Three metaheuristics for solving the flow shop problem with permutation and sequence dependent setup time
Three metaheuristics for solving the flow shop problem with permutation and sequence dependent setup time
复制标题
用于解决具有排列和序列相关设置时间的流水车间问题的三种元启发法
DOI：
10.1109/icoa.2018.8370598
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
K. Allali
中科院分区：
文献类型：
--
作者：
Said Aqil;K. Allali
本文提出了三种元启发式算法来解决带有排列和顺序调整时间的流水作业调度问题。第一种是迭代局部搜索算法，第二种是贪婪随机自适应搜索过程，第三种是贪婪迭代算法。目标是最小化所有作业的总运行时间，即完工时间。在三种元启发式算法中，在改进阶段，我们提出了一套针对所研究问题所采用的局部研究方法。通过改变每个元启发式算法的参数，对一组实例进行了比较研究。结果表明，迭代贪婪算法比其他两种元启发式算法具有更好的性能。
We present in this paper, three metaheuristics for the resolution of the flow shop scheduling problem with permutation and sequence dependent setup time. The first metaheuristic is the iterative local search algorithm, the second is the greedy randomized adaptive search procedure and the third is the greedy iterative algorithm. The goal is to minimize the total running time of all jobs, the makespan. In the three metaheuristics, during the improvement phase, we suggest a set of local research methods that we adopt for the studied problem. A comparative study is conducted on a set of instances by varying the parameters for each metaheuristic. The results obtained show good performances of the iterative greedy algorithm compared to two other metaheuristics.