Wasp Colony with a Multiobjective Local Optimizer for Dynamic Task Planning in a Production Plant

  • Luis Fernando Gutierrez-Marfileno
  • Eunice Ponce-de-Leon
  • Elva Diaz-Diaz
  • Leoncio Ibarra-Martinez
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 175)


Dynamic task scheduling is a time-dependent optimization issue. In this work, we modeled the process that is performed at a production plant as a task scheduling issue, in which a production line sends trucks to a painting plant with several stations. The objective is to attain efficient task scheduling, taking into account three conflicting objectives: number of color changes in booths, work tardiness, and makespan. In order to solve this problem, we developed a hybrid technique, which comprises a Wasp Colony algorithm and a set of priority rules. Both the problem and its solution were modeled through Agent Unified Modeling Languague (AUML) so as to achieve implementation. The results were a remarkable decrease in the number of color changes and work tardiness and the preservation of the number of painted trucks within an acceptable magnitude.


Assembly Line Multiagent System Task Schedule Response Threshold Priority Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Luis Fernando Gutierrez-Marfileno
    • 1
  • Eunice Ponce-de-Leon
    • 1
  • Elva Diaz-Diaz
    • 1
  • Leoncio Ibarra-Martinez
    • 1
  1. 1.Universidad Autonoma de AguascalientesAguascalientesMexico

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