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Managing risk in production scheduling under uncertain disruption

Published online by Cambridge University Press:  09 June 2015

Ruhul Sarker ,
Daryl Essam ,
S.M. Kamrul Hasan  and
A.N. Mustafizul Karim
Ruhul Sarker*
Affiliation:
School of Engineering and Information Technology, University of New South Wales at Canberra, ADFA Campus, Canberra, Australia
Daryl Essam
Affiliation:
School of Engineering and Information Technology, University of New South Wales at Canberra, ADFA Campus, Canberra, Australia
S.M. Kamrul Hasan
Affiliation:
School of Engineering and Information Technology, University of New South Wales at Canberra, ADFA Campus, Canberra, Australia
A.N. Mustafizul Karim
Affiliation:
Department of Manufacturing and Materials Engineering, International Islamic University Malaysia, Gombak, Kuala Lumpur, Malaysia
*
Reprint requests to: Ruhul Sarker, School of Engineering and Information Technology, University of New South Wales at Canberra, ADFA Campus, Canberra, Australia2600. E-mail: r.sarker@adfa.edu.au
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Abstract

The job scheduling problem (JSP) is considered as one of the most complex combinatorial optimization problems. JSP is not an independent task, but is rather a part of a company business case. In this paper, we have studied JSPs under sudden machine breakdown scenarios that introduce a risk of not completing the jobs on time. We have first solved JSPs using an improved memetic algorithm and extended the algorithm to deal with the disruption situations, and then developed a simulation model to analyze the risk of using a job order and delivery scenario. This paper deals with job scheduling under ideal conditions and rescheduling under machine breakdown, and provides a risk analysis for a production business case. The extended algorithm provides better understanding and results than existing algorithms, the rescheduling shows a good way of recovering from disruptions, and the risk analysis shows an effective way of maximizing return under such situations.

Keywords

DisruptionGenetic AlgorithmJob SchedulingMemetic AlgorithmRisk Analysis
Type
Regular Articles
Information
AI EDAM , Volume 30 , Special Issue 3: System Architecture Design , August 2016 , pp. 289 - 299
Copyright
Copyright © Cambridge University Press 2015 

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References

REFERENCES

Aarts, E.H.L., Van Laarhoven, P.J.M., Lenstra, J.K., & Ulder, N.L.J. (1994). A computational study of local search algorithms for job shop scheduling. ORSA Journal on Computing 6, 118–125.CrossRefGoogle Scholar
Abumaizar, R.J., & Svestka, J.A. (1997). Rescheduling job shops under random disruptions. International Journal of Production Research 35(7), 2065–2082.Google Scholar
Adams, J., Balas, E., & Zawack, D. (1988). The shifting bottleneck procedure for job shop scheduling. Management Science 34(3), 391–401.Google Scholar
Binato, S., Hery, W., Loewenstern, D., & Resende, M. (2000). A GRASP for Job Shop Scheduling. Dordrecht: Kluwer Academic.Google Scholar
Blackstone, J.H. Jr, Phillips, D.T., & Hogg, G.L. (1982). A state-of-the-art survey of dispatching rules for manufacturing job shop operations. International Journal of Production Research 20(1), 27–45.CrossRefGoogle Scholar
Demir, Y., & Isleyen, S.K. (2014). An effective genetic algorithm for flexible job-shop scheduling with overlapping in operations. International Journal of Production Research 52(13), 3905–3921.CrossRefGoogle Scholar
Fahmy, S.A., Balakrishnan, A., & ElMekkawy, T.Y. (2008). A generic deadlock-free reactive scheduling approach. International Journal of Production Research 46(1), 1–20.Google Scholar
Ghasem, M., & Mehdi, M. (2011). A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search. International Journal of Production Economics 129(1), 14–22.Google Scholar
Hasan, S.M.K., Sarker, R., & Essam, D. (2011). Genetic algorithm for job-shop scheduling with machine unavailability and breakdown. International Journal of Production Research 49(16), 4999–5015.Google Scholar
Hasan, S.M.K., Sarker, R., Essam, D., & Cornforth, D. (2009). Memetic algorithms for solving job-shop scheduling problems. Memetic Computing 1(1), 69–83.Google Scholar
Lawrence, S. (1985). Job Shop Scheduling with Genetic Algorithms: First International Conference on Genetic Algorithms, pp. 136–140. Mahwah, NJ: Erlbaum.Google Scholar
Lei, D. (2011). Simplified multi-objective genetic algorithms for stochastic job shop scheduling. Applied Soft Computing 11(8), 4991–4996.CrossRefGoogle Scholar
Liu, S.Q., Ong, H.L., & Ng, K.M. (2005). Metaheuristics for minimizing the makespan of the dynamic shop scheduling problem. Advances in Engineering Software 36(3), 199–205.Google Scholar
Meeran, S., & Morshed, M.S. (2012). A hybrid genetic tabu search algorithm for solving job shop scheduling problems: a case study. Journal of Intelligent Manufacturing 23(4), 1063–1078.Google Scholar
Nakano, R., & Yamada, T. (1991). Conventional genetic algorithm for job shop problems. Proc. Fourth Int. Conf. Genetic Algorithms, pp. 474–479. San Mateo, CA: Kaufmann.Google Scholar
Nasr, A.-H., & ElMekkawy, T.Y. (2011). Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm. International Journal of Production Economics 132(2), 279–291.Google Scholar
Paredis, J. (1992). Exploiting constraints as background knowledge for genetic algorithms: a case-study for scheduling. In Parallel Problem Solving from Nature, Vol. 2, pp. 229–238. Amsterdam: North-Holland.Google Scholar
Pezzella, F., Morganti, G., & Ciaschetti, G. (2008). A genetic algorithm for the flexible job-shop scheduling problem. Computers & Operations Research 35(10), 3202–3212.Google Scholar
Ponnambalam, S.G., Aravindan, P., & Rao, P.S. (2001). Comparative evaluation of genetic algorithms for job-shop scheduling. Production Planning & Control 12(6), 560–674.Google Scholar
Qing-dao-er-ji, R., & Wang, Y. (2012). A new hybrid genetic algorithm for job shop scheduling problem. Computers & Operations Research 39(10), 2291–2299.CrossRefGoogle Scholar
Qiu, X., & Lau, H.Y.K. (2014). An AIS-based algorithm for static job shop scheduling problem. Journal of Intelligent Manufacturing 25, 489–503.CrossRefGoogle Scholar
Subramaniam, V., Raheja, A.S., & Reddy, K.R.B. (2005). Reactive repair tool for job shop schedules. International Journal of Production Research 43(1), 1–23.Google Scholar
Wu, S.D., Storer, R.H., & Pei-Chann, C. (1993). One-machine rescheduling heuristics with efficiency and stability as criteria. Computers & Operations Research 20(1), 1–14.Google Scholar
Yamada, T. (2003). Studies on metaheuristics for jobshop and flowshop scheduling problems. PhD Thesis, Kyoto University, Department of Applied Mathematics and Physics, pp. 1–120.Google Scholar
Yamada, T., & Nakano, R. (1997). Genetic algorithms for job-shop scheduling problems. In Modern Heuristic for Decision Support, pp. 67–81. UNICOM seminar, London.Google Scholar
Zhang, L., Gao, L., & Li, X. (2013). A hybrid genetic algorithm and tabu search for a multi-objective dynamic job shop scheduling problem. International Journal of Production Research 51(12), 3516–3531.Google Scholar