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Статті в журналах з теми "Rolling planning"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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1

Kaganovich, Michael. "Rolling planning: Optimality and decentralization." Journal of Economic Behavior & Organization 29, no. 1 (January 1996): 173–85. http://dx.doi.org/10.1016/0167-2681(95)00056-9.

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2

Butkevičius, Jonas, Leonas Povilas Lingaitis, and Gediminas Vaičiūnas. "ROLLING STOCK PLANNING FOR PASSENGER TRANSPORTATION." TRANSPORT 19, no. 5 (October 31, 2004): 202–6. http://dx.doi.org/10.3846/16484142.2004.9637977.

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Анотація:

The planning of the optimal rolling stock for passenger carriage should be based on passenger flow forecasts, close examination of the available locomotives and cars and properly chosen methods of their writing off and plans for rolling stock renewal. The strategy of renewing the existing rolling stock for passenger transportation (including plans for writing off, modernization and purchasing of cars and locomotives) is offered by the authors which is based on forecasts of passenger flow variation and expenses.

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3

Suzuki, Kodai, Mikhail Svinin, and Shigeyuki Hosoe. "Motion Planning for Rolling-Based Locomotion." Journal of Robotics and Mechatronics 17, no. 5 (October 20, 2005): 537–45. http://dx.doi.org/10.20965/jrm.2005.p0537.

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The basic motivation of this paper is to study locomotion on a hemisphere. To understand the problem we resort to a simplified quasi-static model in which the locomotion object is represented by a mass point. In this formulation, the driving principle is based on controlling the position of the center of mass of the object, exploiting non-holonomic rolling constraint to propel the hemisphere. The principle is tested under simulation using two motion planning algorithms. The simulation results show the possibility of steering the loocmotion system to the desired configurations by moving the center of mass through multiple generalized figure eights on the main hemisphere plane.

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4

Rickards, Robert C., and Rolf Ritsert. "Rediscovering Rolling Planning: Controller's Roadmap for Implementing Rolling Instruments in SMEs." Procedia Economics and Finance 2 (2012): 135–44. http://dx.doi.org/10.1016/s2212-5671(12)00073-1.

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5

Wang, Zhaotian, Yezhuo Li, and Yan-An Yao. "Motion and path planning of a novel multi-mode mobile parallel robot based on chessboard-shaped grid division." Industrial Robot: An International Journal 45, no. 3 (May 21, 2018): 390–400. http://dx.doi.org/10.1108/ir-01-2018-0001.

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Анотація:

Purpose The purpose of this paper is to put forward a rolling assistant robot with two rolling modes, and the multi-mode rolling motion strategy with path planning algorithm, which is suitable to this multi-mode mobile robot, is proposed based on chessboard-shaped grid division (CGD). Design/methodology/approach Based on the kinematic analysis and motion properties of the mobile parallel robot, the motion strategy based on CGD path planning algorithm of a mobile robot with two rolling modes moving to a target position is divided into two parts, which are local self-motion planning and global path planning. In the first part, the mobile parallel robot can move by switching and combining the two rolling modes; and in the second part, the specific algorithm of the global path planning is proposed according to the CGD of the moving ground. Findings The assistant robot, which is a novel 4-RSR mobile parallel robot (where R denotes a revolute joint and S denotes a spherical joint) integrating operation and rolling locomotion (Watt linkage rolling mode and 6R linkage rolling mode), can work as a moving spotlight or worktable. A series of simulation and prototype experiment results are presented to verify the CGD path planning strategy of the robot, and the performance of the path planning experiments in simulations and practices shows the validation of the path planning analysis. Originality/value The work presented in this paper is a further exploration to apply parallel mechanisms with two rolling modes to the field of assistant rolling robots by proposing the CGD path planning strategy. It is also a new attempt to use the specific path planning algorithm in the field of mobile robots for operating tasks.

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6

de Sampaio, Raimundo J. B., Rafael R. G. Wollmann, and Paula F. G. Vieira. "A flexible production planning for rolling-horizons." International Journal of Production Economics 190 (August 2017): 31–36. http://dx.doi.org/10.1016/j.ijpe.2017.01.003.

