Quantum-Inspired Optimization for Resilient and Sustainable Aerospace Logistics

The aerospace industry faces a critical imperative to achieve net-zero emissions, with global supply chain logistics representing a significant bottleneck. This paper addresses the complex logistics network design problem by explicitly minimizing transportation-related carbon emissions. By leveraging the Product Breakdown Structure (PBS) to map the strict hierarchical dependencies of aerospace assemblies, the proposed model optimally allocates geographically dispersed manufacturing facilities to each sub-component. To navigate the resulting NP-hard combinatorial space, a GPU-accelerated Quantum-Inspired Evolutionary Optimization (QIEO) algorithm, as part of the QuantumNOW™ solver, is implemented and benchmarked against a classical Genetic Algorithm (GA). Empirical results demonstrate that QIEO’s quantum superposition mechanisms effectively bypass local minima, yielding superior solution stability and quality. Specifically, in a high-dimensional scenario with 23 components and 17 potential sites, the parallelized QIEO achieved a staggering 20X computational speed-up and uncovered configurations with nearly 45% lower logistical costs than the GA. These findings establish QIEO as a highly robust and scalable framework for designing sustainable, resilient aerospace supply chains.