Consumers’ opinions toward product design alternatives are often subjective and perceptual, which reflect their perception about a product and can be described using Kansei adjectives. Therefore, Kansei evaluation is often employed to determine consumers’ preference. However, how to identify and improve the reliability of consumers’ Kansei evaluation opinions toward design alternatives has an important role in adding additional insurance and reducing uncertainty to successful product design. To solve this problem, this study employs a consensus model to measure consistence among consumers’ opinions, and an advanced particle swarm optimization (PSO) algorithm combined with Linearly Decreasing Inertia Weight (LDW) method is proposed for consensus reaching by minimizing adjustment of consumers’ opinions. Furthermore, the process of the proposed method is presented and the details are illustrated using an example of electronic scooter design evaluation. The case study reveals that the proposed method is promising for reaching a consensus through searching optimal solutions by PSO and improving the reliability of consumers’ evaluation opinions toward design alternatives according to Kansei indexes.
As the international market is globalizing at high speed, new opportunities have been opened up for businesses with successful new products. In an era of highly competitive and uncertain market, companies that listen to their consumers are more likely to be successful [
When planning a product development process, concept evaluation is often involved as it facilitates the assessment of the overall feasibility of design alternatives. Through concept evaluation, both time and cost can be saved as 70%–80% of the final product quality and 70% of the product entire life-cycle cost are committed in the early product design phase [
Product Kansei evaluation is a systemic and important step in determining consumers’ preference using Kansei criteria against product design alternatives. The main objective is help to determine that the final design solution reflects consumers’ subjective preference and can reach a certain degree of satisfaction and consensus. To achieve this goal, three procedures should be conducted: (
The rest of this paper is organized as follows: Section
Let
Let vector
Euclidean distance is used to calculate the opinion difference between consumer
Then, the similarity of opinions by consumer
Consensus measurement of consumers’ opinions upon
Finally, the total score of
It is likely not to reach a consensus easily due to cognitive difference among consumers. Therefore appropriate measures should be taken to seek consensus with minimum adjustment of consumers’ opinions, among which particle swarm optimization (PSO) is chosen here as it makes few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions by iteration [
By asking consumers who participate in the evaluation process, the upper limit and lower limit of each consumer’s opinion can be gained, between which consumers’ opinions will be adjusted for consensus reaching. The upper limit vector and lower limit vector are shown as follows:
To seek the optimal solution in PSO, each candidate solution, called a particle, flies in the
Set
The procedure for consensus reaching with PSO.
If
Calculate consensus of
Calculate the deviation de between
Calculate the deviation between
Update
The minimal deviation between the set of
A case study of electric scooter was used to determine the proposed method’s ability for reaching consensus in product Kansei evaluation process. The author’s previous study has gained six primary Kansei needs through investigating and clustering about the target product, seen in [
Scores of No. 1.
Consumers | Kansei indexes | |||||
---|---|---|---|---|---|---|
Untechnical–technological | Inactive–dynamic | Outdated–futuristic | Feminine–manly | Dimmed–vivid | Partial–integral | |
1 | 3 | 2 | 2 | 3 | 3 | −2 |
2 | 2 | 1 | 3 | 2 | 2 | 1 |
3 | 2 | 3 | 0 | −1 | 2 | 1 |
4 | 1 | 2 | 2 | 2 | 2 | 2 |
5 | −2 | 3 | 3 | 3 | 2 | −2 |
6 | 2 | 2 | 2 | 3 | 3 | 1 |
7 | 3 | 2 | −2 | 2 | 2 | 2 |
8 | 2 | 2 | 2 | 0 | 1 | 1 |
9 | 2 | 2 | 1 | 3 | 2 | 2 |
Scores of No. 2.
Consumers | Kansei indexes | |||||
---|---|---|---|---|---|---|
Untechnical–technological | Inactive–dynamic | Outdated–futuristic | Feminine–manly | Dimmed–vivid | Partial–integral | |
1 | 0 | 2 | 1 | 1 | −2 | 3 |
2 | 1 | 1 | 1 | 2 | −1 | 2 |
3 | −1 | 2 | 0 | 1 | 0 | 0 |
4 | 1 | 1 | 1 | 1 | −1 | 1 |
5 | 2 | 1 | 2 | 2 | −1 | 1 |
6 | −2 | 2 | 1 | 2 | −2 | 2 |
7 | 0 | 1 | −1 | 2 | 1 | 2 |
8 | 1 | 2 | −2 | 3 | 1 | −1 |
9 | 2 | 2 | 0 | 0 | 0 | 3 |
Scores of No. 3.
Consumers | Kansei indexes | |||||
---|---|---|---|---|---|---|
Untechnical–technological | Inactive–dynamic | Outdated–futuristic | Feminine–manly | Dimmed–vivid | Partial–integral | |
1 | 2 | 3 | 2 | 2 | 2 | 3 |
2 | 2 | 2 | 2 | 3 | 3 | 3 |
3 | 3 | 2 | 1 | −2 | 3 | 2 |
4 | 2 | 2 | 3 | 2 | 2 | 3 |
5 | 2 | 2 | 2 | −1 | 3 | 2 |
6 | 3 | 3 | 1 | 0 | 2 | 2 |
7 | 2 | 2 | 2 | 3 | 2 | 1 |
8 | 2 | 2 | 2 | 2 | 1 | 2 |
9 | 1 | 3 | −1 | −1 | 2 | 3 |
Design solutions.
