Recycling-and-disassembly collaborative scheduling in remanufacturing system with routing and disassembly line balancing consideration

Research on the recycling and disassembly of end-of-life (EOL) products has garnered escalating academic and industrial attention. Specifically, vehicle routing and product pickup scheduling decisions dictate the batch arrival time and quantity of EOL products at disassembly facilities, while precedence-constrained disassembly task scheduling decisions dominate the rationality of task allocation and the efficiency of workstation utilization. This inherent temporal-resource coupling effect significantly amplifies the complexity of integrated decision-making, since the multidimensional routing and disassembly decisions are highly interdependent and thus pose formidable challenges to coordinated optimization. There, this study introduces the Recycling-and-Disassembly Collaborative Scheduling Problem (RD-CSP), which unifies recycling routing and disassembly line scheduling within a single framework to jointly optimize both stages. And a bi-objective mathematical model is formulated to minimize total operational cost and total synchronization delay. To solve the RD-CSP, a Q-learning-guided variable neighborhood hybrid evolutionary algorithm (QVHEA) is developed. In the QVHEA, a three-layer encoding scheme is designed to represent the coupled routing and disassembly decisions. Then, a Q-learning-driven variable neighborhood search module is proposed to adaptively select among six problem-tailored neighborhood operators, balancing search diversification and intensification. Extensive experiments across a wide range of instance scales demonstrate that QVHEA outperforms several representative state-of-the-art algorithms, while statistical tests and ablation analyses further validate its effectiveness and key design components. Overall, this study establishes an integrated modeling framework for coordinated recycling routing and disassembly line scheduling and provides decision-support and managerial implications for improving cross-stage synchronization and resource utilization in recycling and remanufacturing operations.