混合交叉熵算法求解模糊分布式装配流水线低碳调度问题

Hybrid cross-entropy algorithm for fuzzy distributed assembly permutation flow-shop low-carbon scheduling problem

中文关键词  分布式装配流水线调度  模糊加工时间  模糊装配时间  低碳  多目标优化  交叉熵算法

英文关键词  distributed assembly permutation flow-shop scheduling  fuzzy processing time  fuzzy assembly time  low-carbon  multi-objective optimization  cross-entropy algorithm

中文摘要

      本文针对实际生产过程中普遍存在的不确定性, 采用模糊数表示工件的加工时间与产品的装配时间, 以同时最小化模糊最大完工时间和模糊总能耗为优化目标, 建立模糊分布式装配流水线低碳调度问题(fuzzy distributed assembly permutation flow-shop low-carbon scheduling problem, FDAPFLSP)的模型, 进而提出一种混合交叉熵算法(hybrid cross-entropy algorithm, HCEA)进行求解. 首先, 通过分析现有三角模糊数排序准则特点, 并考虑生产调度问题的基本约束, 设计一种实用的三角模糊数排序修正准则. 其次, 为增强算法性能, 设计一种自适应变邻域局部搜索以实现对解空间不同区域的有效搜索. 最后, 仿真实验与算法对比验证HCEA可有效求解FDAPFLSP.

英文摘要

      Considering the widely existing uncertainty in the real-world production process, this paper uses fuzzy numbers to represent each job’s processing time and each product’s assembly time, and constructs a model for the fuzzy distributed assembly permutation flow-shop low-carbon scheduling problem (FDAPFLSP), whose criteria are the minimization of both the fuzzy maximum completion time and the fuzzy total energy consumption. Then, a hybrid cross-entropy algorithm (HCEA) is proposed for solving the FDAPFLSP. Firstly, a practical ranking correction rule of triangular fuzzy number is designed via analyzing the characteristics of the commonly used ranking rules of triangular fuzzy number and considering the basic constraints of the production scheduling problem. Secondly, HCEA adopts variable neighborhood local search with adaptive selection probability, which can efficiently search different regions in solution space and further enhance the performance of the algorithm. Finally, simulations and comparisons demonstrate that HCEA can effectively solve FDAPFLSP.