Objective Functions for Minimizing Rescheduling Changes in Production Control
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| Опубликовано в:: | Automation vol. 6, no. 3 (2025), p. 30-47 |
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| Главный автор: | |
| Другие авторы: | , |
| Опубликовано: |
MDPI AG
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| Online-ссылка: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 100 | 1 | |a Kulcsár Gyula |u Institute of Information Science, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Egyetemváros, 3515 Miskolc, Hungary; gyula.kulcsar@uni-miskolc.hu (G.K.); monika.kulcsarne@uni-miskolc.hu (M.K.F.) | |
| 245 | 1 | |a Objective Functions for Minimizing Rescheduling Changes in Production Control | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This paper presents an advanced rescheduling approach that jointly applies two sets of objective functions within a novel multi-objective search algorithm and a production simulation of the manufacturing system. The role of the first set of objective functions is to optimize the performance of production systems, while the second newly proposed set of objective functions aims to minimize the intervention changes from the original schedule, thereby supporting schedule stability and smooth manufacturing processes. The combined use of these two objective sets is ensured by a flexible candidate-qualification method, which allows for priorities to be assigned to each objective function, offering precise control over the rescheduling process. The applicability of this approach is presented through an example of an extended flexible flow shop manufacturing system. A new test problem containing 16 objective functions has been developed. The effectiveness of the proposed new objective functions and rescheduling method is validated by a simulation model. The obtained numerical results are also presented in this paper. The aim of this study is not to compare different search algorithms but rather to demonstrate the beneficial impact of change-minimizing objective functions within a given search framework. | |
| 653 | |a Schedules | ||
| 653 | |a Scheduling | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Simulation models | ||
| 653 | |a Rescheduling | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Intervention | ||
| 653 | |a Optimization | ||
| 653 | |a Neural networks | ||
| 653 | |a Search algorithms | ||
| 653 | |a Job shops | ||
| 653 | |a Breakdowns | ||
| 653 | |a Production planning | ||
| 653 | |a Manufacturing | ||
| 653 | |a Production controls | ||
| 653 | |a Fuzzy logic | ||
| 653 | |a Business metrics | ||
| 700 | 1 | |a Kulcsárné Forrai Mónika |u Institute of Information Science, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Egyetemváros, 3515 Miskolc, Hungary; gyula.kulcsar@uni-miskolc.hu (G.K.); monika.kulcsarne@uni-miskolc.hu (M.K.F.) | |
| 700 | 1 | |a Cservenák Ákos |u Institute of Logistics, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Egyetemváros, 3515 Miskolc, Hungary | |
| 773 | 0 | |t Automation |g vol. 6, no. 3 (2025), p. 30-47 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3254466167/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3254466167/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3254466167/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |