This repository contains the core research papers and theoretical frameworks underpinning Tawabiry, a next-generation queue management platform. The documents herein detail the novel integration of classical computer science theories with modern machine learning techniques to solve complex real-world scheduling and prediction problems.
- Domain: Machine Learning, Queuing Theory, Evolutionary Computation.
- Summary: Presents a "Blackbox" prediction system that achieves 92% accuracy in wait time consistency.
- Key Technologies:
- Finite State Machine (FSM) for state modeling.
- Dynamic Erlang-C for baseline estimation.
- Long Short-Term Memory (LSTM) networks for residual correction.
- Genetic Algorithms (GA) for server assignment optimization.
- Domain: Operating Systems, Scheduling Theory, Human-Computer Interaction.
- Summary: A formal analysis of the Dynamic Preemptive Priority Scheduling with Grace Period Adjustment (DPPSGPA) algorithm.
- Key Concepts:
- Adaptation of OS scheduling algorithms (MLFQ, Round Robin) to human queues.
- Formal definitions of extensive grace periods and "ghosting" protocols.
- Handling of starvation and priority inversion in service environments.
These papers represent the intersection of Theory of Computation and Mass-Scale Infrastructure Engineering. They move beyond theoretical abstractions to address stochastic behaviors in heterogeneous service environments, validated by commercial deployment data.
If referencing these methodologies, please cite the respective documents as internal technical reports for Tawabiry.
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Copyright © 2025 Tawabiry. All Rights Reserved.
Principal Researcher: Hatem Soliman
This documentation is the sole property of Tawabiry. Unauthorized reproduction or distribution is strictly prohibited.