Robotic Cell Scheduling with a Proactive–Reactive Approach, Considering Machine Breakdown

Document Type : Research Paper

Authors

1 PhD student, Department of System Modeling and Data Analysis, Faculty of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran

2 Assistant Professor, Department of System Modeling and Data Analysis, Faculty of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran.

Abstract

In this paper, we try to provide a schedule for robotic cells in the event of a machine breakdown by using a proactive -reactive approach. We first develop a proactive model for a robotic m-machine cell under the condition of no disturbance. After the machine breakdown, we provide reactive programming in two time periods during the repair and after the repair of the broken machine. Finally, by stating the criteria of stability and Robustness in a robotic cell, we evaluate the efficiency of the presented model with a numerical example.

Keywords

Main Subjects

References

  • Vaisi, B., Farughi, H., & Raissi, S. (2018). Two-machine robotic cell sequencing under different uncertainties. Int. J. Simul. Model, 17, 284-294. https://doi.org/10.2507/IJSIMM17(2)434
  • Vaisi, B., Farughi, H., & Raissi, S. (2018). Bi-criteria robotic cell scheduling and operation allocation in the presence of break-downs. International journal of industrial engineering & production research, 29(3), 343-357..
  • Gultekin, H., Coban, B., & Akhlaghi, V. E. (2018). Cyclic scheduling of parts and robot moves in m-machine robotic cells. Computers & operations research, 90, 161-172. https://doi.org/10.1016/j.cor.2017.09.018
  • Yildiz, S., Akturk, M. S., & Karasan, O. E. (2011). Bicriteria robotic cell scheduling with controllable processing times. International Journal of Production Research, 49(2), 569-583. https://doi.org/10.1080/00207540903491799
  • Saedi, F., & Kianfar, K. (2023). Scheduling Cellular Manufacturing Systems Based on Human Factors and Due Date of Orders. Journal of Industrial Engineering Research in Production Systems, 10(21), 51-69. doi: 10.22084/ier.2023.27096.210. (in Persian).
  • Moradi, V., Yousefi Nejad Attari, M., & Farughi, H. (2018). Modeling for Minimizing Cycle Time in a three-Machine Robotic Cell with ‎Assumption of Tool Switching. Journal of Industrial Engineering Research in Production Systems, 6(12), 1-17. doi: 10.22084/ier.2017.12669.1578. (in Persian).
  • Sethi, S.P. , Sriskandarajah, C. , Sorger, G. , Blazewicz, J. , Kubiak, W. (1992) . Sequencing of parts and robot moves in a robotic cell. Int. J. Flexible Manuf. Syst. 4 (3–4), 331–358. https://doi.org/10.1007/BF01324886
  • Kats, V., & Levner, E. (1997). A strongly polynomial algorithm for no-wait cyclic robotic flowshop scheduling. Operations Research Letters, 21(4), 171-179. https://doi.org/10.1016/S0167-6377(97)00036-9
  • Hall, N. G., Kamoun, H., & Sriskandarajah, C. (1997). Scheduling in robotic cells: classification, two and three machine cells. Operations Research, 45(3), 421-439. https://doi.org/10.1287/opre.45.3.421
  • Crama, Y., & Van De Klundert, J. (1997). Cyclic scheduling of identical parts in a robotic cell. Operations Research, 45(6), 952-965. https://doi.org/10.1287/opre.45.6.952
  • Levner, E., Kats, V., & Levit, V. E. (1997). An improved algorithm for cyclic flowshop scheduling in a robotic cell. European Journal of Operational Research, 97(3), 500-508. https://doi.org/10.1016/S0377-2217(96)00272-X
  • Brauner, N., & Finke, G. (2001). Cycles and permutations in robotic cells. Mathematical and Computer Modelling, 34(5-6), 565-591. https://doi.org/10.1016/S0895-7177(01)00084-X
  • Kats, V., Levner, E., & Meyzin, L. (1999). Multiple-part cyclic hoist scheduling using a sieve method. IEEE Transactions on Robotics and Automation, 15(4), 704-713. https://doi.org/10.1109/70.781993
  • Che, A., Zhou, Z., Chu, C., & Chen, H. (2011). Multi-degree cyclic hoist scheduling with time window constraints. International journal of production research, 49(19), 5679-5693. https://doi.org/10.1080/00207543.2010.503200
  • Zhou, Z., Che, A., & Yan, P. (2012). A mixed integer programming approach for multi-cyclic robotic flowshop scheduling with time window constraints. Applied Mathematical Modelling, 36(8), 3621-3629. https://doi.org/10.1016/j.apm.2011.10.032
  • Li, X., & Fung, R. Y. (2014). A mixed integer linear programming solution for single hoist multi-degree cyclic scheduling with reentrance. Engineering Optimization, 46(5), 704-723. https://doi.org/10.1080/0305215X.2013.795560
  • Elmi, A., & Topaloglu, S. (2016). Multi-degree cyclic flow shop robotic cell scheduling problem: Ant colony optimization. Computers & Operations Research, 73, 67-83. https://doi.org/10.1016/j.cor.2016.03.007
  • Lee, J. H., & Kim, H. J. (2022). Reinforcement learning for robotic flow shop scheduling with processing time variations. International Journal of Production Research, 60(7), 2346-2368. https://doi.org/10.1080/00207543.2021.1887533
  • Che, A., Kats, V., & Levner, E. (2017). An efficient bicriteria algorithm for stable robotic flow shop scheduling. European Journal of Operational Research, 260(3), 964-971. https://doi.org/10.1016/j.ejor.2017.01.033
  • Kumar, S., Ramanan, N., & Sriskandarajah, C. (2005). Minimizing cycle time in large robotic cells. IIE Transactions, 37(2), 123-136 https://doi.org/10.1080/07408170590885279
