Evidence map›Paper›PMID 40941120›Full record

ArticleFoods (Basel, Switzerland)2025

Generative AI and Blockchain-Integrated Multi-Agent Framework for Resilient and Sustainable Fruit Cold-Chain Logistics.

Abhirup Khanna, Sapna Jain, Anushree Sah, Sarishma Dangi, Abhishek Sharma, Sew Sun Tiang, Chin Hong Wong, Wei Hong Lim

Abstract read
In one paragraph

Article in Foods (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Abhirup KhannaSchool of Computer Science, UPES, Dehradun 248007, India.ORCID 0000-0003-4451-0558
Sapna JainApplied Science Cluster (Chemistry), School of Advanced Engineering, UPES, Dehradun 248007, India.ORCID 0000-0003-1454-3500
Anushree SahSchool of Computer Science, UPES, Dehradun 248007, India.ORCID 0000-0003-3444-5860
Sarishma DangiDepartment of Computer Science and Engineering, Graphic Era Deemed to Be University, Dehradun 248002, India.ORCID 0000-0002-7798-5691
Abhishek SharmaDepartment of Computer Science and Engineering, Graphic Era Deemed to Be University, Dehradun 248002, India.ORCID 0000-0003-4041-9802
Sew Sun TiangFaculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia.ORCID 0000-0001-8433-8663
Chin Hong WongMaynooth International Engineering College, Maynooth University, W23 A3HY Maynooth, Ireland.ORCID 0000-0002-6343-7635
Wei Hong LimFaculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia.ORCID 0000-0003-1673-8088

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The cold-chain supply of perishable fruits continues to face challenges such as fuel wastage, fragmented stakeholder coordination, and limited real-time adaptability. Traditional solutions, based on static routing and centralized control, fall short in addressing the dynamic, distributed, and secure demands of modern food supply chains. This study presents a novel end-to-end architecture that integrates multi-agent reinforcement learning (MARL), blockchain technology, and generative artificial intelligence. The system features large language model (LLM)-mediated negotiation for inter-enterprise coordination, Pareto-based reward optimization balancing spoilage, energy consumption, delivery time, and climate and emission impact. Smart contracts and Non-Fungible Token (NFT)-based traceability are deployed over a private Ethereum blockchain to ensure compliance, trust, and decentralized governance. Modular agents-trained using centralized training with decentralized execution (CTDE)-handle routing, temperature regulation, spoilage prediction, inventory, and delivery scheduling. Generative AI simulates demand variability and disruption scenarios to strengthen resilient infrastructure. Experiments demonstrate up to 50% reduction in spoilage, 35% energy savings, and 25% lower emissions. The system also cuts travel time by 30% and improves delivery reliability and fruit quality. This work offers a scalable, intelligent, and sustainable supply chain framework, especially suitable for resource-constrained or intermittently connected environments, laying the foundation for future-ready food logistics systems.

Indexed as

blockchaincold-chain logisticsgenerative AImulti-agent reinforcement learningsustainable food systems

Identifiers

PMID40941120
PMCPMC12427963

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.