A Genetic Algorithm-Based Heuristic for Part-Feeding Mobile Robot Scheduling Problem

  • Quang-Vinh DangEmail author
  • Izabela Ewa Nielsen
  • Grzegorz Bocewicz
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 157)


This present study deals with the problem of sequencing feeding tasks of a single mobile robot with manipulation arm which is able to provide parts or components for feeders of machines in a manufacturing cell. The mobile robot has to be scheduled in order to keep machines within the cell producing products without any shortage of parts. A method based on the characteristics of feeders and inspired by the (s, Q) inventory system, is thus applied to define time windows for feeding tasks of the robot. The performance criterion is to minimize total traveling time of the robot in a given planning horizon. A genetic algorithm-based heuristic is developed to find the near optimal solution for the problem. A case study is implemented at an impeller production line in a factory to demonstrate the result of the proposed approach.


Scheduling Mobile Robot Genetic Algorithm Part Feeding 


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  1. 1.
    Crama, Y., van de Klundert, J.: Cyclic scheduling of identical parts in a robotic cell. Oper. Res. 45, 952–965 (1997)zbMATHCrossRefGoogle Scholar
  2. 2.
    Crama, Y., van de Klundert, J.: Cyclic scheduling in 3-machine robotic flow shops. J. Sched. 2, 35–54 (1999)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Crama, Y., Kats, V., van de Klundert, J., Levner, E.: Cyclic scheduling in robotic flow shops. Ann. Oper. Res. 96, 97–124 (2000)MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Dang, Q.V., Nielsen, I.E., Steger-Jensen, K.: Scheduling a single mobile robot for feeding tasks in a manufacturing cell. In: Proc. of Int. Conf. Adv. Prod. Manag. Syst., Norway (2011)Google Scholar
  5. 5.
    Dror, M., Stulman, A.: Optimizing robot’s service movement: a one dimensional case. Comput. Ind. Eng. 12, 39–46 (1987)CrossRefGoogle Scholar
  6. 6.
    Goldberg, D.E.: Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, New York (1989)zbMATHGoogle Scholar
  7. 7.
    Kats, V., Levner, E.: Parametric algorithms for 2-cyclic robot scheduling with interval processing times. J. Sched. (2010), doi:10.1007/s10951-010-0166-0Google Scholar
  8. 8.
    Kats, V., Levner, E.: A faster algorithm for 2-cyclic robotic scheduling with a fixed robot route and interval processing times. Eur. J. Oper. Res. 209, 51–56 (2011)MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Maimon, O., Braha, D., Seth, V.: A neural network approach for a robot task sequencing problem. Artif. Intell. Eng. 14, 175–189 (2000)CrossRefGoogle Scholar
  10. 10.
    Potvin, J.Y.: Genetic algorithms for traveling salesman problem. Ann. Oper. Res. 63, 339–370 (1996)zbMATHCrossRefGoogle Scholar
  11. 11.
    Suárez, R., Rosell, J.: Feeding sequence selection in a manufacturing cell with four parallel machines. Robot Comput. Integrated Manuf. 21, 185–195 (2005)CrossRefGoogle Scholar
  12. 12.
    Shyu, J.-H., Liu, A., Kao-Shing, H.: A multi-agent architecture for mobile robot navigation control. In: Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence, pp. 50–57 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Quang-Vinh Dang
    • 1
    Email author
  • Izabela Ewa Nielsen
    • 1
  • Grzegorz Bocewicz
    • 2
  1. 1.Dept. of Mechanical and Manufacturing EngineeringAalborg UniversityAalborgDenmark
  2. 2.Dept. of Computer Science and ManagementKoszalin University of TechnologyKoszalinPoland

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