车间作业调度问题的变体模型与策略

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摘要翻译：
最近，各种约束规划和布尔可满足性方法被引入调度问题。它们的共同点是使用相对简单的传播机制和一种自适应的方法来关注问题中最受约束的部分。在某些情况下，这些方法优于依赖于全局一元或累积资源约束的传播算法和专用搜索启发式的更经典的约束规划方法。特别地，我们描述了一种将重启、通用自适应启发式和解引导分支结合在一个基于析取约束分解的简单模型上的方法。在本文中，我们介绍了一种适用于一类重要子类作业车间调度问题(JSPs)，其中目标函数包括提前/拖期费用最小化。我们进一步表明，我们的技术可以通过为JSP的一个变体添加特定于领域的信息来改进（涉及时滞约束）。特别地，我们引入了一个专用的贪婪启发式，以及一个针对最大时滞为0的情况的改进模型（也称为无等待JSP）。
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英文标题：
《Models and Strategies for Variants of the Job Shop Scheduling Problem》
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作者：
Diarmuid Grimes (4C UCC), Emmanuel Hebrard (LAAS)
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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：
  Recently, a variety of constraint programming and Boolean satisfiability approaches to scheduling problems have been introduced. They have in common the use of relatively simple propagation mechanisms and an adaptive way to focus on the most constrained part of the problem. In some cases, these methods compare favorably to more classical constraint programming methods relying on propagation algorithms for global unary or cumulative resource constraints and dedicated search heuristics. In particular, we described an approach that combines restarting, with a generic adaptive heuristic and solution guided branching on a simple model based on a decomposition of disjunctive constraints. In this paper, we introduce an adaptation of this technique for an important subclass of job shop scheduling problems (JSPs), where the objective function involves minimization of earliness/tardiness costs. We further show that our technique can be improved by adding domain specific information for one variant of the JSP (involving time lag constraints). In particular we introduce a dedicated greedy heuristic, and an improved model for the case where the maximal time lag is 0 (also referred to as no-wait JSPs).
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PDF链接：
https://arxiv.org/pdf/1109.5920

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关键词：Presentation Intelligence Minimization Constraints Programming 模型 适应 表明 problems constraints