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IJAT Vol.15 No.5 pp. 661-668
doi: 10.20965/ijat.2021.p0661
(2021)

Paper:

Reactive Scheduling Based on Adaptive Manipulator Operations in a Job Shop Configuration with Two Machines

Ryo Yonemoto and Haruhiko Suwa

Setsunan University
17-8 Ikeda-naka-machi, Neyagawa, Osaka 572-8508, Japan

Corresponding author

Received:
February 27, 2021
Accepted:
June 2, 2021
Published:
September 5, 2021
Keywords:
reactive scheduling, material-handling manipulator system, automated scheduling system, machine failures, job-shop scheduling
Abstract

Manufacturing systems are affected by uncertainties, such as machine failure or tool breakage, which result in system downtime and productivity deterioration. In machining processes, system downtime must be reduces. This study aims to establish an automated scheduling technique that flexibly responds to unforeseen events, such as machine failure, based on adaptive operations of the handling manipulator instead of an operation schedule for the machine tools. We propose an “adaptive manipulation” procedure for establishing a reactive revision policy. The reactive revision policy modifies a portion of the manipulator operation sequence, followed by the machine operation sequence. We conduct a physical scheduling simulation on a material-handling manipulator system imitating a job-shop manufacturing system. Through simulations involving machine breakdown scenarios, the applicability of the reactive revision policy based on adaptive manipulation is demonstrated.

Cite this article as:
Ryo Yonemoto and Haruhiko Suwa, “Reactive Scheduling Based on Adaptive Manipulator Operations in a Job Shop Configuration with Two Machines,” Int. J. Automation Technol., Vol.15, No.5, pp. 661-668, 2021.
Data files:
References
  1. [1] M. Fujishima, M. Mori, K. Nishimura, and K. Ohno, “Study on Quality Improvement of Machine Tools,” Procedia CIRP, Vol.59, pp. 156-159, 2017.
  2. [2] X. Gu, X. Jin, J. Ni, and Y. Koren, “Manufacturing System Design for Resilience,” Procedia CIRP, Vol.36, pp. 135-140, 2015.
  3. [3] C. Zheng, Y. Zhang, J. Li, J. Bai, X. Qin, and B. Eynard, “Survey on Design Approach for Robotic Manufacturing Systems in SMEs,” Procedia CIRP (29th CIRP Design 2019), Vol.80, pp. 16-21, 2019.
  4. [4] M. Dawande, H. N. Geisma, S. P. Sethi, and C. Sriskandarajah, “Sequence and Scheduling in robotic cells: recent developments,” J. of Scheduling, Vol.8, pp. 387-426, 2005.
  5. [5] J. Carlier, M. Hauari, M. Kharbeche, and A. Moukrim, “An optimization-based heuristic for the roboic cell problem,” European J. of Operational Research, Vol.202, pp. 636-645, 2010.
  6. [6] T. Ogata, T. Okubo, H. Nagai, M. Yamamoto, M. Sugi, and J. Ota, “A Novel Algorithm for Continuous Steel Casting Scheduling with Focus on Quality Property Constraint and Slab Width Maximization,” Int. J. Automation Technol., Vol.9, No.3, pp. 235-247, 2015.
  7. [7] T. Samukawa and H. Suwa, “An Optimization of Energy-Efficiency in Machining Manufacturing Systems Based on a Framework of Multi-Mode RCPSP,” Int. J. Automation Technol., Vol.10, No.6, pp. 985-992, 2016.
  8. [8] X. Yang, C. Peng, L. Jin, and Q. Li, “Unrelated Parallel-Machine Scheduling with Maintenance Activities and Rejection Penalties for Minimizing Total Cost,” Int. J. Automation Technol., Vol.13, No.6, pp. 787-795, 2019.
  9. [9] A. Ishigaki and Y. Matsui, “Effective Neighborhood Generation Method in Search Algorithm for Flexible Job Shop Scheduling Problem,” Int. J. Automation Technol., Vol.13, No.3, pp. 389-396, 2019.
  10. [10] T. Sakaguchi, K. Matsumoto, and N. Uchiyama, “Nesting Scheduling in Sheet Metal Processing Based on Coevolutionary Genetic Algorithm in Different Environments,” Int. J. Automation Technol., Vol.12, No.5, pp. 730-738, 2018.
  11. [11] T. Tanizaki, H. Katagiri, and A. O. N. René, “Scheduling Algorithms Using Metaheuristics for Production Processes with Crane Interference,” Int. J. Automation Technol., Vol.12, No.3, pp. 297-307, 2018.
  12. [12] D. Morita and H. Suwa, “An Optimization Method for Critical Chain Scheduling Toward Project Greenality,” Int. J. Automation Technol., Vol.6, No.3, pp. 331-337, 2012.
  13. [13] J. Liu and L. MacCarthy, “General Heuristic Procedures and Solution Strategies for FMS scheduling,” Int. J. of Production Research, Vol.37, No.14, pp. 3305-3333, 1999.
  14. [14] K. Shanker and B. K. Modi, “A Branch and Bound Based Heuristic for Multi-Product Resource Constrained Scheduling Problem in FMS environment,” European J. of Operational Research, Vol.113, pp. 80-90, 1999.
  15. [15] T. F. Abdelmaguid, A. O. Nassef, B. A. Kamal, and M. F. Hassan, “A Hybrid GA/Heuristic Approach to the Simultaneous Scheduling of Machines and Automated Guided Vehicle,” Int. J. of Production Research, Vol.42, No.2, pp. 267-181, 2004.
  16. [16] M. S. Akturk, H. Gultekin, and O. E. Karasan, “Robotic cell scheduling with operational flexibility,” Discrete Applied Mathematics, Vol.145, pp. 334-348, 2005.
  17. [17] K. Sawada, S. Shin, K. Kumagai, and H. Yoneda, “Optimal Scheduling of Automatic Guided Vehicle System via State Space Realization,” Int. J. Automation Technol., Vol.7, No.5, pp. 571-580, 2013.
  18. [18] A. Ham, “Transfer-robot task scheduling in job shop,” Int. J. of Production Research, doi: 10.1080/00207543.2019.1709671, 2019.
  19. [19] D. Ouelhadj and S. Petrovic, “A Survey of Dynamic Scheduling in Manufacturing Systems,” J. of Scheduling, Vol.12, pp. 417-431, 2009.
  20. [20] F. E. Minguillon and N. Stricker, “Robust Predictive-Reactive Scheduling and Its Effect on Machine Disturbance Mitigation,” CIRP Annals – Manufacturing Technology, Vol.29, pp. 401-404, 2020.
  21. [21] H. Suwa and H. Sandoh, “Online Scheduling in Manufacturing – A Cumulative Delay Approach –,” Springer, London, 2012.
  22. [22] S. D. Wu, R. H. Storer, and P. C. Chang, “One-Machine Rescheduling Heuristics with Efficiency and Stability as Criteria,” Computers and Operations Research, Vol.20, No.1, pp. 1-14, 1993.

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Last updated on Sep. 19, 2021