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A Discrete Particle Swarm Optimization Algorithm for Single Machine Total Earliness and Tardiness Problem with a Common Due Date

机译:具有共同到期日的单机总提前期和延误问题的离散粒子群优化算法

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In this paper, a discrete particle swarm optimization (DPSO) algorithm is presented to solve the single machine total earliness and tardiness penalties with a common due date. A modified version of HRM heuristic presented by Hino et al. in [7], here we call it M_HRM, is also presented to solve the problem. In addition, the DPSO algorithm is hybridized with the neighborhood search algorithm to further improve the solution quality. The performance of the proposed DPSO algorithm is tested on 280 benchmark instances up to 1000 jobs from the OR Library. The computational experiments showed that the proposed DPSO algorithm has generated better results, in terms of both percent deviations from the upper bounds in Biskup and Feldmann [1] and computational time, than the existing approaches in the literature.
机译:本文提出了一种离散粒子群优化算法(DPSO)来解决具有共同到期日的单机总提前期和延误惩罚。 Hino等人提出的HRM启发式方法的修改版本。在[7]中,我们也将其称为M_HRM,以解决该问题。另外,将DPSO算法与邻域搜索算法混合在一起,可以进一步提高解决方案的质量。所提出的DPSO算法的性能在OR库中的280个基准实例(最多1000个作业)上进行了测试。计算实验表明,与文献中的现有方法相比,从Biskup和Feldmann [1]的上限的百分比偏差和计算时间来看,所提出的DPSO算法都产生了更好的结果。

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