In this paper, a linear programming (LP) problem is considered where some or all of its coefficients in the objective function and/or constraints are rough intervals. In order to solve this problem, we will construct two LP problems with interval coefficients. One of these problems is an LP where all of its coefficients are upper approximations of rough intervals and the other is an LP where all of its coefficients are lower approximations of rough intervals. Via these two LPs, two newly solutions (completely and rather satisfactory) are defined. Some examples are given to demonstrate the results.

This paper presents a set of multi-objective programming problems in a rough environment. These problems are classi ed into ve classes according to the location of the roughness in the objective functions or the feasible set. We study the class in which all of the objective functions are crisp and the feasible region is a rough set and, in particular, discuss the properties of the complete and e cient (Pareto optimal) solutions of rough multi-objective programming problems. In order to obtain these solutions, we need certain theorems, which we derive. Finally, we illustrate our results by examples.

Data envelopment analysis (DEA) is a powerful tool for evaluating the efficiency of decision-making units for ranking and comparison purposes and to differentiate efficient and inefficient units. Classic DEA models are ill-suited for the problems where decision-making units consist of multiple stages with intermediate products and those where inputs and outputs are imprecise or nondeterministic, which is not uncommon in the real world. This paper presents a new DEA model for evaluating the efficiency of decision-making units with two-stage structures and triangular intuitionistic fuzzy data. The paper first introduces two-stage DEA models, then explains how these models can be modified with intuitionistic fuzzy coefficients, and finally describes how arithmetic operators for intuitionistic fuzzy numbers can be used for a conversion into crisp two-stage structures. In the end, the proposed method is used to solve an illustrative numerical example.

Classical methods of Data Envelopment Analysis operate by measuring the efficiency of decision-making units (DMUs) compared to similar units, without taking their internal structure into account. However, some DMUs consist of two stages, with the first stage producing an intermediate product, which is then consumed in the second stage to produce the final output. The efficiency of this type of DMU is often measured using two-stage Network Data Envelopment Analysis. In real world, most data are vague; therefore the inputs and outputs of systems with vagueness data create uncertainty challenges for DMUs. As a result, when uncertainty appears, intuitionistic fuzzy sets can show more information than classical fuzzy sets. This paper presents a model of two-stage Network Data Envelopment Analysis based on intuitionistic fuzzy data, which measures the efficiency of the first and second stages of each DMU, and ultimately the overall efficiency measures based on the stage efficiencies.