Objective Functions for Minimizing Rescheduling Changes in Production Control

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Опубликовано в::Automation vol. 6, no. 3 (2025), p. 30-47
Главный автор: Kulcsár Gyula
Другие авторы: Kulcsárné Forrai Mónika, Cservenák Ákos
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MDPI AG
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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 
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