Product family configuration optimisation considering after-sale service: an adaptive quantum evolutionary algorithm approach

ABSTRACT

By providing after-sale services, manufacturing companies can gain more chances to interact with customers, improve brand preference and loyalty, occupy new bases in the value chain, and create more cross-selling opportunities. Considering after-sale service, we propose a new method for product family configuration optimisation in this study. Mathematical models for product family configuration optimisation are established with the objective of maximising the overall profit of the family of products and after-sale services. An adaptive quantum evolutionary algorithm (AQEA) is developed to solve the established optimisation models. An example of an e-book reader product is used to illustrate the proposed method in a case study. An industry case study shows that: (1) the proposed method outperforms the traditional method; (2) the proposed AQEA has better performance when compared with the other three meta-heuristic algorithms; and (3) the proposed method is more useful for industrial cases where the proportion of service profit is relatively large.

KEYWORDS:

• Product family
• quantum evolutionary algorithm
• optimisation
• after-sale service

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was financially supported by the National Natural Science Foundation of China (NSFC Proj. 71831006, 71771070) and Zhejiang Provincial Natural Science Foundation of China under grant number LZ20G010001.