Modeling multi-stage decision optimization problems

TL;DR

This paper introduces a simplified annotation method for multi-stage decision problems under uncertainty, enabling easier modeling and solution, demonstrated through an R implementation.

Contribution

It proposes a novel simplification for modeling multi-stage decision problems, contrasting with complex existing extensions, and defines meta models adaptable across programming languages.

Findings

Simplifies the annotation process for multi-stage decision problems.

Enables implementation of meta models in various programming languages.

Demonstrates the approach with an R example.

Abstract

Multi-stage optimization under uncertainty techniques can be used to solve long-term management problems. Although many optimization modeling language extensions as well as computational environments have been proposed, the acceptance of this technique is generally low, due to the inherent complexity of the modeling and solution process. In this paper a simplification to annotate multi-stage decision problems under uncertainty is presented - this simplification contrasts with the common approach to create an extension on top of an existing optimization modeling language. This leads to the definition of meta models, which can be instanced in various programming languages. An example using the statistical computing language R is shown.