Integration of a GA and PSO for discussing the impact of 3C product engineering changes on customisation degree

Computer, Communication, Consumer (3C) electronics products have high uncertainty of demand as customers often change their demands. Therefore, manufacturers have to deal with Engineering Change (EC) situations to meet the needs of customers. Based on a literature review, this study has found that a Genetic Algorithm (GA) can yield optimal solutions for ECs, and that Particle Swarm Optimisation (PSO) can more efficiently converge to the optimal solution. Therefore, this study integrated the GA and PSO to determine the optimal manufacturing makespan upon ECs. For 3C products, customer preference is affected by customisation degree. This study developed a deviation utility loss-based customisation degree model that can indicate the gap between the makespan required by customers and optimal makespan from manufacturers upon ECs, as well as the impact of changes on unit price and fixed cost of product of the customisation degree upon EC and parameter change of the customisation degree model. The findings can provide 3C manufacturers with references to select the optimal parameters for difference situations.