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首页> 外文期刊>Annals of Operations Research >Time symmetry of resource constrained project scheduling with general temporal constraints and take-give resources
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Time symmetry of resource constrained project scheduling with general temporal constraints and take-give resources

机译:具有一般时间约束和取舍资源的资源受限项目调度的时间对称性

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The paper studies a lacquer production scheduling problem formulated as a resource constrained project scheduling problem with general temporal constraints (i.e., positive and negative time-lags). This real-world scheduling problem requires so-called take-give resources that are needed from the beginning of an activity to the completion of another activity of the production process. Furthermore, we consider sequence dependent changeover times on take-give resources. We formulate this problem by mixed integer linear programming and we suggest a parallel heuristic to solve the problem. This heuristic exploits a time symmetry mapping which allows an easy construction of a schedule in the backward time orientation. In the second part of the paper, it is proven that the time symmetry mapping is bijective and involutive even for the problem with general temporal constraints, changeover times, and take-give resources. The motivation to use this mapping is to improve the performance of the heuristic and to simplify its implementation. Finally, the performance of the heuristic algorithm is evaluated on a set of lacquer production benchmarks requiring take-give resources and on standard benchmarks for the resource constrained project scheduling problem with general temporal constraints where we found new better solutions in 16 and 12 instances out of 90 for UBO500 and UBO1000 respectively.
机译:本文研究了漆生产调度问题,该问题被表述为具有一般时间约束(即正负时滞)的资源受限项目调度问题。从生产活动的开始到生产过程的另一活动的完成,这个实际的调度问题需要所谓的“让步”资源。此外,我们考虑了基于给定资源的序列相关转换时间。我们通过混合整数线性规划来表述此问题,并建议使用并行启发式方法来解决该问题。这种启发式方法利用了时间对称映射,从而可以轻松地按向后的时间方向构造时间表。在本文的第二部分中,证明了时间对称映射是双射的,并且即使对于具有一般时间约束,转换时间和允许资源的问题,也都是不复杂的。使用此映射的动机是提高启发式算法的性能并简化其实现。最后,启发式算法的性能是在一组需要付出资源的清漆生产基准上进行评估的,并在具有一般时间约束的资源受限项目调度问题的标准基准上进行了评估,我们在16个和12个实例中发现了新的更好的解决方案UBO500和UBO1000分别为90。

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