The aim of this paper is to propose a robust reliable bi-objective supply chain network design (SCND) model that is capable of controlling different kinds of uncertainties, concurrently. In this regard, stochastic bi-level scenario based programming approach which is used to model various scenarios related to strike of disruptions. The well-known method helps to overcome adverse effects of disr...

This paper discusses an implicit reformulation of the MPEC (mathematical program with complementarity constraints) problem in order to solve a robust structural optimization with a non-probabilistic uncertainty model of the static load. We first show the relation among the robust constraint satisfaction, worst scenario detection, and robust structural optimization, and derive the MPEC formulati...

We consider the design of tapers for coupling power between uniform and slowlight periodic waveguides. We describe new optimization methods for designing robust tapers, which not only perform well under nominal conditions, but also over a given set of parameter variations. When the set of parameter variations models the inevitable variations typical in the manufacture or operation of the couple...

Many engineering problems can be cast as optimization problems subject to convex constraints that are parameterized by an uncertainty or 'instance' parameter. Two main approaches are generally available to tackle constrained optimization problems in presence of uncertainty: robust optimization and chance-constrained optimization. Robust optimization is a deterministic paradigm where one seeks a...

Open-Pit Production Scheduling (OPPS) problem focuses on determining a block sequencing and scheduling to maximize Net Present Value (NPV) of the venture under constraints. The scheduling model is critically sensitive to the economic value volatility of block, block weight, and operational capacity. In order to deal with the OPPS uncertainties, various approaches can be recommended. Robust opti...

Traditional optimization approaches for handling uncertainty and risk typically require severe assumptions that are often not satisfied in complex practical settings. In an effort to overcome such limitations, several methods have been developed to handle uncertainty when the data and associated real world parameters do not behave according to classical assumptions. Two of the leading and most ...

This paper proposes an equation-based multi-scenario iterative robust optimization methodology for analog/mixed-signal circuits. We show that due to local circuit performance monotonicity in random variations constraint maximization can be used to efficiently find critical constraints and worst-case scenarios of random process variations and populate them into a multi-scenario optimization. Thi...

The present paper addresses an effective cyber defense model by applying information fusion based game theoretical approaches‎. ‎In the present paper, we are trying to improve previous models by applying stochastic optimal control and robust optimization techniques‎. ‎Jump processes are applied to model different and complex situations in cyber games‎. ‎Applying jump processes we propose some m...

Most existing distance metric learning methods assume perfect side information that is usually given in pairwise or triplet constraints. Instead, in many real-world applications, the constraints are derived from side information, such as users’ implicit feedbacks and citations among articles. As a result, these constraints are usually noisy and contain many mistakes. In this work, we aim to lea...