Publication Type : Journal Article
Publisher : International Journal of Industrial and Systems Engineering
Source : International Journal of Industrial and Systems Engineering, Volume 7, Number 3, p.317-340 (2011)
Keywords : ACO, Algorithms, Ant colony optimisation, Artificial intelligence, Completion-time variance, CTV, Flow-shop scheduling, Particle swarm optimisation algorithm, Particle swarm optimization (PSO), PSOA
Campus : Coimbatore
School : School of Engineering
Department : Mechanical Engineering
Year : 2011
Abstract : In this paper, the problem of scheduling in the permutation flowshop scheduling problem is considered with the objective of minimising the completion-time variance of jobs (CTV). Two particle swarm optimisation algorithms (PSOAs) are proposed and analysed. The first PSOA is inspired from the solution construction procedures that are used in ant colony optimisation algorithms. The second algorithm is a newly developed one. The proposed algorithms are applied to a set of benchmark flowshop scheduling problems, and performances of the algorithms are evaluated by comparing the obtained results with the results published in the literature. The performance analysis demonstrates the effectiveness of the proposed algorithms in solving the permutation flowshop sequencing problem with the CTV objective. © 2011 Inderscience Enterprises Ltd.
Cite this Research Publication : K. Ramesh Kumar, Rajendran, Cb, and Mohanasundaram, K. Mc, “Discrete particle swarm optimisation algorithms for minimising the completion-time variance of jobs in flowshops”, International Journal of Industrial and Systems Engineering, vol. 7, pp. 317-340, 2011.