Understanding and Modeling Middle-Mile Logistics Automation
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Summary
This study addresses the critical role of middle-mile logistics, specifically drayage operations, within the national supply chain. Despite covering short distances, drayage accounts for a disproportionately large share of shipping costs and exacerbates traffic congestion in metropolitan areas. The research investigates the potential of vehicle automation to improve the efficiency, resiliency, and sustainability of these operations, a topic that has received limited academic attention. The project aims to fill this gap by combining qualitative stakeholder insights with quantitative optimization modeling to evaluate the prospects, challenges, and economic impacts of deploying autonomous trucks in drayage. The methodology employs a mixed-methods approach. Qualitatively, the researchers conducted semi-structured interviews with nine stakeholders from public sectors, operators, and labor organizations in major freight hubs like Chicago and Los Angeles between May and August 2024. These interviews explored issues such as labor shortages, regulatory environments, and public acceptance. Quantitatively, the team developed an integer linear programming model within a time-expanded network to determine optimal container and truck flows. The model minimizes system total cost, comprising truck operating costs, depreciation, and container time penalties for violating pickup/delivery windows. The analysis compared various fleet composition scenarios, including conventional-only, autonomous-only, and mixed fleets. The quantitative results indicate that increasing the penetration of autonomous trucks reduces system total costs. This benefit stems from the ability of autonomous trucks to operate for longer hours without mandatory driver breaks, leading to timelier container movements and reduced time-window penalty costs. Furthermore, the optimal fleet size for autonomous trucks is significantly larger than for conventional trucks; despite higher individual operating and depreciation costs, the larger autonomous fleet reduces overall system costs by minimizing time penalties. Qualitatively, stakeholders emphasized that successful adoption requires autonomous driving systems to demonstrate high safety standards, cost competitiveness, and regulatory compliance. They highlighted the need for incremental deployment, thorough testing, and public acceptance campaigns to overcome concerns regarding liability and cybersecurity. The study concludes that while automation offers significant operational and economic benefits for middle-mile logistics, widespread adoption depends on addressing technical, regulatory, and social challenges. The findings suggest that autonomous trucks can enhance supply chain efficiency by enabling larger, more flexible fleets and reducing costs associated with time-sensitive deliveries. However, the transition requires a cautious approach, including pilot programs and stakeholder engagement, to ensure safety and public trust. The research provides a framework for policymakers and industry leaders to navigate the integration of automation into drayage operations, supporting broader goals of economic strength and sustainability in freight transportation.
Key finding
Increasing the penetration of autonomous trucks in drayage fleets reduces total system costs and allows for larger optimal fleet sizes due to extended operational hours and reduced time penalties, though successful implementation depends on overcoming regulatory, safety, and public acceptance challenges.
Methodology
mixed_methods
Sample size: 9
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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