init_set_covering is the original greedy weighted scheme (Türkay & Grossmann 1996): weight uncovered disjuncts, solve MILP, NLP, repeat, set_cover_iterlim=8, warn on failure. Slides 20-22 of the 2020 deck sketched a max-coverage selection that certifies covering all nonlinear disjuncts in fewer NLP solves (8-process example: 2 vs 3 iterations).
Scope:
- Exact covering objective (maximize newly covered disjuncts, or a true set-cover MILP over predicted selections).
- Certify coverage or report exactly which disjuncts remain unlinearized (today: warning only).
- Coverage-aware iteration limit instead of the hard-coded 8.
No newer set-covering variant exists in the 2020-2026 literature — small paper + implementation.
Part of roadmap epic #2 (item B5).
init_set_coveringis the original greedy weighted scheme (Türkay & Grossmann 1996): weight uncovered disjuncts, solve MILP, NLP, repeat,set_cover_iterlim=8, warn on failure. Slides 20-22 of the 2020 deck sketched a max-coverage selection that certifies covering all nonlinear disjuncts in fewer NLP solves (8-process example: 2 vs 3 iterations).Scope:
No newer set-covering variant exists in the 2020-2026 literature — small paper + implementation.
Part of roadmap epic #2 (item B5).