Showing papers by "Yao Chen published in 2013"
TL;DR: It is demonstrated that under general network structures, the multiplier and envelopment network DEA models are two different approaches and the envelopment model’s divisional efficiency may actually be the overall efficiency.
150 citations
TL;DR: In this article, the authors proposed to modify the directional distance function by selecting proper feasible reference bundles so that the resulting N-L measure of super-efficiency is always feasible. But this model does not suffer infeasibility problem as in the conventional radial super efficiency DEA models and can produce an infeasible solution in two special situations.
Abstract: The problem of infeasibility arises in conventional radial super-efficiency data envelopment analysis (DEA) models under variable returns to scale (VRS). To tackle this issue, a Nerlove–Luenberger (N–L) measure of super-efficiency is developed based on a directional distance function. Although this N–L super-efficiency model does not suffer infeasibility problem as in the conventional radial super-efficiency DEA models, it can produce an infeasible solution in two special situations. The current paper proposes to modify the directional distance function by selecting proper feasible reference bundles so that the resulting N–L measure of super-efficiency is always feasible. As a result, our modified VRS super-efficiency model successfully addresses the infeasibility issues occurring either in conventional VRS models or the N–L super-efficiency model. Numerical examples are used to demonstrate our approach and compare results obtained from various super-efficiency measures.
89 citations
TL;DR: A new Data Envelopment Analysis (DEA) model for DfE performance evaluation where the simultaneous increase of desirable outputs and decrease of undesirable outputs are considered with a focus on identifying inefficiency as a result of higher levels of undesirable performance is developed.
Abstract: Design for the environment (DfE) has been recognized as one of the most important practices for achieving sustainability. One major challenge in DfE implementation is how to effectively deal with the mix of the both the desirable outputs (to maintain or improve traditional product attributes) and the undesirable outputs (to reduce environmental impacts) in the design process. In this paper, we develop a new Data Envelopment Analysis (DEA) model for DfE performance evaluation where the simultaneous increase of desirable outputs and decrease of undesirable outputs are considered with a focus on identifying inefficiency as a result of higher levels of undesirable performance. We show that our proposed method is capable of establishing a benchmark with lower undesirable outputs for an inefficient decision making unit (DMU) than other models in the existing literature. We also use empirical data to demonstrate the applications of our DEA model in evaluating the sustainable design performances of different vehicle designs for the automobile industry. By treating the undesirable factors as the dominating factors in evaluating a DMU's sustainable design performance, our model can help identify the most eco-efficient way to improve the performance attributes of a product while minimizing its various negative impacts, such as greenhouse gas emissions, water pollution, and solid wastes, on the natural environment.
73 citations
TL;DR: The current paper modifies the DDF approach so that integer values can be incorporated under the concept of super-efficiency and is able to reveal subtle nuances such as the impact of mood on performance with a decision support system.
12 citations