Queueing Cuts in XpressMP

2026-01-19 · Source: OR in an OB World · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, short

Summary

An analysis of Benders decomposition using the XpressMP optimizer (Java API) explores a "cut queue" approach to address a limitation where XpressMP's `PreIntsol` callback could not add Benders cuts from heuristic solutions, unlike CPLEX. The updated Java code implements a `CutRoundCallback` interface to queue cuts generated from heuristic solutions and add them via `XpressProblem.addManagedCut()`. Performance testing on a sample problem revealed that a single-threaded execution of the Benders model with the cut queue was slightly faster (578 ms vs. 597 ms) than without it, while the basic MIP model took 162 ms. With five threads, the cut queue significantly improved performance, reducing runtime from 2557 ms to 817 ms, compared to the MIP model's 168 ms. The queue-enabled model generated more cuts overall, including from heuristic solutions, which appears to be more beneficial in multi-threaded scenarios.

Key takeaway

For optimization engineers using XpressMP for Benders decomposition, consider implementing a cut queue, especially in multi-threaded environments. This approach can significantly improve performance by allowing the generation of Benders cuts from heuristic solutions, which XpressMP's standard `PreIntsol` callback might otherwise miss. Evaluate the performance impact across different thread counts, as the benefits appear to scale with increased parallelism.

Key insights

A cut queue can enable Benders decomposition to utilize heuristic solutions for cut generation in XpressMP.

Principles

• Callback limitations can be circumvented with queues.
• Multi-threading impacts optimization algorithm performance.

Method

Implement a `CutRoundCallback` to queue Benders cuts from heuristic solutions, then add them via `XpressProblem.addManagedCut()` when Xpress generates cuts.

In practice

• Use a cut queue for Benders decomposition in XpressMP.
• Test single- vs. multi-threaded performance for optimization.

Topics

• Benders Decomposition
• XpressMP Optimizer
• Optimization Callbacks
• Cut Generation
• Heuristic Search

Best for: Machine Learning Engineer, Software Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by OR in an OB World.