A course by prof. Michele Monaci
Date:
Event location: Sala del Consiglio, School of Engineering, viale del Risorgimento 2, 40136 Bologna
Type: Course
Mixed Integer Linear Programming (MILP) plays a central role in modeling difficult-to-solve (NP-hard) combinatorial problems.
However, the exact solution of the resulting models often cannot be reached for the problem sizes of interest in real-world applications; hence, one is interested in effective heuristic methods.
The last years have seen a tremendous improvement in the capability of primal heuristics to compute very good solutions for MILP problems.
Many of the proposed methods are general techniques built on top of state-of-the-art MILP solvers, and are hence called matheuristics.
Typically these algorithms compute, in a short computing time, near optimal solutions for hard optimization problems of different types, which allow to have either an approximate solution of the problem, or a starting point for an exact enumerative approach.
In this course, we will review the main techniques that have been proposed in this area and will present some computational experiments on benchmarks from the literature.