Fleet, Driver & Supply Chain Optimization Achieving First- and Last-Mile Excellence through SYNAPSE Orchestration
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I4P106Keywords:
AI-driven logistics, predictive-prescriptive orchestration, multi-objective reinforcement learning, digital twins, supply chain resilienceAbstract
Modern logistics systems operate as fragmented collections of predictive tools, routing engines, and visibility platforms that lack coordinated decision-making across fleet, driver, routing, and supply chain domains. This paper introduces SYNAPSE, a unified predictive–prescriptive orchestration framework designed to integrate multimodal data streams and enable real-time, cross-domain operational optimization. The framework fuses heterogeneous signals—including fleet health indicators, driver physiological and behavioral metrics, graph-based routing predictions, and multi-tier supply chain forecasts—within a centralized insight fusion layer. A novel Multi-Objective Decision Engine (MODE–DDR) leverages reinforcement learning to generate prescriptive actions that balance cost efficiency, service reliability, safety risk, carbon emissions, and regulatory compliance. A high-fidelity digital twin simulates complex transportation networks, enabling large-scale training and evaluation under diverse stochastic disruptions. Experimental results across 12,000 shipments and 3,000 simulated disruptions demonstrate substantial improvements over industry-standard baselines, including a 6× reduction in exception resolution latency, 17% lower operational cost, 34% reduction in delay impact, 28% reduction in emissions, and 52% lower safety-risk exposure. Ablation studies confirm that cross-domain data fusion is critical to policy quality, validating the central hypothesis of architectural convergence
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