The hub location–allocation problem under uncertainty is a real-world task arising in the areas such as public and freight transportation and telecommunication systems. In many applications, the demand is considered as inexact because of the forecasting inaccuracies or human’s unpredictability. This study addresses the robust uncapacitated multiple allocation hub location problem with a set of ...

in this study, a hybrid algorithm is presented to tackle multi-variables robust design problem. the proposed algorithm comprises neural networks (nns) and co-evolution genetic algorithm (cga) in which neural networks are as a function approximation tool used to estimate a map between process variables. furthermore, in order to make a robust optimization of response variables, co-evolution algor...

in this article, an effective method to control a power system during emergency conditions is presented. based on fuzzy linear programming (flp), a new technique is developed to solve the load shedding and generation reallocation (lsgr) optimization problem. the objective function consists of terms of load curtailments and deviations in generation schedules. the constraints are power system var...

risk management is one of the most important aspects of project management that identifies, assesses and responds to project risks. although many papers have been published in project risk response, presented tools and methods are poor. hence, in this paper, we present an optimization model to respond project risk that seeks to optimize two key criteria of project: cost and time. the proposed m...

We provide a method for deriving robust solutions to certain stochastic optimization problems, based on mean-covariance information about the distributions underlying the uncertain vector of returns. We prove that for a general class of objective functions, the robust solutions amount to solving a certain deterministic parametric quadratic program. We first prove a general projection property f...

In this paper we consider a multiobjective optimization problem, and we prove Mond-Weir duality results under second-and higher-order conditions of the objective and constraint functions.

Detecting community structure is crucial for uncovering the links between structures and functions in complex networks. Most of contemporary community detection algorithms employ single optimization criteria (e.g., modularity), which may have fundamental disadvantages. This paper considers the community detection process as a Multi-Objective optimization Problem (MOP). To solve the community de...

In this paper, we present a study on the sensitivity of aggregation methods with respect to the weights associated with objective functions of a multiobjective optimization problem. To do this study, we have developped some measurements such as the speed metric or the distribution metric. We have performed this study on a set of biobjective optimization test problems: a convex, a non-convex, a ...

a real-time optimization (rto) strategy incorporating the fuzzy sets theory is developed, where the problem constraints obtained from process considerations are treated in fuzzy environment. furthermore, the objective function is penalized by a fuzzified form of the key process constraints. to enable using conventional optimization techniques, the resulting fuzzy optimization problem is then re...

Deterministic optimization does not consider uncertainties. This may lead to designs which are not robust or reliable. The use of safety factors is the common approach to cope with this problem. The main weaknesses of the achieved results are overdesign (too expensive) or underdesign (unreliable) because safety factors do not necessarily consider the special problem. Therefore robust design opt...