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A genetic algorithm for permutation flowshop scheduling under practical make-to-order production system

Published online by Cambridge University Press:  06 June 2016

Humyun Fuad Rahman ,
Ruhul Sarker  and
Daryl Essam
Humyun Fuad Rahman*
Affiliation:
School of Engineering and Information Technology, University of New South Wales, Canberra, Australia
Ruhul Sarker
Affiliation:
School of Engineering and Information Technology, University of New South Wales, Canberra, Australia
Daryl Essam
Affiliation:
School of Engineering and Information Technology, University of New South Wales, Canberra, Australia
*
Reprint requests to: Humyun Fuad Rahman, School of Engineering and Information Technology, University of New South Wales, Canberra ACT 2600, Australia. E-mail: m.rahman4@adfa.edu.au
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Abstract

The aim of this work is to bridge the gap between the theory and actual practice of production scheduling by studying a problem from a real-life production environment. This paper considers a practical Sanitaryware production system as a number of make-to-order permutation flowshop problems. Due to the wide range of variation in its products, real-time arrival of customer orders, dynamic batch adjustments, and time for machine setup, Sanitaryware production system is complex and also time sensitive. In practice, many such companies run with suboptimal solutions. To tackle this problem, in this paper, a memetic algorithm based real-time approach has been proposed. Numerical experiments based on real data are also been presented in this paper.

Keywords

Flowshop SchedulingGenetic AlgorithmMemetic AlgorithmOrder AcceptancePractical Make-to-Order System

Information

Type
Regular Articles
Information
AI EDAM , Volume 31 , Issue 1 , February 2017 , pp. 87 - 103
Copyright
Copyright © Cambridge University Press 2016 

