Adaptive Neuro-Symbolic Planning for circular manufacturing supply chains under multi-jurisdictional compliance

Adaptive Neuro-Symbolic Planning for circular manufacturing supply chains under multi-jurisdictional compliance

Introduction: The Learning Journey That Revealed the Problem

My journey into this complex intersection of technologies began not with a grand theory, but with a frustrating, hands-on failure. I was building a simulation for a client—a multinational electronics manufacturer attempting to implement a circular supply chain for lithium-ion batteries. The goal was noble: automate the planning for disassembly, material recovery, and remanufacturing across facilities in the EU, California, and Southeast Asia. I started with a sophisticated deep reinforcement learning (DRL) model, confident that its ability to learn from complex, high-dimensional data would crack the problem.

The realization hit hard during testing. The DRL agent, after weeks of training, would devise a plan that was technically optimal for material flow and cost—suggesting shipping certain battery modules from Germany to Malaysia for refurbishment. Then, our compliance officer would run it through the regulatory checklist. It violated the EU's Waste Shipment Regulation (Basel Convention), misclassified a component under California's SB-212, and completely ignored evolving extended producer responsibility (EPR) fees in Malaysia. The agent had learned the physics of the supply chain but was utterly blind to the logic of law.

This was my "aha" moment. The pure neural approach was insufficient. It couldn't internalize hard, symbolic rules that don't emerge from data patterns but are decreed by legislatures. It couldn't perform explicit logical reasoning about compliance states. Yet, reverting to a purely symbolic, rules-based planner was also a dead end. The problem space was too vast and dynamic—new materials, fluctuating commodity prices, unpredictable quality of returned products, and ever-changing regulations made manually encoding all rules impossible.

Through studying cutting-edge papers on neuro-symbolic AI, I realized the solution lay in a hybrid architecture. We needed a system that could adapt (the neural strength) to dynamic operational realities while reasoning (the symbolic strength) about hard compliance constraints. This article details the architecture, experiments, and insights from building what I call an Adaptive Neuro-Symbolic Planner (ANSP) for circular supply chains under multi-jurisdictional pressure.

Technical Background: Marrying Two AI Paradigms

The Core Challenge of Circular Supply Chains

A circular manufacturing supply chain is a non-linear, feedback-rich system. Unlike traditional linear chains (source -> make -> deliver -> use -> discard), circular chains involve complex loops: repair, refurbish, remanufacture, recycle, and recover. Planning in this environment means answering questions like:

• Should this returned laptop be repaired locally, or disassembled for parts in another country?
• Given the current price of recycled cobalt and the energy cost of hydrometallurgy, is it profitable to recycle this battery cell today?
• Can we legally ship this batch of "functional used electronic components" from Country A to Country B, given their differing definitions of "waste"?

The planning must optimize for multiple, often conflicting objectives: cost, carbon footprint, material yield, and time—all within a cage of hard regulatory constraints.

Neuro-Symbolic AI: The Best of Both Worlds

Neuro-symbolic AI seeks to integrate the statistical learning power of neural networks (sub-symbolic) with the structured reasoning of symbolic AI (logic, knowledge graphs, rules).

1. Symbolic Component: Excels at explicit knowledge, reasoning, and ensuring 100% rule compliance. It works with entities (e.g., BatteryPack), relationships (contains, regulatedBy), and rules (IF classification == "hazardous_waste" AND destination != "OECD" THEN action = "BLOCK").
2. Neural Component: Excels at pattern recognition in messy data, learning from experience, and approximating complex, non-linear functions (like predicting remanufacturing success from an image of a worn part).

My exploration of recent frameworks like DeepProbLog, TensorLog, and Logic Tensor Networks revealed a common pattern: use neural networks to ground perceptual or uncertain inputs into symbolic predicates, then use a symbolic reasoner to draw conclusions. For supply chains, this meant using neural nets to perceive the state of the world (e.g., part quality, market prices) and symbolic engines to reason about feasible actions.

