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A fuzzy-genetic tactical resource planner for workforce allocation

Mohamed, A and Hagras, H and Shakya, S and Owusu, G (2013) A fuzzy-genetic tactical resource planner for workforce allocation. In: UNSPECIFIED, ? - ?.

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Abstract

For the recent few years, resource planning has become an interesting research topic for many companies, especially within telecommunications domain. Resource planning is basically trying to provide a high quality of service while trying to keep the cost as low as possible. The main aim of resource planning is to utilize the available resources as much as possible so that they can match the expected demand for services. Tactical resource planning looks at medium-term planning periods, i.e. weeks to months, and aims to establish coarse-grain resource deployments. In our previous work we introduced an experimental fuzzy based resource planning approach modeled for a delivery unit in British Telecom (BT) [1]. We presented a hierarchical based fuzzy logic system, which calculates the compatibility between resources and the allocated tasks, and then matches the most compatible tasks and resources to each other. The proposed hierarchical fuzzy logic based system (in an experimental setting) was able to achieve very good results in comparison to the original system, where the proposed system was able to achieve 12.2% improvement in tasks done per resource. In this paper, we introduce a hierarchical fuzzy logic based system that uses evolutionary systems to tune the fuzzy membership functions, which result in an improvement in the overall output of the system. The new fuzzy-genetic based system was able achieve better improvement in tasks done per resource than the hierarchical fuzzy logic based system that was tuned by experts. © 2013 IEEE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: Proceedings of the 2013 IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 08 Jan 2015 16:12
Last Modified: 23 Jan 2019 00:18
URI: http://repository.essex.ac.uk/id/eprint/12216

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