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In recent years, remanufacturing has received increased attention as a sustainable and profitable product recovery strategy. To facilitate the remanufacturing of used product returns, factors which affect remanufacturability should be considered during the product design stage. The selection of fastening method during the product design stage is one of the critical decisions which affects the remanufacturability as well as the total cost of disassembly and re-assembly of used products. Hence, both product assembly and disassembly issues should be considered in the product design stage for the selection of fastening methods. Simultaneous consideration of product assembly and disassembly in the product design stage for the fastening method selection has not been properly addressed in previous studies. In this paper, a methodology for selecting appropriate fastening method from a remanufacturing perspective is proposed in which both product assembly and disassembly are addressed. In the proposed methodology, an optimization model is formulated with the objective of minimizing the total cost of product assembly and disassembly. The genetic algorithm is employed to solve the model. A case study on the selection of fastening method for a laptop computer is conducted to illustrate the proposed methodology and to evaluate its effectiveness. The effect of the degree of product disassembly and the demand size for remanufactured products on the total cost of product assembly and disassembly was also investigated. The results showed the proposed methodology provide significant cost savings in the total product assembly and disassembly cost. [ABSTRACT FROM AUTHOR]

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]

The success of a new product is very much related to the customer satisfaction level of the product. Therefore, it is important to estimate the customer satisfaction level of a new product in its design stage. Quality function deployment is commonly used to develop customer satisfaction models for product design. Relationships between customer satisfaction and design attributes are highly fuzzy and nonlinear, but these relationship characteristics cannot be captured by existing customer satisfaction models. In this paper, we propose a novel chaos-based fuzzy regression (FR) approach with which fuzzy customer satisfaction models with second- and/or higher order terms, and interaction terms can be developed. The proposed approach uses a chaos optimization algorithm to generate the polynomial structures of customer satisfaction models. Thereafter, it employs an FR method to determine the fuzzy coefficients of the individual terms of models. To illustrate and validate the proposed approach, it is applied in the development of a customer satisfaction model for a mobile phone design. Five validation tests are conducted to compare modeling results from the chaos-based FR with those from statistical regression, FR, and fuzzy least-squares regression. Results of the validation tests show that the proposed approach outperforms the other three approaches in terms of mean relative errors and variance of errors and customer satisfaction models with second- and/or higher order terms, and interaction terms can be developed effectively using the proposed chaos-based FR approach. [ABSTRACT FROM AUTHOR]

Product line design is a key decision area that a product development team has to deal with in the early stages of product development. Previous studies of product line design have focused on single-objective optimization. However, several optimization objectives may be simultaneously pursued, and the solutions that can address the objectives are required in many practical scenarios. In this research, we propose a one-step multiobjective optimization approach for product line design. The proposed optimization model has three objectives: 1) maximizing the market share of a company's products; 2) minimizing the total product development cost of a product line; and 3) minimizing the total product development cycle time. A curve-fitting method is introduced into the part-worth utility models so that the optimization model can be applied to products with level-based attributes and attributes that have continuous values. A multiobjective genetic algorithm is adopted to solve the optimization model, obtaining a set of nondominated solutions. With the solutions, a new product development team can select a preferred solution interactively in a 2-D graph. An example of the optimal design of a product line of digital cameras is used to illustrate the proposed approach. [ABSTRACT FROM PUBLISHER]

For the sake of gaining competitive advantages, it is important to evaluate the satisfaction level of a product or service from the users? perspective. This can be done by investigating the relationship among customer attributes (customer requirements) and design attributes (product configurations). However, such relationship would be highly non-linear in nature. In this regard, many approaches have been proposed over traditional linear methods. Particularly, the Adaptive Neuro-Fuzzy Inference System (ANFIS) method has been prevalently utilized in modeling such vague and complex relationship among these attributes and evaluating user satisfaction towards certain products or services. Despite the fact that the ANFIS method can explicitly model the non-linear relation among these attributes, it may be restricted if uncertain information can be observed due to subjectivity and incompleteness. To overcome these limitations, a belief rule base (BRB) approach with evidential reasoning (ER) is applied in this paper. For justification purpose, both the ANFIS and BRB methods are applied to the same case. Comparison results indicate that the BRB is capable of minimizing the human biases in evaluating user satisfaction and rectifying the inappropriateness associated with the ANFIS method. Also, the BRB method can generate more rational and informative evaluation results. [ABSTRACT FROM AUTHOR]