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首页> 外文会议>International Symposium on Advanced Intelligent Systems;International Conference on Soft Computing and Intelligent Systems >Multiobjective Two-Level Simple Recourse Programming Problems with Discrete-Type Fuzzy Random Variables and Optimistic and Pessimistic Pareto Stackelberg Solutions
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Multiobjective Two-Level Simple Recourse Programming Problems with Discrete-Type Fuzzy Random Variables and Optimistic and Pessimistic Pareto Stackelberg Solutions

机译：多目标两级简单追索程序编程问题，采用离散式模糊随机变量和乐观和悲观帕累托Stackelberg解决方案

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摘要

In this paper, we focus on multiobjective two-level simple recourse programming problems with discrete-type LR fuzzy random variables, in which each of the decision makers called the leader and the follower optimizes his/her multiple objective functions independently, shortages and excesses arising from the violation of the constraints with discrete-type LR fuzzy random variables are penalized, and the sum of the objective function and the expectation of the amount of the penalties is minimized. To deal with such problems, we introduce new solution concepts called an optimistic and pessimistic Pareto Stackelberg solutions for the leader. It is shown that such optimistic and pessimistic Pareto Stackelberg solutions can be obtained by solving weighting problems of the leader. We propose an interactive algorithm to obtain a satisfactory solution of the leader from among an optimistic or pessimistic Pareto Stackelberg solution set.
机译：在本文中，我们专注于多目标二级简单追索程序编程问题，其中包括离散类型的LR模糊随机变量，其中每个决策者称为领导者和追随者独立优化他/她的多目标功能，产生的短缺和过度从违反离散型LR模糊随机变量的违法行为受到处罚的惩罚，目标职能和罚款金额的总和最小化。要处理此类问题，我们介绍了新的解决方案概念，称为领导者的乐观和悲观的帕累托斯贝尔伯格解决方案。结果表明，通过解决领导者的加权问题，可以获得这种乐观和悲观的帕累托·贝尔伯格解决方案。我们提出了一种互动算法，以获得来自乐观或悲观的Pareto Stackelberg解决方案集的领导者的令人满意的解决方案。

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