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A new memetic algorithm for optimizing the partitioning problem of tandem AGV systems

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  • ElMekkawy, T.Y.
  • Liu, S.

Abstract

In tandem automated guided vehicle (AGV) systems, all the work stations (processing centers) are partitioned into non-overlapping zones, where each zone is served by a dedicated vehicle. Beside the system input/output stations, additional pickup/drop-off (P/D) points are installed to link these zones as transfer points. In this paper, a new memetic algorithm (MA) is proposed to optimize the partitioning problem of tandem AGV systems. MAs are hybrid evolutionary algorithms (EAs) that combine the global and local search by using a genetic algorithm (GA) to perform exploration and a local search method to perform exploitation. Hence, a local search method has been designed and combined with a GA to refine the individuals of the population (i.e. improve their fitness). The objective is to minimize the maximum AGVs' workload in order to balance the workload among all the zones and hence avoid the presence of bottlenecks. The MA performance is evaluated by comparing the obtained results with the reported results in the literature as well as the pure GA. Furthermore, some newly designed test cases are proposed and solved using both, MA and GA. The results show that, overall, the developed MA outperforms both pure GA and the other reported methods in the literature.

Suggested Citation

  • ElMekkawy, T.Y. & Liu, S., 2009. "A new memetic algorithm for optimizing the partitioning problem of tandem AGV systems," International Journal of Production Economics, Elsevier, vol. 118(2), pages 508-520, April.
    • Handle: RePEc:eee:proeco:v:118:y:2009:i:2:p:508-520
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    References listed on IDEAS

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    1. Asef-Vaziri, Ardavan & Laporte, Gilbert, 2005. "Loop based facility planning and material handling," European Journal of Operational Research, Elsevier, vol. 164(1), pages 1-11, July.
    2. Franca, Paulo M. & Mendes, Alexandre & Moscato, Pablo, 2001. "A memetic algorithm for the total tardiness single machine scheduling problem," European Journal of Operational Research, Elsevier, vol. 132(1), pages 224-242, July.
    3. Bozer, Yavuz A. & Srinivasan, Mandyam M., 1992. "Tandem AGV systems: A partitioning algorithm and performance comparison with conventional AGV systems," European Journal of Operational Research, Elsevier, vol. 63(2), pages 173-191, December.
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    Cited by:

    1. Lacomme, Philippe & Larabi, Mohand & Tchernev, Nikolay, 2013. "Job-shop based framework for simultaneous scheduling of machines and automated guided vehicles," International Journal of Production Economics, Elsevier, vol. 143(1), pages 24-34.
    2. Torres, Isidro Ramos & Romero Dessens, Luis Felipe & Martínez Flores, José Luis & Olivares Benítez, Elías, 2015. "Review of Comprehensive Approaches in Optimizing AGV Systems," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Blecker, Thorsten & Kersten, Wolfgang & Ringle, Christian M. (ed.), Operational Excellence in Logistics and Supply Chains: Optimization Methods, Data-driven Approaches and Security Insights. Proceedings of the Hamburg , volume 22, pages 203-232, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    3. Fragapane, Giuseppe & de Koster, René & Sgarbossa, Fabio & Strandhagen, Jan Ola, 2021. "Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda," European Journal of Operational Research, Elsevier, vol. 294(2), pages 405-426.
    4. Ho, William & Ji, Ping, 2010. "Integrated component scheduling models for chip shooter machines," International Journal of Production Economics, Elsevier, vol. 123(1), pages 31-41, January.

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