فهرست مطالب a. amirteimoori

Generally exploring the exact solution of linear programming problems in which all variables and parameters are  Z-numbers, is either not possible or difficult. Therefore, a few numerical methods to find the numerical solutions do act an  important role in these problems. In this paper, we concentrate on introducing a new numerical method to solve such  problems based on the ranking function. After proving the necessary theories, for more illustrations and the correctness  of the topic, some theoretical and practical examples are also provided. Finally, the results obtained from the proposed  method have been compared with some existing methods.

Recently, Farzipoor Saen [Journal of the Operational Research Society, 60(11), 1575–1582 (2009)] proposed a method based on data envelopment analysis to identify optimistic efficient suppliers in the presence of nondiscretionary factors-imprecise data. This short communication aims at showing a computational error in computing the value of preference intensity parameter in Farzipoor Saen’s [1] article. Then, a ranking method is used to identify the suppliers with the best performance.

Because of the piecewise linear nature of the data envelopment analysis (DEA) frontier, the optimal multipliers of the DEA models may not be unique. Choosing weights from alternative optimal solutions of dual multiplier models is one of the most frequently studied subjects in the context of DEA. In this paper, the authors have been inspired by the idea of Cooper et al. (2011) to propose a linear programming problem in which a specific decision making unit chooses the optimal solution of a linear program as the profile of weights for use in the cross-efficiency calculation. The approach proposed in this paper to determine input/output weights, prohibit the large differences in weights in cross efficiency evaluation. A real case on Chinese cities and special economic zones is given to illustrate the applicability of the proposed approach.