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Academic Reference Monograph OPEN ACCESS

END-TO-END PREDICTIVE OPTIMIZATION OF LOGISTICS PROCESSES BASED ON ARTIFICIAL INTELLIGENCE

Dmitry Chistyakov
Chief Technology Officer (CTO) | Scalable AI and Distributed Infrastructure Systems Architect Huntington Beach, California, USA
DOI Crossref DOI 10.37547/tajet/end-01
AI-Driven Logistics Optimization-2026
FULL TEXT PP. 1-32
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VOLUME —
ISSUE —
YEAR 2026
PAGES 1-32
RESEARCH OVERVIEW

Abstract

This methodological guide addresses the design and deployment of AI-driven end-to-end predictive logistics optimization systems in regulated, high-throughput operational environments. The guide focuses on the Cross-Layer Predictive Logistics Optimization Model (CPLOM), an architectural framework developed and validated at Rx2Go.ai, where it supports more than 100,000 pharmaceutical deliveries per day across 16 US states. Drawing on peer-reviewed literature in machine learning, operations research, and supply chain management, as well as on operational data from a production healthcare logistics platform, the guide systematically examines demand forecasting models, real-time dynamic routing algorithms, multi-state regulatory compliance automation, and closed-loop feedback architectures. Key results demonstrate that LSTM-based demand forecasting achieves forecast accuracy above 90%, AI-driven route optimization reduces delivery time by 15 to 20 percent relative to rule-based dispatch, and automated compliance engines reduce process deviations by 65 to 80 percent compared with manual workflows. The guide is intended for logistics technology architects, supply chain engineers, and graduate researchers working at the intersection of artificial intelligence and regulated logistics operations. The findings are directly applicable to the national priority of building resilient, digitally sovereign logistics infrastructure within the United States.

RESEARCH TOPICS

Keywords

predictive logistics optimization artificial intelligence CPLOM demand forecasting

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