本文選題：多目標優(yōu)化　切入點：置換流水車間調(diào)度　出處：《系統(tǒng)工程理論與實踐》2017年01期 　論文類型：期刊論文

【摘要】：針對最小化最大完工時間,總流程時間及總延遲時間的多目標置換流水車間調(diào)度問題,提出一種改進的混沌雜草優(yōu)化算法,該算法采用基于熵值權重的灰熵關聯(lián)度適應值分配策略,引入快速非支配排序法生成外部檔案,并將進化種群的更新和最優(yōu)位置的混沌搜索相結(jié)合,用于維護外部檔案,提升算法的尋優(yōu)性能.通過與NSGA-Ⅱ算法進行OR-Library典型測試算例的對比實驗,驗證該算法的有效性.
[Abstract]:In order to minimize the maximum completion time, total process time and total delay time, an improved chaotic weed optimization algorithm is proposed for income job-shop scheduling problem. In this algorithm, the grey entropy correlation fitness allocation strategy based on entropy weight is adopted, and the fast non-dominated sorting method is introduced to generate external files, and the update of evolutionary population is combined with the chaotic search of optimal location to maintain the external files. To improve the performance of the algorithm, the effectiveness of the algorithm is verified by the comparison of OR-Library typical test cases with NSGA- 鈪，

本文編號：1581022