Quantum annealing for CAD-based disassembly sequence optimization

Abstract Disassembly is the core of sustainable recovery processes, enabling the reuse, remanufacturing, and recycling of end-of-life products. Disassembly Sequence Optimization (DSO) is the key to achieving practical and efficient disassembly applications in industrial scenarios. This study presents a novel approach to DSO by leveraging Quantum Annealing (QA) through Quadratic Unconstrained Binary Optimization (QUBO) and Constrained Quadratic Model (CQM) formulations integrated with CAD-based assembly designs. The proposed methods aim to address computational challenges in industrial disassembly applications. We evaluated these methods against classical heuristics using two distinct product designs. For a smaller, denser assembly, the hybrid CQM model demonstrated superior performance in terms of solution quality and computational time. Conversely, for a larger, sparser assembly, a classical heuristic achieved better results in solution quality when constraining computational time compared to the hybrid quantum approaches. The QUBO model, although did not overcome classical, showed promise in exploring solution spaces, obtaining better solution quality than CQM for the larger problem. These findings indicate that the current practical utility of quantum annealing for DSO is influenced by problem size, structure, and hybrid workflow efficiency. Future research should focus on optimizing the balance between classical and quantum resources to further enhance efficiency. As quantum hardware continues to advance, these formulations have the potential to revolutionize sustainable manufacturing by optimizing disassembly planning with greater efficiency and adaptability to large assemblies.