KaalPath is a cutting-edge framework that redefines logistics optimization for global supply chains. Harnessing the power of quantum-inspired optimization, fuzzy logic ranking, and deep learning prediction, KaalPath tackles the complexities of multi-modal routing
A framework to evaluate algorithmic solution quality and time‐complexity performance for CVRP across Python, C++, Rust, and Julia. • Profiled runtime execution and memory allocation strategies, demonstrating a 2–3x performance variance driven by underlying language architectures and software behaviours that directly impact operational costs.
Professional SVG Visualization & Logic Engine. Engineered with Sovereign-Kaplan Universal Protocol. Human-AI Hybrid Architecture.
A machine learning-driven supply chain optimization project presented as a Jupyter Notebook. It integrates demand forecasting, inventory level optimization, and logistics route optimization using time series models, classification, and graph-based methods, with visualizations and performance metrics to guide decision-making.
A delivery optimization project using causal inference and logistic optimization
Work on clients’ data to help it understand the primary causes of unfulfilled requests as well as come up with solutions that recommend drivers locations that increase the fraction of complete orders.
Work on clients’ data to help it understand the primary causes of unfulfilled requests as well as come up with solutions that recommend drivers locations that increase the fraction of complete orders.
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