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Dive into the research topics where Aida Khajavirad is active.

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Featured researches published by Aida Khajavirad.


Mathematical Programming | 2013

Convex envelopes generated from finitely many compact convex sets

Aida Khajavirad; Nikolaos V. Sahinidis

We consider the problem of constructing the convex envelope of a lower semi-continuous function defined over a compact convex set. We formulate the envelope representation problem as a convex optimization problem for functions whose generating sets consist of finitely many compact convex sets. In particular, we consider nonnegative functions that are products of convex and component-wise concave functions and derive closed-form expressions for the convex envelopes of a wide class of such functions. Several examples demonstrate that these envelopes reduce significantly the relaxation gaps of widely used factorable relaxation techniques.


Journal of Mechanical Design | 2008

A Decomposed Gradient-Based Approach for Generalized Platform Selection and Variant Design in Product Family Optimization

Aida Khajavirad; Jeremy J. Michalek

A core challenge in product family optimization is to jointly determine (1) the optimal selection of components to be shared across product variants and (2) the optimal values for design variables that define those components. Each of these subtasks depends on the other; however, due to the combinatorial nature and high computational cost of the joint problem, prior methods have forgone optimality of the full problem by fixing the platform a priori, restricting the platform configuration to all-or-none component sharing, or optimizing the joint problem in multiple stages. In this paper, we address these restrictions by (1) introducing an extended metric to account for generalized commonality, (2) relaxing the metric to the continuous space to enable gradient-based optimization, and (3) proposing a decomposed single-stage method for optimizing the joint problem. The approach is demonstrated on a family of ten bathroom scales. Results indicate that generalized commonality dramatically improves the quality of optimal solutions, and the decomposed single-stage approach offers substantial improvement in scalability and tractability of the joint problem, providing a practical tool for optimizing families consisting of many variants.


Journal of Global Optimization | 2012

Convex envelopes of products of convex and component-wise concave functions

Aida Khajavirad; Nikolaos V. Sahinidis

In this paper, we consider functions of the form


Journal of Mechanical Design | 2009

A Deterministic Lagrangian-Based Global Optimization Approach for Quasiseparable Nonconvex Mixed-Integer Nonlinear Programs

Aida Khajavirad; Jeremy J. Michalek


design automation conference | 2007

AN EXTENSION OF THE COMMONALITY INDEX FOR PRODUCT FAMILY OPTIMIZATION

Aida Khajavirad; Jeremy J. Michalek

{\phi(x,y)=f(x)g(y)}


48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference | 2007

A Decomposed Genetic Algorithm for Solving the Joint Product Family Optimization Problem

Aida Khajavirad; Jeremy J. Michalek; Timothy W. Simpson


Mathematical Programming | 2014

Relaxations of factorable functions with convex-transformable intermediates

Aida Khajavirad; Jeremy J. Michalek; Nikolaos V. Sahinidis

over a box, where


design automation conference | 2007

A Single-stage Gradient-based Approach for Solving the Joint Product Family Platform Selection and Design Problem Using Decomposition

Aida Khajavirad; Jeremy J. Michalek


Mathematics of Operations Research | 2017

A Polyhedral Study of Binary Polynomial Programs

Alberto Del Pia; Aida Khajavirad

{f(x), x\in {\mathbb R}}


design automation conference | 2008

A Deterministic Lagrangian-Based Global Optimization Approach for Large Scale Decomposable Problems

Aida Khajavirad; Jeremy J. Michalek

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Alberto Del Pia

University of Wisconsin-Madison

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Timothy W. Simpson

Pennsylvania State University

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