We consider a generalization of the classical quadratic assignment problem, where coordinates of locations are uncertain and only upper and lower bounds are known for each coordinate. We develop a mixed integer linear programming model as a robust counterpart of the proposed uncertain model. A key challenge is that, since the uncertain model involves nonlinear objective function of the ...

Robust Optimization is a rapidly developing methodology for handling optimization problems affected by non-stochastic “uncertain-butbounded” data perturbations. In this paper, we overview several selected topics in this popular area, specifically, (1) recent extensions of the basic concept of robust counterpart of an optimization problem with uncertain data, (2) tractability of robust counterpa...

Department of Industrial Engineering, Khajeh Nasir Toosi University, Tehran, Iran b Louvain School of Management, Center of Operations Research and Econometrics (CORE), Universite Catholique de Louvain, 34 Voie du Roman Pays, B-1348 Louvain-le-Neuve, Belgium Management Department, Lindback Distinguished Chair of Information Systems, La Salle University, Philadelphia, PA 19141, USA Department of...

this paper proposes a new approach for determining efficient dmus in dea models using inverse optimi-zation and without solving any lps. it is shown that how a two-phase algorithm can be applied to detect effi-cient dmus. it is important to compare computational performance of solving the simultaneous linear equa-tions with that of the lp, when computational issues and complexity analysis are a...

The problem of determining an optimal benchmark to inefficient decision-making units (DMUs) is important issue in the field performance analysis. Previous methods for projection points DMUs have only focused on one objective and other features been ignored. This paper attempts determine best point each DMU when inputs outputs data are stochastic form presents alternative definition by consideri...

in this paper imprecise target models has been proposed to investigate the relation between imprecise data envelopment analysis (idea) and mini-max reference point formulations. through these models, the decision makers' preferences are involved in interactive trade-off analysis procedures in multiple objective linear programming with imprecise data. in addition, the gradient projection type me...

Data envelopment analysis (DEA) is a method frequently used to evaluate relative firm performance. However, high values in a few indicators can lead to a company being regarded as ‘efficient’, despite valuing poorly in other essential indicators. The Inefficiency Countervailed DEA (IC-DEA) method is thus developed. The method first defines an inefficient frontier using the proposed Reverse DEA ...

optimization of reservoir parameters is an important issue in petroleum exploration and production. the ant colony optimization(aco) is a recent approach to solve discrete and continuous optimization problems. in this paper, the ant colony optimization is usedas an intelligent tool to estimate reservoir rock properties. the methodology is illustrated by using a case study on shear wave velocity...