Non-linear multi-objective optimization model of production planning based on fuzzy logic and machine learning
This research introduces a nonlinear multi-objective optimization model that is designed to simultaneously optimize profit and customer satisfaction in production systems. The investigated problem includes optimization in complex and uncertain conditions of production, which is faced with resource a...
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| Format: | Article |
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University of Qom
2024-09-01
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| Series: | مدیریت مهندسی و رایانش نرم |
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| Online Access: | https://jemsc.qom.ac.ir/article_3051_8396cf4986ba31c33c78f28056b54de2.pdf |
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| author | Zahra Saeidi Mobarakeh Hossein Amoozadkhalili |
| author_facet | Zahra Saeidi Mobarakeh Hossein Amoozadkhalili |
| author_sort | Zahra Saeidi Mobarakeh |
| collection | DOAJ |
| description | This research introduces a nonlinear multi-objective optimization model that is designed to simultaneously optimize profit and customer satisfaction in production systems. The investigated problem includes optimization in complex and uncertain conditions of production, which is faced with resource and time limitations. The proposed model provides optimal solutions for managers by using non-linear objective functions and detailed analysis of operating conditions. This fuzzy logic is combined with machine learning algorithms such as neural networks and reinforcement learning to create an intelligent and flexible model that effectively adapts to sudden changes in dynamic environments. This model uses the combination of non-dominant fourth sorting genetic algorithms (NSGA-IV) and variable selection network (VSN) in a hybrid framework and provides an advanced and multi-faceted approach to solving complex multi-objective optimization problems. Pareto-optimal results obtained from this model indicate its efficient and optimal performance. The proposed model can be used as a practical and strategic source for managers and decision makers in optimizing production and improving customer satisfaction in uncertain and dynamic conditions. |
| format | Article |
| id | doaj-art-f1a633b8ef9c439b9d71d37f4b3cb8e8 |
| institution | Kabale University |
| issn | 2538-6239 2538-2675 |
| language | fas |
| publishDate | 2024-09-01 |
| publisher | University of Qom |
| record_format | Article |
| series | مدیریت مهندسی و رایانش نرم |
| spelling | doaj-art-f1a633b8ef9c439b9d71d37f4b3cb8e82025-01-30T20:19:19ZfasUniversity of Qomمدیریت مهندسی و رایانش نرم2538-62392538-26752024-09-0110115518910.22091/jemsc.2024.11186.11973051Non-linear multi-objective optimization model of production planning based on fuzzy logic and machine learningZahra Saeidi Mobarakeh0Hossein Amoozadkhalili1Department of Industrial Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran. Email: z.saeedi2020@gmail.comDepartment of Industrial Engineering, sari Branch, Islamic Azad University, sari, Iran. Email: Amoozad92@yahoo.comThis research introduces a nonlinear multi-objective optimization model that is designed to simultaneously optimize profit and customer satisfaction in production systems. The investigated problem includes optimization in complex and uncertain conditions of production, which is faced with resource and time limitations. The proposed model provides optimal solutions for managers by using non-linear objective functions and detailed analysis of operating conditions. This fuzzy logic is combined with machine learning algorithms such as neural networks and reinforcement learning to create an intelligent and flexible model that effectively adapts to sudden changes in dynamic environments. This model uses the combination of non-dominant fourth sorting genetic algorithms (NSGA-IV) and variable selection network (VSN) in a hybrid framework and provides an advanced and multi-faceted approach to solving complex multi-objective optimization problems. Pareto-optimal results obtained from this model indicate its efficient and optimal performance. The proposed model can be used as a practical and strategic source for managers and decision makers in optimizing production and improving customer satisfaction in uncertain and dynamic conditions.https://jemsc.qom.ac.ir/article_3051_8396cf4986ba31c33c78f28056b54de2.pdfmulti-objective optimizationfuzzy logicmachine learninghybrid multi-objective meta-heuristic algorithm |
| spellingShingle | Zahra Saeidi Mobarakeh Hossein Amoozadkhalili Non-linear multi-objective optimization model of production planning based on fuzzy logic and machine learning مدیریت مهندسی و رایانش نرم multi-objective optimization fuzzy logic machine learning hybrid multi-objective meta-heuristic algorithm |
| title | Non-linear multi-objective optimization model of production planning based on fuzzy logic and machine learning |
| title_full | Non-linear multi-objective optimization model of production planning based on fuzzy logic and machine learning |
| title_fullStr | Non-linear multi-objective optimization model of production planning based on fuzzy logic and machine learning |
| title_full_unstemmed | Non-linear multi-objective optimization model of production planning based on fuzzy logic and machine learning |
| title_short | Non-linear multi-objective optimization model of production planning based on fuzzy logic and machine learning |
| title_sort | non linear multi objective optimization model of production planning based on fuzzy logic and machine learning |
| topic | multi-objective optimization fuzzy logic machine learning hybrid multi-objective meta-heuristic algorithm |
| url | https://jemsc.qom.ac.ir/article_3051_8396cf4986ba31c33c78f28056b54de2.pdf |
| work_keys_str_mv | AT zahrasaeidimobarakeh nonlinearmultiobjectiveoptimizationmodelofproductionplanningbasedonfuzzylogicandmachinelearning AT hosseinamoozadkhalili nonlinearmultiobjectiveoptimizationmodelofproductionplanningbasedonfuzzylogicandmachinelearning |