Abbaspour Ghadim Bonab, Akbar, yousefi nejad attari, mahdi. (1401). A predictive data-driven state-dependent decision approach to determine inventory system states for critical spare parts. , 7(2), 205-231. doi: 10.22070/jqepo.2022.16129.1233
Akbar Abbaspour Ghadim Bonab; mahdi yousefi nejad attari. "A predictive data-driven state-dependent decision approach to determine inventory system states for critical spare parts". , 7, 2, 1401, 205-231. doi: 10.22070/jqepo.2022.16129.1233
Abbaspour Ghadim Bonab, Akbar, yousefi nejad attari, mahdi. (1401). 'A predictive data-driven state-dependent decision approach to determine inventory system states for critical spare parts', , 7(2), pp. 205-231. doi: 10.22070/jqepo.2022.16129.1233
Abbaspour Ghadim Bonab, Akbar, yousefi nejad attari, mahdi. A predictive data-driven state-dependent decision approach to determine inventory system states for critical spare parts. , 1401; 7(2): 205-231. doi: 10.22070/jqepo.2022.16129.1233

A predictive data-driven state-dependent decision approach to determine inventory system states for critical spare parts

Journal of Quality Engineering and Production Optimization
مقاله 11، دوره 7، شماره 2، اسفند 2022، صفحه 205-231    اصل مقاله (995.67 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22070/jqepo.2022.16129.1233
نویسندگان
Akbar Abbaspour Ghadim Bonab1؛ mahdi yousefi nejad attari* 2
1Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
2Department of Industrial Engineering, Bonab Branch, Islamic Azad University, Bonab, Iran.
چکیده
The Markov chain is widely used in state-dependent inventory control of spare parts because of its ability to model the gradual degradation process of components and predict their condition. Also, according to previous studies, considering system information causes a significant reduction in costs. Therefore, the present study tries to extract the system information using a machine learning algorithm and provide it as a transition matrix to the Markov decision process (MDP) to determine the future states of the critical spare parts inventory system. In the presented method, the machine learning algorithm, here Adaptive Neuro-Fuzzy Inference System (ANFIS), is in charge of the training data. The Markov chain uses the trained data to predict the future states of the inventory system. For this purpose, four states have been considered, each representing a level of tension and demand in the inventory system. Applying the model to the data collected for a critical component showed that the model has good accuracy in predicting the following states of the system. Also, the presented model offers a lower error rate, RMSE, and MAPE, compared to the ARIMA model for predicting the next state of the inventory system.
کلیدواژه‌ها
MDP؛ Machine learning؛ state-dependent spare parts؛ ANFIS؛ Inventory
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