Skip to main content Accessibility help

Managing risk in production scheduling under uncertain disruption

  • Ruhul Sarker (a1), Daryl Essam (a1), S.M. Kamrul Hasan (a1) and A.N. Mustafizul Karim (a2)

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.

Corresponding author
Reprint requests to: Ruhul Sarker, School of Engineering and Information Technology, University of New South Wales at Canberra, ADFA Campus, Canberra, Australia2600. E-mail:
Hide All
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, 118125.
Abumaizar, R.J., & Svestka, J.A. (1997). Rescheduling job shops under random disruptions. International Journal of Production Research 35(7), 20652082.
Adams, J., Balas, E., & Zawack, D. (1988). The shifting bottleneck procedure for job shop scheduling. Management Science 34(3), 391401.
Binato, S., Hery, W., Loewenstern, D., & Resende, M. (2000). A GRASP for Job Shop Scheduling. Dordrecht: Kluwer Academic.
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), 2745.
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), 39053921.
Fahmy, S.A., Balakrishnan, A., & ElMekkawy, T.Y. (2008). A generic deadlock-free reactive scheduling approach. International Journal of Production Research 46(1), 120.
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), 1422.
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), 49995015.
Hasan, S.M.K., Sarker, R., Essam, D., & Cornforth, D. (2009). Memetic algorithms for solving job-shop scheduling problems. Memetic Computing 1(1), 6983.
Lawrence, S. (1985). Job Shop Scheduling with Genetic Algorithms: First International Conference on Genetic Algorithms, pp. 136140. Mahwah, NJ: Erlbaum.
Lei, D. (2011). Simplified multi-objective genetic algorithms for stochastic job shop scheduling. Applied Soft Computing 11(8), 49914996.
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), 199205.
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), 10631078.
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.
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), 279291.
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. 229238. Amsterdam: North-Holland.
Pezzella, F., Morganti, G., & Ciaschetti, G. (2008). A genetic algorithm for the flexible job-shop scheduling problem. Computers & Operations Research 35(10), 32023212.
Ponnambalam, S.G., Aravindan, P., & Rao, P.S. (2001). Comparative evaluation of genetic algorithms for job-shop scheduling. Production Planning & Control 12(6), 560674.
Qing-dao-er-ji, R., & Wang, Y. (2012). A new hybrid genetic algorithm for job shop scheduling problem. Computers & Operations Research 39(10), 22912299.
Qiu, X., & Lau, H.Y.K. (2014). An AIS-based algorithm for static job shop scheduling problem. Journal of Intelligent Manufacturing 25, 489503.
Subramaniam, V., Raheja, A.S., & Reddy, K.R.B. (2005). Reactive repair tool for job shop schedules. International Journal of Production Research 43(1), 123.
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), 114.
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.
Yamada, T., & Nakano, R. (1997). Genetic algorithms for job-shop scheduling problems. In Modern Heuristic for Decision Support, pp. 6781. UNICOM seminar, London.
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), 35163531.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

  • ISSN: 0890-0604
  • EISSN: 1469-1760
  • URL: /core/journals/ai-edam
Please enter your name
Please enter a valid email address
Who would you like to send this to? *



Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed