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Job Shop Scheduling with Setup Times and Maximal Time-Lags: A Simple Constraint Programming Approach

机译:具有建立时间和最大时滞的Job Shop调度:一种简单的约束编程方法

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In previous work we introduced a simple constraint model that combined generic AI strategies and techniques (weighted degree heuristic, geometric restarts, nogood learning from restarts) with naive propagation for job shop and open shop scheduling problems. Here, we extend our model to handle two variants of the job shop scheduling problem: job shop problems with setup times; and job shop problems with maximal time lags. We also make some important additions to our original model, including a solution guidance component for search. We show empirically that our new models often outperform the state of the art techniques on a number of known benchmarks for these two variants, finding a number of new best solutions and proving optimality for the first time on some problems. We provide some insight into the performance of our approach through analysis of the constraint weighting procedure.
机译:在先前的工作中,我们引入了一个简单的约束模型,该模型结合了通用的AI策略和技术(加权度启发式算法,几何重启,无法从重启中学习)与朴素的传播方式相结合,解决了作业车间和开放车间调度问题。在这里,我们扩展了模型以处理车间调度问题的两个变体:带有设置时间的车间问题;和工作车间的问题与最大的时间滞后。我们还对原始模型进行了一些重要的补充,包括用于搜索的解决方案指南组件。我们凭经验证明,在这两个变量的许多已知基准上,我们的新模型通常都优于最新技术,从而找到了许多新的最佳解决方案,并首次在某些问题上证明了最优性。通过分析约束加权过程,我们可以对我们的方法的性能提供一些见识。

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