Your Paper Publication Details:
Title: Inventory Management with Deep Q Learning and Genetic Analytical Hierarchical Pr
DOI (Digital Object Identifier) :
Pubished in Volume: 9 | Issue: 1 | Year: March 2024
Publisher Name : IJSMER-Rems Publishing House | www.ejournal.rems.co.in | ISSN : 2455-6203
Subject Area: Science and Technology
Author type: Indian Author
Pubished in Volume: 9
Issue: 1
Pages: 1-13
Year: March 2024
Downloads: 50
E-ISSN Number: 2455-6203
Abstract
The contemporary landscape of inventory management, particularly in the realm of imperfect production, necessitates innovative approaches to enhance efficiency and accuracy. Existing models often grapple with limitations in handling ameliorating and deteriorating items simultaneously, and lack sophistication in adapting to dynamic market conditions. This study introduces a groundbreaking model that addresses these challenges, blending the theoretical and practical aspects of inventory management under varying pricing policies. Our proposed model is a synthesis of advanced methodologies: it leverages Deep Q Learning for solving intricate inventory tasks, marking a significant leap over traditional models in terms of flexibility and adaptability. By considering both ameliorating and deteriorating items, it presents a more comprehensive framework for inventory management. Additionally, the integration of Genetic Analytical Hierarchical Processing for item preservation showcases an innovative approach to mitigate deterioration rates effectively. A pivotal component of our model is the application of the Apriori Model for market-driven recommendations. This facet underscores the model's responsiveness to fluctuating market dynamics, ensuring that pricing strategies are both relevant and optimized. Tested on electronic and healthcare products, our model demonstrated superior performance over existing methods, with improvements of 3.9% in precision, 4.5% in accuracy, 8.5% in recall, 3.5% in AUC, 2.9% in specificity, and a notable 4.9% reduction in delay. The impacts of this work are manifold. It not only elevates the precision and efficacy of inventory management in imperfect production contexts but also offers a pragmatic and effective solution adaptable to real-world scenarios. The model's enhanced predictive capabilities and responsiveness to market trends set a new standard for inventory management strategies, particularly in sectors where product deterioration is a significant concern. This study not only fills a critical gap in the existing literature but also paves the way for more nuanced and sophisticated approaches to inventory management in diverse industrial domains.
Keywords
Inventory Management, Deep Q Learning, Genetic Analytical Hierarchical Processing, Ameliorating Items, Deteriorating Items
License
Creative Commons Attribution 4.0 and The Open Definition
Puja Verma
Research Scholar, Eklavya University Damoh,Madhya Pradesh,India
Dr. Gopi Sao
Head, Department of Mathematics Eklavya University,Damoh,Madhya Pradesh,India, Email: gopisao0104@gmail.com
Khushboo Rawat
Research Scholar, Eklavya University Damoh,Madhya Pradesh,India
Inventory Management, Deep Q Learning, Genetic Analytical Hierarchical Processing, Ameliorating Items, Deteriorating Items
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