Novel slack-based robust scheduling rule for a semiconductor manufacturing system with uncertain processing time

Abstract

The NP-hard scheduling problems of semiconductor manufacturing systems (SMSs) are further complicated by stochastic uncertainties. Reactive scheduling is a common dynamic scheduling approach where the scheduling scheme is refreshed in response to real-time uncertainties. The scheduling scheme is overly sensitive to the emergence of uncertainties because the optimization of performance (such as minimum make-span) and the system robustness cannot be achieved simultaneously by conventional reactive scheduling methods. To improve the robustness of the scheduling scheme, we propose a novel slack-based robust scheduling rule (SR) based on the analysis of robustness measurement for SMS with uncertain processing time. The decision in the SR is made in real time given the robustness. The proposed SR is verified under different scenarios, and the results are compared with the existing heuristic rules. Simulation results show that the proposed SR can effectively improve the robustness of the scheduling scheme with a slight performance loss.

Keywords

semiconductor manufacturing system / uncertain processing time / dynamic scheduling / slack-based robust scheduling rule

References

[1] Aydilek H, Aydilek H, Allahverdi A (2015). Production in a two-machine flowshop scheduling environment with uncertain processing and setup times to minimize makespan. International Journal of Production Research, 53(9): 2803–2819

[2] Chiang T C (2013). Enhancing rule-based scheduling in wafer fabrication facilities by evolutionary algorithms: Review and opportunity. Computers & Industrial Engineering, 64(1): 524–535

[3] Chiang T C, Fu L C (2012). Rule-based scheduling in wafer fabrication with due date-based objectives. Computers & Operations Research, 39(11): 2820–2835

[4] Choi B, Chung K (2016). Min-max regret version of a scheduling problem with outsourcing decisions under processing time uncertainty. European Journal of Operational Research, 252(2): 367–375

[5] Hazır Ö, Haouari M, Erel E (2010). Robust scheduling and robustness measures for the discrete time/cost trade-off problem. European Journal of Operational Research, 207(2): 633–643

[6] Jamrus T, Chien C F, Gen M, Sethanan K (2017). Hybrid particle swarm optimization combined with genetic operators for flexible job-shop scheduling under uncertain processing time for semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing, 28(3): 353–366

[7] Kouvelis P, Daniels L R, Vairaktarakis G (2000). Robust scheduling of a two-machine flow shop with uncertain processing times. IIE Transactions, 32(5): 421–432

[8] Kumar P R (1993). Re-entrant lines. Queueing Systems, 13(1–3): 87–110

[9] Leon V J, Wu S D, Storer R H (1994). Robustness measures and robust scheduling for job shops. IIE Transactions, 26(5): 32–43

[10] Murata T (1989). Petri nets: Properties, analysis and applications. Proceedings of the IEEE, 77(4): 541–580

[11] Ouelhadj D, Petrovic S (2009). A survey of dynamic scheduling in manufacturing systems. Journal of Scheduling, 12(4): 417–431

[12] Pinedo M, Hadavi K (1992). Scheduling: Theory, algorithms, and systems. IIE Transactions, 28(8): 695–697

[13] Vieira G E, Herrmann J W, Lin E (2003). Rescheduling manufacturing systems: A framework of strategies, policies, and methods. Journal of Scheduling, 6(1): 39–62

[14] Wang S G, You D, Seatzu C (2017). A novel approach for constraint transformation in Petri nets with uncontrollable transitions. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(8): 1403–1410

[15] Wein L M (1988). Scheduling semiconductor wafer fabrication. IEEE Transactions on Semiconductor Manufacturing, 1(3): 115–130

[16] Yugma C, Blue J, Dauzère-Pérès S, Obeid A (2015). Integration of scheduling and advanced process control in semiconductor manufacturing: Review and outlook. Journal of Scheduling, 18(2): 195–205

[17] Zhang J, Ding G, Zou Y, Qin S, Fu J (2017). Review of job shop scheduling research and its new perspectives under industry 4.0. Journal of Intelligent Manufacturing, (3): 1–22

[18] Zhang J, Qin W, Wu L H, Zhai W B (2014). Fuzzy neural network-based rescheduling decision mechanism for semiconductor manufacturing. Computers in Industry, 65(8): 1115–1125

RIGHTS & PERMISSIONS

2018 The Author(s) 2018. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)