M.A. Kazantsev1, I.A. Pinchuk2, E.E. Noskova3, D.G. Gaifulin4

1-4 Joint-Stock Company «Special-Purpose Enterprise «Radiosvyaz» (Krasnoyarsk, Russia)

1 mkaz@mail.ru, 2 pinchuk.ivan@yandex.ru, 3 een90@mail.ru, 4 me@dgayfulin.ru

Problem statement. Modern radio -electronic manufacturing is characterized by a large product range and high complexity of items, which requires advanced shop-floor planning tools. The operational scheduling problem for job-shop–type production remains one of the most challenging in scheduling theory and production control. The development of a production plan requires a comprehensive consideration of the principal components of a manufacturing system that connect the input data and the applied solution methods.

Objective. To construct an optimization model of shop -floor planning as an information model aimed at formalizing the scheduling problem statement in the form of a technical optimization task; to implement planning algorith ms for job -shop–type production; to analyze the influence of quantitative and qualitative input data characteristics on algorithm performance.

Results. An optimization model of shop -floor planning has been developed based on adapted branch -and-bound and ta bu search methods, taking into account the specifics of job -shop-type production. Computational experiments were performed using input datasets with different characteristics generated by a data generator for optimization models of production planning. The obtained results provide an assessment of the efficiency of planning methods in constructing production schedules depending on the manufacturing system operating time and the total idle time of equipment.

Practical significance. The obtained results enable the development of a strategy for selecting algorithms from a planning library based on the analysis of input data properties, which allows evaluating the nature of a scheduling problem and predicting the efficiency of a planning method before computatio n. The use of metrics for input data analysis makes algorithm behavior predictable. This approach opens up the possibility of adapting existing solutions to the specifics of individual shops and manufacturing systems, taking into account their technologica l processes and resource constraints. The proposed optimization model and the experimental results can be used to design an intelligent scheduling agent solving production planning optimization problems.

Kazantsev M.A., Pinchuk I.A., Noskova E.E., Gaifulin D.G. Analysis of input da ta in the implementation of an optimization scheduling model. Dynamics of complex systems. 2026. V. 20. № 1. P. 50−61. DOI: 10.18127/j19997493-202601-05 (in Russian).

1. Galeev R.G., Kapulin D.V., Kazancev M.A. Proizvodstvennaya logistika priborostroitel`nogo predpriyatiya: Ucheb. posobie. Krasnoyarsk: Sibirskij federal`ny`juniversitet, 2021. 265 s.
2. Maue`rgauz Yu.E. Dinamicheskie raspisaniya dlya gibkixproizvodstv: 12 komp`yuterny`xprogramm. M.: NGSS. 2018. 312 s.
3. Guo H., Liu J., Wang Y., & Zhuan g C. An improvedgenetic programming hyper-heuristic for the dynamicflexible job shopscheduling problem with reconfigurable manufacturing cells. Journal of Manufacturing Systems. 2024. V. 74. P. 252–263. https://doi.org/10.1016/ j.jmsy.2024.03.009.
4. Kuznetsov A.S., & Noskova E.E. Assessment of planning methods at job-order manufacturing facilities. IOP Conference Series: Mate rials Science and Engineering. 2020. V. 862. № 4. https://doi.org/10.1088/1757-899X/862/4/042024.
5. Kenneth R. Baker, Dan Trietsch. Principles of Sequencing and Scheduling. Hoboken, New Jersey: John Wiley & Sons. 2009. 648 s.
6. Madraki G., & Mousavian S. Theoretical enhancements in calculations of perturbed schedule in job shop systems. Computers and Industrial Engineering. 2025. V. 201. https://doi.org/10.1016/j.cie.2025.110927.
7. Sun J., Zhang Z., Zhang G., & Huang Z. Multi-objective evolutionary algorithm based flexibleassembly job-shoprescheduling with component sharing for orderinsertion. Computers and Operations Research. 2024. V. 169. https://doi.org/10.1016/j.cor.2024.106744.
8. Mirmozaffari M., Hejazi S.M., Karamizadeh N., & Montazeri A. A mixed-integer non-linear no-waitopen-shopscheduling model for minimizing makespan and totaltardiness in manufacturing. Decision Analytics Journ al. 2024. V. 10. https://doi.org/10.1016/ j.dajour.2024.100403.
9. Kazancev M.A., Pin`chuk I.A., Noskova E.E. Planovo-optimizacionny`juroven` razvitiya informacionnoj sredy` na radioe`lektronny`xproizvodstvax/ Dinamika slozhny`xsistem – 21 vek. 2025. T. 19. № 1. S. 14–23. https://doi.org/10.18127/j19997493-202501-02. 1
10. Svidetel`stvo ogosudarstvennoj registracii programmy` dlya E`VM № 2025682067 Rossijskaya Federaciya. Programma generacii isxodny`xdanny`xdlya optimizacionny`xmodelej proizvodstvennogo planirovaniya: zayavl. 08.07.2025: opubl. 20.08.2025 / M.A. Kazancev, I.A. Pin`chuk, E.E. Noskova; zayavitel` Akcionernoe obshhestvo «Nauchno-proizvodstvennoe predpriyatie «Radiosvyaz`».