Knowledge based or Intelligent Control of Flexible Manufacturing Systems (FMS) means a continuous or frequent observation and evaluation of the status and condition of the system performance, decision making based on the evaluation results and on pre-defined knowledge, and then the operation according to the decisions. This kind of process is applied if any kind of disturbance or irregularity h...

Reconstructability analysis (RA) is a method for detecting and analyzing the structure of multivariate categorical data. Jones and his colleagues extended the original variablebased formulation of RA to encompass models defined in terms of system states (Jones 1982; Jones 1985; Jones 1985; Jones 1986; Jones 1989). In this paper, we demonstrate that Jones’ previous work comprises two separable i...

The main purpose of this paper is to develop a new optimum output quantity decision analysis of a duopoly market under a fuzzy decision environment. To efficiently handle the fuzziness of the decision variables, the linguistic values, subjectively represented by the trapezoidal fuzzy numbers, are used to act as the evaluation tool of decision variables such as fixed cost and unit variable cost....

How do expert pilots interpret flight-related problem situations and what aspects do they find important for making decisions? These questions are not as easy to answer as they at first might appear. Aviation decision situations are often complex and a number of factors may influence decision making. Candidate variables are to name just a few phase of flight, aircraft type, problem type, availa...

This paper addresses the influence of managerial decision-making processes on the evolution of industries. Managers face very complex investment decisions due to uncertainties about customer acceptance, market size, technology, actions of competitors, and a dynamic complex feedback system. Managerial decision-making processes are almost the most influential variable to manipulate the evolution ...

Important ecological phenomena are often observed indirectly. Consequently, probabilistic latent variable models provide an important tool, because they can include explicit models of the ecological phenomenon of interest and the process by which it is observed. However, existing latent variable methods rely on handformulated parametric models, which are expensive to design and require extensiv...

In this paper, we combine for the first time the methods of dynamic mechanism design with techniques from decentralized decision making under uncertainty. Consider a multi-agent system with self-interested agents acting in an uncertain environment, each with private actions, states and rewards. There is also a social planner with its own actions, rewards, and states, acting as a coordinator and...

Today’s highly competitive, fast pace business environment makes it an absolute requirement on managers to continuously make the right decisions in the shortest possible time. There is no room for error in making managerial decisions in this global environment marked by mergers, acquisitions, and ever-increasing economic instability. Success (or merely survivability) depends on the managers mak...

The framework of this paper is supervised learning using classification trees. Two types of variables play a role in the definition of the classification rule, namely a response variable and a set of predictors. The tree classifier is built up by a recursive partitioning of the prediction space such to provide internally homogeneous groups of objects with respect to the response classes. In the...

Partially observable Markov decision processes (POMDPs) are an appealing tool for modeling planning problems under uncertainty. They incorporate stochastic action and sensor descriptions and easily capture goal oriented and process oriented tasks. Unfortunately, POMDPs are very difficult to solve. Exact methods cannot handle problems with much more than 10 states, so approximate methods must be...