Data envelope analysis (DEA) is an approach to estimate the relative efficiency of decision making units (DMUs). Several studies were conducted in order to prioritize efficient units and some useful models such as cross-efficiency matrix (CEM) were presented. Besides, a number of DEA models with interval data have been developed and ranking DMUs with such data was solved. However, presenting an obtained crisp data derived interval data is a critical problem, so that many researches were implemented so as to compute weights and averaging the interval data. In this paper we propose the new algorithm to find more suitable weight applying a data mining approach of DMU's data. For this purpose, we employed clustering 2 and pair-wise comparison matrix on given relative efficiency from CEM. Results indicate there is meaningful different between efficiency of DMUs with lower bound and that of DMUs with upper bound.
Concurrent engineering (CE) is one of the widest known techniques for simultaneous planning of product and process design. In concurrent engineering, design processes are often complicated with multiple conflicting criteria and discrete sets of feasible alternatives. Thus multi-criteria decision making (MCDM) techniques are integrated into CE to perform concurrent design. This paper proposes a design framework governed by MCDM technique, which are in conflict in the sense of competing for common resources to achieve variously different performance objectives such as financial, functional, environmental, etc. The Pareto MCDM model is applied to polyethylene pipe concurrent design governed by four criteria to determine the best alternative design to Pareto-compromise design.
The suggestions system is a part of total quality management to create individual and group spirit of partnership between staff and increase efficiency in the organization. Also, diagnosis and improvement process is one of the steps of the chain in the processes of suggestions system. In this study, an approach has been proposed to evaluate efficiency of organizations in performing suggestions system with these aims: (1) Reviewing all the elements in the successful implementation of the suggestions system and (2) providing an effective scientific approach to evaluate the organizations on implementing this system considering the uncertainty in the data. Methodology used in this study included the following techniques: (1) Factor analyzing to clarify the internal correlation between significant criteria and detect the major criteria and (2) using robust data envelopment analysis (RDEA) model to evaluate efficiency of organizations in performing suggestions system. The method is based on 3 inputs and 17 outputs in which some outputs are uncertain scores in form of intervals with uncertain bounds. This model has been solved for different Gs, and a value of weights and rankings for each Decision Making Unit (DMU) has been saved by using the obtained values. In the following a simulation has been used to compute the conformity of the rankings from the RDEA model with reality. Doing so shows that the maximum conformity occurs G = 6. Therefore, we can conclude that specific values of G can maximize conformity and thus more authentic final rankings for the DMUs in this interval of G may be expected.
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