Π³Ρ€Π°Ρ„Ρ–ΠΊΡƒ Ρ€ΡƒΡ…Ρƒ ΠΏΠΎΡ—Π·Π΄Ρ–Π² Π· урахування Π·Π°Π΄Π°Π½ΠΎΡ— ΠΏΠ΅Ρ€Π²ΠΈΠ½Π½ΠΎΡ— Π·Π°Ρ‚Ρ€ΠΈΠΌΠΊΠΈ. ΠŸΡ€ΠΎΠ²Π΅Π΄Π΅Π½Ρ– Π΅ΠΊΡΠΏΠ΅Ρ€ΠΈΠΌΠ΅Π½Ρ‚Π°Π»ΡŒΠ½Ρ– дослідТСння модСлювання ΠΏΠΎΡˆΠΈΡ€Π΅Π½Π½Ρ Π·Π°Ρ‚Ρ€ΠΈΠΌΠΊΠΈ ΠΏΠΎΡ—Π·Π΄Ρ–Π² Ρƒ Π½ΠΎΡ€ΠΌΠ°Ρ‚ΠΈΠ²Π½ΠΎΠΌΡƒ Π³Ρ€Π°Ρ„Ρ–ΠΊΡƒ Ρ€ΡƒΡ…Ρƒ ΠΏΠΎΡ—Π·Π΄Ρ–Π² Π· урахуванням взаємоув’язки Π·Π°Π»Ρ–Π·Π½ΠΈΡ‡Π½ΠΈΡ… Π΄Ρ–Π»ΡŒΠ½ΠΈΡ†

    Ѐормування ΠΌΠΎΠ΄Π΅Π»Ρ– планування ΡˆΠ²ΠΈΠ΄ΠΊΠΎΡ— доставки ΠΊΠΎΠ½Ρ‚Π΅ΠΉΠ½Π΅Ρ€Ρ–Π² Π·Π°Π»Ρ–Π·Π½ΠΈΡ†Π΅ΡŽ Π² ΡƒΠΌΠΎΠ²Π°Ρ… Ρ–Π½Ρ‚Π΅Ρ€ΠΌΠΎΠ΄Π°Π»ΡŒΠ½ΠΈΡ… ΠΏΠ΅Ρ€Π΅Π²Π΅Π·Π΅Π½ΡŒ Π½Π° основі робастної ΠΎΠΏΡ‚ΠΈΠΌΡ–Π·Π°Ρ†Ρ–Ρ—

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    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 processДослідТСно ΠΌΠΎΠΆΠ»ΠΈΠ²Ρ–ΡΡ‚ΡŒ створСння Ρ‚Π΅Ρ…Π½ΠΎΠ»ΠΎΠ³Ρ–Ρ— швидкого Π·Π°Π»Ρ–Π·Π½ΠΈΡ‡Π½ΠΎΠ³ΠΎ сполучСння для транспортування ΠΊΠΎΠ½Ρ‚Π΅ΠΉΠ½Π΅Ρ€Ρ–Π² ΠΌΡ–ΠΆ ΠΏΠΎΡ€Ρ‚ΠΎΠΌ Ρ‚Π° підприємствами-ΠΊΠ»Ρ–Ρ”Π½Ρ‚Π°ΠΌΠΈ Π² Ρ…ΠΎΠ΄Ρ– здійснСння Ρ–Π½Ρ‚Π΅Ρ€ΠΌΠΎΠ΄Π°Π»ΡŒΠ½ΠΈΡ… ΠΏΠ΅Ρ€Π΅Π²Π΅Π·Π΅Π½ΡŒ. ΠœΠ΅Ρ‚ΠΎΡŽ Ρ€ΠΎΠ·Ρ€ΠΎΠ±ΠΊΠΈ Ρ‚Π΅Ρ…Π½ΠΎΠ»ΠΎΠ³Ρ–Ρ— Ρ” змСншСння частки використання автотранспорту Π½Π° Ρ–Π½Ρ‚Π΅Ρ€ΠΌΠΎΠ΄Π°Π»ΡŒΠ½ΠΈΡ… ΠΌΠ°Ρ€ΡˆΡ€ΡƒΡ‚Π°Ρ… Ρ‚Π° Π²ΠΈΡ€Ρ–ΡˆΠ΅Π½Π½Ρ Ρ‚Π°ΠΊΠΈΠΌ Ρ‡ΠΈΠ½ΠΎΠΌ Ρ†Ρ–Π»ΠΎΡ— Π½ΠΈΠ·ΠΊΠΈ ΠΏΠΎΠ²'язаних Π· Π½ΠΈΠΌ Π΅ΠΊΠΎΠ»ΠΎΠ³Ρ–Ρ‡Π½ΠΈΡ…, транспортних, ΠΌΡƒΠ½Ρ–Ρ†ΠΈΠΏΠ°Π»ΡŒΠ½ΠΈΡ… Ρ‚Π° Π΅ΠΊΠΎΠ½ΠΎΠΌΡ–Ρ‡Π½ΠΈΡ… ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌ. Π‘Ρ„ΠΎΡ€ΠΌΠΎΠ²Π°Π½Π° тСхнологія Π±Π°Π·ΡƒΡ”Ρ‚ΡŒΡΡ Π½Π° ΠΏΡ€ΠΈΠ½Ρ†ΠΈΠΏΠ°Ρ… максимального наблиТСння Π·Π°Π»Ρ–Π·Π½ΠΈΡ†Ρ– Π΄ΠΎ ΠΊΡ–Π½Ρ†Π΅Π²ΠΈΡ… ΠΏΡƒΠ½ΠΊΡ‚Ρ–Π² ΠΌΠ°Ρ€ΡˆΡ€ΡƒΡ‚Ρƒ, ΠΌΡ–Π½Ρ–ΠΌΡ–Π·Π°Ρ†Ρ–Ρ— ΠΊΡ–Π»ΡŒΠΊΠΎΡΡ‚Ρ– ΠΏΡ€ΠΎΠΌΡ–ΠΆΠ½ΠΈΡ… Π²ΠΈΠ΄Ρ–Π² транспорту Ρ‚Π° забСзпСчСння ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡŒΠ½ΠΎΡ— ΡˆΠ²ΠΈΠ΄ΠΊΠΎΡΡ‚Ρ– просування ΠΊΠΎΠ½Ρ‚Π΅ΠΉΠ½Π΅Ρ€Ρ–Π² Π·Π°Π»Ρ–Π·Π½ΠΈΡ†Π΅ΡŽ. Π— Ρ†Ρ–Ρ”ΡŽ ΠΌΠ΅Ρ‚ΠΎΡŽ Π·Π°ΠΏΡ€ΠΎΠΏΠΎΠ½ΠΎΠ²Π°Π½Π΅ застосування Π²Π°Π½Ρ‚Π°ΠΆΠ½ΠΈΡ… Ρ‚Π΅Ρ€ΠΌΡ–Π½Π°Π»Ρ–Π² MetroCargoβ„’ Ρ‚Π° ΠΌΠΎΠ΄ΡƒΠ»ΡŒΠ½ΠΈΡ… ΠΏΠΎΡ—Π·Π΄Ρ–Π² CargoSprinter. Π’ Ρ…ΠΎΠ΄Ρ– дослідТСння Π±ΡƒΠ»Π° поставлСна Ρ– Π²ΠΈΡ€Ρ–ΡˆΠ΅Π½Π° Π·Π°Π΄Π°Ρ‡Π° Π½Π°Π΄Ρ–ΠΉΠ½ΠΎΠ³ΠΎ ΠΎΠΏΠ΅Ρ€Π°Ρ‚ΠΈΠ²Π½ΠΎΠ³ΠΎ планування Ρ€ΠΎΠ±ΠΎΡ‚ΠΈ ΠΏΠ°Ρ€ΠΊΡƒ Ρ‚Π°ΠΊΠΈΡ… ΠΏΠΎΡ—Π·Π΄Ρ–Π² для здійснСння доставки ΠΊΠΎΠ½Ρ‚Π΅ΠΉΠ½Π΅Ρ€Ρ–Π² ΠΌΡ–ΠΆ ΠΏΠΎΡ€Ρ‚ΠΎΠΌ Ρ– підприємствами Π² ΡƒΠΌΠΎΠ²Π°Ρ… "Π·Π°Π±Ρ€ΡƒΠ΄Π½Π΅Π½ΠΈΡ…" Π²ΠΈΡ…Ρ–Π΄Π½ΠΈΡ… Π΄Π°Π½ΠΈΡ…. Π— Ρ†Ρ–Ρ”ΡŽ ΠΌΠ΅Ρ‚ΠΎΡŽ Π·Π°Π΄Π°Ρ‡Π° Ρ„ΠΎΡ€ΠΌΠ°Π»Ρ–Π·ΠΎΠ²Π°Π½Π° Ρƒ вигляді ΠΌΠΎΠ΄Π΅Π»Ρ– Π·ΠΌΡ–ΡˆΠ°Π½ΠΎΠ³ΠΎ програмування, Ρ‰ΠΎ Π±Π°Π·ΡƒΡ”Ρ‚ΡŒΡΡ Π½Π° ΠΏΡ€ΠΈΠ½Ρ†ΠΈΠΏΠ°Ρ… робастної ΠΎΠΏΡ‚ΠΈΠΌΡ–Π·Π°Ρ†Ρ–Ρ—. Для здійснСння ΠΎΠΏΡ‚ΠΈΠΌΡ–Π·Π°Ρ†Ρ–Ρ— ΠΌΠΎΠ΄Π΅Π»Ρ– Ρ–Π· урахуванням ΠΏΡ€ΠΈΠ½Ρ†ΠΈΠΏΡ–Π² робастності