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7

Yan, Feng-Lin, Lin Hua, and Yong-Qiao Wu. "Planning feed speed in cold ring rolling." International Journal of Machine Tools and Manufacture 47, no. 11 (September 2007): 1695–701. http://dx.doi.org/10.1016/j.ijmachtools.2007.01.009.

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8

Piccinocchi, S., A. Bicchi, A. Marigo, and M. Ceccarelli. "Planning Motions of Polyhedral Parts by Rolling." Algorithmica 26, no. 3-4 (March 1, 2000): 560–76. http://dx.doi.org/10.1007/s004539910024.

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9

Bai, Yang, Mikhail Svinin, and Motoji Yamamoto. "2P1-F03 Reduced Dynamic Model Based Motion Planning for a Spherical Rolling Robot." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2015 (2015): _2P1—F03_1—_2P1—F03_3. http://dx.doi.org/10.1299/jsmermd.2015._2p1-f03_1.

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10

Khodaee, Alireza, and Arne Melander. "Process Planning of Gear Rolling with the Finite Element Method." Key Engineering Materials 622-623 (September 2014): 986–92. http://dx.doi.org/10.4028/www.scientific.net/kem.622-623.986.

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Анотація:

Gear rolling is a new gear manufacturing process which can partly replace traditional gear milling processes. High gear wheels with modules of 4mm up to 6mm are of interest to truck manufacturing. The process is of interest since it involves no material removal and since it has the potential to give good performance of the gear wheels. The process must be adopted for the large plastic deformations which occur for gear rolling with large modules. In this paper special emphasis will be put to loads and torques during the gear rolling process of gear wheels with large modulus. The FE method will be used to model the plastic deformation process to fully form a gear wheel with the gear rolling method. The radial and axial loads and the torques in this process are predicted. The loads of the process are high compared to the situation for small gear wheels so simulation of load level is essential for the design of rolling machines for high gears.

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11

Liu, Li Jun, Zhi Wen, Fu Yong Su, Rui Feng Dou, Xun Liang Liu, and Guo Feng Lou. "Research and Application of Integrated Scheduling Management System of Steelmaking-Casting-Rolling Based on Heat Process Model." Advanced Materials Research 760-762 (September 2013): 1017–22. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1017.

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Анотація:

by studying steelmaking-casting-rolling production process, a mathematical model of integrated batch planning is established innovatively to minimize processing costs of whole line and computed by an intelligent optimization ant algorithm. On the premise of utilizing and coordinating capacity of steelmaking and rolling, the mathematical model of integrated batch planning can create integrated batch planning to combine steelmaking-casting-rolling closely according to each process constraint conditions and optimization objective. Furthermore, another mathematical model of job scheduling based on heat process model is proposed to not only guarantee the logistics balance between continuous casting and hot rolling, but also acquire the highest ratio of DHCR and lowest energy consumption.

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12

Suzuki, K., M. Svinin, and S. Hosoe. "A Study on Motion Planning for Rolling-Based Locomotion(Grasping(OS),Session: MP2-A)." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2004.4 (2004): 32. http://dx.doi.org/10.1299/jsmeicam.2004.4.32_2.

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13

Jia, Xue Mei, and Qi Yuan Sun. "An Improved Algorithm of Path Planning for a Mobile Robot." Applied Mechanics and Materials 392 (September 2013): 253–56. http://dx.doi.org/10.4028/www.scientific.net/amm.392.253.

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Анотація:

Navigation is a key technology of mobile robot. The information of path planning for navigation is always incomplete in dynamic time-varying environment. In this paper, based on grid environmental model, an improved algorithm of path planning is proposed combining ant colony optimization with rolling window for an unknown complex environment with obstacles. A sub-target point can be searched within a rolling window according to the heuristic information about a target point, the improved algorithm can plan a local optimal path for the mobile robot in a rolling window. Simulations show that the improved method is feasible for path planning in an unknown environment.

