Improving the interactive genetic algorithm for customer-centric product design by automatically scoring the unfavorable designs

Abstract

IntroductionAppearance of products is one of the factors of customer attraction. This necessitates customer-centric design of products and product customization [1][2][3]. One of the methods proposed in this area is the direct use of designs proposed by users for designing the products. However, offering a design requires specialized knowledge and necessary skills for working with design software and tools and customers lack such requirements. Besides, strong presence in competitive markets requires quick development of AbstractOne of the effective factors in increasing sales is the consistency of products with the preference of the customers. Designing the products consistent with customer needs requires the engagement of customers in the product design process. One way to achieve this goal is the use of interactive evolutionary algorithms. During the running of such algorithms, the customer acts as a fitness function and imparts his/her opinion directly to the design process. Since these algorithms are usually iterated frequently, the user fatigue problem during interaction with them is a major challenge. The present study develops a method to tackle the user fatigue problem in the interactive genetic algorithm using the candidate elimination algorithm. In this method, customer preferences are gradually learned by applying the candidate elimination algorithm on the designs evaluated by the user in the early stages of algorithm. Using the learned preferences, designs which may not meet the customer preferences are discovered and automatically receive a predefined low score from the algorithm. The proposed method has been evaluated on the customer-centric design of book covers and its results have been compared with those of the two simple interactive genetic algorithm and multi-stage interactive genetic algorithm. The results are indicative of a considerable reduction of the number of algorithm generations, the number of chromosomes being evaluated by user, and the evaluating time in comparison with the two aforementioned methods. Reduction of these criteria leads to decrease of user fatigue. In addition, the proposed method has increased the user satisfaction. Keywords:Interactive design, Customer-centric product design, Interactive genetic algorithm, User fatigue, Candidate elimination algorithm Open Access© The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Sheikhi Darani and Kaedi Hum. Cent. Comput. Inf. Sci. (2017) Page 2 of 18 Sheikhi Darani and Kaedi Hum. Cent. Comput. Inf. Sci. (2017) 7:38 customer-centric designs for products and services. These factors have paved the way for the emergence of interactive evolutionary algorithms in the design ...

Cognitive Association in Interactive Evolutionary Design Process for Product Styling and Application to SUV Design

“…In related studies, the evaluation time is often used to determine the difficulty of the evaluation process [36,37]. As such, the initial evaluation time of the users is recorded and the total evaluation time in Table 3 is used to study the performance of each method in facilitating efficiency of similar and dissimilar information.…”

Pepper, just show me the way! How robotic shopping assistants should look and act

“…In particular, by combining both external and internal factors of robots, this research studies the appearance and attributes of an RSA as two important determinants of an effective RSA's design and investigates an effective appearance‐feature combination. Currently, there is no single best customer‐centric design of a frontline robot (Darani & Kaedi, 2017; Lu et al, 2020; Wang & Tseng, 2011). Thus, our research responds to Lu et al's (2020) call for research to explore the determinants of an effective design of robots.…”

“…Overall, this research investigates consumer responses to RSA attributes ( internal factors of robots) in relation to the RSA appearance ( external factors of robots). Insights from the research may help retailers and robot manufacturers increase customer receptivity of RSAs, deploy them more effectively and incorporate customer‐centric innovation in design (Darani & Kaedi, 2017; Lu et al, 2020; Verganti et al, 2020; Wang & Tseng, 2011). In particular, this research aims to answer the following two research questions: (1) How should hedonic/utilitarian attributes be equipped in humanoid/non‐humanoid robots without triggering negative responses from customers?…”

Proceedings of the Second Workshop on Bridging Human--Computer Interaction and Natural Language Processing

“…• Human factors introduce technical considerations that may impact interaction and performance success; for instance, the learning set-up should accommodate human fatigue (Darani and Kaedi, 2017;Llorà et al, 2005).…”