In this work, we introduce a new and efficient solution approach for the problem of decision making under uncertainty, which can be formulated as in belief space, over possibly high-dimensional state space. Typically, to solve problem, one should identify optimal action from set candidates, according some objective. We claim that often generate an analogous yet simplified solved more efficientl...

This paper processes a combined method, based on VIKOR and Data Envelopment Analysis (DEA) to select the units with most efficiency. We utilize the VIKOR as compromise solution method. This research is a two-stage model designed to fully rank the alternatives, where each alternative has multiple inputs and outputs. The problem involves BELIEF parameters in the solution procedure. First, the alt...

In this research, the decision on belief (DOB) approach was employed to analyze and classify the states of uni-variate quality control systems. The concept of DOB and its application in decision making problems were introduced, and then a methodology for modeling a statistical quality control problem by DOB approach was discussed. For this iterative approach, the belief for a system being out-...

The belief decision tree (BDT) approach is a decision tree in an uncertain environment where the uncertainty is represented through the Transferable Belief Model (TBM), one interpretation of the belief function theory. The uncertainty can appear either in the actual class of training objects or attribute values of objects to classify. In this paper, we develop a post-pruning method of belief de...

We introduce a new approach to distribution fitting, called Decision on Beliefs (DOB). The objective is to identify the probability distribution function (PDF) of a random variable X with the greatest possible confidence. It is known that f X is a member of = { , , }. 1 m S f L f To reach this goal and select X f from this set, we utilize stochastic dynamic programming and formulate this proble...