Cooperative self-configuring nature-inspired algorithm for a scheduling problem : доклад, тезисы доклада | Научно-инновационный портал СФУ

Cooperative self-configuring nature-inspired algorithm for a scheduling problem : доклад, тезисы доклада

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: III International Conference on Advanced Technologies in Aerospace, Mechanical and Automation Engineering - MIST: Aerospace-III-2020; 9-th International Workshop on Mathematical Models and their Applications (IWMMA-2020); Krasnoyarsk; Krasnoyarsk

Год издания: 2021

Идентификатор DOI: 10.1088/1757-899X/1047/1/012080

Аннотация: One of the crucial challenges related to operational manufacturing planning is an optimal plan search in the current situation using a workflow model. The problem solving is greatly hindered by the rapid growth of the search space with an increase in dimension or the so-called combinatorial explosion. This paper uses two different approaches to solving a hierarchical scheduling problem based on different solution representations. The first approach assumes a search of an optimal project order and then solving of resource-constrained project scheduling problem (RCPSP) for each of the projects using a model based on the greedy principle. In the second approach we are searching for priorities of all actions of all projects and then use them in the process of building a schedule if there are any conflicts when choosing the next action. To solve the problem with both approaches, the paper considers some nature-inspired algorithms such as the intelligent water drops algorithm (IWDs), a genetic algorithm (GA) and ant colony optimization (ACO) as well as a self-configuring version of the last two. The paper shows the efficiency of the application of the coevolution algorithm using IWDs, self-configuring GA and ACO

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Издание

Журнал: IOP Conference Series: Materials Science and Engineering

Выпуск журнала: 1047

Номера страниц: 12080

Место издания: Krasnoyarsk, Russian Federation

Издатель: IOP Publishing Ltd

Авторы

  • Semenkina O E (Reshetnev Siberian State University of Science and Technologies)
  • Popov E A (Reshetnev Siberian State University of Science and Technologies)
  • Semenkin E S (Reshetnev Siberian State University of Science and Technologies)
  • Krasnoyarsk Science and Technology City Hall.

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