Designing Adaptive Reverse Logistics Systems for EV Batteries Using Multi-Agent Simulation and Geospatial Intelligence
Bibliographic record
Abstract
Collecting and tracking electric vehicle batteries for reverse logistics requires intelligent adaptive systems capable of handling uncertainty and operational complexity. This study presents a unified framework for optimizing electric vehicle battery return logistics by integrating concepts from the Dabbawala delivery model, game theory, agent-based simulation, machine learning, graph theory, geospatial analysis, and stochastic simulation. The research models a decentralized multi-agent logistics environment where collection points, sorting hubs, and transportation modes such as vans and trucks operate under strategic and resource-constrained interactions. Game theory captures competitive and cooperative behavior among agents, while agent-based modeling simulates their dynamic movements and decisions in real time across a mapped urban network. Graph theory structures the road network and optimizes routing using real geospatial data, allowing the model to evaluate distance, accessibility, and travel time across the Greater Toronto Area. Machine learning predicts demand patterns and optimizes collection routes based on historical and spatial data. Geospatial tools enhance realism by mapping collection flows and region-specific return volumes, while a bi-objective optimization model minimizing both cost and carbon emissions, guides decision making. A Pareto frontier analysis evaluates trade-offs between environmental and economic goals, and scenario based stochastic simulations evaluate the robustness of the logistics system under varying return rates and uncertainties. The proposed framework demonstrates how combining sociological logistical insights with advanced computational methods can yield scalable sustainable and intelligent reverse logistics systems for the growing electric vehicle battery recovery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".