Multiobjective optimization under uncertainty: A multiobjective robust (relative) regret approach

TL;DR: Wang et al. as discussed by the authors proposed an efficient and robust dual-sourcing model for disrupted supply chain considering various risk attitudes and interruption uncertain situations, and applied the real case study to analyze validity and stability of model.

TL;DR: In this article, a sequential sampling strategy for the surrogate based solution of multi-objective reliability based robust design optimization problems is proposed, which can be combined with any surrogate model because there is no free lunch but possibly a budget one.

TL;DR: In this article , the authors study Pareto optimality in multi-objective multi-armed bandit with both stochastic and adversarial settings and present new algorithms assuming both with and without prior information of the multiobjective bandit setting.

TL;DR: This note formulates a convex mathematical programming problem in which the usual definition of the feasible region is replaced by a significantly different strategy via set containment.

TL;DR: This work proposes a robust integer programming problem of moderately larger size that allows controlling the degree of conservatism of the solution in terms of probabilistic bounds on constraint violation, and proposes an algorithm for robust network flows that solves the robust counterpart by solving a polynomial number of nominal minimum cost flow problems in a modified network.

TL;DR: This paper presents four approaches to handle Uncertainty in Decision Making using a Robust Discrete Optimization Framework and results show how these approaches can be applied to real-world problems.