single-au.php

IJAT Vol.15 No.6 pp. 804-812
doi: 10.20965/ijat.2021.p0804
(2021)

Paper:

Optimization of Cutting Tool Allocation to Enhance Workload Balance and Total Completion Time in Parallel-Type FMS

Takumi Shimada and Haruhiko Suwa

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

Corresponding auhor,

Received:
March 28, 2021
Accepted:
July 28, 2021
Published:
November 5, 2021
Keywords:
parallel machine scheduling, flexible manufacturing systems, cutting tool management, mathematical model, machine eligibility
Abstract

This study aims to build a machine scheduling method that involves the cutting tool management in parallel-type flexible manufacturing systems. These systems consist of multi-axis CNC machine tools and are equipped with an automated tool changer and a large-capacity tool magazine. The target scheduling problem could be described as a multi-objective parallel scheduling problem. We consider the availability of cutting tools stored in the magazine as so-called “machine eligibility,” and propose a two-phase scheduling method for tool allocation and job sequencing on machines to minimize the workload balance between machines and the total completion time. Two mathematical models for tool allocation are provided: a machine-eligibility-based model and an enhanced version of the model that considers each cutting tool. A series of computational experiments demonstrates the effectiveness of the proposed method. We also clarify the relationship between schedule performance measures and job routing flexibility in the system.

