A Deep Reinforcement Learning Approach for Inventory Control under Stochastic Lead Time and Demand

Shakya, Manoj; Lee, Bu-Sung; Ng, Huey Yuen

doi:10.1109/ssci51031.2022.10022256

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…develop two AA-based systems to automatically detect such events, integrating Principal Component Analysis (PCA), RFID, and stochastic prediction models. In contrast,Meisheri et al (2022), emphasizes uncertain demand and cross-restrictions between products.Similarly, Shakya, Lee and Ng (2022) and Punia, Singh and Madaan (2020) address stochastic demand and supply problems, with reinforcement learning and data-based approaches, respectively Shakya et al (2022). model a linear supply chain problem with stochastic delivery time and demand within the Markov Decision Processes (MDP) framework and compare the efficiency of traditional reinforcement learning (i.e., Q-learning) and rule-based learning (i.e., the (R, S) policy) with their Deep Q Network (DQN) model.Punia et al (…”

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Integration of machine learning in the supply chain for decision making: A systematic literature review

Polo-Triana,

Gutierrez,

Leon-Becerra

2024

JIEM

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Purpose: This study presents a systematic literature review that provides a broad and holistic view of how machine learning can be used and integrated to enhance decision-making in various areas of the supply chain, highlighting its combination with other techniques and models.Design/methodology/approach: An exhaustive literature review used three sets of keywords in the Scopus and Web of Science (WoS) databases. Through a rigorous filtering process, 70 articles were selected from an initial total of 410, focusing on those that specifically addressed the intersection of machine learning and decision-making in supply chain management.Findings: Machine learning has proven to be an essential tool in the supply chain, with applications in inventory management, logistics, and transportation, among others. Its integration with other techniques has led to significant advances in decision-making, improving efficiency in complex environments. Combining machine learning methods with traditional techniques has been particularly effective, and integration with emerging technologies has opened up new application possibilities.Originality/value: Unlike previous studies that focused on specific areas, this study offers a broad perspective on the application of machine learning in the supply chain. Additionally, combining machine learning techniques with other models is highlighted, representing added value for the scientific community and suggesting new avenues for future research.

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confidence: 99%

“…Traditional reinforcement learning (Q-learning), Deep Network Q (DQN) modelintegrates with Markov Decision Processes (MDP) to model stochastic lead time and demand(Shakya et al, 2022) …”

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confidence: 99%