OR-Ops: A framework for continuous deployment and operationalization of optimization models in real-world environments

The operationalization of Information Technology services, embedded within business processes, required the integration of business domains, data and software engineering, and computational infrastructure. This movement consolidated paradigms such as DevOps, DataOps, and MLOps, highlighting the maturity achieved in the integration of development, data, and continuous operations within machine learning implementation workflows. Operational Research still lacks systematic approaches aimed at the resilient operationalization of optimization models in production environments, particularly in scenarios involving large volumes of data and the integration of multiple technical domains. This work proposes OR-Ops (Operational Research Operations), a framework of principles and practices that integrates mathematical modeling, data engineering, computational infrastructure, and business processes.