Replication package for an integrated readiness-cost optimization framework

Description

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.

Steps to reproduce

Download both ZIP archives from this repository. Extract the dataset archive and the code archive. The code archive contains the Python implementation, while the dataset archive contains the public benchmark workbook and the associated documentation. Install Python 3.10 or later. From the root folder of the extracted code package, install the required dependencies using: pip install -r requirements.txt Run the complete reproducibility pipeline from the package root using: python main.py The script loads the benchmark workbook, validates the data contract, performs the empirical calibration, runs the decision-dependent Markov readiness model, approximates the readiness-cost Pareto frontier, executes the algorithm comparison, validates the analytical model through discrete-event simulation, runs the disruption scenarios, and computes the Sobol' sensitivity indices. Generated tables are written to outputs/tables/ and generated figures are written to outputs/figures/. For a faster smoke test, reduce the NSGA-II population size, number of generations, simulation replications, and Sobol' sample size in src/config.py. The full configuration used for the manuscript is documented in the README file.

Institutions

• University of Pisa

  Tuscany, Pisa
• University of Florence

  Tuscany, Florence
• Università degli Studi eCampus

  Lombardy, Novedrate
• University of Rome Tor Vergata

  Lazio, Rome

Categories

Licence