PAMSO: Parametric Autotuning Multi-time Scale Optimization Algorithm

TL;DR

PAMSO is a scalable algorithm that autotunes parameters in low-fidelity models to improve solutions of complex multi-time scale optimization problems, enabling efficient transfer of parameters across similar problems.

Contribution

The paper introduces PAMSO, a novel autotuning method that leverages low-fidelity models and derivative-free optimization to enhance multi-time scale optimization scalability and transferability.

Findings

Effective on large-scale MINLP and MILP models

Reduces computational complexity in multi-time scale optimization

Demonstrates successful parameter transfer across similar problems

Abstract

Optimization models with decision variables in multiple time scales are widely used across various fields such as integrated planning and scheduling. To address scalability challenges in these models, we present the Parametric Autotuning Multi-Time Scale Optimization (PAMSO) algorithm. PAMSO tunes parameters in a low-fidelity model to assist in solving a higher-fidelity multi-time scale optimization model. These parameters represent the mismatch between the two models. PAMSO defines a black-box function with tunable parameters as inputs and multi-scale cost as output, optimized using Derivative-Free Optimization methods. This scalable algorithm allows optimal parameters from one problem to be transferred to similar problems. Case studies demonstrate its effectiveness on an MINLP model for integrated design and scheduling in a resource task network with around 67,000 variables and an MILP model for integrated planning and scheduling of electrified chemical plants and renewable resources with around 26 million variables.