How blockchain improves the supply chain: case study alimentary supply chain

Abstract Current supply chain is a linear economy model that directly or indirectly fulfills supply needs. But this model has some disadvantages, such as the relationships between the members of the supply chain or the lack of information for the consumer about the origin of the products. In this paper we propose a new model of supply chain via blockchain. This new model enables the concept of circular economy and eliminates many of the disadvantages of the current supply chain. In order to coordinate all the transactions that take place in the supply chain a multi-agent system is created for this paper.

[1]  Leonor Becerra-Bonache,et al.  Linguistic models at the crossroads of agents, learning and formal languages , 2014, DCAI 2014.

[2]  Demetrio A. Ovalle,et al.  Multi-agent system for Knowledge-based recommendation of Learning Objects , 2015, DCAI 2015.

[3]  Juan M. Corchado,et al.  A polarity analysis framework for Twitter messages , 2015, Appl. Math. Comput..

[4]  Roberto Casado-Vara,et al.  Blockchain for Democratic Voting: How Blockchain Could Cast off Voter Fraud , 2018 .

[5]  Juan M. Corchado,et al.  How Blockchain Could Improve Fraud Detection in Power Distribution Grid , 2018, SOCO-CISIS-ICEUTE.

[6]  Satoshi Nakamoto Bitcoin : A Peer-to-Peer Electronic Cash System , 2009 .

[7]  Juan M. Corchado,et al.  Neuro-symbolic System for Business Internal Control , 2004, ICDM.

[8]  Sebastian Lehnhoff,et al.  Decentralized Coalition Formation with Agent-based Combinatorial Heuristics , 2017, DCAI 2017.

[9]  Roberto Di Pietro,et al.  CONNECT: CONtextual NamE disCovery for blockchain-based services in the IoT , 2017, 2017 IEEE International Conference on Communications (ICC).

[10]  Yuan Yong,et al.  Towards blockchain-based intelligent transportation systems , 2016 .

[11]  Juan M. Corchado,et al.  Smart Contract for Monitoring and Control of Logistics Activities: Pharmaceutical Utilities Case Study , 2018, SOCO-CISIS-ICEUTE.

[12]  Juan M. Corchado,et al.  A multi-agent system for the classification of gender and age from images , 2018, Comput. Vis. Image Underst..

[13]  Soumya Banerjee,et al.  A Multi-Agent System Approach to Load-Balancing and Resource Allocation for Distributed Computing , 2015, ArXiv.

[14]  Bogdan Okreša Ðuri Organisational Metamodel for Large-Scale Multi-Agent Systems: First Steps Towards Modelling Organisation Dynamics , 2017, DCAI 2017.

[15]  Zita Vale,et al.  Enabling Communications in Heterogeneous Multi-Agent Systems: Electricity Markets Ontology , 2016 .

[16]  Juan M. Corchado,et al.  Decentralized Control of DR Using a Multi-agent Method , 2018 .

[17]  Davor Svetinovic,et al.  Security and Privacy in Decentralized Energy Trading Through Multi-Signatures, Blockchain and Anonymous Messaging Streams , 2018, IEEE Transactions on Dependable and Secure Computing.

[18]  Rafael H. Bordini,et al.  A Multi-Agent Extension of a Hierarchical Task Network Planning Formalism , 2017, DCAI 2017.

[19]  Sara Rodríguez,et al.  A Hash Based Image Matching Algorithm for Social Networks , 2017, PAAMS.

[20]  Juan M. Corchado,et al.  Integrating hardware agents into an enhanced multi-agent architecture for Ambient Intelligence systems , 2013, Inf. Sci..

[21]  Juan M. Corchado,et al.  Intelligent business processes composition based on multi-agent systems , 2014, Expert Syst. Appl..

[22]  Juan M. Corchado,et al.  Agents and Computer Vision for Processing Stereoscopic Images , 2010, HAIS.

[23]  Juan M. Corchado,et al.  Obtaining Relevant Genes by Analysis of Expression Arrays with a Multi-agent System , 2015, PACBB.

[24]  J. Bajo,et al.  Hybrid multi-agent architecture as a real-time problem-solving model , 2008, Expert Syst. Appl..

[25]  Juan M. Corchado,et al.  Organization-based Multi-Agent structure of the Smart Home Electricity System , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

[26]  Juan M. Corchado,et al.  A particle dyeing approach for track continuity for the SMC-PHD filter , 2014, 17th International Conference on Information Fusion (FUSION).

[27]  Nicholas R. Jennings,et al.  Intelligent agents: theory and practice , 1995, The Knowledge Engineering Review.

[28]  Juan M. Corchado,et al.  Random finite set-based Bayesian filters using magnitude-adaptive target birth intensity , 2014, 17th International Conference on Information Fusion (FUSION).

[29]  Juan M. Corchado,et al.  Algorithm design for parallel implementation of the SMC-PHD filter , 2016, Signal Process..

[30]  A. Costa,et al.  Increased performance and better patient attendance in an hospital with the use of smart agendas , 2012, Logic Journal of the IGPL.

[31]  Juan Manuel Corchado,et al.  Bladder Carcinoma Data with Clinical Risk Factors and Molecular Markers: A Cluster Analysis , 2015, BioMed research international.