Библиографическое описание:Hierarchical Co-Evolution of Self-Configuring Bio-Inspired Algorithms for Production Scheduling with Priorities : доклад, тезисы доклада / O. E. Semenkina, E. A. Popov, E. S. Semenkin. - [S. l. : s. n.], 2022. - Текст : непосредственный // HYBRID METHODS OF MODELING AND OPTIMIZATION IN COMPLEX SYSTEMS : Proceedings of the International Workshop “Hybrid methods of modeling and optimization in complex systems” (HMMOCS 2022) / Krasnoyarsk Regional Science and Technology City Hall, Russia Siberian Federal University, Reshetnev Siberian State University of Science and Technology ; International Workshop “Hybrid methods of modeling and optimization in complex systems” (HMMOCS 2022) (2022 ; 22.11 - 24.11 ; Krasnoyarsk). - London, United Kingdom : European Proceedings, 2022. - P. 167-177. - ISBN 9781802969603, DOI 10.15405/epct.23021.21.
Аннотация:This article is focused on the consideration of universal formulations of the scheduling problem, which can be used in the broadest sense for any existing production model. The model must have some external parameters for control, but inside it can contain any aspects and moments that are difficult to formalize, for example, dynamically appearing operations, merging and splitting batches, not a fixed order of operations, accumulating a certain weight to start an operation, and anything else. Four universal scheduling problem statements for operational production planning are considered: permutation of the lots processing order, permutation of operation priorities, real operation priorities, and lot order with machine tool priorities nested problem. The second goal of this paper is to suggest a universal optimization approach for solving such problems. A cooperative co-evolutionary method based on self-configuring bio-inspired algorithms for combinatorial and/or real optimization is proposed. For lot order with machine tool priorities problem a hierarchical co-evolution method with both combinatorial and real optimization is proposed. This optimization method helps not only to adjust the parameters of the algorithm in the process of solving the problem but also to eliminate the need to choose an algorithm suitable for a particular problem. That is, a fully automatic adjustment of the optimization method to the optimization problem is achieved, that simplifies the use of intelligent technologies in practice. The effectiveness of the application of this approach to the scheduling problem is shown.
