Handling uncertainties in modelling manufacturing processes with hybrid swarm intelligence

Abstract

Seldom has research regarding manufacturing process modelling considered the two common types of uncertainties which are caused by randomness as in material properties and by fuzziness as in the inexact knowledge in manufacturing processes. Accuracies of process models can be downgraded if these uncertainties are ignored in the development of process models. In this paper, a hybrid swarm intelligence algorithm for developing process models which intends to achieve significant accuracies for manufacturing process modelling by addressing these two uncertainties is proposed. The hybrid swarm intelligence algorithm first applies the mechanism of particle swarm optimisation to generate structures of process models in polynomial forms, and then it applies the mechanism of fuzzy least square regression algorithm to determine fuzzy coefficients on polynomials so as to address the two uncertainties, fuzziness and randomness. Apart from addressing the two uncertainties, the common feature in manufacturing processes, nonlinearities between process parameters, which are not inevitable in manufacturing processes, can also be addressed. The effectiveness of the hybrid swarm algorithm is demonstrated by modelling of the solder paste dispensing process.

Applying genetic algorithm to optimise personal worklist management in workflow systems

“…The dynamic nature of workflow mainly causes three types of uncertainties in managing personal work lists (Chan, Dillon, and Kwong 2012;Doulamis and Matsatsinis 2012). The first type of uncertainty comes from the activity instances themselves.…”