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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.

, , , и . CEC, стр. 5068-5075. IEEE, (2016)

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A genetic type-2 fuzzy logic based approach for the optimal allocation of mobile field engineers to their working areas., , , и . FUZZ-IEEE, стр. 1-8. IEEE, (2015)A multi-objective genetic type-2 fuzzy logic based system for mobile field workforce area optimization., , , и . Inf. Sci., (2016)A Genetic Algorithm Based Approach for the Simultaneous Optimisation of Workforce Skill Sets and Team Allocation., , , и . SGAI Conf., стр. 253-266. Springer, (2016)Monte Carlo simulation of the global nitrogen cycle., и . CSC, стр. 139-144. CSREA Press, (2009)Interval Type-2 Fuzzy Logic Based Stacked Autoencoder Deep Neural Network For Generating Explainable AI Models in Workforce Optimization., , , и . FUZZ-IEEE, стр. 1-8. IEEE, (2018)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., , , и . CEC, стр. 5068-5075. IEEE, (2016)Stacked Auto Encoder Based Hybrid Genetic Algorithm for Workforce Optimization., , , и . CEEC, стр. 236-241. IEEE, (2018)iPatch: A Many-Objective Type-2 Fuzzy Logic System for Field Workforce Optimization., , , и . IEEE Trans. Fuzzy Syst., 27 (3): 502-514 (2019)Multiobjective evolutionary algorithms for strategic deployment of resources in operational units., , , и . Eur. J. Oper. Res., 282 (2): 729-740 (2020)Many-objective genetic type-2 fuzzy logic based workforce optimisation strategies for large scale organisational design.. University of Essex, Colchester, UK, (2018)British Library, EThOS.