The De-Novo programming problem proposed by Zeleny is well-known for its value on designing an optimal system by extending existed resources instead of finding the optimum in a given system with fixed resources. Since few papers are dedicated to explore the De-Novo programming problem with multiple stages and its resolution approach, the De-Novo programming problem is innovatively extended to a...

Quadratic programming (QP) is an optimization problem wherein one minimizes (or maximizes) a quadratic function of a finite number of decision variable subject to a finite number of linear inequality and/ or equality constraints. In this paper, a quadratic programming problem (FFQP) is considered in which all cost coefficients, constraints coefficients, and right hand side are characterized by ...

Probabilistic or stochastic programming is a framework for modeling optimization problems that involve uncertainty.In this paper, we focus on multi-objective linear programmingproblems in which the coefficients of constraints and the righthand side vector are fuzzy random variables. There are several methodsin the literature that convert this problem to a stochastic or<b...

This paper presents a novel approach to achieving the goals of data envelopment analysis in the process of reconstruction and integration of decision-making units by using multiple objective linear programming. In this regard, first, we review inverse data envelopment analysis models for data reconstruction and integration. We present a model with multi-objective linear programming structure in...

A characteristic of Data Envelopment Analysis (DEA) is to allow individual decision making units (DMUs) to select the factor weights which are the most advantageous for them in calculating their efficiency scores. This flexibility in selecting the weights, on the other hand, deters the comparison among DMUs on a common base. For dealing with this difficulty and assessing all the DMUs on the sam...