Enhancing Hybrid Flow Shop Scheduling Problem with a Hybrid Metaheuristic and Machine Learning Approach for Dynamic Parameter Tuning
This paper addresses the Hybrid Flow Shop Scheduling Problem (HFSSP) by integrating metaheuristic (MHs) and machine learning (ML) approaches. Specifically, we propose a hybrid algorithm by combining Ant Colony Optimization (ACO) and Iterated Local Search (ILS) to form ACOILS. To further enhance the...
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LPPM ISB Atma Luhur
2024-11-01
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| Series: | Jurnal Sisfokom |
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| Online Access: | https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/2290 |
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| author | Ahmed Abdulmunem Hussein |
| author_facet | Ahmed Abdulmunem Hussein |
| author_sort | Ahmed Abdulmunem Hussein |
| collection | DOAJ |
| description | This paper addresses the Hybrid Flow Shop Scheduling Problem (HFSSP) by integrating metaheuristic (MHs) and machine learning (ML) approaches. Specifically, we propose a hybrid algorithm by combining Ant Colony Optimization (ACO) and Iterated Local Search (ILS) to form ACOILS. To further enhance the performance of this hybrid approach, we employ Proximal Policy Optimization (PPO), which is used for dynamic tuning of key parameters within the hybrid algorithm. The introduction of PPO allows real-time adjustment of key parameters, such as pheromone evaporation rates and local search intensity, to balance exploration and exploitation more effectively. Comparative experiments against the non-learning version of ACOILS and Simulated Annealing (SA) show that the learning based LACOILS significantly reduces the percentage deviation from the lower bound while maintaining stable performance through dynamic tuning. In terms of numerical results, LACOILS consistently outperforms SA and ACOILS. For smaller instances (N=20), it achieves up to 56.52% improvement over ACOILS and 12.5% over SA. For larger instances (N=150), LACOILS shows up to 29.82% improvement over ACOILS and 9.09% over SA, demonstrating its superior solution quality and efficiency. |
| format | Article |
| id | doaj-art-dc8bcbfd205146f9b7f672dc6b593b21 |
| institution | OA Journals |
| issn | 2301-7988 2581-0588 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | LPPM ISB Atma Luhur |
| record_format | Article |
| series | Jurnal Sisfokom |
| spelling | doaj-art-dc8bcbfd205146f9b7f672dc6b593b212025-08-20T02:23:31ZengLPPM ISB Atma LuhurJurnal Sisfokom2301-79882581-05882024-11-0113338839510.32736/sisfokom.v13i3.2290896Enhancing Hybrid Flow Shop Scheduling Problem with a Hybrid Metaheuristic and Machine Learning Approach for Dynamic Parameter TuningAhmed Abdulmunem Hussein0University of SamarraThis paper addresses the Hybrid Flow Shop Scheduling Problem (HFSSP) by integrating metaheuristic (MHs) and machine learning (ML) approaches. Specifically, we propose a hybrid algorithm by combining Ant Colony Optimization (ACO) and Iterated Local Search (ILS) to form ACOILS. To further enhance the performance of this hybrid approach, we employ Proximal Policy Optimization (PPO), which is used for dynamic tuning of key parameters within the hybrid algorithm. The introduction of PPO allows real-time adjustment of key parameters, such as pheromone evaporation rates and local search intensity, to balance exploration and exploitation more effectively. Comparative experiments against the non-learning version of ACOILS and Simulated Annealing (SA) show that the learning based LACOILS significantly reduces the percentage deviation from the lower bound while maintaining stable performance through dynamic tuning. In terms of numerical results, LACOILS consistently outperforms SA and ACOILS. For smaller instances (N=20), it achieves up to 56.52% improvement over ACOILS and 12.5% over SA. For larger instances (N=150), LACOILS shows up to 29.82% improvement over ACOILS and 9.09% over SA, demonstrating its superior solution quality and efficiency.https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/2290hybrid flow shop scheduling problemant colony optimizationiterated local searchproximal policy optimizationmachine learning. |
| spellingShingle | Ahmed Abdulmunem Hussein Enhancing Hybrid Flow Shop Scheduling Problem with a Hybrid Metaheuristic and Machine Learning Approach for Dynamic Parameter Tuning Jurnal Sisfokom hybrid flow shop scheduling problem ant colony optimization iterated local search proximal policy optimization machine learning. |
| title | Enhancing Hybrid Flow Shop Scheduling Problem with a Hybrid Metaheuristic and Machine Learning Approach for Dynamic Parameter Tuning |
| title_full | Enhancing Hybrid Flow Shop Scheduling Problem with a Hybrid Metaheuristic and Machine Learning Approach for Dynamic Parameter Tuning |
| title_fullStr | Enhancing Hybrid Flow Shop Scheduling Problem with a Hybrid Metaheuristic and Machine Learning Approach for Dynamic Parameter Tuning |
| title_full_unstemmed | Enhancing Hybrid Flow Shop Scheduling Problem with a Hybrid Metaheuristic and Machine Learning Approach for Dynamic Parameter Tuning |
| title_short | Enhancing Hybrid Flow Shop Scheduling Problem with a Hybrid Metaheuristic and Machine Learning Approach for Dynamic Parameter Tuning |
| title_sort | enhancing hybrid flow shop scheduling problem with a hybrid metaheuristic and machine learning approach for dynamic parameter tuning |
| topic | hybrid flow shop scheduling problem ant colony optimization iterated local search proximal policy optimization machine learning. |
| url | https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/2290 |
| work_keys_str_mv | AT ahmedabdulmunemhussein enhancinghybridflowshopschedulingproblemwithahybridmetaheuristicandmachinelearningapproachfordynamicparametertuning |