Carnegie Mellon University
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Improved Big-M Reformulation for Generalized Disjunctive Programs

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journal contribution
posted on 2014-01-01, 00:00 authored by Francisco Trespalacios, Ignacio E. Grossmann

In this work, we present a new Big-M reformulation for Generalized Disjunctive Programs. The proposed MINLP reformulation is stronger than the traditional Big-M, and it does not require additional variables or constraints. We present the new Big-M, and analyze the strength in its continuous relaxation compared to that of the traditional Big-M. The new formulation is tested by solving several instances of process networks and muli-product batch plant problems with an NLP-based branch and bound method. The results show that, in most cases, the new reformulation requires fewer nodes and less time to find the optimal solution.




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