Decision Diagrams for Discrete Optimization: A Survey of Recent Advances

TL;DR

This survey reviews recent advances in decision diagrams for discrete optimization, highlighting their classifications, advantages, challenges, and future research directions in modeling and solving optimization problems.

Contribution

It provides a comprehensive classification and description of recent decision diagram techniques in discrete optimization, emphasizing their applications and challenges.

Findings

Decision diagrams enable efficient modeling of optimization problems.

Recent methods improve solution quality using approximate DDs.

DDs are increasingly integrated into state-of-the-art optimization frameworks.

Abstract

In the last decade, decision diagrams (DDs) have been the basis for a large array of novel approaches for modeling and solving optimization problems. Many techniques now use DDs as a key tool to achieve state-of-the-art performance within other optimization paradigms, such as integer programming and constraint programming. This paper provides a survey of the use of DDs in discrete optimization, particularly focusing on recent developments. We classify these works into two groups based on the type of diagram (i.e., exact or approximate) and present a thorough description of their use. We discuss the main advantages of DDs, point out major challenges, and provide directions for future work.