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14

Long, Jun, Cong Li, Lei Zhu, Shilong Chen, and Junfeng Liu. "An Efficient Task Autonomous Planning Method for Small Satellites." Information 9, no. 7 (July 20, 2018): 181. http://dx.doi.org/10.3390/info9070181.

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Анотація:

Existing on-board planning systems do not apply to small satellites with limited onboard computer capacity and on-board resources. This study aims to investigate the problem of autonomous task planning for small satellites. Based on the analysis of the problem and its constraints, a model of task autonomous planning was implemented. According to the long-cycle task planning requirements, a framework of rolling planning was proposed, including a rolling window and planning unit in the solution, and we proposed an improved genetic algorithm (IGA) for rolling planning. This algorithm categorized each individual based on the compliance of individuals with a time partial order constraint and resource constraint, and designed an appropriate crossover operator and mutation operator for each type of individual. The experimental result showed that the framework and algorithm can not only respond quickly to observation tasks, but can produce effective planning programs to ensure the successful completion of observation tasks.

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15

Cacchiani, Valentina, Alberto Caprara, Laura Galli, Leo Kroon, Gábor Maróti, and Paolo Toth. "Railway Rolling Stock Planning: Robustness Against Large Disruptions." Transportation Science 46, no. 2 (May 2012): 217–32. http://dx.doi.org/10.1287/trsc.1110.0388.

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16

Woodruff, J. Zachary, Shufeng Ren, and Kevin M. Lynch. "Motion Planning and Feedback Control of Rolling Bodies." IEEE Access 8 (2020): 31780–91. http://dx.doi.org/10.1109/access.2020.2973416.

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17

Specian, Andrew, Caio Mucchiani, Mark Yim, and Jungwon Seo. "Robotic Edge-Rolling Manipulation: A Grasp Planning Approach." IEEE Robotics and Automation Letters 3, no. 4 (October 2018): 3137–44. http://dx.doi.org/10.1109/lra.2018.2849828.

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18

Bødal, Espen Flo, Audun Botterud, and Magnus Korpås. "Capacity Expansion Planning with Stochastic Rolling Horizon Dispatch." Electric Power Systems Research 205 (April 2022): 107729. http://dx.doi.org/10.1016/j.epsr.2021.107729.

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19

Jonaitis, Jonas. "PLANNING OF THE AMOUNT OF TRAINS NEEDED FOR TRANSPORTATION BY RAIL." TRANSPORT 22, no. 2 (June 30, 2007): 83–89. http://dx.doi.org/10.3846/16484142.2007.9638104.

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Анотація:

The article analyses the importance of planning process of rail transportation. Railway planning problems are presented in this paper. Planning the railways for years, months, weeks or days ahead leads to substantially different problems; in this regard railway planning problems can be strategic, tactical, operational and short‐term. Another way to classify railway planning problems is based on their target: they concern the timetable, the rolling stock and the crew. Planning the structure and volume of the rolling stock is a key factor in achieving maximum efficiency of transportation by rail as well as forecasting the demand for these transport facilities. The demand for trains is a time‐dependant variable which in each case should be determined by two main approaches. The first method allows us to determine quantitative parameters of rolling stock (i.e. kilometers logged, efficiency, turnover, etc.). The second is based on specially developed mathematical models relying on qualitative characteristics such as relative expenses, efficiency of the particular train, relative expenditure of resources, cost of the trains, etc. Planning the volume of the rolling stock the determination of optimal service life of the trains plays an important role. The calculations involve repair costs, number of overhauls, current expenses and operational characteristics.

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20

Kaminskas, Saulius. "STRATEGIC PLANNING OF THE ROLLING STOCK IN TRANSPORTATION BY RAIL." TRANSPORT 17, no. 6 (December 31, 2002): 230–33. http://dx.doi.org/10.3846/16483840.2002.10414049.

