The aircraft landings scheduling problem at an airport has become very challenging due to the increase of air traffic. Traditionally, this problem has been widely studied by formulating it as an optimization model solved by various operation research approaches. However, these approaches are not able to capture the dynamic nature of the aircraft landing scheduling problem appropriately and handle uncertainty easily. A systems approach provides an alternative to solve such a problem from a systematic perspective. In this regard, the concept of general systems problem solving (GSPS) was first introduced in 1970s, and yet the power of the GSPS methodology is not fully discovered as it had only been applied to few domains. In this paper, a new general systems problem solving framework integrating computational intelligence techniques (GSPS-CI) is introduced. The two main functions of the framework are: (1) adaptive network based fuzzy inference system (ANFIS) to predict flight delays, and (2) fuzzy decision making procedure to schedule aircraft landings. The effectiveness of the GSPS-CI framework is tested on the JFK airport in USA, one of the most complex real-life systems.

In a recent paper, a functional AM (ambiguity measure) is introduced and an attempt is made to show that this functional qualifies as a measure of total aggregated uncertainty in the Dempster-Shafer theory. We show that this attempt fails due to a particular error in the proof of one of the principal theorems in the paper. Some additional remarks are made regarding recent research pertaining to the subject of the discussed paper.

This paper discusses recent applications of fuzzy sets and the theory of approximate reasoning. The primary focus is on fuzzy logic control (FLC). We begin with a brief history of the key ideas, a survey of recent applications, and a discussion of the genesis of FLC in Japan. We then turn to a study of the general principles of FLC, considering it as a combination of ideas from conventional control theory, artificial intelligence, and fuzzy sets theory. We next provide a detailed analysis of a simple application in consumer electronics, namely, a fuzzy washing machine developed by Hitachi Corporation. In concluding sections we briefly consider other types of applications, including recent work on pilotless helicopters, fuzzy expert systems, and the concept of a fuzzy computer, and we discuss the potential for future developments. It is our opinion that the subject of FLC is still very much in its infancy, and that recent events mark the beginning of an entirely new genre of "intelligent" control.>