Вероятностная модель обслуживания терминалов

Translated title of the contribution: PROBABILISTIC MODEL OF TERMINAL SERVICES

Research output: Contribution to journalArticlepeer-review

Abstract

The article assumes that several companies have transport terminals located in close proximity. Each company provides transportation and storage of goods. If a terminal of one company is crowded, then the company can rent a storage space in the terminal of another company. If a terminal of some company is not loaded, then the part of the premises may be leased to another company. If there is demand for a product that cannot be met in view of its absence from the terminal, the company can purchase goods at the other terminal and transfer them to another your terminal. Rental of the terminal to another company and the transportation of goods from one terminal to another terminal require additional costs. It is desirable to anticipate the need for the rental of the premises of another company, as the organization of rent and transportation of goods require some additional time and financial resources. Therefore, it is necessary to make long-term planning of financial resources in order to provide additional costs. Cargo flow is stochastic in nature and is not fully known in advance. The paper discusses two terminals maintenance tasks in the framework of a simplified mathematical model. The dynamics of the boot process of the terminal is determined by the stochastic equation with control. The probabilistic approaches to the problem of optimal control with the purpose to ensure acceptable conditions for the functioning of the terminal are formulated. Refs 12.
Translated title of the contributionPROBABILISTIC MODEL OF TERMINAL SERVICES
Original languageRussian
Pages (from-to)32-38
JournalВЕСТНИК САНКТ-ПЕТЕРБУРГСКОГО УНИВЕРСИТЕТА. СЕРИЯ 10: ПРИКЛАДНАЯ МАТЕМАТИКА, ИНФОРМАТИКА, ПРОЦЕССЫ УПРАВЛЕНИЯ
Volume12
Issue number3
StatePublished - 2016

Scopus subject areas

  • Mathematics(all)

Keywords

  • terminal
  • the Bernoulli scheme
  • convolution of distributions

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