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Анотація:

Planning the structure and volume of the rolling stock is a key factor of achieving maximum efficiency of transportation by rail as well as forecasting the demand for these it transport facilities. The demand for trains is a time — dependant variable which in each case should be determined by two main approaches. The first method allows us to determine quantitative parameters of rolling stock (i.e. kilometers logged, efficiency, turnover, etc.). The second is based on specially developed mathematical models relying on qualitative characteristics such as relative expenses, efficiency of the particular train, relative expenditure of resources, cost of the trains, etc. Planning the volume of the rolling stock the determination of optimal service life of the trains plays an important role. The calculations involve repair costs, number of overhauls, current expenses and other operational characteristics.

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21

Yang, Wang Lin, Hai Tong Xu, Song Lin Yang, and Sheng Zhang. "System Identification of Unmanned Planning Boat Rolling Motion Mode." Advanced Materials Research 823 (October 2013): 285–90. http://dx.doi.org/10.4028/www.scientific.net/amr.823.285.

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In this paper, the author took an unmanned planning boat as the object of study and carried out a series of rolling decay ship model test by changing the draft. The author established nine kinds of mathematical model of rolling decay motion model system identification by using different damping and righting moment and established the optimization calculation of the objective function based on the principle of system identification. Then the author adapted the genetic algorithm of system identification program which is based on the Visual Basic 6.0 and got 15 kinds of identification programs. By doing research on the first three cycles of the series of rolling angular velocity curve and identifying respectively the resulting 15 kinds of identification programs, the author confirmed the feasibility of the adapted program. Comparing different drafts and the initial roll angle identification results, the author found a reasonable hydrostatic roll motion equation of the unmanned planning boat in the case of different drafts and the initial roll angle, and made a preliminary analysis.

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22

Li, Mingyu, and Peter Schütz. "Planning Annual LNG Deliveries with Transshipment." Energies 13, no. 6 (March 21, 2020): 1490. http://dx.doi.org/10.3390/en13061490.

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Анотація:

The introduction of transshipment ports in the liquefied natural gas (LNG) supply chain in recent years offers additional flexibility, but also challenges to the planning of the annual delivery program. We present a new variant of the LNG-annual delivery program (ADP) planning problem by considering transshipment as well as time-dependent sailing times. We present a continuous time formulation for the LNG-ADP problem and propose a rolling horizon heuristic to solve the problem. Both the model and heuristic were used to solve a case inspired by the Yamal LNG project. The computational results show that the heuristic provides good solutions within a relatively short amount of time, especially compared to the exact solution methods. However, there is a trade-off between computational time and solution quality when designing the rolling horizon heuristic. The results also show the impact storage capacity at the transshipment port has on the total cost.

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23

Hu, Zhengbiao, Dongfeng He, Wei Song, and Kai Feng. "Model and Algorithm for Planning Hot-Rolled Batch Processing under Time-of-Use Electricity Pricing." Processes 8, no. 1 (January 1, 2020): 42. http://dx.doi.org/10.3390/pr8010042.

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Анотація:

Batch-type hot rolling planning highly affects electricity costs in a steel plant, but previous research models seldom considered time-of-use (TOU) electricity pricing. Based on an analysis of the hot-rolling process and TOU electricity pricing, a batch-processing plan optimization model for hot rolling was established, using an objective function with the goal of minimizing the total penalty incurred by the differences in width, thickness, and hardness among adjacent slabs, as well as the electricity cost of the rolling process. A method was provided to solve the model through improved genetic algorithm. An analysis of the batch processing of the hot rolling of 240 slabs of different sizes at a steel plant proved the effectiveness of the proposed model. Compared to the man–machine interaction model and the model in which TOU electricity pricing was not considered, the batch-processing model that included TOU electricity pricing produced significantly better results with respect to both product quality and power consumption.

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24

LIANG, HELAN, and SUJIAN LI. "CC-DHCR PLANNING AND SCHEDULING METHOD BASED ON SLAB CLUSTER." Journal of Advanced Manufacturing Systems 07, no. 02 (December 2008): 249–52. http://dx.doi.org/10.1142/s0219686708001437.

