Andrew Starkey

Orcid: 0000-0001-6168-4045

According to our database1, Andrew Starkey authored at least 20 papers between 2009 and 2020.

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Bibliography

2020
NNIR: N-Non-Intersecting-Routing Algorithm for Multi-Path Resilient Routing in Telecommunications Applications.
Int. J. Comput. Intell. Syst., 2020

Multiobjective evolutionary algorithms for strategic deployment of resources in operational units.
Eur. J. Oper. Res., 2020

2019
iPatch: A Many-Objective Type-2 Fuzzy Logic System for Field Workforce Optimization.
IEEE Trans. Fuzzy Syst., 2019

A Fuzzy Genetic System for Resilient Routing in Uncertain & Dynamic Telecommunication Networks.
Proceedings of the 2019 IEEE International Conference on Fuzzy Systems, 2019

2018
Many-objective genetic type-2 fuzzy logic based workforce optimisation strategies for large scale organisational design.
PhD thesis, 2018

A Genetic Algorithm Based System for Simultaneous Optimisation of Workforce Skills and Teams.
Künstliche Intell., 2018

Explainable AI and Fuzzy Logic Systems.
Proceedings of the Theory and Practice of Natural Computing - 7th International Conference, 2018

Enhancing Human Decision Making for Workforce Optimisation Using a Stacked Auto Encoder Based Hybrid Genetic Algorithm.
Proceedings of the Artificial Intelligence XXXV, 2018

Interval Type-2 Fuzzy Logic Based Stacked Autoencoder Deep Neural Network For Generating Explainable AI Models in Workforce Optimization.
Proceedings of the 2018 IEEE International Conference on Fuzzy Systems, 2018

A Big-Bang Big-Crunch Type-2 Fuzzy Logic System for Generating Interpretable Models in Workforce Optimization.
Proceedings of the 2018 IEEE International Conference on Fuzzy Systems, 2018

Stacked Auto Encoder Based Hybrid Genetic Algorithm for Workforce Optimization.
Proceedings of the 2018 10th Computer Science and Electronic Engineering Conference, 2018

2017
Fuzzy dominance rules for real-world many objective optimization.
Proceedings of the 2017 IEEE International Conference on Fuzzy Systems, 2017

2016
A cloud computing based many objective type-2 fuzzy logic system for mobile field workforce area optimization.
Memetic Comput., 2016

A multi-objective genetic type-2 fuzzy logic based system for mobile field workforce area optimization.
Inf. Sci., 2016

A Genetic Algorithm Based Approach for the Simultaneous Optimisation of Workforce Skill Sets and Team Allocation.
Proceedings of the Research and Development in Intelligent Systems XXXIII, 2016

A many-objective genetic type-2 fuzzy logic system for the optimal allocation of mobile field engineers.
Proceedings of the 2016 IEEE International Conference on Fuzzy Systems, 2016

A comparison of particle swarm optimization and genetic algorithms for a multi-objective Type-2 fuzzy logic based system for the optimal allocation of mobile field engineers.
Proceedings of the IEEE Congress on Evolutionary Computation, 2016

2015
A Genetic Algorithm Based Approach for Workforce Upskilling.
Proceedings of the Research and Development in Intelligent Systems XXXII, 2015

A genetic type-2 fuzzy logic based approach for the optimal allocation of mobile field engineers to their working areas.
Proceedings of the 2015 IEEE International Conference on Fuzzy Systems, 2015

2009
Monte Carlo simulation of the global nitrogen cycle.
Proceedings of the 2009 International Conference on Scientific Computing, 2009


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