Chance constraints are an important modeling tool in stochastic optimization, providing probabilistic guarantees that a solution “succeeds” in satisfying a given constraint. While they control the probability of “success,” they provide no control whatsoever in the event of a “failure.” That is, they do not distinguish between a slight overor under-shoot of the bounds, and more catastrophic viol...

In this paper, Mond-Weir type duality results for a uncertain multiobjective robust optimization problem are given under generalized invexity assumptions. Also, weak vector saddle-point theorems are obtained under convexity assumptions.

This paper presents a new, bi-criteria mixed-integer programming model for scheduling cells and pieces within each cell in manufacturing cellular system. The objective of this is to minimize the makespan inter-cell movements simultaneously, while considering sequence-dependent setup times. In CMS design planning, three main steps must be considered, namely formation (i.e., piece families machin...

This paper presents a methodology for design of instrumentation sensor networks in non-linear chemical plants. The method utilizes a robust extended Kalman filter approach to provide an efficient dynamic data reconciliation. A weighted objective function has been introduced to enable the designer to incorporate each individual process variable with its own operational importance. To enhance...

modeling of fermentation processes is so complicated and uncertain; therefore it is necessary to provide a robust and appropriate dynamic optimization method. in order to obtain the maximum amount of yeast (saccharomyces cerevisiae), the bioreactor must be operated under optimal conditions. to determine substrate feeding in a fed-batch bioreactor, a simulated annealing (sa) approach was examine...

A suboptimal approach to distributed robust MPC for uncertain systems consisting of polytopic subsystems with coupled dynamics subject to both state and input constraints is proposed. The approach applies the dynamic dual decomposition method and reformulates the original centralized robust MPC problem into a distributed robust MPC problem. It is based on distributed on-line optimization by app...

Endogenous, i.e. decision-dependent, uncertainty has received increased interest in the stochastic programming community. In robust optimization context, however, it rarely been considered. This work addresses multistage mixed-integer with decision-dependent sets. The proposed framework allows us to consider both continuous and integer recourse, including recourse decisions that affect set. We ...

Abstract Currently, few approaches are available for mixed-integer nonlinear robust optimization. Those that do exist typically either require restrictive assumptions on the problem structure or not guarantee protection. In this work, we develop an algorithm convex optimization problems where a key feature is method does rely specific of inner worst-case (adversarial) and allows latter to be no...