Mixed-integer linear programming (MILP) is highly significant in operations research, now capable of solving complex problems quickly due to modern solvers.
The versatility of MILP solvers has led to widespread applications in transportation, logistics, supply chain, finance, and other sectors.
This article reviews major advancements in MILP solution methods, focusing on computational improvements and experiments.
It details branch-and-cut methods, Dantzig-Wolfe decomposition, and Benders decomposition as key areas of recent progress.
The research highlights ongoing challenges and future opportunities in MILP, indicating it's still an active research field.
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