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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. [ABSTRACT FROM PUBLISHER]

Affective design is an important aspect of new product development that can enhance customer satisfaction of new products. Previous studies generally conducted customer surveys based on questionnaires and interviews to collect customers’ views and preferences of affective design of products. However, the process could be time-consuming and the survey data does not contain much sentiment expression. Presently, a large number of online customer reviews on products can be found on various websites that contain rich information of customer opinions and expectations. However, the generation of useful information based on online customer reviews for affective design has not been addressed in previous studies. In this paper, a methodology for generating association rules for supporting affective design based on online customer reviews is proposed which mainly involves opinion mining of affective dimensions from online customer reviews and association rule mining based on multi-objective particle swarm optimisation (PSO). Opinion mining is adopted to analyze online reviews and conduct sentiment analysis for affective dimensions. Based on the mined information and morphological analysis of products, a multi-objective PSO approach is proposed to generate association rules that depict the relationships between affective dimensions and design attributes. A case study was conducted to illustrate the proposed methodology. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports published within the issue which include the use of particle swarm optimization in customer reviews, the use of machine learning framework to collect product information, and the affective properties of a new product.

Quality function deployment (QFD) is a widely adopted customer-oriented methodology to assist in product design and development. In a traditional QFD-based product planning process, the importance weights of customer requirements (CRs) in house of quality are obtained by analyzing and generalizing the requirements of various customers, and the purpose of the subsequent optimal setting of engineering characteristics (ECs) and process parameters is to achieve a higher level of overall customer satisfaction (OCS). However, nowadays customers’ requirements are increasingly diversified and customers usually have heterogeneous preferences. Consequently, customers in a product market may have different purchasing choice behaviors and satisfaction criteria toward a new product. Therefore, heterogeneity of CRs should be considered in QFD-based optimization models to describe the relationship between CRs and ECs and to model the OCS. In this paper, a novel QFD-based product planning approach is proposed for a product market with diversified CRs by integrating consumer choice behavior analysis. The contributions of this paper include: 1) customers’ purchase choice rules are introduced into QFD-based product planning process to simulate the purchase behavior of a customer toward a product; 2) two new QFD-based optimization models under deterministic and multinomial logit consumer choice rules are proposed to help firms improve the product quality under environment of diversified CRs; 3) the established model under deterministic consumer choice rule is transformed into an equivalent linear model to facilitate the solving process; and 4) the established optimization models are further extended for products with both continuous and discrete target values of ECs. [ABSTRACT FROM PUBLISHER]