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Abstract. We present an algorithm for the crew pairing problem, an optimization problem that is part of the airline crew scheduling procedure. A pairing is a round trip starting and ending at the home base, which is susceptible to constraints that arise due to laws and regulations. The purpose of the crew pairing problem is to generate a set of pairings with minimal cost, covering all flight legs that the company has to carry out during a predefined time period. The proposed solution is a two-phase procedure. For the first phase, the pairing generation, a depth first search approach is employed. The second phase deals with the selection of a subset of the generated pairings with near optimal cost. This problem, which is modelled by a set covering formulation, is solved with a genetic algorithm. The presented method was tested on actual flight data of Olympic Airways.

Concept mapping is a knowledge elicitation technique which stimulates learners to articulate and synthesize their actual states of knowledge during the learning process. Several approaches have been proposed for automating the assessment procedure of learners' concept maps based on an expert's map as a reference point. However, these approaches do not handle cases where learners have misspelled a concept or they have used a synonym or a concept related to the appropriate one. In this paper we present an alternative approach in which the process of the error identification is performed through the use of an expert map and of WordNet which is an electronic lexical database containing semantic relationships between words. This way we handle cases such as misspelled concepts, synonyms and related concepts. After error detection WordNet is also employed for providing the learner with appropriate feedback based on the identified errors, with the intention of helping the learner to correct them.

Abstract:In this paper we propose a method that implements student diagnosis in the context of the Adaptive Hypermedia Educational System INSPIRE -INtelligent System for Personalized Instruction in a Remote Environment. The method explores ideas from the fields of fuzzy logic and multicriteria decisionmaking in order to deal with uncertainty and incorporate in the system a more complete and accurate description of the expert's knowledge as well as flexibility in student's assessment. To be more precise, an inference system, using fuzzy logic and the Analytic Hierarchy Process to represent the knowledge of the teacher-expert on student's diagnosis, analyzes student's answers to questions of varying difficulty and importance, and estimates the student's knowledge level. Preliminary experiments with real students indicate that the method is characterized by effectiveness in handling the uncertainty of student diagnosis, and is found to be closer to the assessment performed by a human teacher, when compared to a more traditional method of assessment.