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A learn-and-construct framework for general mixed-integer programming problems
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DOI：10.1111/itor.12578.png)
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Abstract
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In this paper, we propose a new framework for finding an initial feasible solution from a mixed-integer programming (MIP) model. We call it learn-and-construct since it first exploits the structure of the model and its linear relaxation solution and then uses this knowledge to try to produce a feasible solution. In the learning phase, we use an unsupervised learning algorithm to cluster entities originating the MIP model. Such clusters are then used to decompose the original MIP in a number of easier sub-MIPs that are solved by using a black box solver. Computational results on three well-known problems show that our procedure is characterized by a success rate larger than both the feasibility pump heuristic and a state-of-the-art MIP solver. Furthermore, our approach is more scalable and uses less computing time on average.
Keywords:
mixed-integer programming
heuristics
feasible solution
unsupervised learning
optimization
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