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A fast Lagrangian heuristic for large-scale capacitated lot-size problems with restricted cost structures

机译:快速拉格朗日启发式算法,用于受限于成本结构的大规模批量批量问题

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摘要

summary:In this paper, we demonstrate the computational consequences of making a simple assumption on production cost structures in capacitated lot-size problems. Our results indicate that our cost assumption of increased productivity over time has dramatic effects on the problem sizes which are solvable. Our experiments indicate that problems with more than 1000 products in more than 1000 time periods may be solved within reasonable time. The Lagrangian decomposition algorithm we use does of course not guarantee optimality, but our results indicate surprisingly narrow gaps for such large-scale cases - in most cases significantly outperforming CPLEX. We also demonstrate that general CLSP's can benefit greatly from applying our proposed heuristic.
机译:摘要:在本文中,我们演示了在容量受限的批量问题中对生产成本结构进行简单假设的计算结果。我们的结果表明,随着时间的推移生产率提高的成本假设对可解决的问题规模产生了巨大影响。我们的实验表明,可以在合理的时间内解决1000多个产品中1000多个问题。我们使用的拉格朗日分解算法当然不能保证最优性,但是我们的结果表明,在这种大规模案例中,差距出人意料地很窄-在大多数情况下,它们的性能明显优于CPLEX。我们还证明,一般的CLSP可以从应用我们提出的启发式方法中受益匪浅。

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