Data uncertainty is an inherent property in various applications due to reasons such as outdated sources or imprecise measurement. When data mining techniques are applied to these data, their uncertainty has to be considered to obtain high quality results. We present UK-means clustering, an algorithm that enhances the K-means algorithm to handle data uncertainty. We apply UKmeans to the particu...

Data envelopment analysis (DEA) is a non-parametric method for measuring the efficiency of peer operating units that employ multiple inputs to produce multiple outputs. Several DEA methods have been proposed for clustering operating units. However, to the best of our knowledge, the existing methods in the literature do not simultaneously consider the priority between the clusters (classes) and ...

The main goal of the paper is a consideration of cost efficiency evaluation models related to some supply chain when dealing with imprecise data. Data envelopment analysis (DEA) method is a non-parametric mathematical programming approach to assess the performance. This method is proposed for deterministic data and it can be generalized to inaccurate data, while considering real world applicati...

  In this paper imprecise target models has been proposed to investigate the relation between imprecise data envelopment analysis (IDEA) and mini-max reference point formulations. Through these models, the decision makers' preferences are involved in interactive trade-off analysis procedures in multiple objective linear programming with imprecise data. In addition, the gradient projection type...

The Truck and Trailer Routing Problem consists of a heterogeneous fleet composed of trucks and trailers to serve a set of customers. This problem has been solved previously considering accurate data available. But in a real-world the available knowledge about some data and parameters involves a vague nature. In this study, we propose the application of the Soft Computing to model and solve the ...