Abstract
Much of the research on operations scheduling problems has ignored dynamic events in real-world environments where there are complex constraints and a variety of unexpected disruptions. Besides, while most scheduling problems which have been discussed in the literature assume that machines are incessantly available, in most real life industries a machine can be unavailable for many reasons, such as unanticipated breakdowns (stochastic unavailability), or due to a scheduled preventive maintenance where the periods of unavailability are determined in advance (deterministic unavailability). This paper describes how we can integrate simulation into genetic algorithm to the dynamic scheduling of a flexible job shop with machines that suffer stochastic breakdowns. The objectives are the minimization of two criteria, expected makespan and expected mean tardiness. An overview of the flexible job shops and scheduling under the stochastic unavailability of machines are presented. Subsequently, the details of integrating simulation into genetic algorithm are described and implemented. Consequently, problems of various sizes are used to test the performance of the proposed algorithm. The results obtained reveal that the relative performance of the algorithm for both abovementioned objectives can be affected by changing the levels of the breakdown parameters.
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References
Allaoui H., Artiba A. (2004) Integrating simulation and optimization to schedule a hybrid flow shop with maintenance constraints. Computers & Industrial Engineering 47: 431–450 doi:10.1016/j.cie.2004.09.002
Baker K.R. (1974) Introduction to sequencing and scheduling. John Wiley and Sons, New York
Brandimarte P. (1993) Routing and scheduling in a flexible job shop by tabu search. Annals of Operations Research 41: 157–183 doi:10.1007/BF02023073
Chryssolouris G., Subramaniam V. (2001) Dynamic scheduling of manufacturing job shops using genetic algorithms. Journal of Intelligent Manufacturing 12(3): 281–293 doi:10.1023/A:1011253011638
Cowling P.I., Johansson M. (2002) Using real-time information for effective dynamic scheduling. European Journal of Operational Research 139(2): 230–244 doi:10.1016/S0377-2217(01)00355-1
Fattahi P., Mehrabad M.S., Jolai F. (2007) Mathematical modeling and heuristic approaches to flexible job shop scheduling problems. Journal of Intelligent Manufacturing 18(3): 331–342 doi:10.1007/s10845-007-0026-8
Gao J., Sun L., Gen M. (2008) A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems. Computers & Operations Research 35(9): 2892–2907 doi:10.1016/j.cor.2007.01.001
Garey M.R., Johnson D.S., Sethi R. (1976) The complexity of flow shop and job shop scheduling. Mathematics of Operations Research 1(2): 117–129
Gholami, M., Zandieh, M., Alem-Tabriz, A. Scheduling hybrid flow shop with sequence-dependant setup times and machines with random breakdowns. International Journal of Advanced Manufacturing Technology. doi:10.1007/s00170-008-1577-3.
Ho N.B., Tay J.C., Lai E.M.-K. (2007) An effective architecture for learning and evolving flexible job-shop schedules. European Journal of Operational Research 179: 316–333 doi:10.1016/j.ejor.2006.04.007
Holthaus O. (1999) Scheduling in job shops with machine breakdowns: An experimental study. Computers & Industrial Engineering 36: 137–162 doi:10.1016/S0360-8352(99)00006-6
Jain A.S., Meeran S. (1998) Deterministic job-shop scheduling: Past, present and future. European Journal of Operational Research 113(2): 390–434 doi:10.1016/S0377-2217(98)00113-1
Kacem I., Hammadi S., Borne P. (2002) Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems. IEEE Transactions on Systems, Man, and Cybernetics 32(1): 1–13 doi:10.1109/TSMCC.2002.1009117
Pezzella F., Morganti G., Ciaschetti G. (2008) A genetic algorithm for the flexible job-shop scheduling problem. Computers & Operations Research 35(10): 3202–3212 doi:10.1016/j.cor.2007.02.014
Pinedo M. (1995) Scheduling theory, algorithms, and systems. Prentice-Hall, Englewood Cliffs, NJ
Sabuncuoglu I., Bayiz M. (2000) Analysis of reactive scheduling problems in a job shop environment. European Journal of Operational Research 126(3): 567–586 doi:10.1016/S0377-2217(99)00311-2
Stoop P.P.M., Weirs V.C.S. (1996) The complexity of scheduling in practice. International Journal of Operations & Production Management 16(10): 37–53 doi:10.1108/01443579610130682
Suresh V., Chaudhuri D. (1993) Dynamic scheduling a survey of research. International Journal of Production Economics 32(1): 53–63 doi:10.1016/0925-5273(93)90007-8
Tay J.C., Ho N.B. (2008) Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Computers & Industrial Engineering 54(3): 453–473 doi:10.1016/j.cie.2007.08.008
Vieira G.E., Hermann J.W., Lin E. (2003) Rescheduling manufacturing systems: a framework of strategies, policies and methods. Journal of Scheduling 6(1): 39–62 doi:10.1023/A:1022235519958
Vilcot G., Billaut J.-C. (2008) A tabu search and a genetic algorithm for solving a bicriteria general job shop scheduling problem. European Journal of Operational Research 190(2): 398–411 doi:10.1016/j.ejor.2007.06.039
Yamamoto M., Nof S.Y. (1985) Scheduling/rescheduling in the manufacturing operating system environment. International Journal of Production Research 23(4): 705–722 doi:10.1080/00207548508904739
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Gholami, M., Zandieh, M. Integrating simulation and genetic algorithm to schedule a dynamic flexible job shop. J Intell Manuf 20, 481–498 (2009). https://doi.org/10.1007/s10845-008-0150-0
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DOI: https://doi.org/10.1007/s10845-008-0150-0