Circular economy (CE) triage is the assessment of products to determine which sustainable pathway they can follow once they reach the end of their usefulness as they are currently being used. Effective CE triage requires adaptive decisions that balance retained value against the costs and constraints of processing and labour. This paper presents a novel decision-making framework as a simple deterministic solver over a state-augmented Disassembly Sequencing Planning (DSP) graph. By encoding the disassembly history into the state, our framework enforces the Markov property, enabling optimal, recursive evaluation by ensuring each decision only depends on the previous state. The triage decision involves choices between continuing disassembly or committing to a CE option. The model integrates condition-aware utility based on diagnostic health scores and complex operational constraints. We demonstrate the framework's flexibility with a worked example: the hierarchical triage of electric vehicle (EV) batteries, where decisions are driven by the recursive valuation of components. The example illustrates how a unified formalism enables the accommodation of varying mechanical complexity, safety requirements, and economic drivers. This unified formalism therefore provides a tractable and generalisable foundation for optimising CE triage decisions across diverse products and operational contexts.

翻译：循环经济分类是指对产品进行评估，以确定其在当前使用方式下达到使用寿命终点后可以遵循的可持续路径。有效的循环经济分类需要做出适应性决策，以平衡产品保留价值与加工处理及人工的成本和约束。本文提出一种新颖的决策框架，将其构建为基于状态增强的拆解序列规划图的简单确定性求解器。通过将拆解历史编码至状态中，我们的框架强制满足马尔可夫性质，确保每个决策仅依赖于前一状态，从而实现最优的递归评估。分类决策涉及继续拆解或选择特定循环经济方案之间的选择。该模型整合了基于诊断健康评分的状态感知效用函数以及复杂的操作约束。我们通过一个工作实例——电动汽车电池的层次化分类决策——展示了该框架的灵活性，其中决策由组件的递归估值驱动。该实例说明了统一的形式化方法如何能够兼容不同的机械复杂性、安全要求和经济驱动因素。因此，这一统一的形式化为在不同产品和操作场景中优化循环经济分类决策提供了可处理且可推广的基础。