This workshop aims to bring together researchers that work on developing methods, systems, and tools that rely on state-based modeling and solving.  In particular, two streams of research have recently emerged around domain independent solving methodologies based on dynamic programming: 

• Decision diagram-based optimization (DDOpt)

• Domain independent dynamic programming (DIDP)

Both approaches use dynamic programming models (or, more generally, labeled transition systems) to represent and solve discrete optimization models.  This is a new approach that relies on 1) a dynamic programming formulation, and 2) generic solvers that solve a given DP formulation as a ‘model and solve’ framework similar to constraint programming or mixed-integer programming.

The goal of the workshop is to provide a forum for interested researchers to discuss recent advances in DP modeling and solving as well as strategies towards broader impact of this technology. The workshop contains tutorial-style presentations on background methodology, an invited talk by Ryan O'Neil (cofounder and CTO of Nextmv), technical presentations, and a discussion session.

The DPSOLVE 2023 workshop takes place at the CP 2023 conference location.