RecFOR

RecFOR is a software framework and methodology designed to support Recursive Forecasting and Optimization for Resources in real-time dynamic systems. It provides a modular architecture that combines recursive estimation, online optimization, and decision-support tools to update forecasts and reallocate resources as new data arrives.

The framework centers on a core recursive engine that processes streaming data, a flexible solver interface for online optimization, and a library of models and adapters. It is designed to be extensible, with plug-ins for estimators, scenario generators, and problem formulations, and it offers bindings for commonly used programming languages to facilitate integration with data pipelines and control systems.

RecFOR stands for Recursive Forecasting and Optimization for Resources. In practice, it aims to enable rapid, data-driven decisions in environments where conditions evolve continuously and forecasts must be updated on the fly. The approach is particularly suited to problems that couple prediction with optimization, such as deciding short-term resource allocations under uncertainty.

Applications span energy systems (for example, microgrids and demand-response), manufacturing and logistics operations, telecommunications, and finance risk management. In these settings, RecFOR can update predictive models as new observations arrive and immediately re-solve optimization problems to adjust dispatch, scheduling, or inventory policies.

Development and adoption of RecFOR are rooted in concepts from online optimization, model predictive control, and recursive estimation. While implementations vary, common strengths include real-time responsiveness, modularity, and the ability to handle streaming data. Limitations include computational load for large-scale models and the need for careful model specification and tuning.

Related concepts include online optimization, model predictive control, recursive least squares, and Bayesian updating.