@techreport{TR-IC-05-02,
  number = {IC-05-02},
  author = {Greg\'{o}rio Baggio Tramontina and Jacques Wainer},
  title = {Using Genetic Algorithms for the Dynamic Job Shop Scheduling Problem
with Processing Time Uncertainties},
  month = {February},
  year = {2005}, 
  institution = {Institute of Computing, University of Campinas},
  note = {In English, 15 pages.
    \par\selectlanguage{english}\textbf{Abstract}
       A possible solution to the job shop scheduling problem is using
       genetic algorithms. Although research on the topic can be
       considered evolved, it mainly concentrates on deterministic
       static problems. This work reports an experience on using
       genetic algorithms with the dynamic job shop problem with
       processing time uncertainties, based on two approaches: the
       guess and solve, and the decomposition of dynamic job shops
       into static instances. The guess and solve consists of making a
       controlled guess on the processing time of the jobs and solving
       the resulting deterministic scheduling problem with a suitable
       technique, in this report the genetic algorithms, and the
       decomposition treats the dynamic job shop as a series of static
       problems. Processing time uncertainties prevent the direct use
       of the decomposition approach, so an adjustment to this
       technique is also proposed. Simulation results show that the
       guess and solve with genetic algorithms together with the
       proposed adjustment is an adequate way to handle the dynamic
       job shop with processing time uncertainties.
  }
}