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Improved Big-M reformulation for generalized disjunctive programs
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DOI：10.1016/j.compchemeng.2015.02.013.png)
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En 中文
In this work, we present a new Big-M reformulation for Generalized Disjunctive Programs. Unlike the traditional Big-M reformulation that uses one M-parameter for each constraint, the new approach uses multiple M-parameters for each constraint. Each of these M-parameters is associated with each alternative in the disjunction to which the constraint belongs. In this way, the proposed MINLP reformulation is at least as tight as 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 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. (C) 2015 Elsevier Ltd. All rights reserved.
Keyword:
Disjunctive programming
Mixed-integer programming
Big-M
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被引数:
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