@inproceedings{0c4858e7eca54853b399ec7e1ca6e3d4,
title = "A many-objective genetic type-2 fuzzy logic system for the optimal allocation of mobile field engineers",
abstract = "In real world optimization problems there are often multiple objectives to consider. However with regular multiobjective genetic algorithms the more objectives there are the more of a problem this becomes for the Pareto front. This is why solutions for Many Objective Problems should be explored. Many objective problems differ from multi-objective problems in that they have more than three objectives [1], [2], [3]. The problem faced by many objective systems is that the more objectives there are the more likely that more solutions will appear on the Pareto front, especially if the objectives are conflicting. This is a problem in two instances, the first is that the genetic algorithm finds it difficult to distinguish between solutions for parent selection, the second is that the output of the system will usually give a big portion of the entire population set. This means that it might be very difficult for users to choose a single solution to apply to the given real-world problem. This paper presents a novel many objective genetic type-2 fuzzy logic based system for mobile field workforce area optimization. This system was employed in real world scheduling problems to handle the high uncertainty levels associated with these domains. We will present a distance measure to avoid the problems associated with the selection of one solution from the Pareto front of Many Objective Problems. The system in this paper uses five objectives from a real world many-objective problem where the objectives are conflicting and as a result the Pareto front becomes saturated with solutions. The distance metric will help to evaluate if optimizing fuzzy systems using a genetic algorithm improves the performance of the system, comparing both optimized and un-optimized type-1 and type-2 fuzzy sets. The results show that optimizing the membership functions of fuzzy sets using a genetic algorithm improved the overall performance of the fuzzy systems and that the distance metric helps to distinguish between the better solutions on the Pareto front. Such optimization improvements of the working areas will result in better utilization of the mobile field workforce in utilities companies.",
keywords = "Genetic algorithms, Many-objective, Multiobjective, Type-2 fuzzy logic, Work area optimization",
author = "Andrew Starkey and Hani Hagras and Sid Shakya and Gilbert Owusu",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016 ; Conference date: 24-07-2016 Through 29-07-2016",
year = "2016",
month = nov,
day = "7",
doi = "10.1109/FUZZ-IEEE.2016.7737944",
language = "British English",
series = "2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2051--2058",
booktitle = "2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016",
address = "United States",
}