A hybrid surrogate-assisted evolutionary algorithm for technician routing and scheduling problems

Technician routing and scheduling problems (TRSP) that involve time windows and skill-based requirements represent complex optimization tasks frequently encountered in areas such as maintenance and field service operations. Because these problems belong to the class of NP-hard combinatorial challenges, obtaining exact solutions within realistic scales is computationally prohibitive. To address this challenge, the present study proposes a hybrid surrogate-assisted evolutionary algorithm (HSAEA) that combines several artificial intelligence (AI) strategies to obtain high-quality solutions more efficiently. At its core, the framework employs a Genetic Algorithm (GA) that is specifically designed to allocate and schedule service jobs among available technicians. To enhance this GA, the paper incorporates: a surrogate model – a Multi-Layer Perceptron (MLP) regressor trained on domain-specific features – to approximate the expensive fitness evaluation of candidate schedules; a reinforcement learning (RL) based adaptive mutation operator selection mechanism that dynamically chooses mutation operators based on their past performance, aiming to balance exploration and exploitation; and a simulated annealing (SA) procedure applied as a local search for intensification, refining individual solutions by exploiting neighborhood improvements. The proposed method is formalized and each component’s role is detailed. To sum up, the proposed hybrid framework combines several complementary ideas: surrogate models are used to cut down computational effort, reinforcement learning helps direct the search process more intelligently, and simulated annealing refines the solutions at a local level. When tested on standard benchmark datasets, the method consistently produced better-quality solutions than the baseline algorithms, and it did so within practical computation times.