Manoj Shakya
Papers
1
Total Citations
11
H-Index
1
About
Manoj Shakya is an emerging researcher at the intersection of operations research and artificial intelligence, with a primary focus on inventory management and supply chain optimization. His most cited work, "A Deep Reinforcement Learning Approach for Inventory Control under Stochastic Lead Time and Demand" (2022, 11 citations), addresses a critical challenge in logistics: how to make optimal stock-ordering decisions when both customer demand and supplier lead times are unpredictable. By framing inventory control as a sequential decision-making problem, Shakya demonstrates how deep reinforcement learning can outperform traditional heuristics, offering a data-driven path to reduce costs and stockouts. This contribution is particularly notable for bridging the gap between cutting-edge AI techniques and practical operations management—a field where real-world uncertainty often defies classical models. While his citation count is still growing, Shakya’s work signals a promising direction for researchers seeking to apply reinforcement learning to complex, stochastic environments. His research is especially relevant for students and practitioners interested in how modern machine learning can transform classic industrial engineering problems into adaptive, intelligent systems.
Research Focus
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Top Papers
- 1