Decision Diagrams for Discrete Optimization - Generic Julia Implementation
Author rschwarz
2 Stars
Updated Last
4 Months Ago
Started In
May 2020

Decision Diagrams for Discrete Optimization in Julia.

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Provides a generic implementation of decision diagrams (top-down construction of layered state transition graph). Implements a branch-and-bound algorithms with subproblems defined by nodes in an exact cutset of the diagram.

To support new problem classes, the several methods have to be implemented that are dispatched on the user-defined types for the instance, describing the states and transition functions.

The solver behavior (restrictions, relaxations, variable order, diagram width) can also be fully customized through user-defined layer processing.


The package is mostly written as a learning experiment. Implementing an idea gives a deeper understanding of the challenges than just reading about it.

The appeal of decision diagrams for discrete optimization is two-fold:

  1. The simplicity of the algorithm makes implementation from scratch a reasonable endeavor.
  2. It seems that the DD-based branch-and-bound lends itself to parallelization, yielding better speed-ups than MIP solvers.


This is (still) mostly a naive text book implementation. I'm sure there's room for improvement in the choice of data structures and avoing frequent allocation.

It's currently assumed that the objective function is to be maximized, and the transition values are combined by addition. That is, we're looking for a longest path in the diagram, using as arc weights the values of the transitions. In principle, one could also choose minimization or use another operator (multiplication, maximum), but this would require even more type parametrization.

The decision diagram does not keep all transition arcs, but computes the longest path on the fly. That is, after a new layer is created, each node only remembers a single ingoing arc. This simplification works OK for the purpose of finding an optimal solution, but it rules out other use cases, such as enumeration of feasible solutions or post-optimality analysis.

Problem Classes

Models and methods for some specific problem classes are also implemented in the context of this package as submodules. The main motivation is test-driving the current API, to make sure it's sufficiently general and not too verbose.

Currently included are:


The implementation is informed by the book Decision Diagrams for Optimization by D Bergman, A Cire, WJ van Hoeve and J Hooker.

The MDD website also contains a lot of valuable resources, in particular the INFORMS article Discrete Optimization with Decision Diagrams.


Pull requests with various forms of contributions are very welcome. In particular, I would appreciate suggestions to simplify the current interface, improve overall performance or cover more problem classes.

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