量子退火在可扩展机器人装配线优化中的应用：一项案例研究
Application of quantum annealing for scalable robotic assembly line optimization: a case study
AI总结：
针对大规模定制下传统方法难以高效求解机器人装配线平衡（RALB）问题的挑战，研究将量子退火应用于该问题，通过转化为二次无约束二元优化问题、用D-Wave Advantage 4.1的混合算法求解，案例验证了其提升制造效率的潜力，也指出量子技术仍需进一步发展以增强适用性。
摘要：
在制造领域，跨资源与工作站的任务均匀分配与优化是一项核心流程，其目标是最大化效率、生产率与利润率，这一问题被称为机器人装配线平衡（Robotic Assembly Line Balancing，RALB）。随着大规模定制对制造复杂度的要求不断提升，传统计算方法难以高效求解RALB问题。为应对这些可扩展性挑战，我们研究了将量子计算（尤其是量子退火）应用于该基于真实场景的问题的方法。我们将整数规划公式转化为二次无约束二元优化问题，随后在D-Wave Advantage 4.1量子计算机上采用混合量子-经典算法对其进行求解。在一项案例研究中，我们将量子求解方案与精确解进行了对比，证明了量子计算具备提升制造生产率、降低成本的潜力。不过，量子退火存在的局限性（包括硬件约束与特定问题挑战）表明，量子技术仍需持续发展，才能提升其在RALB制造优化场景中的适用性。
英文摘要：
The even distribution and optimization of tasks across resources and workstations is a critical process in manufacturing aimed at maximizing efficiency, productivity, and profitability, known as Robotic Assembly Line Balancing (RALB). With the increasing complexity of manufacturing required by mass customization, traditional computational approaches struggle to solve RALB problems efficiently. To address these scalability challenges, we investigate applying quantum computing, particularly quantum annealing, to the real-world based problem. We transform the integer programming formulation into a quadratic unconstrained binary optimization problem, which is then solved using a hybrid quantum-classical algorithm on the D-Wave Advantage 4.1 quantum computer. In a case study, the quantum solution is compared to an exact solution, demonstrating the potential for quantum computing to enhance manufacturing productivity and reduce costs. Nevertheless, limitations of quantum annealing, including hardware constraints and problem-specific challenges, suggest that continued advancements in quantum technology will be necessary to improve its applicability to RALB manufacturing optimization.
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