Fuzzy-logic controlled genetic algorithm for the rail-freight crew-scheduling problem

Elena Khmeleva, Adrian Alan Hopgood, Lucian Tipi, Malihe Shahidan

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This article presents a fuzzy-logic controlled genetic algorithm designed for the solution of the crew-scheduling problem in the rail-freight industry. This problem refers to the assignment of train drivers to a number of train trips in accordance with complex industrial and governmental regulations. In practice, it is a challenging task due to the massive quantity of train trips, large geographical span and significant number of restrictions. While genetic algorithms are capable of handling large data sets, they are prone to stalled evolution and premature convergence on a local optimum, thereby obstructing further search. In order to tackle these problems, the proposed genetic algorithm contains an embedded fuzzy-logic controller that adjusts the mutation and crossover probabilities in accordance with the genetic algorithm's performance. The computational results demonstrate a 10% reduction in the cost of the schedule generated by this hybrid technique when compared with a genetic algorithm with fixed crossover and mutation rates.
Original languageEnglish
Pages (from-to)61-75
Number of pages15
JournalKünstliche Intelligenz
Issue number1
Early online date27 Oct 2017
Publication statusPublished - 3 Feb 2018


  • fuzzy Logic
  • genetic algorithm (GA)
  • hybrid
  • crew scheduling
  • rail freight


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