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Minimizing total tardiness in a scheduling problem with a learning effect

机译:使具有学习效果的调度问题中的总拖延最小化

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Minimizing of total tardiness is one of the most studied topics on single machine problems. Researchers develop a number of optimizing and heuristic methods to solve this NP-hard problem. In this paper, the problem of minimizing total tardiness is examined in a learning effect situation. The concept of learning effects describes the reduction of processing times arising from process repetition. A 0-1 integer programming model is developed to solve the problem. Also, a random search, the tabu search and the simulated annealing-based methods are proposed for the problem and the solutions of the large size problems with up to 1000 jobs are found by these methods. To the best of our knowledge, no works exists on the total tardiness problem with a learning effect tackled in this paper.
机译:减少总延迟是单机问题研究最多的主题之一。研究人员开发了许多优化和启发式方法来解决此NP难题。在本文中,研究了在学习效果情况下将总拖延最小化的问题。学习效果的概念描述了减少过程重复产生的处理时间。开发了0-1整数编程模型来解决该问题。此外,针对该问题提出了随机搜索,禁忌搜索和基于模拟退火的方法,并通过这些方法找到了多达1000个工作的大型问题的解决方案。据我们所知,目前尚无关于总迟缓问题的著作,但本文研究的学习效果没有得到解决。

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