M.A.Kazantsev1, I.A. Pinchuk2, E.E. Noskova3

1–3 Joint-Stock Company «Special-Purpose Enterprise «Radiosvyaz» (Krasnoyarsk, Russia)
1 mkaz@mail/ru, 2 pinchuk.ivan@yandex.ru, 3 een90@mail.ru

In the current economic conditions, manufacturing enterprises striving for sustainable operation are compelled to undergo a digital transformation process. Digital transformation primarily refers to the integration of digital technologies into all aspects of an enterprise’s business activities. For manufacturing enterprises, digital transformation is impossible without a well-developed digital ecosystem, which consists of a comprehensive set of information resources, technologies, systems, and processes to control and support the enterprise’s design and production activities.

The digital ecosystem in radio-electronic manufacturing ensures the collection, processing, storage, transmission, and analysis of data necessary for the effective execution of design and production tasks. However, the specifics of producing complex radio-electronic systems, which are high-tech, knowledge-intensive products with a complex engineering assembly structure, require that the digital ecosystem in radio-electronic enterprises not only address tasks of accounting and tracking but, most importantly, tackle the optimization tasks of production planning and control.

Objective. To analyze the development potential of the digital ecosystem in radio-electronic manufacturing to support control tasks at the planning-optimization level, and to expand the functionality of the operational production planning subsystem within the department of automated enterprise management systems JSC «SPE «Radiosvyaz» by developing methods for optimal planning and control.

Results. Solutions for implementing the digital ecosystem of radio-electronic manufacturing at the current stage are presented, and pathways for its development are identified, considering the shift from the accounting and management level to the planning and optimization level. An analysis was conducted on the functional capabilities of modern operational control methods when approaching production planning as an optimization task for different types of manufacturing. An evaluation of the computational complexity of operational control algorithms was proposed, and the effectiveness of the developed approaches for generating operational scheduling plans for the production of radio-electronic systems with varying engineering assembly structure was demonstrated.

Practical Significance. The obtained results expand the capabilities of the information environment in radio-electronic manufacturing by enabling the transition to the planning and optimization level in intra-workshop scheduling, allowing for the resolution of not only accounting and tracking tasks but also optimal control tasks in the production of radio-electronic systems as products with complex engineering and technological assembly structure.

Kazantsev M.A., Pinchuk I.A., Noskova E.E. The Planning-Optimization Level of Digital Ecosystem Development in Radio-Electronic Manufacturing. Dynamics of complex systems. 2025. V. 19. № 1. P. 14−23. DOI: 10.18127/j19997493-202501-02 (in Russian).

1. Kazancev M.A., Noskova E.E. Rol' avtomatizirovannyh sistem tekhnologicheskoj podgotovki proizvodstva v processe cifrovoj transformacii predpriYatiYa. Uspekhi sovremennoj radioelektroniki. 2024. T. 78. № 1. S. 8–18. https://doi.org/10.18127/j20700784-202401-02 (in Russian).
2. Galeev R.G., Kapulin D.V., Kazancev M.A. ProizvodstvennaYa logistika priborostroitel'nogo predpriYatiYa: uchebnoe posobie. KrasnoYarsk: Sibirskij federal'nyj universitet. 2021. 265 s. (in Russian).
3. Frolov E., Garaeva Yu. Istoki effektivnosti IT [Elektronnyj resurs]. ECM-Journal: [sajt]. [2009]. URL: https://ecm-journal.ru/material/ Istoki-ehffektivnosti-IT (data obrashcheniYa: 20.10.2024) (in Russian).
4. Noskova E.E., Kapulin D.V., Russkih P.A. Metody sinhronnogo planirovaniYa dlYa uluchsheniYa processov pozakaznogo proizvodstva: na puti k cifrovoj transformacii. CifrovizaciYa. 2022. T. 3. № 2. S. 41–48. https://doi.org/10.37993/2712-8733-2022-3-2-41-48 (in Russian).
5. Hajji M.K., Hamlaoui O., Hadda H. A simulated annealing metaheuristic approach to hybrid flow shop scheduling problem. Advances in Industrial and Manufacturing Engineering. 2024. № 9. https://doi.org/10.1016/j.aime.2024.100144.
6. Boufellouh R., Belkaid F. Bi-objective optimization algorithms for joint production and maintenance scheduling under a global resource constraint: Application to the permutation flow shop problem. Computers and Operations Research. 2020. № 122. https://doi.org/ 10.1016/j.cor.2020.104943.
7. Shao W., Shao Z., Pi D. Modelling and optimization of distributed heterogeneous hybrid flow shop lot-streaming scheduling problem. Expert Systems with Applications. 2023. 214. https://doi.org/10.1016/j.eswa.2022.119151.
8. Khatami M., Salehipour A., Cheng T.C.E. Flow-shop scheduling with exact delays to minimize makespan. Computers and Industrial Engineering. 2023. № 183. https://doi.org/10.1016/j.cie.2023.109456.
9. Norenkov I.P. AvtomatizirovannYe informacionnYe sistemy: Ucheb. posobie. M.: MGTU im. N.E. Baumana. 2011. 343 s. (in Russian).
10. Big (O) Notation Explanation [Elektronnyj resurs]. Java Challengers: [sajt]. [2023]. URL: https://javachallengers.com/big-o-notation-explanation/ (data obrashcheniYa: 20.10.2024).
11. Kuznetsov A.S., Noskova, E.E. Assessment of planning methods at job-order manufacturing facilities. IOP Conference Series: Materials Science and Engineering. 2020. № 862(4). https://doi.org/10.1088/1757-899X/862/4/042024.