In 2011, the Hellenic Quality Assurance and Accreditation Agency prepared and distributed a course outline template, in paper format, to be filled in by the university tutors. This template contains details about learning outcomes, generic skills, teaching and assessment procedures for each course. The teaching staff, having little knowledge on the aforementioned topics, faced serious difficulties in filling it in. Thus, we developed a web-based information system for outlining courses in our institution. It guides the user (tutor) step-by-step to properly record, document and store every detail of a course, and export it to a pdf file. Additionally, by recording the information in a database, it allows any kind of queries, thus it offers various statistics in university/school/departmental level on the usage of verbs of the Bloom’s taxonomy, about the nurtured generic skills, about the students’ workload per course, etc. The system is user-friendly, according to the results of a short survey, and it is fully expandable. This paper focuses on the presentation of the web-based system application along with the benefits it introduces firstly for the tutors and secondly for the quality assurance team of the university.

A methodology for analysing and measuring experience of students is presented, which was applied during an engineering course, focusing along with engineering topics on the development of academic writing, presentation and teamwork. The methodology based on multi-criteria principles, offers flexibility in the sense that a tutor focusing on specific skills or competencies can adapt it according to his/her effort. The multi-criteria method used, estimates a set of indices, which offer valuable information about the level of competencies that students may have or have acquired. Moreover, in the cases that we have applied the methodology at least twice in different courses, we can calculate the contribution of our effort to the development of competencies. Or, in other words, by quantifying the contribution of a course on a three-level scale as slight - moderate - substantive, we can `prove' compliance with quality assurance issues. The methodology is based on a pre- and post-experience self-evaluation approach about competencies/skills, that students may have before entering and after the completion of a course, respectively. Behind this effort it is assumed firstly that courses are the `vehicles' for the development of students' competencies and secondly that tutors apply teaching - learning and assessment - feedback methods or techniques other than the usual lecturing. An application in a Greek Engineering School is discussed, too. The obstacles, limitations, strengths and weaknesses as well as some theoretical issues are presented. More generally speaking, this kind of initiatives require the following: low number of students per class, investment in time at least for the initial redesign of a course, a few pioneers who may be individuals other than those mostly oriented on research than teaching, and finally fewer teaching topics because time is spent for both engineering and competencies issues.

Resource allocation and scheduling are fundamental issues in a Workflow Management System (WfMS). Effective resource management in WfMS should examine resource allocation together with task scheduling since these problems impose mutual constraints. Optimization of the one factor is subject to the other constraints and vice versa. Thus, an ideal algorithm should take into account not only performance metrics of the infrastructure, such as the number of resources and their utilization, but also quality criteria such as the percentage of tasks undergone violation in their temporal restrictions. In this paper, we propose an innovative algorithm which jointly optimizes the two aforementioned contradictory criteria. The algorithm, called Resource Conflicts Joint Optimization (Re.Co.Jo.Op.), minimizes resource conflicts subject to temporal constraints and simultaneously optimizes throughput or utilization subject to resources constraints. To achieve the optimization, the two factors are formulated in a matrix form and the optimal solution is found by applying concepts of the generalized eigenvalue analysis. A rough outline of an agent-based architecture is proposed to achieve runtime integration of our algorithm into a functional WfMS, while experimental results under different load environments and tasks assumption reveal the superiority of the proposed strategy than the other conventional approaches.

The optimal product line design problem consists of predicting the acceptance of new products in the market, prior of the actual production phase. It requires a unique blend of techniques and resources as it deals with multiple parameters and hard optimization problems. This paper proposes a service oriented architecture which outlines the basic features that a marketing decision support system should provide in order to address this problem. Each feature corresponds to a distinct service while the overall functionality is achieved though service orchestration. The proposed breakdown of the problem into distinct services fits intrinsically the way that marketing managers anticipate the optimal product line process, while it allows them the opportunity to get a solution without getting obstructed of the various, complex optimization parameters and needing to coordinate the whole process.

UTARec, a Recommender System that incorporates Multiple Criteria Analysis methodologies is presented. The system's performance and capability of addressing certain shortfalls of existing Recommender Systems is demonstrated in the case of movie recommendations. UTARec's accuracy is measured in terms of Kendall's tau and ROC curve analysis and is also compared to a Multiple Rating Collaborative Filtering (MRCF) approach. The results indicate that the proposed Multiple Criteria Analysis methodology can certainly improve the recommendation process by producing highly accurate results, from a user oriented perspective.

Designing optimal product lines is essential for any firm to stay competitive. Whereas a large number of optimization algorithms have been applied for solving the problem, most of them adopt the first choice rule to simulate the consumer's choice process. Researchers avoid using probabilistic choice models, such as the logit, since they tend to produce duplicate products in the line, due to the IIA problem. Furthermore preference heterogeneity among consumers is a factor usually neglected, although its representation form has a substantial impact in the design of the product line. We propose a probabilistic choice model that can be used with algorithms that solve the optimal product line design problem, using the share of choices criterion. Our model deals with the IIA problem, by incorporating the similarity among products through the use of a corrective method. In addition, preference heterogeneity among consumers is effectively represented, while the model's predictive accuracy is optimized through the use of Stochastic Logarithmic Search and Genetic Algorithms.