文献类型：期刊文献

中文题名：考虑工时不确定性的混流U型装配线平衡的分布鲁棒优化方法

英文题名：A Distributionally Robust Optimization Method for Mixed-Model U Shaped Assembly Line Balancing Considering Task Time Uncertainty

作者：周剑扬[1];张凌波[1]

机构：[1]华东理工大学信息科学与工程学院,上海200237

年份：2023

卷号：49

期号：6

起止页码：862

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：Scopus;北大核心:【北大核心2020】；CSCD:【CSCD_E2023_2024】；

基金：国家自然科学基金(62076095)。

语种：中文

中文关键词：分布鲁棒优化;装配线平衡;混流U型装配线;Wasserstein距离;遗传算法

外文关键词：distributionally robust optimization;assembly line balancing;mixed-model U-shaped assembly line;Wasserstein distance;genetic algorithm

摘要：在以人工装配为主的流水线生产中,工时的不确定性是影响生产节拍的重要因素。考虑到随机优化要求精确的概率分布信息和较高的鲁棒优化保守性,本文针对工时不确定条件下混流U型装配线平衡问题,采用以经验分布为中心、Wasserstein距离为半径的模糊集对工时的不确定性进行描述,并以最小化生产节拍为优化目标,建立装配线平衡问题的分布鲁棒优化模型。为了降低模型的复杂性,利用强对偶理论将模型转换为易于求解的形式;为保证解的鲁棒性,设计了一种鲁棒性指标并将其作为模型的约束条件。针对上述模型,通过设计一种基于区间数的解码方式,并引入自适应交叉和变异概率,给出了一种改进的遗传算法。最后通过标准算例和断路器抽架生产实例进行了数值仿真实验,结果表明相较于随机优化和鲁棒优化方法,所建立模型在降低结果保守性的同时保持较高的鲁棒性,并且针对问题所提出的改进遗传算法具有良好的寻优能力。
In the assembly line production dominated by manual assembly,the uncertainty of task time is an important factor affecting the cycle time.Considering that stochastic optimization requires precise probability distribution information and high conservatism in robust optimization.In this paper,we focuse on the balance problems of a mixed-model U-shaped assembly line under uncertain task time.A fuzzy set centered on empirical distribution and with Wasserstein distance as the radius is used to describe the uncertainty of working hours.The optimization goal is to minimize production time,and a distributed robust optimization model for the assembly line balance problem is established.In order to reduce the complexity of the model,the strong duality theory is used to transform the model into a form that is easy to solve.To guarantee the robustness of the solution,a robustness metric is designed and used as a constraint condition for the model.Based on the above model,an improved genetic algorithm is presented by designing a decoding method based on interval number and introducing adaptive crossover and mutation probabilities.Finally,numerical simulation experiments are carried out through standard examples and production examples of circuit breaker chassis.Compared to stochastic optimization and robust optimization methods,the established model reduces the conservatism of results while maintaining high robustness,and the improved genetic algorithm proposed for the problem has good optimization ability.