This repository provides a modular research framework for optimisation in Reconfigurable Manufacturing Systems (RMS). The architecture follows a layered design comprising configuration management, data ingestion, simulation stubs, algorithmic portfolios, experiment orchestration, visualisation, reporting, and validation utilities. The goal is to enable rapid prototyping of novel optimisation strategies while meeting reproducibility requirements expected from Q1 journal submissions.
python -m venv .venv
source .venv/bin/activate
pip install -e .
python scripts/run_experiments.py --config config/base_config.yamlThe baseline script executes a small suite of dispatching rules on the configured datasets, exports aggregated metrics, and generates a publication-ready bar chart together with a markdown summary report.
config/: Pydantic-backed configuration models and sample YAML filesdata/: Data loading, validation, synthetic generation, cachingcore/: Shared domain abstractions (problem, solution, metrics)algorithms/: Portfolios including classical, metaheuristic, RL, and hybrid stubsexperiments/: Experiment manager orchestrating runs and persistencevisualization/: Publication-quality plotting utilitiesreporting/: Automated report generation helpersvalidation/: Theoretical and empirical validation skeletonsscripts/: Command-line interfaces for executing experiments
The framework is intentionally modular so additional algorithms, simulators, or validation routines can be contributed without touching the existing components.