Abstract
Customer requirement preference is an important part of customer satisfaction. In view of similar case retrieval technology for existing product level, in the process of solving similar cases, there is no consideration for customer requirement preference. This article proposes a similar case solution method considering customer requirement preference. First, we deal with the expression of customer requirements and transform them into operable parameter forms according to the mapping model. Second, the preference graph is used to analyze the customer’s requirement preference, to determine the preference weight, and to weigh the final weight of the requirement node with the initial weight determined by the fuzzy analytic hierarchy process. Finally, the similarity degree solving model of requirement node and product case attribute parameters is established. By integrating the weights of the above-mentioned nodes, the similarity of the product case is obtained, and a more satisfied case of the customer is obtained. Taking the automated guided vehicle car product as an example, the effectiveness of the proposed method is verified.
Highlights
With the rise of personalized customized products, customers have more and more individualized requirements for products
The acquisition of similar cases is the key to product case retrieval and has a great impact on the final product reasoning
The above works mainly focus on the efficiency and accuracy of solving the similarity model between requirement attributes and product case attributes, but they cannot be analyzed from the requirement attributes weight, customer satisfaction, and so on
Summary
With the rise of personalized customized products, customers have more and more individualized requirements for products. The above works mainly focus on the efficiency and accuracy of solving the similarity model between requirement attributes and product case attributes, but they cannot be analyzed from the requirement attributes weight, customer satisfaction, and so on. Customer requirement representation is processed by mapping model to obtain operable customer requirement nodes and parameterized range space vector R = (R1, R2, R3, ..., Rn). The sum of the elements of the Nth row of the dominance matrix D is defined as dN, which represents the preference value of the Nth requirement node directly or indirectly relative to other requirement nodes. It gets d1 = 2, d2 = 4, d3 = 1, d4 = 1, d5 = 0 from dominance matrix D We substitute it in formulas (3) and (4) to get the customer requirement node preference weight vector as v = (0.231, 0.385, 0.154, 0.154, 0.077).
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