Iterative combinatorial auctions for managing product transitions in semiconductor manufacturing

Iterative combinatorial auctions for managing product transitions in semiconductor manufacturing
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用于管理半导体制造中产品转换的迭代组合拍卖

DOI:
10.1080/24725854.2019.1651951
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发表时间:
2020
期刊:
影响因子:
2.6
通讯作者:
Kempf, Karl
Kempf, Karl
中科院分区:
工程技术3区
文献类型:
--
作者:
Bansal, Ankit;Uzsoy, Reha;Kempf, Karl

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半导体行业产品转型的成功管理需要制造和产品开发活动的有效协调。制造单位必须满足对现有产品的需求,同时还要为产品开发单位分配生产能力,以进行原型制造,从而支持及时将新产品引入大批量制造。只有在每个单位内部才能了解详细的业务限制和能力,因此无法使用一个拥有所有单位完整信息的中央规划模式。然而,各单位使用的决策支持工具提供了一个分散的决策框架的可能性,使用这些本地模型作为组件,以迅速获得相互接受的,可实施的解决方案。我们开发了迭代组合拍卖(ICA),实现所有单位的协调决策,以最大限度地提高公司的利润,同时激励所有单位如实分享信息。计算结果表明,使用列生成更新价格的伊卡优于使用次梯度搜索,在较低的CPU时间获得接近最优的公司利润。
Successful management of product transitions in the semiconductor industry requires effective coordination of manufacturing and product development activities. Manufacturing units must meet demand for current products while also allocating capacity to product development units for prototype fabrication that will support timely introduction of new products into high-volume manufacturing. Knowledge of detailed operational constraints and capabilities is only available within each unit, precluding the use of a centralized planning model with complete information of all units. However, the decision support tools used by the individual units offer the possibility of a decentralized decision framework that uses these local models as components to rapidly obtain mutually acceptable, implementable solutions. We develop Iterative Combinatorial Auctions (ICAs) that achieve coordinated decisions for all units to maximize the firm’s profit while motivating all units to share information truthfully. Computational results show that the ICA that uses column generation to update prices outperforms that using subgradient search, obtaining near-optimal corporate profit in low CPU times.
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