在不相同作业规模的单机问题中最小化总加权延迟

M. Bijari, Omid Rajabi
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引用次数: 1

摘要

本文研究了单机问题中总加权延迟的最小化问题。作业大小不同,也考虑了批量处理的假设。我们提出了一种新的混合整数线性规划(MILP)来解决这个问题。该模型比以前的模型更快地解决问题;由于所提出的模型限制了求解空间。为了评估所提出的模型,生成了一些实例问题。将所提模型的求解时间与旧模型的求解时间进行比较,表明了新模型的有效性。计算结果表明,该模型在减少90%以上实例的CPU时间。在某些情况下,CPU时间减少了大约70%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Minimizing total weighted tardiness in single machine problem with non-identical job sizes
In this paper, minimizing total weighted tardiness in single machine problem has been considered. Jobs have different size, also batch processing assumption is considered. We developed a new Mixed Integer Linear Programming (MILP) to the problem. The model solves the problem faster than previous model; due to the proposed model restricted the solution space. Some instances problems are generated in order to evaluate the proposed model. Comparing the solution time of the proposed model with the old model shows the efficiency of the new model. Computational result is shown that the proposed model decrease CPU time at more than 90% instances. In some instance CPU time decreased about 70%.
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