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Focusing on the limitations of the traditional Continuous Casting-Direct Hot Charge Rolling (CC-DHCR) planning and scheduling methods that rarely consider dynamic scheduling problems, a new method is put forward. The key idea is to make out clusters and integrated plans in the planning layer, and then to adjust the rolling sequences according to the slab cluster-based strategy in the dynamic scheduling layer. Results of the test with data from practical production process show that the method can effectively solve the CC-DHCR planning and scheduling problem and increase the DHCR ratio.

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25

Jiravanstit, Rinrada, and Wipawee Tharmmaphornphilas. "Overhaul Resource Planning for Rolling Stock Using MIP Models." Engineering Journal 21, no. 5 (September 29, 2017): 145–59. http://dx.doi.org/10.4186/ej.2017.21.5.145.

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26

Simpson, N. C. "Multiple level production planning in rolling horizon assembly environments." European Journal of Operational Research 114, no. 1 (April 1999): 15–28. http://dx.doi.org/10.1016/s0377-2217(98)00005-8.

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27

Bredström, D., P. Flisberg, and M. Rönnqvist. "A new method for robustness in rolling horizon planning." International Journal of Production Economics 143, no. 1 (May 2013): 41–52. http://dx.doi.org/10.1016/j.ijpe.2011.02.008.

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28

Veksler, E. M., and N. A. Adamova. "Planning regimes of induction hardening of cold-rolling rolls." Metal Science and Heat Treatment 33, no. 4 (April 1991): 306–10. http://dx.doi.org/10.1007/bf00776441.

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29

Kiss, Bálint, Jean Lévine, and Béla Lantos. "On Motion Planning for Robotic Manipulation with Rolling Contacts." IFAC Proceedings Volumes 33, no. 27 (September 2000): 255–60. http://dx.doi.org/10.1016/s1474-6670(17)37938-7.

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30

Alouges, François, Yacine Chitour, and Ruixing Long. "A Motion-Planning Algorithm for the Rolling-Body Problem." IEEE Transactions on Robotics 26, no. 5 (October 2010): 827–36. http://dx.doi.org/10.1109/tro.2010.2053733.

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31

ZHANG, Chungang. "Sub-optimality analysis of mobile robot rolling path planning." Science in China Series F 46, no. 2 (2003): 116. http://dx.doi.org/10.1360/03yf9010.

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32

Sridharan, Sri V., William L. Berry, and V. Udayabhanu. "MEASURING MASTER PRODUCTION SCHEDULE STABILITY UNDER ROLLING PLANNING HORIZONS." Decision Sciences 19, no. 1 (March 1988): 147–66. http://dx.doi.org/10.1111/j.1540-5915.1988.tb00259.x.

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33

Tréfond, S., A. Billionnet, S. Elloumi, H. Djellab, and O. Guyon. "Optimization and simulation for robust railway rolling-stock planning." Journal of Rail Transport Planning & Management 7, no. 1-2 (June 2017): 33–49. http://dx.doi.org/10.1016/j.jrtpm.2017.02.001.

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34

Wang, Xingbo, Xiaotao Wang, Zhongpeng Zhang, and Ying Zhao. "Motion Planning of Kinematically Redundant 12-tetrahedral Rolling Robot." International Journal of Advanced Robotic Systems 13, no. 1 (January 2016): 23. http://dx.doi.org/10.5772/62178.

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35

BOOKBINDER, JAMES H., and BAK-TEE H'NG. "Rolling horizon production planning for probabilistic time-varying demands." International Journal of Production Research 24, no. 6 (November 1986): 1439–58. http://dx.doi.org/10.1080/00207548608919814.

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36

Sridharan, V., William L. Berry, and V. Udayabhanu. "Freezing the Master Production Schedule Under Rolling Planning Horizons." Management Science 33, no. 9 (September 1987): 1137–49. http://dx.doi.org/10.1287/mnsc.33.9.1137.

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37

Bhattacharya, S., and S. K. Agrawal. "Spherical rolling robot: a design and motion planning studies." IEEE Transactions on Robotics and Automation 16, no. 6 (2000): 835–39. http://dx.doi.org/10.1109/70.897794.

