Decision Diagrams for Discrete Optimization: A Survey of Recent Advances

Margarita P. Castro (), Andre A. Cire () and J. Christopher Beck ()
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Margarita P. Castro: Department of Industrial and Systems Engineering, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile
Andre A. Cire: Department of Management, University of Toronto Scarborough and Rotman School of Management, Toronto, Ontario M1E 1A4, Canada
J. Christopher Beck: Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada

INFORMS Journal on Computing, 2022, vol. 34, issue 4, 2271-2295

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.

Keywords: decision diagrams; discrete optimization (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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