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Scheduling Mobile Robots in Flexible Manufacturing System by An Adaptive Large Neighborhood Search

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Published:07 March 2020Publication History

ABSTRACT

Robots play an important role in the production and processing of auxiliary products in flexible manufacturing systems. Mobile robots can quickly and easily transport goods from warehouses to individual production workshops. This paper studies the path planning problem of using mobile robots to provide goods. This problem is called mobile robot path planning (MRPP) problem. An improved algorithm based on adaptive large neighborhood search called ALNSI is proposed. This algorithm integrates the characteristics of this problem into the search framework and has designed various destroy and repair methods according to the problem. Both the destroy and repair methods include strategies for tasks and mobile robots. A path reconstruction algorithm for ensuring the feasibility of scheme is also included in neighborhood search framework. The algorithm proposed in this paper has better performance in experimental verification than comparison algorithms and can be better used in flexible manufacturing systems.

References

  1. Dang, Q. V., Nielsen, I. E., & Bocewicz, G. A Genetic Algorithm-Based Heuristic for Part-Feeding Mobile Robot Scheduling Problem. Trends in Practical Applications of Agents and Multiagent Systems, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  2. Tazaki, Y., & Suzuki, T. Path planning of mobile robots considering position uncertainty and cost of observation. Sice Jcmsi, vol. 7, no. 3, pp. 183--190, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  3. Huisman, R. Scheduling the refuelling activities of multiple heterogeneous autonomous mobile robots, 2014.Google ScholarGoogle Scholar
  4. Hasgül, S., Saricicek, I., Ozkan, M., & Parlaktuna, O. Project-oriented task scheduling for mobile robot team. Journal of Intelligent Manufacturing, vol. 20, no. 2, pp. 151--158, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  5. Mosallaeipour, S., Nejad, M. G., Shavarani, S. M., & Nazerian, R. Mobile robot scheduling for cycle time optimization in flow-shop cells, a case study. Production Engineering, vol. 12, no. 1, pp. 83--94, 2017.Google ScholarGoogle ScholarCross RefCross Ref
  6. Liu, S., Wu, H., Xiang, S., & Li, X. Mobile robot scheduling with multiple trips and time windows, advanced data mining and applications, pp. 608--620, 2017.Google ScholarGoogle Scholar
  7. Nielsen, I., Do, N. A. D., Nielsen, P., & Khosiawan, Y. Material Supply Scheduling for a Mobile Robot with Supply Quantity Consideration---A GA-based Approach. International Conference Information Systems Architecture & Technology, pp. 41--52, 2016.Google ScholarGoogle ScholarCross RefCross Ref
  8. Wang, Q., Luo, H., Xiong, J., Song, Y., Zhang, Z. Evolutionary Algorithm for Aerospace Shell Product Digital Production Line Scheduling Problem. Symmetry, vol. 11, no. 849, 2019.Google ScholarGoogle Scholar
  9. Song, Y., Ma, X., Li, X., Xing, L., Wang, P. Learning-guided nondominated sorting genetic algorithm II for multi-objective satellite range scheduling problem. Swarm and Evolutionary Computation. vol. 49, pp. 194--205, 2019.Google ScholarGoogle ScholarCross RefCross Ref

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  1. Scheduling Mobile Robots in Flexible Manufacturing System by An Adaptive Large Neighborhood Search

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      cover image ACM Other conferences
      ICCDE '20: Proceedings of 2020 6th International Conference on Computing and Data Engineering
      January 2020
      279 pages
      ISBN:9781450376730
      DOI:10.1145/3379247

      Copyright © 2020 ACM

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      New York, NY, United States

      Publication History

      • Published: 7 March 2020

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