Agent-based modeling of service maintenance and repair of rolling stock

Alexandrov, A.; Bannikov, Dmitry; Sirina, Nina

doi:10.1088/1755-1315/403/1/012193

Cited by 9 publications

(4 citation statements)

References 6 publications

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“…From it follows that does not increase with increasing both and for . Therefore, reaches a maximum at and So, according to (14), and Further, on (11), as grows, then does not decrease. It follows from (10) that does not increase with decreasing , But, according to (11), does not…”

Section: Aggregate Control Mechanismmentioning

confidence: 95%

“…In eliminating this uncertainty, the Admin is guided by the principle of the maximum guaranteed result10. Then the Admin's goal function is: (13) The set of optimal choices of the Admin that maximizes the goal function of the Manager (13) is: (14)…”

Section: The Admin's Choicesmentioning

confidence: 99%

“…Let's illustrate the investigated problem on the example of the application of AI in the maintenance of railway operations 13 . One of the areas of such application is maintenance and repair of rolling stock 14 . With the current practice of managing the costs of such maintenance and repairs in a large company with regional departments and enterprises (depots), the regional administrator receives incentives if the regional costs do not exceed the established norm.…”

Section: Introductionmentioning

confidence: 99%

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Digital system for controlling regional costs in a large company

Tsyganov

2023

International Conference on Digital Transformation: Informatics, Economics, and Education (DTIEE2023)

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The problem of digital control of regional costs in a large company under conditions of uncertainty, using artificial procedures and taking into account the human intelligence factor, are analyzed. The undesirable activity of managers of the middle (regional) and lower (production) level of the company, associated with the presence of their own goals, was revealed. Such undesirable activity can lead to overestimation of regional costs. In order to minimize these costs, a digital control model is proposed that uses digital learning procedures at the regional and production levels. As a result of research, a mechanism has been developed to reduce regional costs in each period of time. This mechanism includes, firstly, a digital self-learning procedure for a top manager of a company, as well as a procedure for stimulating a middle manager by him to reduce regional costs. Secondly, this mechanism includes a procedure for digitally supervised training of a middle manager, as well as a procedure for stimulating a lower-level manager for reducing production costs. The statement and solution of the problem is illustrated by the example of the application in the maintenance of rolling stock.

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Section: Aggregate Control Mechanismmentioning

confidence: 95%

Section: The Admin's Choicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Digital system for controlling regional costs in a large company

Tsyganov

2023

International Conference on Digital Transformation: Informatics, Economics, and Education (DTIEE2023)

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“…Let's illustrate the described problem on the example of the application of AI in the maintenance of railway operations [17]. One of the areas of such application is service maintenance and repair of rolling stock [18]. With the current practice of managing the output of such maintenance and repairs in a corporation with branch departments and plants, the branch head receives incentives if the branch output do not exceed the established norm.…”

Section: Introductionmentioning

confidence: 99%

Development of an output management model for a branch of an engineering corporation

Tsyganov

2024

E3S Web Conf.

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The model of output control in a corporation, carried out by its top manager (Chief), is considered. The head of the branch of the corporation (Head) controls the subordinate plant headed by the Director. A corporation’s branch output is affected by random external influences. The Head knows exactly the maximum possible volume of outsourcing production at the branch level. But he does not know the maximum possible volume of production (potential) in the plant. The Adviser helps the Head to eliminate this uncertainty. For his part, the Director knows exactly plant’s potential. At the same time, the Chief does not know this potential, nor the possibilities of outsourcing. Thus, the Head can manipulate branch output to influence the decisions of the Chief in order to increase his own promotion. Therefore, the Chief needs to learn how to control the Head in order to maximize branch output. Similarly, the Director can manipulate plant’s output to influence the Head’s decisions about his own denomination. Therefore, the Head needs to control the Director in order to increase plant’s output. A complex control mechanism has been found, in which total branch output is maximal. The use of such a mechanism is illustrated by the example of the modernization of freight railway platforms.

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Explainable Machine Learning in Service Management of Transport Corporation

Tsyganov

2023

Communications in Computer and Information Science

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Agent-based modeling of service maintenance and repair of rolling stock

Cited by 9 publications

References 6 publications

Digital system for controlling regional costs in a large company

Digital system for controlling regional costs in a large company

Development of an output management model for a branch of an engineering corporation

Explainable Machine Learning in Service Management of Transport Corporation

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