Instant repairman travel in home appliance after‐sales service platform: dynamic routing optimization with multiple service types

Abstract

Home appliance after‐sales services have become a key strategy for improving market share and enhancing customer satisfaction. By using modern technologies, the after‐sales service platform gathers dynamic customer requests and repairman travel data. To reduce response times and provide immediate service, dynamic routing models must be combined with real‐time platform information. This study proposes a dynamic routing model for after‐sales service that integrates multiple service types with hierarchical repairman skills and soft time windows. A rolling improved adaptive genetic algorithm with neighborhood search is further developed to solve the model, which divides the planning horizon into multiple time slices and re‐optimizes routes at each time slice. The algorithm is specifically designed through skill‐embedded encoding, the minimum‐cost insertion option, and a hybrid rolling optimization strategy. Numerical experiments using benchmark and simulation cases show that, in 21 Kilby benchmark instances, the proposed method outperforms previous methods, achieving 10 better “Best” results and 18 better “Average” results. Simulation results show that the multi‐service type model reduces costs by 15.69% to 30.06% compared to the single‐service type model. Sensitivity analysis suggests that the platform should consider the effect of the number of instant customers. The algorithm performs better when the workday is divided into 20 time segments. Additionally, a real‐world case study based on practical after‐sales service data demonstrates the effectiveness and practical applicability of the proposed method.