数学优化
对偶(语法数字)
分解
设施选址问题
拉格朗日松弛
增广拉格朗日法
计算机科学
班级(哲学)
拉格朗日乘数
可微函数
随机规划
分解法(排队论)
惩罚法
整数(计算机科学)
非线性系统
本德分解
数学
人工智能
数学分析
艺术
文学类
物理
程序设计语言
离散数学
生物
量子力学
生态学
标识
DOI:10.1016/j.tre.2018.05.010
摘要
We consider the risk-averse uncapacitated facility location problem under stochastic disruptions. By the Conditional-value-at-risk, we control the risks at each individual customer, while previous works usually control the entire networks. We show that our model provides more reliable solutions than previous ones. The resulting formulation is a mixed-integer nonlinear programming. In response, we develop a multi-dual decomposition algorithm based on the augmented Lagrangian and classic penalty function. A class of decomposed unconstrained subproblems are then solved by an iterative approach not relying on Lagrange multipliers and differentiability. Our experiments show that the algorithm performs well even for some larger problems.
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