3 papers
cs.LG2026
On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions
Tushar Lone, Neha Karanjkar
Graph Neural Networks (GNNs) have emerged as a powerful, differentiable class of learning models for graph-structured systems. Their ability to generalize across topologies opens t…
cs.AI2026
SupplyNetPy: An Open-Source Python Library for High-Fidelity Modeling and Simulation of Arbitrary Supply Chain and Inventory Networks
Tushar Lone, Neha Karanjkar
This paper introduces SupplyNetPy, an open-source, well-documented Python library for modeling and discrete-event simulation of supply chain networks with arbitrary multi-echelon s…
eess.SY2026
On Integrating Resilience and Human Oversight into LLM-Assisted Modeling Workflows for Digital Twins
Lekshmi P, Neha Karanjkar
LLM-assisted modeling holds the potential to rapidly build executable Digital Twins of complex systems from only coarse descriptions and sensor data. However, resilience to LLM hal…