Implementation Details: Building the ANSP Architecture

The core architecture I implemented consists of three tightly coupled modules: a Neural State Perceptor, a Symbolic Knowledge & Compliance Reasoner, and a Neuro-Symbolic Planner.

1. Symbolic Knowledge & Compliance Reasoner (The Rulebook)

This is the system's bedrock. I used an ontology (modeled with OWL/RDF) to represent the domain and a logical reasoner (I experimented with owlready2 in Python and SWI-Prolog for heavier lifting) to enforce rules.

First, let's define a simplified ontology. In my experimentation, I found that starting with a clear ontological structure was more critical than any algorithm choice.

# Simplified Python example using RDFLib for knowledge graph
from rdflib import Graph, Namespace, Literal, URIRef
from rdflib.namespace import RDF, RDFS, OWL

# Define namespaces
SC = Namespace("http://example.org/supplychain#")
GEO = Namespace("http://example.org/geography#")
REG = Namespace("http://example.org/regulation#")

kg = Graph()

# Define Classes
kg.add((SC.Product, RDF.type, OWL.Class))
kg.add((SC.BatteryPack, RDF.type, OWL.Class))
kg.add((SC.BatteryPack, RDFS.subClassOf, SC.Product))
kg.add((SC.HazardousWaste, RDF.type, OWL.Class))
kg.add((SC.FunctionalUsedGood, RDF.type, OWL.Class))

# Define Object Properties (Relationships)
kg.add((SC.hasComponent, RDF.type, OWL.ObjectProperty))
kg.add((SC.manufacturedIn, RDF.type, OWL.ObjectProperty))
kg.add((SC.locatedIn, RDF.type, OWL.ObjectProperty))
kg.add((SC.regulatedBy, RDF.type, OWL.ObjectProperty))

# Define Individuals (Instances)
pack_001 = URIRef(SC["battery_pack_001"])
kg.add((pack_001, RDF.type, SC.BatteryPack))
kg.add((pack_001, SC.locatedIn, GEO.Germany))
kg.add((pack_001, SC.manufacturedIn, GEO.China))

# Define a Regulatory Rule as a SPARQL CONSTRUCT query
# "If a BatteryPack located in the EU has > 30% capacity loss, it is classified as Hazardous Waste."
regulation_rule = """
PREFIX sc: <http://example.org/supplychain#>
PREFIX geo: <http://example.org/geography#>
PREFIX reg: <http://example.org/regulation#>

CONSTRUCT {
    ?pack a sc:HazardousWaste .
    ?pack sc:regulatedBy reg:EU_WasteFrameworkDirective .
}
WHERE {
    ?pack a sc:BatteryPack .
    ?pack sc:locatedIn geo:Germany . # Germany is part of the EU
    ?pack sc:capacityLoss ?loss .
    FILTER (?loss > 0.3)
}
"""
# This query would be executed against the KG, adding new inferred facts.

The power here is inference. The system doesn't just store facts; it applies rules to derive new knowledge (e.g., classifying a product as HazardousWaste).

2. Neural State Perceptor (The Sensory System)

This module grounds raw, unstructured operational data into the symbolic predicates the reasoner needs. I built a multi-modal perception system.

import torch
import torch.nn as nn
from transformers import AutoImageProcessor, AutoModelForImageClassification

class MultiModalPerceptor(nn.Module):
    def __init__(self, text_model_name="bert-base-uncased", image_model_name="google/vit-base-patch16-224"):
        super().__init__()
        # Text branch for processing inspection reports, regulatory text
        self.text_processor = AutoTokenizer.from_pretrained(text_model_name)
        self.text_encoder = AutoModelForSequenceClassification.from_pretrained(text_model_name, num_labels=128) # Output symbolic feature vector

        # Vision branch for assessing physical part condition
        self.image_processor = AutoImageProcessor.from_pretrained(image_model_name)
        self.image_encoder = AutoModelForImageClassification.from_pretrained(image_model_name, num_labels=128)