  • Joo, Y. J. (2009). Operational optimization of an automated electrical die sorting line with single-wafer transfer. Master's thesis, Korea Advanced Institute of Science and Technology.
  • Wu, N., & Zhou, M. (2011). Schedulability analysis and optimal scheduling of dual-arm cluster tools with residency time constraint and activity time variation. IEEE Transactions on Automation Science and Engineering, 9(1), 203-209. https://doi.org/10.1109/TASE.2011.2160452
  • Kim, H. J., Lee, J. H., & Lee, T. E. (2013). Noncyclic scheduling of cluster tools with a branch and bound algorithm. IEEE Transactions on Automation Science and Engineering, 12(2), 690-700. https://doi.org/10.1109/TASE.2013.2293552
  • Wu, X., Yuan, Q., & Wang, L. (2020). Multiobjective differential evolution algorithm for solving robotic cell scheduling problem with batch-processing machines. IEEE transactions on automation science and engineering, 18(2), 757-775. https://doi.org/10.1109/TASE.2020.2969469
  • Vaisi, B., Farughi, H., & Raissi, S. (2020). Schedule-allocate and robust sequencing in three-machine robotic cell under breakdowns. Mathematical problems in engineering, 2020, 1-24. https://doi.org/10.1155/2020/4597827
  • Kim, H. J., & Lee, J. H. (2021). Scheduling of dual-gripper robotic cells with reinforcement learning. IEEE transactions on automation science and engineering, 19(2), 1120-1136. https://doi.org/10.1109/TASE.2020.3047924
  • Zahrouni, W., & Kamoun, H. (2021). Scheduling in robotic cells with time window constraints. European journal of industrial engineering, 15(2), 206-225. https://doi.org/10.1504/EJIE.2021.114001
  • Ghadiri Nejad, M., Shavarani, S. M., Vizvári, B., & Barenji, R. V. (2018). Trade-off between process scheduling and production cost in cyclic flexible robotic cells. The international journal of advanced manufacturing technology, 96, 1081-1091. https://doi.org/10.1007/s00170-018-1577-x
  • Wang, Z., Zhou, B., Trentesaux, D., & Bekrar, A. (2017). Approximate optimal method for cyclic solutions in multi-robotic cell with processing time window. Robotics and autonomous systems, 98, 307-316. https://doi.org/10.1016/j.robot.2017.09.020
  • Majumder, A., & Laha, D. (2016). A new cuckoo search algorithm for 2-machine robotic cell scheduling problem with sequence-dependent setup times. Swarm and evolutionary computation, 28, 131-143. https://doi.org/10.1016/j.swevo.2016.02.001
  • Dawande, M., Geismar, H. N., Sethi, S. P., & Sriskandarajah, C. (2005). Sequencing and scheduling in robotic cells: Recent developments. Journal of Scheduling, 8(5), 387-426. https://doi.org/10.1007/s10951-005-2861-9
  • Geismar, H. N., & Pinedo, M. (2010). Robotic cells with stochastic processing times. IIE Transactions, 42(12), 897-914. https://doi.org/10.1080/0740817X.2010.491505
  • Kim, J. H., & Lee, T. E. (2008). Schedulability analysis of time-constrained cluster tools with bounded time variation by an extended Petri net. IEEE Transactions on Automation Science and Engineering, 5(3), 490-503. https://doi.org/10.1109/TASE.2007.912716
  • Yoon, H. J., & Lee, D. Y. (2003, October). On-line scheduling of robotic cells with post-processing residency constraints. In SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme-System Security and Assurance (Cat. No. 03CH37483) (Vol. 3, pp. 2785-2790). IEEE. https://doi.org/10.1109/ICSMC.2003.1244307
  • Tonke, D., Grunow, M., & Akkerman, R. (2019). Robotic-cell scheduling with pick-up constraints and uncertain processing times. IISE Transactions, 51(11), 1217-1235. https://doi.org/10.1080/24725854.2018.1555727
  • Foumani, M., Razeghi, A., & Smith-Miles, K. (2020). Stochastic optimization of two-machine flow shop robotic cells with controllable inspection times: From theory toward practice. Robotics and Computer-Integrated Manufacturing, 61, 101822. https://doi.org/10.1016/j.rcim.2019.101822
  • Vaisi, B. A. H. A. R. E. H., Farughi, H., & Raissi, S. (2020). Multi-Objective Optimal Model for Task Scheduling and Allocation in a Two Machines Robotic Cell Considering Breakdowns. WSEAS transactions on information science and applications, 17, 1-8. https://doi.org/10.37394/23209.2020.17.a025103-920
  • Vaisi, B., Farughi, H., & Raissi, S. (2021). Utilization of response surface methodology and goal programming based on simulation in a robotic cell to optimize sequencing. Journal of quality engineering and management, 10(4), 327-338.
  • Vaisi, B., Farughi, H., Raissi, S., & Sadeghi, H. (2023). A bi-objective optimal task scheduling model for two-machine robotic-cell subject to probable machine failures. Journal of applied research on industrial engineering, 10(1), 141-154.
  • Jalali, Arash and Gholami, Saideh, 2018, Mathematical modeling of m-machine and k-unit robotic cell problem in workshop flow environment, 16th International Industrial Engineering Conference, Tehran.
  • Rahmani, D. (2017). A new proactive-reactive approach to hedge against uncertain processing times and unexpected machine failures in the two-machine flow shop scheduling problems. scientia Iranica, 24(3), 1571-1584. https://doi.org/10.24200/sci.2017.4136
Journal of Industrial Engineering Research in Production Systems
Volume 12, Issue 24
September 2024
Pages 17-33

Files

Share

How to cite

Statistics