<Under construction. Add chapter intro.>
CSP solvers are already deeply embedded into modern industrial workflows (and have been for quite some time!). There are many real-world problems that can be expressed as formal CSPs, and the answers generated by CSP solvers can be directly implemented onto factory floors, computer system scheduling, etc.
List of CP systems from Lagerkvist, M. Z. (2015). Constraint programming. https://www.kth.se/social/files/56249ef5f276543722593c59/progp15-cplecture.pdf
Some companies make proprietary CSP solvers, a.k.a. “constraint programming” (CP) systems, as a tool to sell to other companies, such as:
There are also many open source CP systems like:
There are also companies that specialize in CP services for particular industries. For example, the small Swedish company Tomologic specializes in optimization and CP for the sheet metal industry. From their website:
Tomologic takes end-to-end control over the optimization process, from part placements in clusters to cutting path optimization. Maintaining reliable production implies taking into account all physical constraints in the cutting process, such as: heat, tension, melting, tilting parts, inductive cutting, struggling sheets etc. All these factors are part of our optimization model making it computationally intensive and requiring high performance computing.
Let’s take a closer look at IBM’s CP Optimizer to see how it works. From their website:
Laborie, P., Rogerie, J., Shaw, P., & Vilı́m, P. (2018). IBM ILOG CP optimizer for scheduling. Constraints, 23(2), 210–250.
From the paper:
“It is clearly not possible to provide an exhaustive description of all industrial applications that are using CP Optimizer. Nevertheless, if we focus only on recent articles published in the last 18 months, we can get an idea of the diversity of scheduling problems successfully addressed by CP Optimizer. These problems cover several domains:
…. Beside the technical articles published in the academic community, here are some companies that are using CP Optimizer to solve their scheduling problems:
<Under construction. more details on how CP optimizer works, with examples from paper>
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Kunda, M. (2022). Triangle AI Book. https://www.triangleaibook.org
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