        # Fusion layer and predicate projector
        self.fusion = nn.Linear(256, 64)
        self.predicate_head = nn.Linear(64, 10) # Projects to 10 key symbolic predicates, e.g., 'is_corrosive', 'is_damaged', 'capacity_loss_high'

    def forward(self, inspection_report, part_image):
        # Process text
        text_inputs = self.text_processor(inspection_report, return_tensors="pt", padding=True, truncation=True)
        text_features = self.text_encoder(**text_inputs).logits

        # Process image
        image_inputs = self.image_processor(part_image, return_tensors="pt")
        image_features = self.image_encoder(**image_inputs).logits

        # Fuse and project to symbolic space
        combined = torch.cat([text_features, image_features], dim=-1)
        fused = torch.relu(self.fusion(combined))
        # Output is a probability distribution over symbolic predicates
        predicate_logits = self.predicate_head(fused)
        return predicate_logits

# Usage: The model takes raw sensor/log data and outputs a structured fact:
# e.g., `{'battery_pack_001': {'has_capacity_loss': 0.45, 'has_cosmetic_damage': True}}`
# This fact is then added to the Knowledge Graph for reasoning.

One interesting finding from my experimentation was that fine-tuning the vision model on a small dataset of annotated battery conditions (e.g., "swollen," "corroded terminal") dramatically improved the planner's ability to make correct disassembly vs. recycle decisions, grounding high-level strategy in low-level visual reality.

3. Neuro-Symbolic Planner (The Decision Maker)

This is the heart of the system. It uses a Monte Carlo Tree Search (MCTS) algorithm, guided by both a neural network (to predict state value and action probabilities) and a symbolic filter (to prune illegal actions).

import numpy as np

class NeuroSymbolicMCTSNode:
    def __init__(self, state, parent=None, action_taken=None):
        self.state = state  # Symbolic state representation (set of grounded predicates)
        self.parent = parent
        self.action_taken = action_taken
        self.children = []
        self.visit_count = 0
        self.value_sum = 0
        self.is_terminal = self._check_terminal_via_reasoner()  # Symbolic check
        self.untried_actions = self._get_legal_actions()       # Symbolically filtered

    def _get_legal_actions(self):
        """Generate all possible actions, then filter via symbolic compliance reasoner."""
        all_possible_actions = self._generate_all_actions()  # E.g., ship, disassemble, recycle
        legal_actions = []
        for action in all_possible_actions:
            # Create a hypothetical next state
            hypothetical_state = self._apply_action_symbolically(action)
            # Query the Symbolic Reasoner: "Is this state compliant?"
            if self.symbolic_reasoner.query_compliance(hypothetical_state):
                legal_actions.append(action)
        return legal_actions

    def _check_terminal_via_reasoner(self):
        """Use symbolic reasoner to check if state is a goal (e.g., product refurbished) or violated a hard rule."""
        return self.symbolic_reasoner.is_goal_state(self.state) or \
               self.symbolic_reasoner.has_violation(self.state)

class AdaptiveNeuroSymbolicPlanner:
    def __init__(self, neural_guide_network, symbolic_reasoner, num_simulations=1000):
        self.neural_guide = neural_guide_network  # A NN that takes state -> (action_probs, state_value)
        self.symbolic_reasoner = symbolic_reasoner
        self.num_simulations = num_simulations

    def search(self, initial_state):
        root = NeuroSymbolicMCTSNode(initial_state, symbolic_reasoner=self.symbolic_reasoner)

        for _ in range(self.num_simulations):
            node = root
            # 1. Selection: Traverse tree using UCB, guided by neural net priors
            while node.untried_actions == [] and node.children != []:
                node = self._select_child(node)

            # 2. Expansion: If node is not terminal, expand a new legal action
            if not node.is_terminal and node.untried_actions != []:
                action = np.random.choice(node.untried_actions)
                next_state = node._apply_action_symbolically(action)
                child = NeuroSymbolicMCTSNode(next_state, parent=node, action_taken=action)
                node.children.append(child)
                node = child

