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Minimizing the number of machines with limited workload capacity for scheduling jobs with interval constraints

机译:最大限度地减少工作负载容量有限的机器的数量,以调度具有时间间隔限制的作业

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In this paper, we consider a parallel machine scheduling problem in which machines have a limited workload capacity and jobs have deadlines and release dates. The problem is motivated by the operation of energy storage management systems for microgrids under emergency conditions and generalizes some problems that have already been studied in the literature for their theoretical value. In this work, we propose heuristic and exact algorithms to solve the problem. The heuristics are adaptations of classical bin packing heuristics in which additional conditions on the feasibility of a solution are imposed, whereas the exact method is a branch-and-price approach. The results show that the branch-andprice approach is able to optimally solve random instances with up to 250 jobs within a time limit of one hour, while the heuristic procedures provide near optimal solution within reduced running times. Finally, we also provide additional complexity results for a special case of the problem. (C) 2019 Elsevier Inc. All rights reserved.
机译:在本文中,我们考虑了并行机器调度问题,其中机器的工作负载能力有限,工作具有截止日期和发布日期。该问题是由微电网的储能管理系统在紧急情况下运行引起的,并且概括了一些已经在文献中对其理论价值进行研究的问题。在这项工作中,我们提出了启发式和精确的算法来解决该问题。启发式方法是对经典装箱式启发式方法的改编,其中对解决方案的可行性施加了附加条件,而确切的方法是分支定价法。结果表明,分支价格法能够在一小时的时间内优化解决多达250个工作的随机实例,而启发式过程则在减少的运行时间内提供了接近最优的解决方案。最后,对于特殊情况,我们还提供了其他复杂性结果。 (C)2019 Elsevier Inc.保留所有权利。

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