An Optimized Hybrid Model for Perishable Product Quality Inference in the Food Supply Chain
The supply chain for perishable products faces significant challenges in monitoring and maintaining product quality. These products are particularly vulnerable to environmental dynamic conditions and variations in distribution and transportation. To address these challenges, leveraging the Internet...
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| Language: | English |
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2025-02-01
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| Series: | Emerging Science Journal |
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| Online Access: | https://ijournalse.org/index.php/ESJ/article/view/2884 |
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| author | Muhammad Asrol . Suharjito Riyanto Jayadi |
| author_facet | Muhammad Asrol . Suharjito Riyanto Jayadi |
| author_sort | Muhammad Asrol |
| collection | DOAJ |
| description | The supply chain for perishable products faces significant challenges in monitoring and maintaining product quality. These products are particularly vulnerable to environmental dynamic conditions and variations in distribution and transportation. To address these challenges, leveraging the Internet of Things (IoT) and quality inference techniques during transportation can provide valuable insights for both consumers and producers. The objective of the research is to develop a model for inferring the quality of perishable products using an IoT sensor dataset to monitor perishable product quality continuously. This research applied a hybrid approach combining a Fuzzy Inference System (FIS), clustering models, and genetic algorithms to infer the product quality during supply chain distribution with IoT sensors. The result shows that the hybrid FIS model, which employs Gaussian membership functions and fuzzy c-means clustering for rule generation, achieves a high accuracy with an R²: 0.873. This research contributes to improving the model by employing genetic algorithms in optimizing the inference model by activating only five out of seven rules. The model optimization achieves optimal computation time while aiming to preserve model accuracy. However, test results indicate that the combination of rules has not yet significantly enhanced the model's accuracy, though it holds potential for future development.
Doi: 10.28991/ESJ-2025-09-01-027
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| format | Article |
| id | doaj-art-3e426e43bcae4d8e94f3b3a7ba002e99 |
| institution | Kabale University |
| issn | 2610-9182 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Ital Publication |
| record_format | Article |
| series | Emerging Science Journal |
| spelling | doaj-art-3e426e43bcae4d8e94f3b3a7ba002e992025-02-08T14:26:27ZengItal PublicationEmerging Science Journal2610-91822025-02-019148550310.28991/ESJ-2025-09-01-027787An Optimized Hybrid Model for Perishable Product Quality Inference in the Food Supply ChainMuhammad Asrol0. Suharjito1Riyanto Jayadi2Industrial Engineering Department, BINUS Graduate Program – Master of Industrial Engineering, Bina Nusantara University, Jakarta, 11480,Industrial Engineering Department, BINUS Graduate Program – Master of Industrial Engineering, Bina Nusantara University, Jakarta, 11480,Information System Management Department, BINUS Graduate Program, Master of Information System, Bina Nusantara University, Jakarta 11480,The supply chain for perishable products faces significant challenges in monitoring and maintaining product quality. These products are particularly vulnerable to environmental dynamic conditions and variations in distribution and transportation. To address these challenges, leveraging the Internet of Things (IoT) and quality inference techniques during transportation can provide valuable insights for both consumers and producers. The objective of the research is to develop a model for inferring the quality of perishable products using an IoT sensor dataset to monitor perishable product quality continuously. This research applied a hybrid approach combining a Fuzzy Inference System (FIS), clustering models, and genetic algorithms to infer the product quality during supply chain distribution with IoT sensors. The result shows that the hybrid FIS model, which employs Gaussian membership functions and fuzzy c-means clustering for rule generation, achieves a high accuracy with an R²: 0.873. This research contributes to improving the model by employing genetic algorithms in optimizing the inference model by activating only five out of seven rules. The model optimization achieves optimal computation time while aiming to preserve model accuracy. However, test results indicate that the combination of rules has not yet significantly enhanced the model's accuracy, though it holds potential for future development. Doi: 10.28991/ESJ-2025-09-01-027 Full Text: PDFhttps://ijournalse.org/index.php/ESJ/article/view/2884supply chain managementoptimizationquality controlfuzzy inferencegenetic algorithms. |
| spellingShingle | Muhammad Asrol . Suharjito Riyanto Jayadi An Optimized Hybrid Model for Perishable Product Quality Inference in the Food Supply Chain Emerging Science Journal supply chain management optimization quality control fuzzy inference genetic algorithms. |
| title | An Optimized Hybrid Model for Perishable Product Quality Inference in the Food Supply Chain |
| title_full | An Optimized Hybrid Model for Perishable Product Quality Inference in the Food Supply Chain |
| title_fullStr | An Optimized Hybrid Model for Perishable Product Quality Inference in the Food Supply Chain |
| title_full_unstemmed | An Optimized Hybrid Model for Perishable Product Quality Inference in the Food Supply Chain |
| title_short | An Optimized Hybrid Model for Perishable Product Quality Inference in the Food Supply Chain |
| title_sort | optimized hybrid model for perishable product quality inference in the food supply chain |
| topic | supply chain management optimization quality control fuzzy inference genetic algorithms. |
| url | https://ijournalse.org/index.php/ESJ/article/view/2884 |
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