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References

REFERENCES

Al-Anzi, F.S., & Allahverdi, A. (2005). Using a hybrid evolutionary algorithm to minimize variance in response time for multimedia object requests. Journal of Mathematical Modelling and Algorithms 4(4), 435–453.Google Scholar
Allahverdi, A. (2000). Minimizing mean flowtime in a two-machine flowshop with sequence-independent setup times. Computers & Operations Research 27(2), 111–127.Google Scholar
Allahverdi, A., & Al-Anzi, F.S. (2006). A branch-and-bound algorithm for three-machine flowshop scheduling problem to minimize total completion time with separate setup times. European Journal of Operational Research 169(3), 767–780.Google Scholar
Allahverdi, A., Ng, C., Cheng, T.E., & Kovalyov, M.Y. (2008). A survey of scheduling problems with setup times or costs. European Journal of Operational Research 187(3), 985–1032.Google Scholar
Bertel, S., & Billaut, J.C. (2004). A genetic algorithm for an industrial multiprocessor flow shop scheduling problem with recirculation. European Journal of Operational Research 159(3), 651–662.Google Scholar
Brucker, P., Knust, S., & Wang, G. (2005). Complexity results for flow-shop problems with a single server. European Journal of Operational Research 165(2), 398–407.Google Scholar
Dudek, R.A., Panwalkar, S.S., & Smith, M.L. (1992). The lessons of flowshop scheduling research. Operations Research 40(1), 7–13.Google Scholar
Ford, F.N., & Bradbard, D. (1987). Use of operations research in production management. Production and Inventory Management 28(3), 59–63.Google Scholar
Glass, C.A., Shafransky, Y.M., & Strusevich, V.A. (2000). Scheduling for parallel dedicated machines with a single server. Naval Research Logistics 47(4), 304–328.Google Scholar
Grabowski, J., & Wodecki, M. (2004). A very fast tabu search algorithm for the permutation flow shop problem with makespan criterion. Computers & Operations Research 31(11), 1891–1909.Google Scholar
Graves, S.C. (1981). A review of production scheduling. Operations Research 29(4), 646–675.Google Scholar
Guerrero, H., & Kern, G. (1988). How to more effectively accept and refuse orders. Production and Inventory Management 29(4), 59–63.Google Scholar
Johnson, S.M. (1954). Optimal two- and three-stage production schedules with setup times included. Naval Research Logistics Quarterly 1(1), 61–68.Google Scholar
Man, K.-F., Tang, K.-S., & Kwong, S. (1996). Genetic algorithms: concepts and applications. IEEE Transactions on Industrial Electronics 43(5), 519–534.Google Scholar
Nawaz, M., Enscore, E.E., & Ham, I. (1983). A heuristic algorithm for the M-machine, N-job flowshop sequencing problem. Omega—International Journal of Management Science 11(1), 91–95.Google Scholar
Ng, C., Allahverdi, A., Al-Anzi, F.S., & Cheng, T.E. (2007). The three-machine flowshop scheduling problem to minimise maximum lateness with separate setup times. International Journal of Operational Research 2(2), 135–155.Google Scholar
Nowicki, E., & Smutnicki, C. (1996). A fast tabu search algorithm for the permutation flow-shop problem. European Journal of Operational Research 91(1), 160–175.Google Scholar
Ogbu, F.A., & Smith, D.K. (1990). The application of the simulated annealing algorithm to the solution of the n/m/C max flowshop problem. Computers & Operations Research 17(3), 243–253.Google Scholar
Onwubolu, G., & Davendra, D. (2006). Scheduling flow shops using differential evolution algorithm. European Journal of Operational Research 171(2), 674–692.Google Scholar
Osman, I.H., & Potts, C.N. (1989). Simulated annealing for permutation flowshop scheduling. Omega—International Journal of Management Science 17(6), 551–557.Google Scholar
Parthasarathy, S., & Rajendran, C. (1997). An experimental evaluation of heuristics for scheduling in a real-life flowshop with sequence-dependent setup times of jobs. International Journal of Production Economics 49(3), 255–263.Google Scholar
Pinedo, M. (2012). Scheduling: Theory, Algorithms, and Systems (4th ed.). New York: Springer.Google Scholar
Rahman, H.F., Sarker, R.A., & Essam, D.L. (2013). A memetic algorithm for permutation flow shop problems. Proc. IEEE Congress on Evolutionary Computation, pp. 1618–1625, Cancun, June 20–23.Google Scholar
Rahman, H.F., Sarker, R.A., & Essam, D. (2015). A genetic algorithm for permutation flow shop scheduling under make to stock production system. Computers & Industrial Engineering 90, 12–24.Google Scholar
Rajendran, C., & Ziegler, H. (2004). Ant-colony algorithms for permutation flowshop scheduling to minimize makespan/total flowtime of jobs. European Journal of Operational Research 155(2), 426–438.Google Scholar
Reisman, A., Kumar, A., & Motwani, J. (1997). Flowshop scheduling/sequencing research: a statistical review of the literature, 1952–1994. IEEE Transactions on Engineering Management 44(3), 316–329.Google Scholar
Rom, W.O., & Slotnick, S.A. (2009). Order acceptance using genetic algorithms. Computers & Operations Research 36(6), 1758–1767.Google Scholar
Ruiz, R., Maroto, C., & Alcaraz, J. (2006). Two new robust genetic algorithms for the flowshop scheduling problem. Omega—International Journal of Management Science 34(5), 461–476.Google Scholar
Ruiz, R., Serifoglu, F.S., & Urlings, T. (2008). Modeling realistic hybrid flexible flowshop scheduling problems. Computers & Operations Research 35(4), 1151–1175.Google Scholar
Slotnick, S.A. (2011). Order acceptance and scheduling: a taxonomy and review. European Journal of Operational Research 212(1), 1–11.Google Scholar
Su, L.-H., & Chou, F.-D. (2000). Heuristic for scheduling in a two-machine bicriteria dynamic flowshop with setup and processing times separated. Production Planning & Control 11(8), 806–819.Google Scholar
Taillard, E. (1990). Some efficient heuristic methods for the flow-shop sequencing problem. European Journal of Operational Research 47(1), 65–74.Google Scholar
Tasgetiren, M.F., Liang, Y.C., Sevkli, M., & Gencyilmaz, G. (2007). A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem. European Journal of Operational Research 177(3), 1930–1947.Google Scholar
Wang, X.L., Xie, X.Z., & Cheng, T.C.E. (2013). Order acceptance and scheduling in a two-machine flowshop. International Journal of Production Economics 141(1), 366–376.Google Scholar
Xiao, Y.Y., Zhang, R.Q., Zhao, Q.H., & Kaku, I. (2012). Permutation flow shop scheduling with order acceptance and weighted tardiness. Applied Mathematics and Computation 218(15), 7911–7926.Google Scholar
Zobolas, G.I., Tarantilis, C.D., & Ioannou, G. (2009). Minimizing makespan in permutation flow shop scheduling problems using a hybrid metaheuristic algorithm. Computers & Operations Research 36(4), 1249–1267.Google Scholar