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On decomposition and multiobjective-based column and disjunctive cut generation for MINLP
Optimization and Engineering ( IF 2.1 ) Pub Date : 2020-11-11 , DOI: 10.1007/s11081-020-09576-x
Pavlo Muts , Ivo Nowak , Eligius M. T. Hendrix

Most industrial optimization problems are sparse and can be formulated as block-separable mixed-integer nonlinear programming (MINLP) problems, defined by linking low-dimensional sub-problems by (linear) coupling constraints. This paper investigates the potential of using decomposition and a novel multiobjective-based column and cut generation approach for solving nonconvex block-separable MINLPs, based on the so-called resource-constrained reformulation. Based on this approach, two decomposition-based inner- and outer-refinement algorithms are presented and preliminary numerical results with nonconvex MINLP instances are reported.



中文翻译:

基于分解和多目标的MINLP列和析取割生成

大多数工业优化问题都很稀疏,可以表述为块可分离的混合整数非线性规划(MINLP)问题,这些问题是通过(线性)耦合约束将低维子问题联系起来而定义的。本文研究了基于资源约束的重构,使用分解和基于多目标的新颖列和割生成方法来解决非凸块可分离的MINLP的潜力。基于这种方法,提出了两种基于分解的内部和外部细化算法,并报告了非凸MINLP实例的初步数值结果。

更新日期:2020-11-12
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