A perfect information lower bound for robust lot-sizing problems
A perfect information lower bound for robust lot-sizing problems
复制标题
鲁棒批量问题的完美信息下界
DOI：
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发表时间：
2018
影响因子：
4.8
通讯作者：
D. Nace
中科院分区：
文献类型：
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作者：
M. C. Santos;M. Poss;D. Nace
鲁棒多阶段线性优化计算量大，只能精确求解小问题。因此，鲁棒多阶段线性问题通常通过决策规则来解决，决策规则为问题的最优解成本提供上限。本文研究了随机规划中完全信息松弛的下界。具体来说，我们研究了无能力约束的鲁棒批量问题，表明当非预期约束放松时，问题的不同版本变得易于处理。因此，我们可以有效地解决所产生的问题，获得了原问题的最优解成本的下界。我们在数值上比较了求解时间和新下界的质量与Kuhn等人提出的对偶仿射决策规则（Math Program 130：177-209，2011）。
Robust multi-stage linear optimization is hard computationally and only small problems can be solved exactly. Hence, robust multi-stage linear problems are typically addressed heuristically through decision rules, which provide upper bounds for the optimal solution costs of the problems. We investigate in this paper lower bounds inspired by the perfect information relaxation used in stochastic programming. Specifically, we study the uncapacitated robust lot-sizing problem, showing that different versions of the problem become tractable whenever the non-anticipativity constraints are relaxed. Hence, we can solve the resulting problem efficiently, obtaining a lower bound for the optimal solution cost of the original problem. We compare numerically the solution time and the quality of the new lower bound with the dual affine decision rules that have been proposed by Kuhn et al. (Math Program 130:177–209, 2011).
DOI：
10.1007/978-3-319-09174-7_6
发表时间：
2014-03
期刊：
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影响因子：
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作者：
F. Baumann;C. Buchheim;A. Ilyina
通讯作者：
F. Baumann;C. Buchheim;A. Ilyina