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Applications of particle swarm optimisation in integrated process planning and scheduling


Reference:

Guo, Y. W., Liu, W. D., Mileham, A. R. and Owen, G. W., 2009. Applications of particle swarm optimisation in integrated process planning and scheduling. Robotics and Computer-Integrated Manufacturing, 25 (2), pp. 280-288.

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Official URL:

http://dx.doi.org/10.1016/j.rcim.2007.12.002

Abstract

Integration of process planning and scheduling (IPPS) is an important research issue to achieve manufacturing planning optimisation. In both process planning and scheduling, vast search spaces and complex technical constraints are significant barriers to the effectiveness of the processes. In this paper, the IPPS problem has been developed as a combinatorial optimisation model, and a modern evolutionary algorithm, i.e., the particle swarm optimisation (PSO) algorithm, has been modified and applied to solve it effectively. Initial solutions are formed and encoded into particles of the PSO algorithm. The particles "fly" intelligently in the search space to achieve the best sequence according to the optimisation strategies of the PSO algorithm. Meanwhile, to explore the search space comprehensively and to avoid being trapped into local optima, several new operators have been developed to improve the particles' movements to form a modified PSO algorithm. Case studies have been conducted to verify the performance and efficiency of the modified PSO algorithm. A comparison has been made between the result of the modified PSO algorithm and the previous results generated by the genetic algorithm (GA) and the simulated annealing (SA) algorithm, respectively, and the different characteristics of the three algorithms are indicated. Case Studies show that the developed PSO can generate satisfactory results in both applications.

Details

Item Type Articles
CreatorsGuo, Y. W., Liu, W. D., Mileham, A. R. and Owen, G. W.
DOI10.1016/j.rcim.2007.12.002
Uncontrolled Keywordsplanning and scheduling, integrated process, genetic algorithm, operation sequencing, particle swarm optimisation, simulated annealing
DepartmentsFaculty of Engineering & Design > Mechanical Engineering
Research CentresInnovative Design & Manufacturing Research Centre (IdMRC)
RefereedYes
StatusPublished
ID Code12518

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