20.500.12556/DKUM-97140 Optimization approaches for solving production scheduling problem: a brief overview and a case study for hybrid flow shop using genetic algorithms The aim of this paper is to investigate scheduling problems in manufacturing. After a brief introduction to the modelling approach to the scheduling problem, the study focuses on the optimization approach to the scheduling problem. Firstly, the different optimization approaches are categorised and their respective advantages and disadvantages are shown. This is followed by a detailed analysis of the characteristics and applicability of each of the commonly used optimization approaches. Finally, a case study is presented. A mathematical model is developed with the objective of minimising the maximum completion time for a mixed flow shop scheduling problem, and a genetic algorithm is used to solve the problem. The validity of the model is verified through the case study, which can provide a reasonable scheduling solution for actual manufacturing. This provides a reference for the selection and use of methods for solving scheduling problems in practical production. scheduling production scheduling hybrid flow shop optimization genetic algorithms completion time optimizacija načrtovanje prosizvodnje genetski algoritmi true false true Fakulteta za strojništvo, Inštitut za proizvodno strojništvo Angleški jezik Slovenski jezik https://apem-journal.org/index.html Content from this work may be used under the terms of the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation, and DOI. Članek v reviji 2026-02-19 13:43:19 2026-02-19 13:43:20 2026-03-20 06:57:34 0000-00-00 00:00:00 2022 0 0 str. 45-56 no. 1 Vol. 17 Mar. 2022 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2021-10-15 2022-03-17 2022-04-29 658.5 269027587 10.14743/apem2022.1.420 1854-6250 APEM17-1_045-056.pdf APEM17-1_045-056.pdf 1 29BCDB754C21F4E7C0E60C72DA487EE4 cdaa63ca8f88b33fcab5feb617a5dacac5e9baa2b47aad73e9ba55741e04c944 d636f392-0d90-11f1-a5b8-00155d580e1a https://dk.um.si/Dokument.php?lang=slv&id=203538 Fakulteta za strojništvo Advances in production engineering & management 0 0 0