Multi-service value chains collaboration for repairperson resources selection using a many-objective evolutionary algorithm with adaptive reference vectors

When facing with rising demand for repair services and technical problems, service stations with limited resources suffer a decline in service quality. However, high-quality service is essential for customer retention. The purpose of this paper is to solve the problem by sharing and allocating repairperson resources across multi-service value chains in order to improve the quality of after-sales repair services. Firstly, based on the multi-service value chain collaboration model in a third-party cloud platform environment, a repairperson resources selection model is proposed to balance the multi-service value chain repairperson resources, considering the interests of resource users, resource providers, and the third-party cloud platform simultaneously. Secondly, a many-objective evolutionary algorithm with adaptive reference vectors(EAARV) is designed to solve the resources selection model with an irregular Pareto front. Finally, a case study is conducted to compare the performance of EAARV with seven state-of-the-art evolutionary optimization algorithms for solving many-objective optimization problems and to validate its viability. The experimental results show that EAARV outperforms others in solving the repairperson resources selection problem, and the selection model considering multi-service value chains collaboration is proven to promote the utilization of service resources. The satisfaction rate of repairperson resources with multi-service value chains collaboration is significantly higher than the single-service value chain collaboration. Meanwhile, the parameters of EAARV are analyzed and an ablation experiment is conducted to further evaluate the influence of each component in EAARV on the performance.(c) 2022 Elsevier B.V. All rights reserved.