Flow Path Network Design for Robust AGV Systems Against Tasks Using Competitive Coevolution

Ryosuke Chiba; Tamio Arai; Jun Ota

doi:10.20965/jrm.2010.p0475

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JRM Vol.22 No.4 pp. 475-484

doi: 10.20965/jrm.2010.p0475

(2010)

Paper:

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Flow Path Network Design for Robust AGV Systems Against Tasks Using Competitive Coevolution

Ryosuke Chiba^*, Tamio Arai^, and Jun Ota^

^*Tokyo Metropolitan University, 6-6 Asahigaoka, Hino-shi, Tokyo 191-0065, Japan

^**The University of Tokyo

Received:

January 4, 2010

Accepted:

May 13, 2010

Published:

August 20, 2010

Keywords:

AGV system, competitive coevolution

Abstract

An effective and robust flow path network is desired in Automated Guided Vehicle (AGV) systems. A design process to obtain the desired flow path network in AGV systems is proposed in this paper. Our proposed method can make flow path networks robust against tasks, which include pick-up point, drop-off point and throughput and number of AGVs . It is important for this robust flow path network that the kinds of tasks be of various and non-linear to the system effectiveness. The problem is solved by the design method of various kinds of tasks that are difficult for AGV systems using Genetic Algorithm (GA). An effective flow path network is designed with GA simultaneously because the difficult tasks and number of AGVs depend on the flow path networks. Competitive coevolution is applied to the simultaneous design. AGV systems can be effective with uni/bi-directional combined flow path networks which utilize just simple routings. Results of the design are shown through simulations, and the designed flow path network makes it possible to complete various tasks with various numbers of AGVs.

Cite this article as:

R. Chiba, T. Arai, and J. Ota, “Flow Path Network Design for Robust AGV Systems Against Tasks Using Competitive Coevolution,” J. Robot. Mechatron., Vol.22 No.4, pp. 475-484, 2010.

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References

[1] C. W. Kim and J. M. A. Tanchoco, “Economical design of material flow path,” Int. J. of Production Research, Vol.31, No.6, pp. 2123-2138, 1993.
[2] D. Sinriech, “Network design models for discrete material flow systems: A literature review,” Int. J. of Advanced Manufacturing Technology, Vol.10, No.4, pp. 227-291, 1995.
[3] R. Chiba, J. Ota, and T. Arai, “Integrated Design with Classification of Transporter Routing for AGV Systems,” Proc. of Int. Conf. Intell. Robots and Systems, pp.1820-1825, 2002.
[4] R. Chiba, T. Arai, and J. Ota, “Integrated Design for AGV Systems using Cooperative Co-evolution,” Advanced Robotics, Vol.24, No.1-2, pp. 25-45, 2010.
[5] J. M. A. Tanchoco and D. Sinriech, “OSL-optimal single loop guide paths for AGVs,” Int. J. of Production Research, Vol.30, No.3, pp. 665-681, 1992.
[6] R. Chiba, T. Arai and J. Ota, “Design and Analysis for AGV Systems using Cooperative Co-evolution,” Distributed Autonomous Robotic Systems 2008, Springer, pp.465-476, 2009.
[7] W. D. Hillis, “Artificial Life II, Co-Evolving Parasites Improve Simulated Evolution as an Optimization Procedures,” ed.by Brooks & Maes, MIT Press, pp. 313-324, 1992.
[8] K. Sims, “Evolving 3D Morphology and Behavior by Competition,” Artificial Life IV Proceedings., ed.by Brooks & Maes, MIT Press, pp. 28-39, 1994.
[9] D. Floreano and S. Nolfi, “God save the red queen! Competition in co-evolutionary robotics,” In Proc. of the second Int. Conf. on Genetic Programming, pp. 398-406, 1997.
[10] M. Nerome, K. Yamada, S. Endo, and H. Miyagi, “Competitive Coevolution Based Game-Strategy Acquisition with Packaging Strategies Method,” Proc. of SMC’97, pp. 4418-4423, 1997.
[11] L. Qiu, W. Hsu, S. Huang, and H. Wang, “Scheduling and routing algorithms for AGVs: a survey,” Int. J. of Production Research, Vol.40, pp. 745-760, 2002.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] C. W. Kim and J. M. A. Tanchoco, “Economical design of material flow path,” Int. J. of Production Research, Vol.31, No.6, pp. 2123-2138, 1993.

[2] [2] D. Sinriech, “Network design models for discrete material flow systems: A literature review,” Int. J. of Advanced Manufacturing Technology, Vol.10, No.4, pp. 227-291, 1995.

[3] [3] R. Chiba, J. Ota, and T. Arai, “Integrated Design with Classification of Transporter Routing for AGV Systems,” Proc. of Int. Conf. Intell. Robots and Systems, pp.1820-1825, 2002.

[4] [4] R. Chiba, T. Arai, and J. Ota, “Integrated Design for AGV Systems using Cooperative Co-evolution,” Advanced Robotics, Vol.24, No.1-2, pp. 25-45, 2010.

[5] [5] J. M. A. Tanchoco and D. Sinriech, “OSL-optimal single loop guide paths for AGVs,” Int. J. of Production Research, Vol.30, No.3, pp. 665-681, 1992.

[6] [6] R. Chiba, T. Arai and J. Ota, “Design and Analysis for AGV Systems using Cooperative Co-evolution,” Distributed Autonomous Robotic Systems 2008, Springer, pp.465-476, 2009.

[7] [7] W. D. Hillis, “Artificial Life II, Co-Evolving Parasites Improve Simulated Evolution as an Optimization Procedures,” ed.by Brooks & Maes, MIT Press, pp. 313-324, 1992.

[8] [8] K. Sims, “Evolving 3D Morphology and Behavior by Competition,” Artificial Life IV Proceedings., ed.by Brooks & Maes, MIT Press, pp. 28-39, 1994.

[9] [9] D. Floreano and S. Nolfi, “God save the red queen! Competition in co-evolutionary robotics,” In Proc. of the second Int. Conf. on Genetic Programming, pp. 398-406, 1997.

[10] [10] M. Nerome, K. Yamada, S. Endo, and H. Miyagi, “Competitive Coevolution Based Game-Strategy Acquisition with Packaging Strategies Method,” Proc. of SMC’97, pp. 4418-4423, 1997.

[11] [11] L. Qiu, W. Hsu, S. Huang, and H. Wang, “Scheduling and routing algorithms for AGVs: a survey,” Int. J. of Production Research, Vol.40, pp. 745-760, 2002.

Flow Path Network Design for Robust AGV Systems Against Tasks Using Competitive Coevolution

Ryosuke Chiba*, Tamio Arai**, and Jun Ota**

Ryosuke Chiba^*, Tamio Arai^, and Jun Ota^