Many decision making problems can be formulated as multi-objective optimization problems (MOP). There hardly exists the solution that optimizes all objective functions in MOP, and then the concept of Pareto optimal solution (or efficient solution) is introduced. Usually, there exist a lot of Pareto optimal solutions, which are considered as candidates of final decision making solution. It is an...

Modification of different zirconium propoxide precursors with H(2)dea was investigated by characterization of the isolated modified species. Upon modification of zirconium n-propoxide and [Zr(O(n)Pr)(O(i)Pr)(3)((i)PrOH)](2) with (1/2) a mol equivalent of H(2)dea the complexes [Zr(2)(O(n)Pr)(6)(OCH(2)CH(2))(2)NH](2) (1) and [Zr(2)(O(n)Pr)(2)(O(i)Pr)(4)(OCH(2)CH(2))(2)NH](2) (2) were obtained. Ho...

It is generally accepted that Data Envelopment Analysis (DEA) is a method for indicating efficiency. The DEA method has many applications in the field of calculating the relative efficiency of Decision Making Units (DMU) in explicit input-output environments. Regarding imprecise data, several definitions of efficiency can be found. The aim of our work is showing an equivalence relation between ...

Data Envelopment Analysis is based on linear programming problems (LPP) in order to find the efficiency of Decision Making Units (DMUs). This process can be computationally intense, as a LPP has to be run for each unit. Besides, a typical DEA LPP has a large number of redundant constraints concerning the inefficient DMUs. That results in degenerate LPPs and in some cases, multiple efficient sol...

In this paper, a BCC-DPSO scheduling algorithm is proposed to solve multi-objective optimization problem for task scheduling on Network-on-Chip (NoC). In our proposal, the relative advantage of the solution is evaluated by calculating its efficiency using BCC model in Data Envelopment Analysis (DEA), and the referred-time method is introduced to rank the BCC-efficient solution. Moreover, a sub-...

this paper gives a new application of dea to evaluate the scheduling solutions of parallel processing. it evaluates the scheduling solutions of parallel processing using the non-convex dea model, fdh model. by introducing each solution of parallel processing scheduling as a dmu with some relevant inputs and outputs this paper shows that how the most efficient schedule(s) can be identified.

Chiou et al. (2010) (A joint measurement of efficiency and effectiveness for non-storable commodities: integrated data envelopment analysis approache s. European Journal of Operationa l Research 201, 477– 489) propose an integrated data envelopment analysis model in measuring decision making units (DMUs) that have a two-stage internal netwo rk structure with multiple inputs, outputs, and consum...

Data envelopment analysis (DEA) is a performance measurement tool that was initially developed without consideration of the decision maker (DM)’s preference structures. Ever since, there has been a wide literature incorporating DEA with value judgements such as the goal and target setting models. However, most of these models require prior judgements on target or weight setting. This paper will...

Data envelopment analysis (DEA) is a widely used mathematical programming approach for comparing the input and output of a set of comparable decision-making units (DMUs) by evaluating their relative efficiency. The traditional DEA methods require accurate measurement of both the inputs and outputs. However, the real evaluation of the DMUs is often characterized by imprecision and uncertainty in...