PRICE DYNAMICS ON VEGETABLE PRODUCTS BASED ON THE Q-LEARNING ALGORITHM
Vegetables are classified as highly perishable products with a limited shelf life, leading consumers to avoid products approaching expiration. The primary challenge for vegetable sellers is to maximize sales before freshness declines. This study aims to optimize vegetable pricing dynamics to enhance...
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id-itb.:832712024-08-06T14:24:31ZPRICE DYNAMICS ON VEGETABLE PRODUCTS BASED ON THE Q-LEARNING ALGORITHM Laila Shabrina, Mutiara Indonesia Final Project pricing dynamics, vegetables, perishable products, Q-learning, deep Q-learning. INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/83271 Vegetables are classified as highly perishable products with a limited shelf life, leading consumers to avoid products approaching expiration. The primary challenge for vegetable sellers is to maximize sales before freshness declines. This study aims to optimize vegetable pricing dynamics to enhance sales and reduce food waste using reinforcement learning with the Q-learning algorithm and its extension, deep Q-learning. Q-learning is an algorithm that discovers the optimal policy, which in this case is the optimal price through experimentation. However, it has limitations due to requiring a large Q-value table. Deep Q-learning overcomes this limitation by using artificial neural networks to estimate Q-values, making it more efficient in handling complex environments. text |
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Institut Teknologi Bandung |
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Institut Teknologi Bandung Library |
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Asia |
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Indonesia Indonesia |
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Institut Teknologi Bandung |
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Digital ITB |
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Indonesia |
| description |
Vegetables are classified as highly perishable products with a limited shelf life, leading consumers to avoid products approaching expiration. The primary challenge for vegetable sellers is to maximize sales before freshness declines. This study aims to optimize vegetable pricing dynamics to enhance sales and reduce food waste using reinforcement learning with the Q-learning algorithm and its extension, deep Q-learning. Q-learning is an algorithm that discovers the optimal policy, which in this case is the optimal price through experimentation. However, it has limitations due to requiring a large Q-value table. Deep Q-learning overcomes this limitation by using artificial neural networks to estimate Q-values, making it more efficient in handling complex environments. |
| format |
Final Project |
| author |
Laila Shabrina, Mutiara |
| spellingShingle |
Laila Shabrina, Mutiara PRICE DYNAMICS ON VEGETABLE PRODUCTS BASED ON THE Q-LEARNING ALGORITHM |
| author_facet |
Laila Shabrina, Mutiara |
| author_sort |
Laila Shabrina, Mutiara |
| title |
PRICE DYNAMICS ON VEGETABLE PRODUCTS BASED ON THE Q-LEARNING ALGORITHM |
| title_short |
PRICE DYNAMICS ON VEGETABLE PRODUCTS BASED ON THE Q-LEARNING ALGORITHM |
| title_full |
PRICE DYNAMICS ON VEGETABLE PRODUCTS BASED ON THE Q-LEARNING ALGORITHM |
| title_fullStr |
PRICE DYNAMICS ON VEGETABLE PRODUCTS BASED ON THE Q-LEARNING ALGORITHM |
| title_full_unstemmed |
PRICE DYNAMICS ON VEGETABLE PRODUCTS BASED ON THE Q-LEARNING ALGORITHM |
| title_sort |
price dynamics on vegetable products based on the q-learning algorithm |
| url |
https://digilib.itb.ac.id/gdl/view/83271 |
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1822998052979343360 |