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38

Li, Hai Tao, Su Jian Li, Di Wu, Fang Han, and Fang Wang. "Hot Rolling Batch Planning Problem Model Based on Genetic Algorithm." Advanced Materials Research 433-440 (January 2012): 2042–46. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.2042.

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Анотація:

To solve the hot rolling batch planning problem in production scheduling of iron and steel enterprises, a hot rolling batch planning model is formulated based on multiple travelling salesmen problem(MTSP) model. The objective is to minimize the total limit penalty value of adjacent stripped steels in width, thickness and hardness. The main constraints include jumps in width, thickness and hardness between adjacent stripped steels, which are essential for steel production process. An improved genetic algorithm is designed to solve the model. A simulation example shows the reasonability of the model and validity of the algorithm.

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39

Borndörfer, Ralf, Thomas Eßer, Patrick Frankenberger, Andreas Huck, Christoph Jobmann, Boris Krostitz, Karsten Kuchenbecker, et al. "Deutsche Bahn Schedules Train Rotations Using Hypergraph Optimization." INFORMS Journal on Applied Analytics 51, no. 1 (February 2021): 42–62. http://dx.doi.org/10.1287/inte.2020.1069.

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Анотація:

Deutsche Bahn (DB) operates a large fleet of rolling stock (locomotives, wagons, and train sets) that must be combined into trains to perform rolling stock rotations. This train composition is a special characteristic of railway operations that distinguishes rolling stock rotation planning from the vehicle scheduling problems prevalent in other industries. DB models train compositions using hyperarcs. The resulting hypergraph models are addressed using a novel coarse-to-fine method that implements a hierarchical column generation over three levels of detail. This algorithm is the mathematical core of DB’s fleet employment optimization (FEO) system for rolling stock rotation planning. FEO’s impact within DB’s planning departments has been revolutionary. DB has used it to support the company’s procurements of its newest high-speed passenger train fleet and its intermodal cargo locomotive fleet for crossborder operations. FEO is the key to successful tendering in regional transport and to construction site management in daily operations. DB’s planning departments appreciate FEO’s high-quality results, ability to reoptimize (quickly), and ease of use. Both employees and customers benefit from the increased regularity of operations. DB attributes annual savings of 74 million euro, an annual reduction of 34,000 tons of CO2 emissions, and the elimination of 600 coupling operations in crossborder operations to the implementation of FEO.

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40

Wang, Lin, Zhiqiang Lu, and Yifei Ren. "A rolling horizon approach for production planning and condition-based maintenance under uncertain demand." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 233, no. 6 (June 10, 2019): 1014–28. http://dx.doi.org/10.1177/1748006x19853671.

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Анотація:

In reality, the forecast of uncertainties often becomes more accurate with the approaching of the forecasted period. This article proposes a rolling horizon approach to dynamically determine the production plan and the maintenance plan for a degradation system under uncertain environment. In each rolling horizon, demand forecasts are updated with new information from customers, and the degradation level of system is confirmed by inspection. By taking advantage of the updated uncertainties, at each decision point, the maintenance plan is determined by an advance-postpone balancing approach and the production plan is optimized by a heuristic algorithm in a two-stage stochastic model. Numerical results validate that the rolling horizon approach has great superiority over traditional stochastic programming approach in terms of real total cost and service level.

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41

Xu, Bao Yu, Yi Lun Liu, Xu Dong Wang, and Fang Dong. "Stochastic Excitation Model of Strip Rolling Mill." Advanced Materials Research 216 (March 2011): 378–82. http://dx.doi.org/10.4028/www.scientific.net/amr.216.378.

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The stochastic excitation power spectral density (PSD) model and ARMA time series model are established based on the stochastic rolling force acquisition data, which is processed into stationary, normal and zero mean from a aluminum hot strip tandem mill. Characteristics of rolling force ARMA time series models are discussed by means of random process theory. The rolling forces PSD function of facilitating engineering application is obtained by utilizing Levenberg-Marquardt combined with generalized global planning algorithm, and the stochastic excitation model is established. It provides the basis for the prediction of rolling force.