Cite this article as:
T. Shimada and H. Suwa, “Optimization of Cutting Tool Allocation to Enhance Workload Balance and Total Completion Time in Parallel-Type FMS,” Int. J. Automation Technol., Vol.15 No.6, pp. 804-812, 2021.
Data files:
References
  1. [1] K.-D. Thoben, S. Wiesner, and T. Wuest, ““Industrie 4.0” and Smart Manufacturing – A Review of Research Issues and Application Examples,” Int. J. Automation Technol., Vol.11, No.1, pp. 4-16, doi: 10.20965/ijat.2017.p0004, 2017.
  2. [2] D. Kokuryo, T. Kaihara, S.Kuik, S. Suginouchi, and K. Hirai, “Value Co-Creative Manufacturing with IoT-Based Smart Factory for Mass Customization,” Int. J. Automation Technol., Vol.11, No.3, pp. 165-180, doi: 10.20965/ijat.2017.p0509, 2017.
  3. [3] Y. Koren and M. Shpitalni, “Design of Reconfigurable Manufacturing Systems,” J. of Manufacturing Systems, Vol.29, No.4, pp. 130-141, doi: 10.1016/j.jmsy.2011.01.001, 2010.
  4. [4] B. S. P. Reddy and C. S. P. Rao, “Flexible Manufacturing Systems Modelling and Performance Evaluation Using AutoMod,” Int. J. of Simulation Modelling, Vol.10, No.2, pp. 78-90, doi: 10.2507/IJSIMM10(2)3.176, 2011.
  5. [5] D. Brenner, F. Kleinert, J. Imiela, and E. Westkmper, “Life Cycle Management for Cutting Tools – Comprehensive Acquisition and Aggregation of Tool Life Data,” Procedia CIRP (The 24th CIRP Conf. on Life Cycle Engineering), Vol.61, pp. 311-316, doi: 10.1016/j.procir.2016.11.168, 2017.
  6. [6] M. Pinedo, “Scheduling – Theory, Algorithm, and Systems,” 4th Ed., Springer, NY, 2012.
  7. [7] A. Setiawan, R. Wangsaputra, Y. Martawirya, and A. Halim, “A Production Scheduling Model Considering Cutting Tools for an FMS to Minimize Makespan,” Proc. of the Asia Pacific Industrial Engineering and Management Systems Conf., 2015.
  8. [8] A. Setiawan, R. Wangsaputra, Y. Martawirya, and A. Halim, “An FMS Dynamic Production Scheduling Algorithm Considering Cutting Tool Failure and Cutting Tool Life,” IOP Conf. Series: Materials Science and Engineering, Vol.114, No.1, 012052, doi: 10.1088/1757-899X/114/1/012052, 2016.
  9. [9] M. Berrada and K. E. Stecke, “A branch and bound approach for machine load balancing in flexible manufacturing systems,” Management Science, Vol.32, pp. 1316-1335, doi: 10.1287/mnsc.32.10.1316, 1986.
  10. [10] R. Yonemoto and H. Suwa, “Task Scheduling of Material-Handling Manipulator for Enhancing Energy Efficiency in Flow-Type FMS,” Int. J. Automation Technol., Vol.14, No.6, pp. 943-950, doi: 10.20965/ijat.2020.p0943, 2020.
  11. [11] E. Bosch and J. Metternich, “Understanding and Assessing Complexity in Cutting Tool Management,” Procedia CIRP (51th CIRP Conf. on Manufacturing Systems), Vol.72, pp. 1499-1504, doi: 10.1016/j.procir.2018.03.108, 2018.
  12. [12] S. Bilgin and M. Azizoğlu, “Capacity and Tool Allocation Problem in Flexible Manufacturing Systems,” J. of Operational Research Society, Vol.57, pp. 670-681, doi: 10.1057/palgrave.jors.2602039, 2006.
  13. [13] H. Iwabe and K. Enta, “Tool Life of Small Diameter Ball End Mill for High Speed Milling of Hardened Steel – Effects of the Machining Method and the Tool Materials –,” Int. J. Automation Technol., Vol.2, No.6, pp. 425-430, doi: 10.20965/ijat.2008.p0425, 2008.
  14. [14] J. Herwan, S. Kano, R. Oleg, H. Sawada, and M. Watanabe, “Comparing Vibration Sensor Positions in CNC Turning for a Feasible Application in Smart Manufacturing System,” Int. J. Automation Technol., Vol.12, No.3, pp. 282-289, doi: 10.20965/ijat.2018.p0282, 2018.
  15. [15] 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, doi: 10.20965/ijat.2019.p0787, 2019.
  16. [16] L. Fanjul-Peyro, “Models and an Exact Method for the Unrelated Parallel Machine Scheduling Problem with Setups and Resources,” Expert Systems with Applications: X, Vol.5, doi: 10.1016/j.eswax.2020.100022, 2020.
  17. [17] Q.-V. Dang, T. van Diessen, T. Martagan, and I. Adan, “A Mathheuristic for Parallel Machine Scheduling with Tool Replacements,” Eur. J. of Operational Research, Vol.291, No.2, pp. 640-660, 2021.
  18. [18] B. Denkena, F. Schinkel, J. Prinary, and S. Wilmsmeier, “Quantum Algorithms for Process Parallel Flexible Job Shop Scheduling,” CIRP J. of Manufacturing Science and Technology, Vol.33, pp. 100-114, doi: 10.1016/j.cirpj.2021.03.006, 2021.
  19. [19] Y. Ouzene, F. Yalaoui, H. Chehade, and A. Yalaoui, “Workload Balancing in Identical Parallel Machine Scheduling Using a Mathematical Programming Method,” Int. J. of Computational Intelligence Systems, Vol.7, pp. 58-67, doi: 10.1080/18756891.2013.853932, 2014.
  20. [20] S. Özpeynirci, B. Gökgür, and B. Hnich, “Parallel machine scheduling with tool loading,” Applied Mathematical Modelling, Vol.40, No.9-10, pp. 5660-5671, doi: 10.1016/j.apm.2016.01.006, 2016.
  21. [21] A. C. Beezão, J.-F. Cordeau, G. Laporte, and H. H. Yanasse, “Scheduling identical parallel machines with tooling constraints,” Eur. J. Oper. Res., Vol.257, No.3, pp. 834-844, doi: 10.1016/j.ejor.2016.08.008, 2017.
  22. [22] J. Blażewicz, K. H. Ecker, E. Pesch, G. Schmidt, and J. Węglarz, “Scheduling Computer and Manufacturing Processes,” Springer-Verlag, Berlin, 1996.
  23. [23] K. C. So, “Some Heuristics for Scheduling Jobs on Parallel Machines with Setups,” Management Science, Vol.36, No.4, pp. 467-475, doi: 10.1287/mnsc.36.4.467, 1990.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 19, 2024