Improved algorithm for weighted matching of employees

Abstract: This report gives the reader a detailed description of a computer engineering master thesis work done at the company Netlight Consulting AB. Netlight Consulting AB is a growing IT consulting company based in Stockholm with offices in major cities across Europe. One of their key success factors is their focus on personal and professional development amongst all employees. An essential part of this development program consist of reoccurring evaluation periods, where every employee receives written constructive feedback from some of their co-workers. This thesis’ focus lies in improving the algorithm that organizes which employee should evaluate who. The original algorithm turned out to harbor a number of flaws, e.g. it was not always able to deliver a satisfactory matching where every participant received the minimum number of evaluations.   In this thesis a new matching algorithm has been implemented that is platform independent and that facilitates future modifications with accessible source code written in Java. The input data for the matching algorithm, i.e. the set of all potential evaluation pairs, is of importance to obtain satisfactory matching results. The number of potential evaluation pairs determines the number of possible matching combinations, which in turn increases the probability to find a satisfactory matching. In this thesis the input data has been extended by utilizing a data mining technique known as SONAR. Two different data mining sources were evaluated, and one of them is shown to extend the number of potential evaluation pairs in the matching input by 20%. Finally, a new feature to support assignment of different evaluation sizes was added to the matching algorithm.