An ontological multi-criteria optimization system for Workforce Management

Cimitile, Marta; Gaeta, Matteo; Loia, Vincenzo

doi:10.1109/cec.2012.6252901

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…The subject of Workforce Management (WFM) is broad [7], and optimisation strategies will differ between workforce types. A field workforce is one that is mobile and will travel to many locations throughout the day.…”

Section: Problemsmentioning

confidence: 99%

“…To help identify strong solutions in the population, we have implemented a distance metric that increases when maximisation objectives are increased and increases when minimisation objectives are decreased, when compared to the original WA design. The distance metric for our objectives is shown by Equation (7). The greater the distance from the original solution design the better the new solution is.…”

Section: The Implemented Distance Metricmentioning

confidence: 99%

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iPatch: A Many-Objective Type-2 Fuzzy Logic System for Field Workforce Optimization

Starkey

Hagras

Shakya

et al. 2019

IEEE Trans. Fuzzy Syst.

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Employing effective optimisation strategies in organisations with large workforces can have a clear impact on costs, revenues, and customer satisfaction. This is particularly true for organisations that employ large field workforces, such as utility companies. Ensuring each member of the workforce is fully utilised is a challenging problem as there are many factors that can impact the organisation's overall performance. We have developed a system that optimises to make sure we have the right engineers, in the right place, at the right time, with the right skills. This system is currently deployed to help solve real-world optimisation problems, which means there are many objectives to consider when optimising, and there is much uncertainty in the environment. The latest version of the system uses a multi-objective genetic algorithm as its core optimisation logic, with modifications such as Fuzzy Dominance Rules (FDRs), to help overcome the issues associated with many-objective optimisation. The system also utilises genetically optimised type-2 fuzzy logic systems to better handle the uncertainty in the data and modelling. This paper shows the genetically optimised type-2 fuzzy logic systems producing better results than the crisp value implementations in our application. We also show that we can help address the weaknesses in the standard NSGA-II dominance calculations by using FDRs. The impact of this work can be measured in a number of ways; productivity benefit of £1million a year, the reduction of over 2,500 metric tonnes of CO2 and a possible prevention of over 100 serious injuries and fatalities on the UK's roads.

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Section: Problemsmentioning

confidence: 99%

Section: The Implemented Distance Metricmentioning

confidence: 99%