Using Fuzzy Cognitive Maps for Prediction of Knowledge Worker Productivity Based on Real Coded Genetic Algorithm

• Main Authors: Asadallah Najafi, Abbas Afrazeh
• Format: Article
• Language: English
• Published: Iran University of Science & Technology 2011-03-01
• Series: International Journal of Industrial Engineering and Production Research
• Subjects: Knowledge work; Knowledge Worker Productivity; Fuzzy Cognetive Maps; Knowledge Management
• Online Access: http://ijiepr.iust.ac.ir/browse.php?a_code=A-10-181-2&slc_lang=en&sid=1

Improving knowledge worker productivity has been one of the most important tasks of the century. However, we have few measures or management interventions to make such improvement possible, and it is difficult to identify patterns that should be followed by knowledge workers because systems and processes in an organization are often regarded as a death blow to creativity. In this paper, we seek to present a method for prediction of Knowledge worker productivity (KWP) that it must be capable of predicting the productivity of the knowledge workers in a one year period of time based on the Fuzzy cognitive maps (FCM) technique Based on Real Coded Genetic Algorithm (RCGA) , as well as presenting the best option from among different options as the knowledge workers’ productivity improving strategy (suggesting solution), based on the results gained from this and the previous section and depending on the requirements. The validity of the suggested model will be tested in an Iranian Company .