Π±ΡƒΠ»Π° Π·Π°ΠΏΡ€ΠΎΠΏΠΎΠ½ΠΎΠ²Π°Π½Π° ΠΏΡ€ΠΎΡ†Π΅Π΄ΡƒΡ€Π°, яка використовує Π΄Π²ΠΎΠΊΠΎΠ½Ρ‚ΡƒΡ€Π½ΠΈΠΉ Π³Π΅Π½Π΅Ρ‚ΠΈΡ‡Π½ΠΈΠΉ Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌ. Π’ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ– модСлювання Π±ΡƒΠ»ΠΎ встановлСно, Ρ‰ΠΎ ΠΎΡ‚Ρ€ΠΈΠΌΠ°Π½ΠΈΠΉ ΠΏΠ»Π°Π½ лишС Π½Π° 6,5 % ΠΏΠΎΡΡ‚ΡƒΠΏΠ°Ρ”Ρ‚ΡŒΡΡ Π·Π° Ρ†Ρ–Π»ΡŒΠΎΠ²ΠΈΠΌ ΠΊΡ€ΠΈΡ‚Π΅Ρ€Ρ–Ρ”ΠΌ ΠΏΠ»Π°Π½Ρƒ, який Π±ΡƒΠ»ΠΎ ΠΎΡ‚Ρ€ΠΈΠΌΠ°Π½ΠΎ Π±Π΅Π· урахування робастності. Π‘ΡƒΠ»ΠΎ Π΄ΠΎΠ²Π΅Π΄Π΅Π½ΠΎ, Ρ‰ΠΎ Ρ€ΠΎΠ·Ρ€ΠΎΠ±Π»Π΅Π½Π° модСль дозволяє ΠΎΡ‚Ρ€ΠΈΠΌΡƒΠ²Π°Ρ‚ΠΈ ΠΎΠΏΠ΅Ρ€Π°Ρ‚ΠΈΠ²Π½ΠΈΠΉ ΠΏΠ»Π°Π½ доставки ΠΊΠΎΠ½Ρ‚Π΅ΠΉΠ½Π΅Ρ€Ρ–Π² Π·Π°Π»Ρ–Π·Π½ΠΈΡ†Π΅ΡŽ, який Ρ” близьким Π΄ΠΎ ΠΎΠΏΡ‚ΠΈΠΌΠ°Π»ΡŒΠ½ΠΎΠ³ΠΎ. Π’ Ρ‚ΠΎΠΉ ΠΆΠ΅ час, ΠΏΠ»Π°Π½ Ρ” Ρ€Π΅Π°Π»Ρ–Π·ΠΎΠ²Π°Π½ΠΈΠΌ Π½Π°Π²Ρ–Ρ‚ΡŒ Ρƒ Π²ΠΈΠΏΠ°Π΄ΠΊΡƒ Π½Π°ΠΉΠ±Ρ–Π»ΡŒΡˆ нСсприятливого Π·Π±Ρ–Π³Ρƒ обставин Ρƒ вигляді Π·Π°Ρ‚Ρ€ΠΈΠΌΠΎΠΊ, зсуву часових Π²Ρ–ΠΊΠΎΠ½ Ρ€ΠΎΠ±ΠΎΡ‚ΠΈ Π²Π°Π½Ρ‚Π°ΠΆΠ½ΠΈΡ… Ρ„Ρ€ΠΎΠ½Ρ‚Ρ–Π² Ρ‚ΠΎΡ‰ΠΎ, Ρ‚ΠΎΠ±Ρ‚ΠΎ Π² Ρ€Π΅Π°Π»ΡŒΠ½ΠΈΡ… ΡƒΠΌΠΎΠ²Π°Ρ… транспортного процСс

    Π ΠΎΠ·Ρ€ΠΎΠ±ΠΊΠ° ΠΌΠ΅Ρ‚ΠΎΠ΄Ρƒ модСлювання Ρ€ΠΎΠ·ΠΏΠΎΠ²ΡΡŽΠ΄ΠΆΠ΅Π½Π½Ρ Π·Π°Ρ‚Ρ€ΠΈΠΌΠΊΠΈ Ρƒ Π½Π΅Ρ†ΠΈΠΊΠ»Ρ–Ρ‡Π½ΠΎΠΌΡƒ Π³Ρ€Π°Ρ„Ρ–ΠΊΡƒ Ρ€ΡƒΡ…Ρƒ ΠΏΠΎΡ—Π·Π΄Ρ–Π² Π½Π° залізницях Π·ΠΌΡ–ΡˆΠ°Π½ΠΎΠ³ΠΎ Ρ€ΡƒΡ…Ρƒ

