In this paper we introduce a linguistic multi-criteria decision-aiding model to support college students with the internship job market application. It considers a fuzzy ordered weighted averaging (FOWA) operator in the matching to capture the inherent uncertainty and vague nature of personnel selection processes. The decision model is integrated in a software tool able to capture data from university student resume and internship databases. The application assesses position characteristics implicitly by means of linguistic descriptions according to each student's preferences. The software tool is enabled with the ability to propose positions according to student preferences. The system selects a reduced list of alternatives from the set of job offers, helping students to decide on which positions to focus their applications.
Assigning papers to reviewers is a large, long and difficult task for conference chairs and scientific committees. The paper reviewer assignment problem is a multi-agent problem which requires understanding reviewer expertise and paper topics for the matching process. This paper proposes to elaborate on some features used to compute reviewer expertise and aggregate multiple factors to find the fittest combination of reviewers for each paper. Expertise information is gathered implicitly from publicly available information and a reviewer profile is generated automatically. An OWA (Ordered Weighted Average) aggregation function is used to summarize information coming from different sources and rank candidate reviewers for each paper. General constraints for the RAP (Reviewer Assignment Problem) have been incorporated into a real case example: (i) conflicts of interest between a reviewer and authors should be avoided, (ii) each paper must have a minimum number of reviewers, and (iii) each reviewer load cannot exceed a certain number of papers.
The concept mapping methodology aims to respond to the non trivial task of conceptualising abstract thoughts by means of a focus group composed by experts from the studied domain. The approach defines a set of general steps that allow experts to lead the generation of ideas, group the ideas in a conceptual map of interrelated concepts using clustering multidimensional scaling and clustering techniques, analysing the quality of the conceptual maps and deciding on a final interpretation. In this sense, this final decision is not trivial because clustering techniques provide a set of potentially conceptual maps so experts must select the one that fits best according to their opinion. For this reason, we present the global index of consensus as an indicator for filtering the most suitable clustering solutions using qualitative reasoning. It promotes the consensus of experts opinions and ensures objectivity in the final interpretation. The index outperforms three of the most well-known clustering validation indexes in a case study focused on the meaning of excellence in hospitality industry. This work presents the global index of consensus as an indicator for filtering the most suitable clustering solutions using qualitative reasoning that promotes the consensus of experts' opinions, which is one of the key aspects in the concept mapping methodology. The index outperforms three of the most well-known clustering validation indexes in a case study focused on the meaning of excellence in hospitality.
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