State-Augmented Graphs for Circular Economy Triage

TL;DR

This paper introduces a state-augmented graph framework for optimizing circular economy triage decisions, enabling adaptive, condition-aware, and recursive evaluation of end-of-life products like EV batteries.

Contribution

It presents a novel deterministic solver over a state-augmented DSP graph that enforces the Markov property for optimal decision-making in CE triage.

Findings

Framework effectively models complex operational constraints.

Demonstrated flexibility with EV battery triage example.

Enables recursive valuation of components for optimal decisions.

Abstract

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.