A type2 fuzzy logic system for workforce management in the telecommunications domain

Summer Kassem, Hani Hagras, Gilbert Owusu, Sid Shakya

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Workforce management is one of the most important factors in the success of any company that provides its customers with services. Hence, in order for the company to achieve objectives like customer satisfaction and maximum resource utilization, there is a need to have a reliable means of efficiently managing the company workforce and making sure that the produced plan always gives a good choice when it comes to assigning the available technicians to the given jobs. As the quantity of services and the workforce grow, the use of an automated workforce management system becomes inevitable. However the automated workforce management system should allow full transparency to allow the user to interact with the generated plans. In addition, the workforce management systems face high levels of uncertainties when dealing with real-world scenarios, which necessitates employing systems, which are able to handle the linguistic and numerical uncertainties available in the real-world scenarios. Fuzzy Logic Systems (FLSs) are credited with providing transparent methodologies that can deal with the imprecision and uncertainties. However the vast majority of the FLSs employ the type-1 FLSs, which cannot directly handle the high levels of uncertainties. Type-2 FLSs which employ type-2 fuzzy sets can handle such high levels of uncertainties to give very good performances. In this paper, we will present a type-2 FLS based workforce management system that is being developed for a delivery unit in British Telecom (BT). We will show how the presented system was able to handle the faced uncertainties to give very good performance that outperformed the automated non-intelligent system and the type-1 FLSs based system.

Original languageBritish English
Title of host publication2012 IEEE International Conference on Fuzzy Systems, FUZZ 2012
DOIs
StatePublished - 2012
Event2012 IEEE International Conference on Fuzzy Systems, FUZZ 2012 - Brisbane, QLD, Australia
Duration: 10 Jun 201215 Jun 2012

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584

Conference

Conference2012 IEEE International Conference on Fuzzy Systems, FUZZ 2012
Country/TerritoryAustralia
CityBrisbane, QLD
Period10/06/1215/06/12

Keywords

  • Type-2 Fuzzy Logic Systems
  • Workforce Management

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