Grey-based Taguchi Method to Optimize the Multi-response Design of Product Form Design

This paper presents a multi-response optimization method that uses the grey-based Taguchi method as the integrative product form design optimization method, and it serves as a tool for product form design to determine the optimal combination of design parameters in Kansei engineering (KE). This method is unique in that it combines the Taguchi method (TM) and grey relational analysis (GRA), allowing it to take advantage of the benefits of both methods. The TM is used to design experiments and generate combinative product form design samples which can be used to improve product quality. The GRA is applied to multi-response optimization problems. Factor effect analysis and analysis of variance (ANOVA) are used to determine which combinations of design parameters will result in the optimal product design. To demonstrate the applicability of the grey-based TM, a case study of a car form design is presented, and a confirmation test is performed to verify the performance of the optimal product design. The results show that the grey-based TM can deal with optimization problems with multiple Kansei responses and determine an optimal car form design that is representative of the consumers' perception in a systematic manner. The confirmation test results also show that the optimal product design generated by the grey-based TM can be used to improve the overall quality of a product form.

Conceptual comparison of the ecogeography-based algorithm, equilibrium algorithm, marine predators algorithm and slime mold algorithm for optimal product design

Vehicle component design is crucial for developing a vehicle prototype, as optimum parts can lead to cost reduction and performance enhancement of the vehicle system. The use of metaheuristics for vehicle component optimization has been commonplace due to several advantages: robustness and simplicity. This paper aims to demonstrate the shape design of a vehicle bracket by using a newly invented metaheuristic. The new optimizer is termed the ecogeography-based optimization algorithm (EBO). This is arguably the first vehicle design application of the new optimizer. The optimization problem is posed while EBO is implemented to solve the problem. It is found that the design results obtained from EBO are better when compared to other optimizers such as the equilibrium optimization algorithm, marine predators algorithm, slime mold algorithm.

Optimizing the design of a contraceptive microarray patch: a discrete choice experiment on women’s preferences in India and Nigeria

Background Efforts are underway to develop an easy-to-use contraceptive microarray patch (MAP) that could expand the range of self-administrable methods. This paper presents results from a discrete choice experiment (DCE) designed to support optimal product design. Methods We conducted a DCE survey of users and non-users of contraception in New Delhi, India (496 women) and Ibadan, Nigeria (two versions with 530 and 416 women, respectively) to assess stated preferences for up to six potential product attributes: effect on menstruation, duration of effectiveness, application pain, location, rash after application, and patch size. We estimated Hierarchical Bayes coefficients (utilities) for each attribute level and ran simulations comparing women’s preferences for hypothetical MAPs with varying attribute combinations. Results The most important attributes of the MAP were potential for menstrual side effects (55% of preferences in India and 42% in Nigeria) and duration (13% of preferences in India and 24% in Nigeria). Women preferred a regular period over an irregular or no period, and a six-month duration to three or one month. Simulations show that the most ideal design would be a small patch, providing 6 months of protection, that would involve no pain on administration, result in a one-day rash, and be applied to the foot. Conclusions To the extent possible, MAP developers should consider method designs and formulations that limit menstrual side effects and provide more than one month of protection.

Fusing Multi-Attribute Decision Models for Decision Making to Achieve Optimal Product Design

Manufacturers need to select the best design from alternative design concepts in order to meet up with the demand of customers and have a larger share of the competitive market that is flooded with multifarious designs. Evaluation of conceptual design alternatives can be modelled as a Multi-Criteria Decision Making (MCDM) process because it includes conflicting design features with different sub features. Hybridization of Multi Attribute Decision Making (MADM) models has been applied in various field of management, science and engineering in order to have a robust decision-making process but the extension of these hybridized MADM models to decision making in engineering design still requires attention. In this article, an integrated MADM model comprising of Fuzzy Analytic Hierarchy Process (FAHP), Fuzzy Pugh Matrix and Fuzzy VIKOR was developed and applied to evaluate conceptual designs of liquid spraying machine. The fuzzy AHP was used to determine weights of the design features and sub features by virtue of its fuzzified comparison matrix and synthetic extent evaluation. The fuzzy Pugh matrix provides a methodical structure for determining performance using all the design alternatives as basis and obtaining aggregates for the designs using the weights of the sub features. The fuzzy VIKOR generates the decision matrix from the aggregates of the fuzzified Pugh matrices and determine the best design concept from the defuzzified performance index. At the end, the optimal design concept is determined for the liquid spraying machine.

Optimizing the Design of a Contraceptive Microarray Patch: A Discrete Choice Experiment on Women’s Preferences in India and Nigeria

Background: Efforts are underway to develop an easy-to-use contraceptive microarray patch (MAP) that could expand the range of self-administrable methods. This paper presents results from a discrete choice experiment (DCE) designed to support optimal product design.Methods: We conducted a DCE survey of users and non-users of contraception in New Delhi, India (496 women) and Ibadan, Nigeria (two versions with 530 and 416 women, respectively) to assess stated preferences for up to six potential product attributes: effect on menstruation, duration of effectiveness, application pain, location, rash after application, and patch size. We estimated Hierarchical Bayes coefficients (utilities) for each attribute level and ran simulations comparing women’s preferences for hypothetical MAPs with varying attribute combinations.Results: The most important attributes of the MAP were potential for menstrual side effects (55% of preferences in India and 42% in Nigeria) and duration (13% of preferences in India and 24% in Nigeria). Women preferred a regular period over an irregular or no period, and a six-month duration to three or one month. Simulations show that the most ideal design would be a small patch, providing 6 months of protection, that would involve no pain on administration, result in a one-day rash, and be applied to the foot. Conclusions: To the extent possible, MAP developers should consider method designs and formulations that limit menstrual side effects and provide more than one month of protection.