Novel robust fuzzy mathematical programming methods

TL;DR: The results indicated that the RPFCCP model is able to handle uncertainty in the parameters of the objective function and constraints more efficiently and was able to provide robust and risk-averse solutions for the problem which are resistant to different scenarios.

TL;DR: In this paper, a flexible-possibilistic stochastic programming (FPSP) method is developed for planning municipal-scale energy system (MES) with cost minimization and emission mitigation.

TL;DR: The MOMRPFP approach improves the efficiency about the average computational time for large-sized networks with the presence of uncertainty in flexible objective functions and constraints as well as in data.

TL;DR: This study proposes a multi-objective mixed-integer programming model to configure a sustainable supply chain network while considering resilience and responsiveness measures, and an improved version of the fuzzy robust stochastic optimization approach is proposed to tackle the uncertain data arising in the dynamic business environment.

TL;DR: A reverse-flow technique is described for the solution of a functional equation arising in connection with a decision process in which the termination time is defined implicitly by the condition that the process stops when the system under control enters a specified set of states in its state space.

TL;DR: In this paper, the authors propose an approach that attempts to make this trade-off more attractive by flexibly adjusting the level of conservatism of the robust solutions in terms of probabilistic bounds of constraint violations.

TL;DR: An approach is proposed that flexibly adjust the level of conservatism of the robust solutions in terms of probabilistic bounds of constraint violations, and an attractive aspect of this method is that the new robust formulation is also a linear optimization problem, so it naturally extend to discrete optimization problems in a tractable way.

TL;DR: If U is an ellipsoidal uncertainty set, then for some of the most important generic convex optimization problems (linear programming, quadratically constrained programming, semidefinite programming and others) the corresponding robust convex program is either exactly, or approximately, a tractable problem which lends itself to efficientalgorithms such as polynomial time interior point methods.