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Trofimova, L. S., and A. P. Zhigadlo. "Activities for road transport enterprises on indicators of workers and rolling stock productivity planning." Russian Automobile and Highway Industry Journal 19, no. 1 (March 17, 2022): 74–83. http://dx.doi.org/10.26518/2071-7296-2022-19-1-74-83.

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Introduction. The emphasis is placed on modern requirements in the development of transport industry by improving the planning of the activities of road transport enterprises in terms of indicators that determine the productivity of workers and rolling stock, which are a function of the demand for transportation. The results of the activities of road transport enterprises depend on the labour productivity of each employee performing a specific function for the implementation of a specific contract and making a profit.Materials and methods. Planning is carried out using the conceptual provisions of the current planning of the work of a trucking company, taking into account the relationship of activities and the demand for transportation, methods of probability theory and mathematical statistics, scientific principles of education and training of workers for the transport industry.Results. The application of a new approach to planning is to combine the productivity of workers and the production of rolling stock of the Motor Transport Enterprises (MTE). This made it possible to develop a mathematical model and methodology, which takes into account that the productivity of workers must ensure the receipt of the planned income for each contract and its value will be planned within the confidence limits. When planning, probabilistic events are taken into account that occur during the development of sectors of the economy of the Russian Federation and affect the need for skilled staff. Mathematical modeling is carried out in relation to the real operating conditions of cargo transportation and specific functions of employees. The number of rolling stock and the required number of employees are determined by the development of rolling stock, by the development of a specific production function by an employee in modern conditions of contractual relations.Discussion and conclusions. The results of the study are intended for the practice of road transport enterprises at the stage of determining indicators of labor productivity of workers and rolling stock.

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Sidorenko, V. G., Chzho Min Aung, V. M. Alekseev, E. N. Rozenberg, and V. I. Umanskii. "Planning Electric-Rolling-Stock Maintenance in Conditions of Limited Resources." Russian Electrical Engineering 88, no. 12 (December 2017): 839–41. http://dx.doi.org/10.3103/s106837121712015x.

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Kamran, Mehdi A., Behrooz Karimi, Nico Dellaert, and Erik Demeulemeester. "Adaptive operating rooms planning and scheduling: A rolling horizon approach." Operations Research for Health Care 22 (September 2019): 100200. http://dx.doi.org/10.1016/j.orhc.2019.100200.

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Liu, Shixin, Jianhai Song, and Mengguang Wang. "VRP-Based Model and Algorithm for Hot Rolling Lot Planning." IFAC Proceedings Volumes 36, no. 24 (October 2003): 133–36. http://dx.doi.org/10.1016/s1474-6670(17)37616-4.

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Jia, Yan-Bin. "Planning the Initial Motion of a Free Sliding/Rolling Ball." IEEE Transactions on Robotics 32, no. 3 (June 2016): 566–82. http://dx.doi.org/10.1109/tro.2016.2544338.

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Kiss, Bálint, Jean Lévine, and Béla Lantos. "On Motion Planning for Robotic Manipulation with Permanent Rolling Contacts." International Journal of Robotics Research 21, no. 5-6 (May 2002): 443–61. http://dx.doi.org/10.1177/027836402321261959.

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Váncza, J., P. Egri, and L. Monostori. "A coordination mechanism for rolling horizon planning in supply networks." CIRP Annals 57, no. 1 (2008): 455–58. http://dx.doi.org/10.1016/j.cirp.2008.03.105.

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Barba, Irene, Andrés Jiménez-Ramírez, Manfred Reichert, Carmelo Del Valle, and Barbara Weber. "Flexible runtime support of business processes under rolling planning horizons." Expert Systems with Applications 177 (September 2021): 114857. http://dx.doi.org/10.1016/j.eswa.2021.114857.

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Gao, Yuan, Jun Xia, Andrea D’Ariano, and Lixing Yang. "Weekly rolling stock planning in Chinese high-speed rail networks." Transportation Research Part B: Methodological 158 (April 2022): 295–322. http://dx.doi.org/10.1016/j.trb.2022.02.005.

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