Next-Gen Joint Optimization Engine for LORA & Spari

 Dear [Hiring Manager / CTO Name],

I am writing to introduce a novel numerical optimization framework that significantly enhances the efficiency of Joint Level of Repair Analysis (LORA) and Spare Parts Requirement Planning.

As a researcher with a background in high-performance computing (formerly with SWIP), I have recently published my findings in Computers & Industrial Engineering (CAIE). My work addresses a critical bottleneck in Integrated Logistics Support (ILS): the computational explosion inherent in large-scale, multi-echelon maintenance systems.

Key Technical Breakthroughs:

  • Unconstrained Transformation: I developed an algorithm that maps complex discrete logical constraints into an unconstrained optimization space. This effectively eliminates the "combinatorial explosion" common in traditional MILP or Branch-and-Bound approaches.

  • Joint Optimization & Pareto Frontier: Unlike standard tools that isolate LORA from Sparing, my engine performs joint optimization, generating a Pareto Frontier of "Maintenance Cost vs. Delay Time." This provides decision-makers with a quantitative trade-off dashboard.

  • HPC Performance: Leveraging Numba-based vectorization, the solver achieves [X]-fold speedup compared to conventional heuristic-based solvers, making it ideal for real-time digital twin simulations of complex assets like aircraft or lithography systems.

I am eager to discuss how my optimization engine could be integrated into your [specific project, e.g., F-35 PBL / Opus Suite / Supply Chain Service] to drive down Life Cycle Costs while maximizing operational availability.

Attached is my white paper and a link to my CAIE publication. I look forward to the possibility of a technical exchange.

Best regards,

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