Autonomous Context Aware Routing (ACAR) for Perishable Logistics: A Multi-Objective AI Framework Integrating Predictive Analytics and Cargo Specific Policy Engines

Patrick Ndabarishye; Priyadharshini V; P Krishna Swami Reddy Kaduluri; Dharshini R; Garima Sinha

Autonomous Context Aware Routing (ACAR) for Perishable Logistics: A Multi-Objective AI Framework Integrating Predictive Analytics and Cargo Specific Policy Engines

Author(s)	Mr. Patrick Ndabarishye, Priyadharshini V, P Krishna Swami Reddy Kaduluri, Dharshini R, Dr. Garima Sinha
Country	India
Abstract	The global supply chain faces a critical challenge, with ap- proximately 1.3 billion tons of food wasted annually. This issue forms a critical point of research that brings together a combination of reduc- tion of expenses on transport which exaggerates prices, reduction of food wastage, and crucially, the reduction of CO2 emissions to mitigate en- vironmental impact. Research has shown that the use of Dijkstra-based shortest-path routing is effective for optimizing routes under a single ob- jective such as distance or cost. However, traditional algorithms fail to account for real-time quality and biological urgency of perishable cargo and the environmental impact associated with transportation, leading to scenarios where goods arrive with a high fraction spoiled. To address this gap, we propose the Autonomous Context Aware Rout- ing (ACAR) framework, introducing a novelty named the Autonomous Policy Engine (APE) to perform context-aware multi-objective decision- making . The proposed solution integrates predictive analytics with rout- ing optimization and achieves high-precision cost prediction using a Ran- dom Forest model with R2 = 0.997, consistent with recent evidence that ensemble learning improves predictive logistics accuracy. The ACAR framework unifies predicted cost, cargo degradation risk, and carbon emissions within a single multi-objective control logic. While ACAR has not yet been deployed in a physical setting, it has been validated through Python-based simulations that emulate an au- tonomous decision-making layer for robotic logistics. The observation of real-time decision behavior through complex scenarios demonstrates the feasibility of embedding ACAR into robotic delivery systems and autonomous vehicle control stacks.
Keywords	Perishable Logistics, Multi-Objective Optimization, Ran- dom Forest, Green Supply Chain, Autonomous Decision-Making, Robotics.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 2, March-April 2026
Published On	2026-04-24

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Autonomous Context Aware Routing (ACAR) for Perishable Logistics: A Multi-Objective AI Framework Integrating Predictive Analytics and Cargo Specific Policy Engines

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