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

Andrew Starkey, Hani Hagras, Sid Shakya, Gilbert Owusu

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Employing effective optimization strategies in organizations with large workforces can have a clear impact on costs, revenues, and customer satisfaction. This is particularly true for organizations that employ large field workforces, such as utility companies. Ensuring each member of the workforce is fully utilized is a challenging problem as there are many factors that can impact the overall performance of the organization. We have developed a system that optimizes 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 optimization problems, which means there are many objectives to consider when optimizing, and there is much uncertainty in the environment. The latest version of the system uses a multiobjective genetic algorithm as its core optimization logic, with modifications such as fuzzy dominance rules (FDRs), to help overcome the issues associated with many-objective optimization. The system also utilizes genetically optimized type-2 fuzzy logic systems to better handle the uncertainty in the data and modeling. This paper shows the genetically optimized 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 £1 million a year, the reduction of over 2500 t of CO 2 and a possible prevention of over 100 serious injuries and fatalities on the UK's roads.

Original languageBritish English
Article number8424502
Pages (from-to)502-514
Number of pages13
JournalIEEE Transactions on Fuzzy Systems
Issue number3
StatePublished - 2019


  • Fuzzy dominance
  • genetic algorithms (GAs)
  • many-objective
  • multiobjective
  • type-2 fuzzy logic
  • workforce optimization


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