Abstract
This paper deals with the m-machine permutation flowshop scheduling problem to minimize the total flowtime, an NP-complete problem, and proposes an improved particle swarm optimization (PSO) algorithm. To enhance the exploitation ability of PSO, a stochastic iterated local search is incorporated. To improve the exploration ability of PSO, a population update method is applied to replace non-promising particles. In addition, a solution pool that stores elite solutions found in the search history is adopted, and in the evolution process each particle learns from this solution pool besides its personal best solution and the global best solution so as to improve the learning capability of the particles. Experimental results on benchmark instances show that the proposed PSO algorithm is competitive with other metaheuristics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Johnson, S.M.: Optimal two- and three-stage production schedules with setup times included. Naval Research Logistics Quarterly 1, 61–68 (1954)
Framinan, J.M., Leisten, R., Ruiz-Usano, R.: Comparison of heuristic for flowtime minimisation in permutation flowshops. Computers & Operations Research 32, 1237–1254 (2005)
Garey, M.R., Johnson, D.S., Sethi, R.: The complexity of flowshop and jobshop scheduling. Mathematics of Operations Research 1, 117–129 (1976)
Bansal, S.P.: Minimizing the sum of completion times of n jobs over m machines in a flowshop – a branch and bound approach. AIIE Transactions 9, 306–311 (1977)
Chung, C.S., Flynn, J., Kirca, O.: A branch and bound algorithm to minimize the total flow time for m-machine permutation flowshop problems. International Journal of Production Economics 79, 185–196 (2002)
Gupta, J.N.D.: Heuristic algorithms for multistage flowshop scheduling problem. AIIE Transactions 4, 11–18 (1972)
Rajendran, C., Ziegler, H.: An efficient heuristic for scheduling in a flowshop to minimize total weighted flowtime of jobs. European Journal of Operational Research 103, 129–138 (1997)
Rajendran, C.: Heuristic algorithm for scheduling in a flowshop to minimize total lowtime. International Journal of Production Economics 29, 65–73 (1993)
Wang, C., Chu, C., Proth, J.M.: Heuristic approaches for n/m/F/∑Ci scheduling problems. European Journal of Operational Research 96, 636–644 (1997)
Liu, J.Y., Reeves, C.R.: Constructive and composite heuristic solutions to the P//∑Ci scheduling problem. European Journal of Operational Research 132, 439–542 (2001)
Framinan, J.M., Leisten, R.: An efficient constructive heuristic for flowtime minimisation in permutation flow shops. OMEGA 31, 311–317 (2003)
Yamada, T., Reeves, C.R.: Solving the Csum permutation flowshop scheduling problem by genetic local search. In: Proceedings of IEEE international conference on evolutionary computation, pp. 230–234 (1998)
Rajendran, C., Ziegler, H.: Ant-colony algorithms for permutation flowshop scheduling to minimize makespan/total flowtime of jobs. European Journal of Operational Research 2004 155, 426–438 (2005)
Tasgetiren, M.F., Liang, Y.C., Sevkli, M., Gencyilmaz, G.: 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 (2007)
Jarboui, B., Ibrahim, S., Siarry, P., Rebai, A.: A combinatorial particle swarm optimisation for solving permutation flowshop problems. Computers and Industrial Engineering 54, 526–538 (2008)
Taillard, E.: Benchmarks for basic scheduling problems. European Journal of Operational Research 64, 278–285 (1993)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Network, pp. 1942–1948 (1995)
Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, pp. 39–43 (1995)
Coello, C.A., Pulido, G.T., Lechuga, M.S.: Handling multiple objectives with particle swarm optimization. EEE Transactions on Evolutionary Computation 8(3), 256–279 (2004)
Nawaz, M., Enscore, E.E., Ham, I.: A heuristic algorithm for the m-machine, n-job sequencing problem. Omega 11, 91–95 (1983)
Congram, R.K.: Polynomially searchable exponential neighbourhoods for sequencing problems in combinatorial optimisation. PhD Thesis, University of Southampton (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, X., Tang, L. (2010). An Improved Particle Swarm Optimization for Permutation Flowshop Scheduling Problem with Total Flowtime Criterion. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13495-1_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-13495-1_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13494-4
Online ISBN: 978-3-642-13495-1
eBook Packages: Computer ScienceComputer Science (R0)