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    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.ΠŸΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½ ΠΌΠ΅Ρ‚ΠΎΠ΄ модСлирования распространСния Π·Π°Π΄Π΅Ρ€ΠΆΠΊΠΈ Π² нСцикличСском Π³Ρ€Π°Ρ„ΠΈΠΊΠ΅ двиТСния ΠΏΠΎΠ΅Π·Π΄ΠΎΠ² с ΡƒΡ‡Π΅Ρ‚ΠΎΠΌ тСхничСских ΠΈ тСхнологичСских особСностСй ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡ€ΠΎΠΆΠ½ΠΎΠΉ сСти смСшанного пассаТирского ΠΈ Π³Ρ€ΡƒΠ·ΠΎΠ²ΠΎΠ³ΠΎ двиТСния. Π Π°Π·Ρ€Π°Π±ΠΎΡ‚Π°Π½Π° ΠΏΡ€ΠΎΡ†Π΅Π΄ΡƒΡ€Π° исслСдования влияния Π·Π°Π΄Π΅Ρ€ΠΆΠΊΠΈ ΠΏΠΎΠ΅Π·Π΄ΠΎΠ² Π² Π½ΠΎΡ€ΠΌΠ°Ρ‚ΠΈΠ²Π½ΠΎΠΌ Π³Ρ€Π°Ρ„ΠΈΠΊΠ΅ двиТСния ΠΏΠΎΠ΅Π·Π΄ΠΎΠ² Π½Π° основС ΠΎΠΏΡ‚ΠΈΠΌΠΈΠ·Π°Ρ†ΠΈΠΎΠ½Π½ΠΎΠΉ матСматичСской ΠΌΠΎΠ΄Π΅Π»ΠΈ построСния Π³Ρ€Π°Ρ„ΠΈΠΊΠ° двиТСния ΠΏΠΎΠ΅Π·Π΄ΠΎΠ² с ΡƒΡ‡Π΅Ρ‚ΠΎΠΌ Π·Π°Π΄Π°Π½ΠΎΠΉ ΠΏΠ΅Ρ€Π²ΠΈΡ‡Π½ΠΎΠΉ Π·Π°Π΄Π΅Ρ€ΠΆΠΊΠΈ. ΠŸΡ€ΠΎΠ²Π΅Π΄Π΅Π½Π½Ρ‹Π΅ ΡΠΊΡΠΏΠ΅Ρ€ΠΈΠΌΠ΅Π½Ρ‚Π°Π»ΡŒΠ½Ρ‹Π΅ исслСдования модСлирования распространСния Π·Π°Π΄Π΅Ρ€ΠΆΠΊΠΈ Π² Π½ΠΎΡ€ΠΌΠ°Ρ‚ΠΈΠ²Π½ΠΎΠΌ Π³Ρ€Π°Ρ„ΠΈΠΊΠ΅ двиТСния ΠΏΠΎΠ΅Π·Π΄ΠΎΠ² Π½Π° ΠΆΠ΅Π»Π΅Π·Π½ΠΎΠ΄ΠΎΡ€ΠΎΠΆΠ½ΠΎΠΉ Π»ΠΈΠ½ΠΈΠΈ Π·Π½Π°Ρ‡ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΠΉ протяТСнностиЗапропоновано ΠΌΠ΅Ρ‚ΠΎΠ΄ модСлювання Ρ€ΠΎΠ·ΠΏΠΎΠ²ΡΡŽΠ΄ΠΆΠ΅Π½Π½Ρ Π·Π°Ρ‚Ρ€ΠΈΠΌΠΊΠΈ Ρƒ Π½Π΅Ρ†ΠΈΠΊΠ»Ρ–Ρ‡Π½ΠΎΠΌΡƒ Π³Ρ€Π°Ρ„Ρ–ΠΊΡƒ Ρ€ΡƒΡ…Ρƒ ΠΏΠΎΡ—Π·Π΄Ρ–Π² Π· урахуванням Ρ‚Π΅Ρ…Π½Ρ–Ρ‡Π½ΠΈΡ… Ρ‚Π° Ρ‚Π΅Ρ…Π½ΠΎΠ»ΠΎΠ³Ρ–Ρ‡Π½ΠΈΡ… особливостСй Π·Π°Π»Ρ–Π·Π½ΠΈΡ‡Π½ΠΎΡ— ΠΌΠ΅Ρ€Π΅ΠΆΡ– Π·ΠΌΡ–ΡˆΠ°Π½ΠΎΠ³ΠΎ Ρ€ΡƒΡ…Ρƒ ΠΏΠ°ΡΠ°ΠΆΠΈΡ€ΡΡŒΠΊΠΈΡ… Ρ– Π²Π°Π½Ρ‚Π°ΠΆΠ½ΠΈΡ… ΠΏΠΎΡ—Π·Π΄Ρ–Π². Π ΠΎΠ·Ρ€ΠΎΠ±Π»Π΅Π½ΠΎ ΠΏΡ€ΠΎΡ†Π΅Π΄ΡƒΡ€Ρƒ дослідТСння Π²ΠΏΠ»ΠΈΠ²Ρƒ Π·Π°Ρ‚Ρ€ΠΈΠΌΠΊΠΈ ΠΏΠΎΡ—Π·Π΄Ρ–Π² Ρƒ Π½ΠΎΡ€ΠΌΠ°Ρ‚ΠΈΠ²Π½ΠΎΠΌΡƒ Π³Ρ€Π°Ρ„Ρ–ΠΊΡƒ Ρ€ΡƒΡ…Ρƒ ΠΏΠΎΡ—Π·Π΄Ρ–Π² Π½Π° основі ΠΎΠΏΡ‚ΠΈΠΌΡ–Π·Π°Ρ†Ρ–ΠΉΠ½ΠΎΡ— ΠΌΠ°Ρ‚Π΅ΠΌΠ°Ρ‚ΠΈΡ‡Π½ΠΎΡ— ΠΌΠΎΠ΄Π΅Π»Ρ– ΠΏΠΎΠ±ΡƒΠ΄ΠΎΠ²ΠΈ Π³Ρ€Π°Ρ„Ρ–ΠΊΡƒ Ρ€ΡƒΡ…Ρƒ ΠΏΠΎΡ—Π·Π΄Ρ–Π² Π· урахування Π·Π°Π΄Π°Π½ΠΎΡ— ΠΏΠ΅Ρ€Π²ΠΈΠ½Π½ΠΎΡ— Π·Π°Ρ‚Ρ€ΠΈΠΌΠΊΠΈ. ΠŸΡ€ΠΎΠ²Π΅Π΄Π΅Π½Ρ– Π΅ΠΊΡΠΏΠ΅Ρ€ΠΈΠΌΠ΅Π½Ρ‚Π°Π»ΡŒΠ½Ρ– дослідТСння модСлювання ΠΏΠΎΡˆΠΈΡ€Π΅Π½Π½Ρ Π·Π°Ρ‚Ρ€ΠΈΠΌΠΊΠΈ ΠΏΠΎΡ—Π·Π΄Ρ–Π² Ρƒ Π½ΠΎΡ€ΠΌΠ°Ρ‚ΠΈΠ²Π½ΠΎΠΌΡƒ Π³Ρ€Π°Ρ„Ρ–ΠΊΡƒ Ρ€ΡƒΡ…Ρƒ ΠΏΠΎΡ—Π·Π΄Ρ–Π² Π· урахуванням взаємоув’язки Π·Π°Π»Ρ–Π·Π½ΠΈΡ‡Π½ΠΈΡ… Π΄Ρ–Π»ΡŒΠ½ΠΈΡ†

    Constructing A Model for the Automated Operative Planning of Local Operations at Railroad Technical Stations

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    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

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    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

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    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 β€œTransEurAsia” 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
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