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A hybrid TP+PLS algorithm for bi-objective flow-shop scheduling problems

机译:TP + PLS混合算法求解双目标流水车间调度问题

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This paper presents a new, carefully designed algorithm for five bi-objective permutation flow shop scheduling problems that arise from the pairwise combinations of the objectives (ⅰ) makespan, (ⅱ) the sum of the completion times of the jobs, and (ⅲ) both, the weighted and non-weighted total tardiness of all jobs. The proposed algorithm combines two search methods, two-phase local search and Pareto local search, which are representative of two different, but complementary, paradigms for multi-objective optimization in terms of Pareto-optimality. The design of the hybrid algorithm is based on a careful experimental analysis of crucial algorithmic components of these two search methods. We compared our algorithm to the two best algorithms identified, among a set of 23 candidate algorithms, in a recent review of the bi-objective permutation flow-shop scheduling problem. We have reimplemented carefully these two algorithms in order to assess the quality of our algorithm. The experimental comparison in this paper shows that the proposed algorithm obtains results that often dominate the output of the two best algorithms from the literature. Therefore, our analysis shows without ambiguity that the proposed algorithm is a new state-of-the-art algorithm for the bi-objective permutation flow-shop problems studied in this paper.
机译:本文针对五种双目标置换流水车间调度问题提出了一种经过精心设计的新算法,该问题是由目标(ⅰ)生成时间,(ⅱ)作业完成时间之和和(the)的成对组合产生的所有工作的加权和非加权总拖延时间。所提出的算法结合了两种搜索方法:两阶段局部搜索和帕累托局部搜索,它们代表了帕累托最优性的两个不同但互补的多目标优化范式。混合算法的设计基于对这两种搜索方法的关键算法组成部分的仔细实验分析。在最近对双目标置换流水车间调度问题的回顾中,我们将我们的算法与确定的23种候选算法中的两种最佳算法进行了比较。为了评估算法的质量,我们已经仔细地重新实现了这两种算法。本文中的实验比较表明,所提出的算法所获得的结果通常支配着文献中两种最佳算法的输出。因此,我们的分析毫不含糊地表明,该算法是针对本文研究的双目标置换流水车间问题的一种最新技术。

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