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A multi-unit combinatorial auction based approach for decentralized multi-project scheduling

机译:基于多单元组合拍卖的分散式多项目调度方法

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摘要

In industry, many problems are considered as the decentralized resource-constrained multi-project scheduling problem (DRCMPSP). Existing approaches encounter difficulties in dealing with large DRCMPSP cases while respecting the information privacy requirements of the project agents. In this paper, we tackle DRCMPSP by formulating it as a multi-unit combinatorial auction (Wellman et al. in Games Econ Behav 35(1):271-303, 2001), which does not require sensitive private project information. To handle the hardness of bidder valuation, we introduce the capacity query which uses different item capacity profiles to efficiently elicit valuation information from bidders. Based on the capacity query, we adopt two existing strategies (Gonen and Lehmann in Proceedings of the 2nd ACM conference on electronic commerce, pp 13-20, 2000) for solving multi-unit winner determination problems to find good allocations of the DRCMPSP auctions. The first strategy employs a greedy allocation process, which can rapidly find good allocations by allocating the bidder with the best answer after each query. The second strategy is based on a branch-and-bound process to improve the results of the first strategy, by searching for a better sequence of granting the bids from the bidders. Empirical results indicate that the two strategies can find good solutions with higher quality than state-of-the-art decentralized approaches, and scale well to large-scale problems with thousands of activities from tens of projects.
机译:在工业中,许多问题被认为是资源分散的多项目调度问题(DRCMPSP)。现有方法在处理大型DRCMPSP案例时遇到困难,同时又要尊重项目代理的信息隐私要求。在本文中,我们通过将DRCMPSP公式化为一个多单元组合拍卖(Wellman等人,在Games Econ Behav 35(1):271-303,2001)中解决了该问题,它不需要敏感的私人项目信息。为了处理投标人估价的难点,我们引入了容量查询,该查询使用不同的项目容量配置文件来有效地从投标人中获取估价信息。根据容量查询,我们采用了两种现有策略(Gonen和Lehmann在2000年第二届ACM电子商务会议论文集,第13-20页)中解决了多单位获胜者确定问题,以找到DRCMPSP拍卖的良好分配。第一种策略采用贪婪的分配过程,该过程可以通过在每次查询后为出价人分配最佳答案来快速找到良好的分配。第二种策略是基于分支定界过程,通过寻找更好的从投标人中标竞标的顺序来改进第一种策略的结果。经验结果表明,这两种策略可以找到质量比最新的分散方法更高质量的好的解决方案,并且可以很好地解决数十个项目中成千上万个活动的大规模问题。

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