Enhancing the recycling efficiency of end-of-life (EOL) products is crucial for promoting a circular economy. Disassembly sequence planning (DSP) is a key technology in this process. However, traditional DSP relies on manually executed sequential operations, resulting in inefficiency and high labor intensity, while fully automated robotic disassembly, without human involvement, struggles to independently complete tasks. To address this, human-robot collaborative disassembly sequence planning (HRCDSP) has emerged, where humans and robots perform disassembly tasks in parallel to enhance efficiency and reduce labor demands. Despite its potential, existing HRCDSP studies often assume deterministic disassembly conditions, overlook real-world uncertainties, and favor complete disassembly, which is often unnecessary in practice. Furthermore, current research has yet to systematically balance the trade-offs among disassembly time, energy consumption, and profit, thereby limiting practical applicability. To address these gaps, this paper proposes a chance-constrained programming-based human-robot collaborative selective disassembly sequence planning (CHRCSDSP) problem. This approach integrates uncertainties and selective disassembly into HRCDSP decision-making, enabling decision-makers to flexibly balance disassembly time, energy consumption, and profit under predefined confidence levels. Considering the complexity of this problem, an enhanced multi-objective bees algorithm (EMOBA) is proposed, integrating reinforcement learning and variable neighborhood search (VNS) strategies to improve solution performance. Experimental results demonstrate that CHRCSDSP outperforms human-only and robot-only disassembly in feasibility and effectiveness, while EMOBA surpasses other advanced algorithms. This study strengthens HRCDSP's real-world applicability, contributing to more efficient EOL product recycling.
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