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The collaborative multi-level lot-sizing problem with cost synergies

机译:具有成本协同效应的协同多级批判问题

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摘要

Collaborative operations planning is a key element of modern supply chains. We introduce the collaborative multi-level lot-sizing problem with cost synergies. This arises if producers can realise reductions of their costs by providing more than one product in a specific time horizon. Since producers are typically not willing to reveal critical information, we propose a decentralised mechanism, where producers do not have to reveal their individual items costs. Additionally, a Genetic Algorithms-based centralised approach is developed, which we use for benchmarking. Our study shows that this approach comes very close to the a central plan, while in the decentralised one no critical information has to be shared. We compare the results to a myopic upstream planning approach, and show that these results are almost 12% worse than the centralised ones. All solution approaches are assessed on available test instances for problems without cost synergies. For the biggest available instances, the proposed centralised mechanism improves the best known solutions on average by 10.8%. The proposed decentralised mechanism can be applied to other problem classes, where collaborative decision makers aim for good plans under incomplete information.
机译:协作运营计划是现代供应链的关键要素。我们介绍了成本协同效应的协同多级批量问题。这是由于生产者通过在特定时间范围内提供多个产品来实现成本的降低。由于生产者通常不愿意揭示关键信息,我们提出了一种分散的机制,生产者不必揭示他们的个人项目成本。另外,开发了一种基于遗传算法的集中方法,我们用于基准测试。我们的研究表明,这种方法非常接近一个中央计划,而在分散的一个中,必须共享任何关键信息。我们将结果与近视上游规划方法进行比较,并表明这些结果差约比集中式更差。所有解决方案方法都会在没有成本协同作用的情况下对现有测试实例进行评估。对于最大的可用情况,所提出的集中机制平均提高了最佳已知解决方案10.8%。拟议的分散机制可以应用于其他问题课程,其中协同决策者旨在根据不完整的信息造成良好的计划。

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