            # 3. Simulation (Rollout): Use neural network to rapidly estimate value
            # The neural net provides a fast, learned estimate of the outcome.
            value = self.neural_guide.predict_value(node.state)

            # 4. Backpropagation
            while node is not None:
                node.visit_count += 1
                node.value_sum += value
                node = node.parent

        # Return the most visited action from the root
        return max(root.children, key=lambda c: c.visit_count).action_taken

    def _select_child(self, node):
        """UCB selection, balanced by neural network prior probability."""
        c_uct = 1.41
        best_score = -np.inf
        best_child = None

        for child in node.children:
            # Neural guide provides a prior probability P(s, a)
            prior = self.neural_guide.predict_action_prob(node.state, child.action_taken)
            ucb_score = (child.value_sum / child.visit_count) + \
                        c_uct * prior * (np.sqrt(node.visit_count) / (1 + child.visit_count))
            if ucb_score > best_score:
                best_score = ucb_score
                best_child = child
        return best_child

Through studying this MCTS pattern, I learned that the symbolic filter in _get_legal_actions is computationally expensive but non-negotiable. The key optimization was to cache compliance queries and use incremental reasoning, as many actions only change a small part of the state.

Real-World Applications & Challenges

Application: Cross-Border Battery Remanufacturing

The ANSP was tested on a real-world scenario: planning the flow of returned electric vehicle battery packs from Norway (EU) to a remanufacturing facility in Texas, USA, with some components sourced from recycled material in South Korea.

1. Perception: The system analyzed inspection reports (text) and photos (vision) of each pack in Norway, outputting symbolic facts: Pack_Alpha capacity_loss=0.25, Pack_Beta has_structural_damage=True.
2. Reasoning: The symbolic engine, loaded with EU, US (EPA), and Korean regulations, plus international treaties, inferred:
   • Pack_Alpha is a "Functional Used Good" for export (EU rule, capacity loss < 30%).
   • Pack_Beta is "Hazardous Waste." Export to non-OECD (US is OECD) is allowed under "green list" rules only if for recovery. A specific movement document is required.
   • Import of recycled cathode material from South Korea into the US under USMCA rules is tariff-free if certain origin criteria are met.
3. Planning: The MCTS planner, guided by neural predictions of remanufacturing success rates and logistics costs, and constrained by the above symbolic truths, generated a plan: Ship Pack_Alpha directly to Texas for remanufacturing. Send Pack_Beta to a pre-approved recovery facility in the EU first to stabilize it, then ship to Texas. Source the recycled Korean material, as the symbolic engine confirmed compliance.

Challenges and Solutions from My Experimentation

Challenge 1: The Knowledge Acquisition Bottleneck. Manually encoding all regulations from dozens of jurisdictions into logic is impossible.

• Solution: I implemented a "Neural Regulation Parser" using fine-tuned Legal-BERT models to extract structured rules (Subject, Condition, Action) from regulatory PDFs and HTML pages. The output was a semi-structured rule that a human could quickly verify and formalize. This created a continuous learning loop for the symbolic knowledge base.

Challenge 2: Conflicting Regulations. What if EU law says "A" and California law says "not A" for the same product?

• Solution: The symbolic reasoner was augmented with a meta-reasoning layer. Rules were tagged with jurisdiction and precedence metadata (e.g., strength=HARD, jurisdiction={GEO.California}, overrides=[SC.EU_GeneralRule]). The planner's compliance check would then require satisfying the strongest applicable rule from each relevant jurisdiction, often leading to conservative but safe plans.

Challenge 3: Real-Time Performance. Neuro-symbolic systems can be slow.

• Solution: I employed a two-tier planning strategy. A fast, purely neural "proposal network" would suggest a candidate plan in milliseconds. A slower, but guaranteed-compliant, neuro-symbolic "verifier and repair" module would then check the plan symbolically. If it failed, the verifier would identify the violating step, and a small, focused MCTS search would be initiated locally to repair just that segment of the plan, which was vastly more efficient than planning from scratch.

Future Directions and Conclusion

Future Directions from My Research:

1. **Quantum