Consumers who participated in the evaluation process weight equally and weights of the six Kansei indexes can be calculated using AHP method [
Generally, particle swarm size ranges from 10 to 50 depending on different applications and problems [
Consensus and deviation of viable solutions against No. 1 using PSO.
Solution 1 | Solution 2 | Solution 3 | Solution 4 | Solution 5 | |
---|---|---|---|---|---|
CON | 0.728 | 0.778 | 0.704 | 0.731 | 0.717 |
de | 0.135 | 0.131 | 0.064 | 0.221 | 0.181 |
Optimal adjusted evaluation value of No. 1.
Consumers | Kansei indexes | |||||
---|---|---|---|---|---|---|
Untechnical–technological | Inactive–dynamic | Outdated–futuristic | Feminine–manly | Dimmed–vivid | Partial–integral | |
1 | 2.00 | 2.92 | 1.79 | 2.68 | 2.05 | −1.82 |
2 | 2.89 | 1.37 | 2.79 | 1.99 | 2.11 | 1.58 |
3 | 2.03 | 2.87 | −0.36 | −1.48 | 2.28 | 0.37 |
4 | 0.70 | 1.31 | 1.36 | 1.58 | 1.78 | 1.90 |
5 | −2.98 | 2.67 | 2.38 | 2.44 | 2.94 | −1.56 |
6 | 2.02 | 1.82 | 2.73 | 2.15 | 2.89 | 0.16 |
7 | 2.11 | 1.13 | −1.27 | 2.63 | 2.43 | 2.10 |
8 | 2.00 | 1.37 | 2.56 | −0.69 | 0.57 | 0.42 |
9 | 2.22 | 2.15 | 1.61 | 2.99 | 1.55 | 1.24 |
Consensus and deviation of viable solutions against No. 2 using PSO.
Solution 1 | Solution 2 | Solution 3 | Solution 4 | Solution 5 | Solution 6 | |
---|---|---|---|---|---|---|
CON | 0.852 | 0.753 | 0.778 | 0.728 | 0.749 | 0.769 |
de | 0.122 | 0.225 | 0.168 | 0.149 | 0.223 | 0.235 |
Optimal adjusted evaluation value of No. 2.
Consumers | Kansei indexes | |||||
---|---|---|---|---|---|---|
Untechnical–technological | Inactive–dynamic | Outdated–futuristic | Feminine–manly | Dimmed–vivid | Partial–integral | |
1 | −0.32 | 2.97 | 1.77 | 1.96 | −1.44 | 2.33 |
2 | 1.30 | 1.94 | 0.64 | 2.42 | −0.59 | 1.69 |
3 | −0.89 | 2.17 | 0.26 | 1.44 | −0.74 | −0.20 |
4 | 0.80 | 0.32 | 1.64 | 0.87 | −0.12 | 1.49 |
5 | 1.10 | 1.59 | 1.21 | 1.77 | −1.40 | 0.50 |
6 | −2.73 | 2.06 | 0.79 | 1.64 | −1.68 | 1.42 |
7 | −0.34 | 1.75 | −0.47 | 1.09 | 0.54 | 2.85 |
8 | 0.13 | 1.44 | −1.02 | 2.36 | 0.27 | −0.44 |
9 | 1.02 | 2.12 | −0.16 | 0.74 | 0.42 | 2.34 |
Using (
Consensus value and score of each design solution.
No. 1 | No. 2 | No. 3 | ||||
---|---|---|---|---|---|---|
Score | Consensus | Score | Consensus | Score | Consensus | |
Considering consensus | 0.754 | 0.704 | 0.612 | 0.852 | 0.799 | 0.704 |
Not considering consensus | 0.732 | 0.531 | 0.558 | 0.630 | 0.799 | 0.704 |
A novel method for consensus reaching in product Kansei evaluation process using advanced particle swarm optimization (PSO) algorithm is proposed in this work. The method demonstrates the capacity and efficiency for reaching consensus by minimizing the adjusted opinions of consumers. An advanced PSO algorithm is presented combined with Linearly Decreasing Inertia Weight (LDW) method to enhance the global exploration ability for searching in a larger space when the evolution speed of the swarm is fast and maintain the particles searching in a small space to find the optimal solution more quickly if the evolution speed of particles slows down. The process of the proposed method is discussed and illustrated using an example of electronic scooter design evaluation for consensus reaching. The results suggest that using advanced PSO helps to reach a consensus and find the optimal solutions with minimal adjustment of original evaluation value and improve the reliability of consumers’ evaluation opinions toward design alternatives according to Kansei indexes. It appears that the proposed method is promising for reaching a consensus in product Kansei evaluation process.
The author declares that they have no competing interests.
This research was supported by the Natural Science Foundation of Shaanxi Province, China (no. 2016JQ5107) and the Fundamental Research Funds for the Central Universities (no. 310825151039). The authors are grateful of their support. The authors would also like to thank Dr. Zhen LEI for providing suggestions and helping with conducting the experiment.