Replication package for an integrated readiness-cost optimization framework

This repository contains the replication package for a study on readiness-cost optimization in mission-critical supply chains. The study examines how supplier selection, spare-parts inventory, maintenance scheduling frequency, and crew capacity affect the operational readiness and cost of multi-subsystem assets. The package includes two ZIP archives. The dataset archive contains a curated public benchmark built from U.S. Government sources: FAA Service Difficulty Reporting records for JASC Chapter 29 hydraulic systems, U.S. GAO readiness and sustainment reports, and USAspending PSC-1650 procurement records. These data are processed into documented benchmark inputs for subsystem failure evidence, supplier archetypes, readiness references, and procurement-cost calibration. The code archive contains the Python implementation used to reproduce the computational results. It includes the decision-dependent Markov availability model, the bi-objective readiness-cost optimization workflow, the NSGA-II and MOEA/D comparison, the discrete-event simulation validation, the disruption scenario analysis, and the Sobol' sensitivity analysis. The main workflow can be run from the package root using `python main.py`. The data support the finding that cost-minimizing policies can generate a substantial readiness gap relative to readiness-oriented policies, while a knee-point policy recovers most of the readiness gain at a limited fraction of the total cost increase. The analyses also show that maintenance scheduling is the dominant readiness lever and that disruption scenarios primarily increase cost rather than reduce attainable readiness. Licensing note: the repository-level license is CC BY 4.0. The dataset archive is released under CC BY 4.0. The code archive includes its own LICENSE file and is released under the MIT License. The underlying FAA, GAO, and USAspending sources are public-domain U.S. Government data.