论文标题

在平行机调度中进行词典制作的词典制作pan优化的答案集编程

Answer-Set Programming for Lexicographical Makespan Optimisation in Parallel Machine Scheduling

论文作者

Eiter, Thomas, Geibinger, Tobias, Musliu, Nysret, Oetsch, Johannes, Skocovsky, Peter, Stepanova, Daria

论文摘要

我们在平行机器上处理了一个具有挑战性的调度问题,该问题依赖序列的设置时间,并释放了半导体工作场所生产的现实应用程序的日期。在那里,只能由专用的机器处理作业,因此,无论其余机器如何安排工作,很少能确定Makepan。当机器故障并需要重新安排工作时,这会导致问题。我们不仅将单个机器跨越了非稳定顺序,而不是仅优化MakePan,并在词典上最小化了所得的元素。这实现了所有机器尽早完成,并增加了时间表的鲁棒性。我们研究答案集编程(ASP)以解决此问题的应用。在ASP放松建模的同时,时间限制和所考虑的目标函数的组合挑战了当前的解决技术。通过使用差异逻辑的ASP扩展来解决前一种问题。对于后者,我们设计了使用多拍求解的不同算法。为了解决工业大小的实例,我们研究了不同的近似和启发式方法。我们的实验结果表明,ASP确实是该问题的有希望的KRR范式,并且与最先进的CP和MIP求解器具有竞争力。在逻辑编程(TPLP)的理论和实践中考虑的。

We deal with a challenging scheduling problem on parallel machines with sequence-dependent setup times and release dates from a real-world application of semiconductor work-shop production. There, jobs can only be processed by dedicated machines, thus few machines can determine the makespan almost regardless of how jobs are scheduled on the remaining ones. This causes problems when machines fail and jobs need to be rescheduled. Instead of optimising only the makespan, we put the individual machine spans in non-ascending order and lexicographically minimise the resulting tuples. This achieves that all machines complete as early as possible and increases the robustness of the schedule. We study the application of Answer-Set Programming (ASP) to solve this problem. While ASP eases modelling, the combination of timing constraints and the considered objective function challenges current solving technology. The former issue is addressed by using an extension of ASP by difference logic. For the latter, we devise different algorithms that use multi-shot solving. To tackle industrial-sized instances, we study different approximations and heuristics. Our experimental results show that ASP is indeed a promising KRR paradigm for this problem and is competitive with state-of-the-art CP and MIP solvers. Under consideration in Theory and Practice of Logic Programming (TPLP).

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