A flow-based MILP reformulation for graph inspection planning solves problems with up to 15,000 vertices and reduces optimality gaps by 30-50% on large instances.
An automatic method for solving discrete programming problems
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A gene importance ranking method splits large MILP problems for metabolic network design into parallel subproblems, recovering most original solutions while raising success rates 37-186% under fixed time limits.
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Scalable Inspection Planning via Flow-based Mixed Integer Linear Programming
A flow-based MILP reformulation for graph inspection planning solves problems with up to 15,000 vertices and reduces optimality gaps by 30-50% on large instances.
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A Gene Ranking Framework Enhances the Design Efficiency of Genome-Scale Constraint-Based Metabolic Networks under Time Limits
A gene importance ranking method splits large MILP problems for metabolic network design into parallel subproblems, recovering most original solutions while raising success rates 37-186% under fixed time limits.