ENTERPRISE AI ANALYSIS
State-Augmented Graphs for Circular Economy Triage
Executive Impact
This paper introduces a novel decision-making framework for Circular Economy (CE) triage, using state-augmented Disassembly Sequencing Planning (DSP) graphs. By encoding disassembly history into the state, it ensures the Markov property, enabling optimal, recursive evaluation for CE options. The framework integrates condition-aware utility and operational constraints, demonstrated with an EV battery example, showcasing flexibility for varying mechanical complexity, safety, and economic drivers. This unified formalism offers a generalisable foundation for optimising CE triage decisions across diverse products and contexts.
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Optimised Triage Decisions
Reduced Disassembly Cost
Increased Value Recovery
Deep Analysis & Enterprise Applications
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The paper presents a novel decision-making framework for Circular Economy (CE) triage. It uses state-augmented Disassembly Sequencing Planning (DSP) graphs, which encode disassembly history into the state to ensure the Markov property. This allows for optimal, recursive evaluation, balancing retained value against processing costs and constraints. The framework integrates condition-aware utility based on diagnostic health scores and complex operational constraints. This approach is designed to be tractable and generalisable across diverse products and contexts.
The framework is demonstrated through a worked example: the hierarchical triage of electric vehicle (EV) batteries. Decisions are driven by recursive valuation of components, illustrating how the unified formalism accommodates varying mechanical complexity, safety requirements, and economic drivers. This specific application highlights the practical utility of the state-augmented graph approach in real-world CE scenarios, particularly where safety and high-value components are critical.
This unified formalism provides a tractable and generalisable foundation for optimising CE triage decisions. It supports dynamic programming (DP) or reinforcement learning (RL) rollouts, bridging the gap between static sequence planning and triage under real-world constraints. Future work will extend the model to explicitly incorporate uncertainty, using sequential decision-making AI and RL, and integrate human feedback. It also aims to incorporate more realistic data from industrial partners and extend the framework to multi-agent coordination.
State-Augmented Disassembly Process
Product EOL Assessment
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Initial Diagnostics
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State-Augmented DSP Graph Creation
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Decision: Continue Disassembly OR Commit to CE Option
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Recursive Utility Evaluation
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Optimal CE Triage Decision
Impact on EV Battery Triage
210 Optimal Utility for Pack Reuse (High Health - Case A)For high-health EV battery packs (e.g., Hp=0.92), the framework identifies 'Pack Reuse' as the optimal CE option with a utility of 210, outperforming disassembly and other routes due to minimal costs and high retained value. This highlights the framework's ability to identify the most economically beneficial pathway early in the triage process.
| Feature | State-Augmented Graphs | Traditional DSP |
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| Markov Property Enforcement |
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| Dynamic Triage Decisions |
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| Condition-Aware Routing |
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| Real-world Constraints |
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This table compares the proposed state-augmented graph framework with traditional Disassembly Sequencing Planning (DSP) methods. The new framework excels in enforcing the Markov property, enabling dynamic and condition-aware triage decisions, and integrating real-world constraints, offering significant advantages for CE applications.
Misalignment in Degraded Battery Triage (Case C)
Problem: For degraded EV battery modules (e.g., HM1=0.30, HM2=0.54), the framework determines that recycling the entire pack (utility UP.RC=30) is economically optimal for the operator, while module-level recycling (UM.RC=-25) is strongly negative. This results in the economically rational but environmentally suboptimal decision to not separate materials at the module level.
Solution: The framework quantifies this utility gap, identifying a clear intervention point. Policy interventions, such as subsidies for module recycling, could make deeper, more valuable disassembly profitable and align economic incentives with environmental goals.
Outcome: By explicitly modeling this economic misalignment, the framework highlights opportunities for targeted policy changes to enhance circular economy outcomes. This ensures that while individual decisions are optimized for the operator, systemic improvements can be identified and advocated for.
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