求解指导下的多点构造性搜索作业车间调度

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摘要翻译：
解引导多点构造性搜索(SGMPCS)是一种新颖的构造性搜索技术，它执行一系列资源受限的树搜索，每次搜索要么从一个空解（如随机重启）开始，要么从搜索过程中遇到的一个解开始。这些“精英解决方案中的一小部分在搜索过程中得到维护。我们介绍了该技术，并对车间作业调度问题进行了三组实验。首先，对SGMPCS进行了系统的、全面的交叉研究，以评估各种参数设置对性能的影响。其次，我们探讨了精英解决方案集的多样性，结果表明，与预期相反，较少多样性的解决方案集会导致更强的性能。最后，我们将前两个实验中的最佳参数设置与时间回溯、有限差异搜索、随机重启和一个复杂的禁忌搜索算法在一组著名的基准问题上进行了比较。结果表明，SGMPCS虽然落后于禁忌搜索，但明显优于其他被测试的构造性技术。
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英文标题：
《Solution-Guided Multi-Point Constructive Search for Job Shop Scheduling》
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作者：
J. C. Beck
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Solution-Guided Multi-Point Constructive Search (SGMPCS) is a novel constructive search technique that performs a series of resource-limited tree searches where each search begins either from an empty solution (as in randomized restart) or from a solution that has been encountered during the search. A small number of these "elite solutions is maintained during the search. We introduce the technique and perform three sets of experiments on the job shop scheduling problem. First, a systematic, fully crossed study of SGMPCS is carried out to evaluate the performance impact of various parameter settings. Second, we inquire into the diversity of the elite solution set, showing, contrary to expectations, that a less diverse set leads to stronger performance. Finally, we compare the best parameter setting of SGMPCS from the first two experiments to chronological backtracking, limited discrepancy search, randomized restart, and a sophisticated tabu search algorithm on a set of well-known benchmark problems. Results demonstrate that SGMPCS is significantly better than the other constructive techniques tested, though lags behind the tabu search.
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PDF链接：
https://arxiv.org/pdf/1110.2743

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关键词：SGMPCS Multi during 进行 结果表明